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Slide 1
Wireless Sensor Wireless Sensor Networks (WSNs)Networks (WSNs)
Slide 2
2005 Prof. I Stavrakakis 2
Technological Revolution
1. Computer NetworkingLANInternet
2. Wireless CommunicationsGSM/UMTSWLAN
3. Wireless Sensing TechnologiesMEMS TechnologyWSNs
1990
2000
2010
During the decade of the 90’s research on computer networking has
evolved to a big technological breakthrough with many consequences
on the social and economical worlds, which can be summarized under
the term ‘Internet’.
During the first decade of the 21st century it became apparent, that
wireless connectivity was a major concern for the costumers. The
demand for wireless telephony was enormous and thus, a new market
boomed suddenly(GSM/UMTS). However, the demand for mobility of
connected users is intense also for data communications, thus new
standards for wireless networking were defined (802.11, HIPERLAN,
etc.)
Nowadays, technological evolution is speeding up due to the increased
use of computers in research and development. The field of micro-
electro-mechanical-systems (MEMS) has shown great advances
recently and, combined together with wireless communications and
digital electronics, has enabled the development of low-cost, low-
power, multifunctional sensor nodes that are small in size and
communicate untethered in short distances. The possibility of these
sensor nodes to be networked together over a wireless medium, and to
provide, through collaborative effort, an overall result of their sensing
functionality is raising a whole, new field of research for networking
engineers and researchers (WSNs).
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2005 Prof. I Stavrakakis 3
Applications for Wireless Sensor Networksoo Military ApplicationsMilitary Applications
(monitoring friendly forces, monitoring equipment, battlefield surveillance, reconnaissance of opposing forces and terrain)
oo Environmental MonitoringEnvironmental Monitoring(flood/forest fire detection, space exploration, biological attack detection))
oo Commercial ApplicationsCommercial Applications((home/office smart environments, health applications. environmental control in buildings)
oo TrackingTracking(targeting in intelligent ammunition, tracking of doctors and patients inside a hospital)
These are some of the possible uses of sensor networks. Generally, it
is assumed that sensor networks will be ubiquitous in the future,
because they will provide new possibilities for the interaction of
humans with their physical world. Sensor networks are going to be
inside houses, offices, hospitals and in the military. Furthermore,
space exploration is a field, to which WSNs are in position to
contribute, because they can be sent there, where people themselves
can not go. It is generally accepted, that WSN technology will be the
groundbreaking technology of the next decade.
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2005 Prof. I Stavrakakis 4
Application Examples
Military applications include hostile environment monitoring,
friendly or hostile forces tracking and surveillance applications. The
main concept is that the sensor network is dropped from an aeroplane
or other flying object to the field of interest, and there the sensors
organize themselves as appropriate in order to fulfil the assigned
tasks. The user of the sensor network is in a remote place. Ideally, the
network would provide the possibility of task re-assignment from the
user.
Health applications for WSNs are also very important, because they
can revolutionize the way patients are treated. For example, the
organization of a big hospital may change completely, if the doctors
carry little sensors on them to provide tracking of personnel. Also,
patients may be provided with new technologies for monitoring.
Environmental monitoring is a major application driver for wireless
sensor networks. Well studied examples in literature include animal
target tracking, forest detection and flood detection. Many different
scenarios are possible.
Commercial applications are expected to emerge as soon as sensor
networks are fully functional. Seismic activity monitoring and smart
environment applications are two important examples. The first one
will introduce new methods of research for geologists, and one can
only hope that they will help for more accurate prediction of seismic
activity. Smart environments are expected to be user-interactive,
integrated and ubiquitous.
Finally, Control and Automation are a field, which sensors have
already penetrated. However, the use of sensor networks will improve
their overall performance and provide new methodologies for
production.
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2005 Prof. I Stavrakakis 5
WSN Model Terminology1.1. SensorsSensors
Make discrete, local samples (measurements) of the phenomenon Communicate over wireless medium, forming a wireless sensor networkDisseminate information about the phenomenon to the observer
2.2. ObserverObserverIs interested in measuring/ monitoring the behaviour of a phenomenonAccepts measurements under specific performance requirements (accuracy or delay)
3.3. PhenomenonPhenomenonEntity of interest to the observer
A model for WSN is made of the above three entities (Individual
sensors forming a sensor network, the observer and the phenomenon).
Each entity of the model interacts with the other entities in ways,
which are listed as bullets:
Sensors are the devices that implement the physical sensing of
environmental phenomena and the reporting of measurements
through wireless communication. For reporting their measurements,
individual sensors organize themselves into a sensor network,
exchange sensor readings and disseminate information as needed to
the observer. The measurements taken by the sensors are discrete
samples of the physical phenomenon subject to individual sensor
measurement accuracy as well as location with respect to the
phenomenon
The observer is the end user interested in information about the
phenomenon. The observer may indicate interests (or queries) to the
network and receive responses to these queries. Multiple observers
may exist in a sensor network.
The phenomenon is being sensed and potentially analyzed/ filtered by
the sensor network. Multiple phenomena may be under observation
concurrently in the same network.
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2005 Prof. I Stavrakakis 6
System ArchitectureCheap, lowCheap, low--power, tiny power, tiny sensors used in sensors used in thousandsthousandsCommunication with Communication with the use of miniaturized the use of miniaturized wirelesswireless transceiverstransceiversData aggregationData aggregationduring data during data propagation or at the propagation or at the sinksinkUnattendedUnattended operation operation of the sensor networkof the sensor networkSink transmits data to Sink transmits data to the endthe end--user at the user at the other endother end of the worldof the world
Internet,Satellite,etc.
SINK
SINK
USER
WSN
WSN
The sensor field is situated far from the user. The sensor network is
supposed to be self-organizing, meaning that there is no need for a
network engineer to set it up. During network operation, many sensor
nodes will die, due to lack of energy or due to other reasons. However,
human intervention is not possible at the sight, so the network must
be able to re-configure itself, so that the operation as a whole will
continue.
In the above figure we can see the nodes being connected via an
aggregation tree. The aggregation tree is representing the information
gathering and data fusion procedures. These functionalities are
essential for any WSN, because they contribute vastly to the overall
energy savings. They are an inherit part of the routing protocol of the
sensor network and therefore they must be taken into consideration
during the network-layer protocol design.
The sinks may be base stations, located near the sensor field, or well-
equipped nodes of the sensor network, which can connect via satellite
or other means to the Internet. The user can collect through the sinks
the requested data about the phenomenon taking place at the sensor
field. In particular, the user obtains an aggregated view of the sensor
field, where the aggregation points are usually the sinks.
Preferably the user should be able to interact with the sensor network
at the field, that means there should be established a bi-directional
link between sinks and user. That is essential for the re-assignment of
tasks or other instructions.
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2005 Prof. I Stavrakakis 7
Sensors Hardware Platform
Node Node characteristicscharacteristics
•• Tiny sizeTiny size•• Low powerLow power•• Low bit rateLow bit rate•• High densitiesHigh densities•• Low cost Low cost
(dispensable)(dispensable)•• AutonomousAutonomous•• AdaptiveAdaptive
Power Unit
Sensor,A/D
Converter
CPU,Memory
DigitalTransceiver
PowerGenerator
Location Finding System Mobilizer
Real world data To user
Sensors are made up of four basic components: a sensing unit, a
processing unit, a transceiver unit and a power unit. They may also
have application dependent additional components such as a location
finding system, a power generator and a mobilizer.
Sensing units are usually composed of two subunits: sensors and
analog to digital converters (ADCs). The analog signals produced by
the sensors based on the observed phenomenon are converted to
digital signals by the ADC, and then fed into the processing unit.
The processing unit manages the procedures that make the sensor
node collaborate with the other nodes to carry out the assigned
sensing tasks.
A transceiver unit connects the node to the network.
One of the most important components of the sensor node is the
power unit. Power units may be supported by a power scavenging unit
such as solar cells.
It is common that a sensor node has a location finding system,
because most of the sensor network routing techniques and sensing
tasks require the knowledge of location with high accuracy.
A mobilizer may sometimes be needed to move sensor nodes when it is
required to carry out the assigned tasks.
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2005 Prof. I Stavrakakis 8
Communication Architecture•• CrossCross--layer designlayer design of of
protocol stackprotocol stack•• IntegrationIntegration of routing of routing
functionality and power functionality and power awareness (energyawareness (energy--aware aware routing)routing)
•• IntegrationIntegration of routing of routing functionality and data functionality and data transport (aggregation)transport (aggregation)
•• InclusionInclusion of mobility as a of mobility as a network control primitivenetwork control primitive
•• Promotes cooperative Promotes cooperative efforts (task management efforts (task management plane)plane)
Application LayerApplication Layer
Transport LayerTransport Layer
Network LayerNetwork Layer
Data Link LayerData Link Layer
Physical LayerPhysical Layer
Power M
anagement Plane
Power M
anagement Plane
Mobility M
anagement Plane
Mobility M
anagement Plane
Task Managem
ent PlaneTask M
anagement Plane
Depending on the sensing tasks, different types of application
software can be built and used on the application layer. The transport
layer helps to maintain the flow of data if the sensor networks
application requires it. The network layer takes care of routing the
data supplied by the transport layer. Since the environment is noisy
and sensor nodes can be mobile, the MAC protocol must be power
aware and able to minimize collision with neighbours’ broadcast. The
physical layer addresses the needs of a simple but robust modulation,
transmission and receiving techniques.
The power management plane manages how a sensor node uses its
power. For example, the sensor node may turn off its receiver after
receiving a message from one of its neighbours. This is to avoid
duplicated messages. Also, when the power level of the node is low,
the sensor node broadcasts to its neighbours that it is low in power
and can not participate in routing messages. The remaining power is
reserved for sensing. The mobility management plane detects and
registers the movement of sensor nodes. So, a route back to the user
is always maintained, and the sensor nodes can keep track of who are
their neighbour sensor nodes. Thus, the sensor nodes can balance
their power and task usage. The task management plane balances and
schedules the sensing tasks given to a specific region. Not all sensor
nodes in that region are required to perform the sensing task at the
same time. As a result, some sensor nodes perform the task more
than the others depending on their power level.
The management planes are needed, so that sensor nodes can work
together in a power efficient way, route data in a mobile sensor
network, and share resources between sensor nodes. From the whole
sensor network standpoint, it is more efficient if sensor nodes can
collaborate with each other, so the lifetime of the sensor networks can
be prolonged.
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2005 Prof. I Stavrakakis 9
WSNs vs. MANETs
SimilaritiesSimilaritiesData communication over wireless mediumAd-hoc network topologyPower and bandwidth are scarce resources
WSNs and MANETs are equivalent networks build for different purposes!
Nodes in sensor networks are resource constrained. They have limited
energy and computing power. Among the existing network models
MANETs are the closest to sensor networks. MANETS and sensor
networks share the following characteristics:
Node are connected to each other by wireless communication links
Network topology is not fixed (ad-hoc)
Power and bandwidth is an expensive resource
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2005 Prof. I Stavrakakis 10
WSNs vs. MANETsDifferencesDifferences
WSNs are deployed and owned by a single userSensor nodes are extremely cheap, tinydevices, not like ad-hoc network nodes (PDAs, laptops, etc.)No general purpose communication network, but a data-gathering, surveillance networkNumber of nodes several orders of magnitudehigher than MANETsEnergy and bandwidth conservation is a primary concern in WSN protocol design
Sensor networks are mainly used to collect information while MANETs
are designed for distribute computing rather than information
gathering.
Usually sensor networks are deployed by one owner while MANETs
could be run by several units without any relationship
The number of sensor nodes in sensor networks can be several orders
of magnitude higher than that of the nodes in MANETs
Unlike a node in an ad-hoc network, a node in a sensor network may
not have a unique ID.
Sensor nodes are much cheaper than nodes in an ad-hoc network and
are usually deployed in thousands.
Power resource of sensor nodes could be very limited because of their
cost and un-attendedness during their lifetime. However, nodes in a
MANET can be re-charged somehow.
Usually, the data in sensor networks are bound either down-stream to
the nodes from a sink or up-stream to a sink from nodes, while in a
MANET the data flows are irregular.
Usually, sensors are deployed once in their lifetime although they may
be re-tasked or moved to other places for various reasons. But nodes
in MANET move really in an ad hoc manner
Sensor nodes are much more limited in their computation and
communication capabilities than their MANET counterparts due to
their low cost.
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2005 Prof. I Stavrakakis 11
WSNs vs. MANETsComparison Summary
YesYesNoNoLowLow--cost nodes of tiny sizecost nodes of tiny size
YesYesYesYesRobust to node failuresRobust to node failures(self(self--healing)healing)
YesYesNoNoExtreme power constraints Extreme power constraints for nodes operationfor nodes operation
YesYesYesYesAdAd--hoc deploymenthoc deployment(unattended operation)(unattended operation)
YesYesYesYesMultiMulti--hop routing protocols hop routing protocols applicableapplicable
WSNWSNMANETMANETFeaturesFeatures
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2005 Prof. I Stavrakakis 12
WSNs vs. MANETsComparison Summary
YesYesNoNoInIn--network data network data processingprocessing
WSNWSNMANETMANETFeaturesFeatures
NoNoYesYesUnique global IP addressesUnique global IP addresses
YesYesYesYesMobility of nodesMobility of nodes
<1000 <1000 <100<100Node densityNode density
NoNoYesYesGeneral purpose General purpose communication networkcommunication network
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2005 Prof. I Stavrakakis 13
Sensor Network Protocols Design Challenges
Energy depletionEnergy depletion is the is the main resource main resource bottleneckbottleneck
Reduce each sensor’s Reduce each sensor’s active duty cycleactive duty cycleMinimize data communicationMinimize data communication over over wireless channelwireless channel
Use computation to reduce data size (data aggregation)Communicate only network state summaries instead of actual data
Maximize total network lifetimeMaximize total network lifetimeMinimum energy routing
Extra slide to focus and make clear on the new, important challenges
that sensor networks impose on the network-layer protocol design
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2005 Prof. I Stavrakakis 14
Sensor Network Protocols Design Challenges
RobustnessRobustness to dynamic environmentto dynamic environmentNetwork should be self-configuringNetwork should be self-healingNetwork should be adaptive (measure and act)
Scalable to thousandsScalable to thousands of nodesof nodesOrganize network in a Organize network in a hierarchicalhierarchical manner manner (possibly with the use of clustering)(possibly with the use of clustering)Use only Use only localizedlocalized algorithmsalgorithms; with localized ; with localized interactions between nodes interactions between nodes
Extra slide to focus and make clear on the new, important challenges
that sensor networks impose on the network-layer protocol design
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2005 Prof. I Stavrakakis 15
Sensor Network Protocols Design Characteristics
Data-centric operation• Focus on application data, not
individual nodes: information gathering is the purpose of sensor networks
Traditional networks: : “What is the temperature “What is the temperature at sensor #27at sensor #27 ?? ””
Sensor Networks: : ““Where areWhere are thethe nodesnodes whose temperatureswhose temperatures
recently exceeded 30 degrees?recently exceeded 30 degrees? ””
Unlike traditional networks, a sensor node doesn’t need an identity
(e.g. address). That is, applications are unlikely to to ask the question:
‘What is the temperature at sensor #27?’ Rather, applications focus
on the data generated by sensors. Data is named by attributes and
applications request data matching certain attribute values. So, the
communication primitive in this system is a request: Where are nodes
whose temperatures recently exceeded 30 degrees? This approach
decouples data from the sensor that produced it. This allows for more
robust application design: even if sensor #27 dies, the data it
generates can be cached in other (possibly neighbouring) sensors for
later retrieval.
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2005 Prof. I Stavrakakis 16
Sensor Network Protocols Design Characteristics
ApplicationApplication--specific designspecific design• WSN networks can be tailored to the
sensing task at hand• Intermediate nodes can perform
application-specific data aggregationand caching
Low energy expenditure at nodesLow energy expenditure at nodes• Use of low duty-cycled sensors• Coordinate groups of sensors to fall to
the sleep stated
Traditional networks are designed to accommodate a wide variety of
applications. On the contrary WSN networks can be tailored to the
sensing task at hand. This means that intermediate nodes can
perform application specific data aggregation and caching or informed
forwarding of requests for data. This is in contrast to routers that
facilitate node to node packet switching n traditional networks.
Traditional networks provide large bandwidths, wall power and
powerful compute elements. Sensor nodes will often be limited in one
or all of these dimensions.
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2005 Prof. I Stavrakakis 17
Classification of Routing Protocols
According to route discoveryAccording to route discovery1.1. ProactiveProactive2.2. ReactiveReactive3.3. HybridHybrid
According to location awarenessAccording to location awareness1.1. Location aware routingLocation aware routing2.2. LocationLocation--less routingless routing
Proactive protocols are too expensive for WSNs in terms of storage and
bandwidth consumption. Reactive or hybrid protocols are preferred
Location awareness is too expensive for a sensor network. However,
geographic protocols are scalable. So geographic protocols without
location information are needed for WSNs
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2005 Prof. I Stavrakakis 18
Classification of Routing Protocols (cont’d)
According to nodes’ participating styleAccording to nodes’ participating style1.1. Direct communicationDirect communication2.2. Flat routingFlat routing3.3. Clustering routing protocolsClustering routing protocols
SINKSINK
SINK
A.Direct communication is out of the question, since the energy
requirements grow with the diameter of the sensor network.
B. In flat routing, simple, multi-hop communication is employed for
information dissemination. Since the nodes near the sinks relay all
the traffic to the sink, their power is depleted very fast.
C. Clustered routing protocols are the most appropriate for sensor
networks, since they have many advantages:
1. Nodes need to store info about the clusterhead only!It is
scalable!
2 . Routes are easily discovered and maintained
3. Energy efficient, since data is collected and processed at the
clusterheads.
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2005 Prof. I Stavrakakis 19
Sensor Network Communication Protocols
Proposed Sensor Network Proposed Sensor Network Performance Performance MetricsMetrics
Energy efficiency/system lifetimeLatencyAccuracyFault-toleranceScalability
Energy efficiency/system lifetime. As sensor nodes are battery-operated, protocols
must be energy-efficient to maximize system lifetime. System lifetime can be
measured by generic parameters such as the time until half of the nodes die or by
application-directed metrics, such as when the network stops providing the
application with the desired information about the phenomena.
Latency. The observer is interested in knowing about the phenomena within a given
delay. The precise semantics of latency are application dependent.
Accuracy. Obtaining accurate information is the primary objective of the observer,
where accuracy is determined by the given application. There is a trade-off between
accuracy, latency and energy efficiency. The given infrastructure should be adaptive
so that the application obtains the desired accuracy and delay with minimal energy
expenditure. For example, the application can either request more frequent data
dissemination from the same sensor nodes or it can direct data dissemination from
more sensor nodes with the same frequency.
Fault-tolerance: Sensors may fail due to surrounding physical conditions or when
their energy runs out. It may be difficult to replace existing sensors; the network must
be fault-tolerant such that non-catastrophic failures are hidden from the application.
Scalability: Scalability for sensor networks is also a critical factor. For large-scale
networks, it is likely that localizing interactions through hierarchy and aggregation
will be critical for ensuring scalability.
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2005 Prof. I Stavrakakis 20
SPANProblemProblem: Need to : Need to minimize the minimize the
energy consumptionenergy consumption of wireless of wireless nodes in a wireless ad hoc nodes in a wireless ad hoc network!network!
IDEA:IDEA:Leverage the time the network Leverage the time the network interface of a node remains interface of a node remains idleidleto to powerpower--downdown the radio of the the radio of the node.node.
Reducing energy consumption in a wireless ad hoc network is the
primary goal of this protocol. It aims also at being completely inter-
operable with different routing protocols running in the ad hoc
network. The approach adopted here is: since the network interface
remains lots of time idle, why not power down the radio of the node
for this time. BUT: it is not always straightforward, when to turn the
radio off, since a node does not only send/receive packets when it
wants to communicate with the rest of the network, but it also
participates in the network as a relaying node.
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2005 Prof. I Stavrakakis 21
SPANDesired CharacteristicsDesired Characteristics
1.1. As many nodes as possibleAs many nodes as possible should should be be in sleep modein sleep mode
2.2. Forwarding ofForwarding of packets packets should occur should occur with with minimal minimal additionaladditional delaysdelays
3.3. Awake nodesAwake nodes should provide should provide as as much total capacitymuch total capacity as original as original networknetwork
4.4. Distributed algorithmDistributed algorithm for so that for so that nodes make nodes make locallocal decisionsdecisions
SPAN is a power saving technique for multi-hop ad hoc wireless
networks that reduces energy consumption without significantly
diminishing the capacity or latency characteristics of the network.
A good power-saving coordination technique for wireless ad-hoc
networks ought to have certain characteristics:
Allow as many nodes as possible to turn their radio receivers off
most of the time, since even an idle receive circuit can consume
almost as much energy as an active transmitter.
It should forward packets between any source and destination with
minimally more delay than if all nodes were awake.
The backbone formed by the awake nodes should provide about as
much total capacity as the original network, since otherwise
congestion may increase.
The algorithm for picking this backbone should be distributed,
requiring each node to make a local decision (localised algorithm).
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2005 Prof. I Stavrakakis 22
SPAN
Span is a powerSpan is a power--saving protocol that saving protocol that operates operates betweenbetween the routing layer and the routing layer and the MAC layer.the MAC layer.
802.11, H802.11, HIIPERLAN/2PERLAN/2
SpanSpan
DSRDSRAODVAODVGPSRGPSRRouting layer
MAC/Phy layer
As shown in the figure above, Span runs above the link and MAC
layers and interacts with the routing protocol. This structuring allows
Span to take advantage of power-saving features of the link layer
protocol, while still being able to affect the routing process. For
example, non-coordinator nodes can periodically turn on their radios
and listen or poll for their packets. Span leverages a feature of modern
power-saving MAC layers, in which if a node has been asleep for a
while, packets destined for it are not lost but are buffered at a
neighbor. When the node awakens, it can retrieve these packets from
the buffering node, typically a coordinator. Span also requires a
modification to the route lookup process at each node – at any time,
only those entries in a node’s routing table that correspond to
currently active coordinators can be used as valid next-hops (unless
the next hop is the destination itself).
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2005 Prof. I Stavrakakis 23
SPANOperation of SPANOperation of SPAN
Certain nodes are elected as Certain nodes are elected as ‘coordinators’‘coordinators’to participate in the backbone network. to participate in the backbone network. Coordinators stay Coordinators stay alwaysalways--onon to provide to provide global connectivity of the network. The rest global connectivity of the network. The rest of nodes remain in of nodes remain in powerpower--save modesave mode and and periodically check to change statusperiodically check to change statusCoordinators are rotated among nodesCoordinators are rotated among nodesAttempt to minimize the number of Attempt to minimize the number of coordinatorscoordinatorsDistributed coordinators election processDistributed coordinators election process
Span adaptively elects “coordinators” from all nodes in the network.
Span coordinators stay awake continuously and perform multi-hop
packet routing within the ad hoc network, while other nodes remain in
power-saving mode and periodically check if they should wake up and
become a coordinator.
Span achieves four goals.
First, it ensures that enough coordinators are elected so that every
node is in radio range of at least one coordinator.
Second, it rotates the coordinators in order to ensure that all nodes
share the task of providing global connectivity roughly equally.
Third, it attempts to minimize the number of nodes elected as
coordinators, thereby increasing network lifetime, but without
suffering a significant loss of capacity or an increase in latency.
Fourth, it elects coordinators using only local information in a
decentralized manner – each node only consults state stored in local
routing tables during the election process.
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2005 Prof. I Stavrakakis 24
SPANSpan is Span is proactiveproactive: each node : each node periodicallyperiodicallybroadcastsbroadcasts HELLOHELLO messages:messages:
1.1. the node’s statusthe node’s status2.2. its current coordinatorsits current coordinators3.3. its current neighborsits current neighbors
From the HELLO messages each node From the HELLO messages each node buildsbuilds
1.1. a list of own neighbors and a list of own neighbors and coordinatorscoordinators
2.2. for each neighbor: a list of its for each neighbor: a list of its neighbors and coordinatorsneighbors and coordinators
Span is proactive: each node periodically broadcasts HELLO messages
that contain the node’s status (i.e., whether or not the node is a
coordinator), its current coordinators, and its current neighbors. From
these HELLO messages, each node constructs a list of the node’s
neighbors and coordinators, and for each neighbor, a list of its
neighbors and coordinators.
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2005 Prof. I Stavrakakis 25
SPANCoordinator announcementCoordinator announcement
Regular nodes Regular nodes periodicallyperiodically wake up and wake up and decide to become decide to become coordinatorscoordinators or not based on or not based on a a coordinator eligibility rulecoordinator eligibility rule
Coordinator eligibility ruleCoordinator eligibility ruleA nonA non--coordinator node should become a coordinator if coordinator node should become a coordinator if it discovers, using only information gathered from local it discovers, using only information gathered from local broadcast messages, that two of its neighbors cannot broadcast messages, that two of its neighbors cannot reach each other either directly or via one or two reach each other either directly or via one or two coordinatorscoordinators
Coordinator announcement
Periodically, a non-coordinator node determines if it should become a
coordinator or not. The following coordinator eligibility rule in Span
ensures that the entire network is covered with enough coordinators:
Coordinator eligibility rule. A non-coordinator node should become
a coordinator if it discovers, using only information gathered from
local broadcast messages, that two of its neighbors cannot reach each
other either directly or via one or two coordinators.
This election algorithm does not yield the minimum number of
coordinators required to merely maintain connectedness. However, it
roughly ensures that every populated radio range in the entire
network contains at least one coordinator. Because packets are routed
through coordinators, the resulting coordinator topology should yield
good capacity.
This election algorithm does not yield the minimum number of
coordinators required to merely maintain connectedness. However, it
roughly ensures that every populated radio range in the entire
network contains at least one coordinator. Because packets are routed
through coordinators, the resulting coordinator topology should yield
good capacity.
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2005 Prof. I Stavrakakis 26
SPANContention resolutionContention resolution
What happens if two nodes decide to become What happens if two nodes decide to become coordinators at the same time?coordinators at the same time?
Introduce a Introduce a randomized randomized backoffbackoff delay delay at each at each node, based onnode, based on
Nodes with Nodes with roughly equal remaining energyroughly equal remaining energyNNii: number of : number of neighborsneighbors at node iat node iCCii: number of additional pairs of nodes to be : number of additional pairs of nodes to be connected if i became a coordinatorconnected if i became a coordinator
0 ≤ Ci ≤ (Ni ov. 2)Define as utilityutility of a node i: of a node i: CCii / (N/ (Nii ovov. 2). 2)
Announcement contention occurs when multiple nodes discover the
lack of a coordinator at the same time, and all decide to become a
coordinator. Span resolves contention by delaying coordinator
announcements with a randomized backoff delay. Each node chooses
a delay value, and delays the HELLO message that announces the
node’s volunteering as a coordinator for that amount of time. At the
end of the delay,
the node re-evaluates its eligibility based on HELLO messages recently
received, and makes its announcement if and only if the eligibility rule
still holds.
We consider a variety of factors in our derivation of the backoff delay.
Consider first the case when all nodes have roughly equal energy,
which implies that only topology should play a role in deciding which
nodes become coordinators.
Let Ni be the number of neighbors for node i and let Ci be the number
of additional pairs of nodes among these neighbors that would be
connected if i were to become a coordinator and forward packets.
We call Ci/Ni the utility of node i.
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2005 Prof. I Stavrakakis 27
SPANContention resolutionContention resolution
Nodes with Nodes with higher higher CCii should volunteer should volunteer more quicklymore quicklythan ones with smaller than ones with smaller CCii
the delay for each node is randomly chosen over an interval proportional to Ni x T
R picked uniformly at random from interval (0,1]
If nodes with high Ci become coordinators, fewer coordinators in total
may be needed in order to make sure every node can talk to a
coordinator; thus a node with high Ci should volunteer more quickly
than one with smaller Ci .
If there are multiple nodes within radio range that all have the same
utility, Span prevents too many of them becoming coordinators. This
is because such coordinators would be redundant – they would not
increase system capacity, but simply drain energy. If the potential
coordinators make their decisions simultaneously, they may all decide
to become coordinators. If, on the other hand, they decide one at a
time, only the first few will become coordinators, and the rest will
notice that there are already enough coordinators and go back to
sleep. To handle this, we use a randomized “slotting-and damping”
method reminiscent of techniques to avoid multiple retransmissions of
lost packets by multicast protocols, such as XTP, IGMP and SRM: the
delay for each node is randomly chosen over an interval proportional
to Ni Χ T , where T is the round-trip delay for a small packet over the
wireless link.
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2005 Prof. I Stavrakakis 28
SPANContention resolutionContention resolution
Nodes with Nodes with unequal remaining energyunequal remaining energyEErr: amount of remaining energy at a node: amount of remaining energy at a nodeEEmm: maximum amount of energy available: maximum amount of energy available
Fairness ruleFairness ruleA node with A node with larger larger EErr/E/Emm should become should become
coordinator coordinator more quickly more quickly
Consider the case when nodes may have unequal energy left in their
batteries. We observe that what matters in a heterogeneous network is
not necessarily the absolute amount of energy available at the node,
but the amount of energy scaled to the maximum amount of energy
that the node can have. Let Er denote the amount of energy at a node
that still remains, and Em be the maximum amount of energy
available
at the same node. A reasonable (but not the only) notion of fairness
can be achieved by ensuring that a node with a larger value of Er/Em
is more likely to volunteer to become a coordinator more quickly than
one with a smaller ratio. Thus, we need to add a decreasing function
of Er/Em that reflects this, to equation (1). There are an infinite
number of such functions, from which we choose a simple linear one:
1−Er/Em.
Observe that the first term does not have a random component; thus
if a node is running low on energy, its propensity to become a
volunteer is guaranteed to diminish relative to other nodes in the
neighborhood with similar neighbors.
In a network with uniform density and energy, our election algorithm
rotates coordinators among all nodes of the network. It achieves
fairness because the likelihood of becoming a coordinator falls as a
coordinator uses up its battery. In practice, however, ad hoc networks
are rarely uniform. Our announcement rule adapts to non-uniform
topology: a node that connects network partitions together will always
be elected
a coordinator. This property preserves capacity over the lifetime of the
network. Because of Span’s emphasis on capacitypreservation to the
extent possible, such critical nodes will unavoidably die before other
less-critical ones. However, in a mobile Span network, a given node is
rarely stuck in such a position, and this improves fairness
dramatically.
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2005 Prof. I Stavrakakis 29
SPANCoordinators withdrawalCoordinators withdrawal
Each coordinator Each coordinator periodicallyperiodically checks if it should checks if it should withdraw as a coordinatorwithdraw as a coordinatorRule to withdrawRule to withdraw: every pair of its : every pair of its neighborsneighborsshould be able to reach each other either should be able to reach each other either directlydirectlyor via or via one or twoone or two other coordinatorsother coordinatorsTo rotate coordinators among all nodes fairly: use To rotate coordinators among all nodes fairly: use of of tentativetentative coordinatorscoordinatorsTentative coordinators: Tentative coordinators: provide the chance for provide the chance for nonnon--coordinators to become coordinatorscoordinators to become coordinatorsCoordinators Coordinators stay tentativestay tentative for Wfor WTT amount of timeamount of time
WWTT= 3 x N= 3 x Nii x T (max. delay for cont. resolution)x T (max. delay for cont. resolution)After WAfter WT T , the tentative bit is removed, the tentative bit is removed
Coordinator withdrawal
Each coordinator periodically checks if it should withdraw as a
coordinator. A node should withdraw if every pair of its neighbors can
reach each other either directly or via one or two other coordinators.
In order to also rotate the coordinators among all nodes fairly, after a
node has been a coordinator for some period of time, it marks itself as
a tentative coordinator if every pair of neighbor nodes can reach each
other via one or two other neighbors, even if those neighbors are not
currently coordinators. A tentative coordinator can still be used to
forward packets. However, the coordinator announcement algorithm
described above treats a tentative coordinator as a non-coordinator.
Thus, by marking itself as tentative, a coordinator gives its neighbors
a chance to become coordinators.
A coordinator stays tentative for WT amount of time, where WT is the
maximum value of equation (2). That is, WT = 3 Χ Ni Χ T. (3) If a
coordinator has not withdrawn afterWT , it clears its tentative bit. To
prevent an unlucky low energy node from draining all of its energy
once it becomes a coordinator, the amount of time a node stays as a
coordinator before turning on its tentative bit is proportional to the
amount of energy it has (Er/Em).
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2005 Prof. I Stavrakakis 30
SPANIllustration of SPAN Illustration of SPAN algalg. at some arbitrary . at some arbitrary momentmoment
+: non-coordinator nodes
*: coordinator nodes
Solid lines: connect neighboring coordinators
Figure shows the result of our election algorithmat a random point in
time on a network of 100 nodes in a 1000 m Χ 1000 m area, where
each radio has an isotropic circular range with a 250 m radius. Solid
lines connect coordinators that are within radio range of each other.
A scenario with 100 nodes, 19 coordinators, and a radio range of 250
m. The nodes marked “�” are coordinators; the nodes marked “+” are
non-coordinator nodes. Solid lines connect coordinators that are
within radio range of each other.
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2005 Prof. I Stavrakakis 31
SPANEnergy consumption characteristicsEnergy consumption characteristics
per-node power usage in networksrunning Span, 802.11 PSM, and 802.11
This section evaluates Span’s ability to save energy. The potential for
savings depends on node density, since the fraction of sleeping nodes
depends on the number of nodes per radio coverage area. The energy
savings also depend on a radio’s power consumption in sleep mode
and the amount of time that sleeping nodes must turn on their
receivers to listen for 802.11 beacons and Span HELLO messages.
Figure shows the per-node power usage in networks running Span,
802.11 PSM, and 802.11. These numbers are calculated from the
initial energy and the energy remaining at each of the 100 mobile
nodes over 500 s. Each value is an average over 5 mobile simulations.
From these results, we find that Span provides a considerable amount
of energy savings over 802.11, while 802.11 PSM saves essentially no
power. This is because geographic forwarding needs to send broadcast
messages. With 802.11 PSM, each time a node receives a broadcast
advertisement, it must stay up for the entire beacon period. This
prevents non-coordinators from going back to sleep. When the node
density is low, the number of broadcast messages in a radio range
decreases, and 802.11 PSM yields a small amount of energy savings.
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SPANProsPros
Achieves high energyAchieves high energy--savings, even with savings, even with regular ad hoc routing protocolsregular ad hoc routing protocolsSlow increase of energy savings with higher Slow increase of energy savings with higher network densities due to periodicitynetwork densities due to periodicityLow latency, low throughput degradationLow latency, low throughput degradation
ConsConsCan not be applied to sensor networks, Can not be applied to sensor networks, because sensing nodes may not be powered because sensing nodes may not be powered up or downup or downHigh communication overheadHigh communication overhead
Span leverages a feature of modern power-saving MAC layers, in
which if a node has been asleep for a while, packets destined for it are
not lost but are buffered at a neighbor.
Performance evaluation shows that Span not only preserves network
connectivity, it also preserves capacity, decreases latency and provides
significant energy savings. E.g. for a practical range of node densities
and a practical energy model, the system lifetime with span is more
than a factor of two better than without span!
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2005 Prof. I Stavrakakis 33
LEACH
LLow ow EEnergy nergy AAdaptive daptive CClustering lustering HHierarchyierarchyA clustering-based protocol utilizing randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the networkLEACH makes the following assumptions:
1. The base station is fixed and located far from the sensors
2. All nodes in the network are homogeneous and energy-constrained
Sensor networks can contain hundreds or thousands of sensing
nodes. It is desirable to make these nodes as cheap and energy-
efficient as possible and rely on their large numbers to obtain high
quality results. Network protocols must be designed to achieve fault
tolerance in the presence of individual node failure while minimizing
energy consumption. In addition, since the limited wireless channel
bandwidth must be shared among all the sensors in the network,
routing protocols for these networks should be able to perform local
collaboration to reduce bandwidth requirements.
Eventually, the data being sensed by the nodes in the network must
be transmitted to a control center or base station, where the end-user
can access the data.
Communication between the sensor nodes and the base station is
expensive, and there are no “high-energy” nodes through which
communication can proceed.
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2005 Prof. I Stavrakakis 34
LEACH
Key features of LEACHKey features of LEACH: : Localized coordination and control for cluster set-up and operationRandomized rotation of the cluster “base stations” or “cluster-heads” and the corresponding clustersLocal compression to reduce global communication
By analyzing the advantages and disadvantages of conventional
routing protocols using our model of sensor networks, we have
developed LEACH (Low-Energy Adaptive Clustering Hierarchy), a
clustering-based protocol that minimizes energy dissipation in sensor
networks.
The use of clusters for transmitting data to the base station leverages
the advantages of small transmit distances for most nodes, requiring
only a few nodes to transmit far distances to the base station.
However, LEACH outperforms classical clustering algorithms by using
adaptive clusters and rotating cluster-heads, allowing the energy
requirements of the system to be distributed among all the sensors. In
addition, LEACH is able to perform local computation in each cluster
to reduce the amount of data that must be transmitted to the base
station. This achieves a large reduction in the energy dissipation, as
computation is much cheaper than communication.
Sensor networks contain too much data for an end-user to process.
Therefore, automated methods of combining or aggregating the data
into a small set of meaningful information is required. In addition to
helping avoid information overload, data aggregation, also known as
data fusion, can combine several unreliable data measurements to
produce a more accurate signal by enhancing the common signal and
reducing the uncorrelated noise. The classification performed on the
aggregated data might be performed by a human operator or
automatically. Both the method of performing data aggregation and
the classification algorithm are application-specific.
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2005 Prof. I Stavrakakis 35
LEACHProtocol descriptionProtocol description
Nodes organize themselves into local clusters, with one node acting as local base station or “cluster-head”Randomized rotation of high-energy cluster-head position so as not to ‘drain’ the energy of a single nodeElection of clusterheads at any given time with a certain probabilitySensors choose their preferred clusterhead to belong to, based on the minimum required energy to communicate withClusterheads create schedules for the nodes in their cluster, so that plain nodes can power-down when they are not scheduled to transmitClusterheads aggregate data from sensors in cluster and transmit compressed data to the base station
LEACH is a self-organizing, adaptive clustering protocol that uses
randomization to distribute the energy load evenly among the sensors
in the network. In LEACH, the nodes organize themselves into local
clusters, with one node acting as the local base station or cluster-
head. If the clusterheads were chosen a priori and fixed throughout
the system lifetime, as in conventional clustering algorithms, it is easy
to see that the unlucky sensors chosen to be cluster-heads would die
quickly, ending the useful lifetime of all nodes belonging to those
clusters. Thus LEACH includes randomized rotation of the high-
energy cluster-head position such that it rotates among the various
sensors in order to not drain
the battery of a single sensor.
Sensors elect themselves to be local cluster-heads at any given time with a certain
probability. These clusterhead nodes broadcast their status to the other sensors in the
network. Each sensor node determines to which cluster it wants to belong by choosing
the cluster-head that requires the minimum communication energy. Once all the nodes
are organized into clusters, each cluster-head creates a schedule for the nodes in its
cluster. This allows the radio components of each non-cluster-head node to be turned
off at all times except during its transmit time, thus minimizing the energy dissipated
in the individual sensors. Once the cluster-head has all the data from the nodes in its
cluster, the cluster-head node aggregates the data and then transmit the compressed
data to the base station. Since the base station is far away in the scenario we are
examining, this is a high energy transmission. However, since there are only a few
cluster-heads, this only affects a small number of nodes.
As discussed previously, being a cluster-head drains the battery of that node. In order
to spread this energy usage over multiple nodes, the cluster-head nodes are not fixed;
rather, this position is self-elected at different time intervals. Thus a set C of nodes
might elect themselves cluster-heads at time t, but at time t d a new set C of nodes
elect themselves as cluster-heads. The decision to become a cluster-head depends on
the amount of energy left at the node. In this way, nodes with more energy remaining
will perform the energy-intensive functions of the network. Each node makes its
decision about whether to be a cluster-head independently of the other nodes in the
Network and thus no extra negotiation is required to determine the
clusterheads . The system can determine, a priori, the optimal
number of clusters to have in the system. This will depend on several
parameters, such as the network topology and the relative costs of
computation versus communication.
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2005 Prof. I Stavrakakis 36
LEACHLEACH operates in LEACH operates in consecutive roundsconsecutive roundsClusterheadsClusterheads areare elected newelected new at at each round of each round of operationoperation
C: set of clusterheads
at time t0
For even energy dissipation
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2005 Prof. I Stavrakakis 37
LEACH
NewNew set of clusterheads C`set of clusterheads C` for the next for the next round round
C`: set of clusterheads
at time t0 + δ0
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2005 Prof. I Stavrakakis 38
LEACHPhases of operationPhases of operation
1. Advertisement Phase• Clusterheads are elected in this phase• Election is based on P (percentage of clusterheads for
the network) and the number of times the node has been a clusterhead so far
• Node n chooses a random number between 0 and 1 and if this number is less than a threshold T(n), the node becomes clusterhead in this round
• Clusterheads broadcast advertisement messages using CSMA MAC protocol using the same energy
• Receiving nodes decide which clusterehad to belong to based on the received advertisement signal strength
2. Cluster Set-up Phase• Nodes inform the clusterheads that they want to join
their cluster• Again a CSMA MAC protocol is used
Advertisement phase
Initially, when clusters are being created, each node decides whether
or not to become a cluster-head for the current round. This decision is
based on the suggested percentage of cluster heads for the network
(determined a priori) and the number of times the node has been a
cluster-head so far. This decision is made by the node n choosing a
random number between 0 and 1. If the number is less than a
threshold Tn, the node becomes a cluster-head for the current round.
Each node that has elected itself a cluster-head for the current round broadcasts an
advertisement message to the rest of the nodes. For this “cluster-head-advertisement”
phase, the cluster-heads use a CSMA MAC protocol, and all cluster-heads transmit
their advertisement using the same transmit energy. The non-cluster-head nodes must
keep their receivers on during this phase of set-up to hear the advertisements of all the
cluster-head nodes. After this phase is complete, each non-cluster-head node decides
the cluster to which it will belong for this round. This decision is based on the
received signal strength of the advertisement. Assuming symmetric propagation
channels, the cluster-head advertisement heard with the largest signal strength is the
cluster-head to whom the minimum amount of transmitted
Cluster set-up phase
After each node has decided to which cluster it belongs, it must inform the cluster-
head node that it will be a member of the cluster. Each node transmits this information
back to the cluster-head again using a CSMA MAC protocol. During this phase, all
cluster-head nodes must keep their receivers
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2005 Prof. I Stavrakakis 39
LEACHPhases of operationPhases of operation
3. Schedule Creation Phase• Clusterheads receive all messages from nodes to be
included in cluster• Based on the number of nodes in the cluster,
clusterhead creates TDMA schedule• Schedule is broadcast to all cluster nodes
4. Data Transmission Phase• Assuming nodes have data to send, they wait for
their allocated time to send data to the clusterhead• The rest of the time they power down their radio to
conserve energy• Clusterhead performs data fusion so as to send
compressed data to the sink• This final transmission is a high-energy data
transmission
Schedule creation
The cluster-head node receives all the messages for nodes that would like to be
included in the cluster. Based on the number of nodes in the cluster, the cluster-head
node creates a TDMA schedule telling each node when it can transmit. This schedule
is broadcast back to the nodes in the cluster.
Data transmission
Once the clusters are created and the TDMA schedule is fixed, data transmission can
begin. Assuming nodes always have data to send, they send it during their allocated
transmission time to the cluster head. This transmission uses a minimal amount of
energy (chosen based on the received strength of the cluster-head advertisement). The
radio of each non-cluster-head node can be turned off until the node’s allocated
transmission time, thus minimizing energy dissipation in these nodes. The cluster-
head node must keep its receiver on to receive all the data from the nodes in the
cluster. When all the data has been received, the cluster head node performs signal
processing functions to compress the data into a single signal. For example, if the data
are audio or seismic signals, the cluster-head node can beamform the individual
signals to generate a composite signal. This composite signal is sent to the base
station. Since the base station is far away, this is a high-energy transmission.
This is the steady-state operation of LEACH networks. After a certain
time, which is determined a priori, the next round begins with each
node determining if it should be a cluster-head for this round and
advertising this information.
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2005 Prof. I Stavrakakis 40
LEACHNormalized total system energy dissipated versus the percent of nodes that are cluster-heads.
Optimal point of LEACH operation
Over a factor of 7 for reduction in energy dissipation when optimal number of clusterheads
Normalized total system energy dissipated versus the percent of nodes
that are cluster-heads. Note that direct transmission is equivalent to 0
nodes being cluster-heads or all the nodes being cluster-heads.
The system can determine, a priori, the optimal number of clusters to
have in the system. This will depend on several parameters, such as
the network topology and the relative costs of computation versus
communication. Figure shows how the energy dissipation in the
system varies as the percent of nodes that are cluster-heads is
changed. Note that 0 cluster-heads and 100% clusterheads is the
same as direct communication.
From this plot, we find that there exists an optimal percent of nodes N
that should be cluster-heads. If there are fewer than N clusterheads,
some nodes in the network have to transmit their data very far to
reach the cluster-head, causing the global energy in the system to be
large. If there are more than N clusterheads, the distance nodes have
to transmit to reach the nearest cluster-head does not reduce
substantially, yet there are more cluster-heads that have to transmit
data the long-haul distances to the base station, and there is less
compression being performed locally. For our system parameters and
topology, N is roughly equal to 5%.
Figure also shows that LEACH can achieve over a factor of 7 reduction
in energy dissipation compared to direct communication with the base
station, when using the optimal number of cluster-heads. The main
energy savings of the LEACH protocol is due to combining lossy
compression
with the data routing. There is clearly a trade-off between the quality
of the output and the amount of compression achieved. In this case,
some data from the individual signals is lost, but this results in a
substantial reduction of the overall energy dissipation of the system.
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2005 Prof. I Stavrakakis 41
LEACH
Up to 8x reduction in energy dissipation between LEACH and conventional routing protocols
Total system energy dissipated using direct communication, MTE and LEACH for a 100-node random network
Total system energy dissipated using direct communication, MTE and
LEACH for a 100-node random network.
Direct communication: All sensor nodes communicate directly with
base station
MTE: “Minimum Transmission Energy”, power-aware protocol, where
nodes route data destined ultimately for the base station through
intermediate nodes. Thus nodes act as routers for other nodes’ data in
addition to sensing the environment. These protocols differ in the way
the routes are chosen. In this case the intermediate nodes are chosen
such that the transmit amplifier energy is minimized.
The figure shows how these algorithms compare with LEACH using
energy dissipated per bit as Eelec=50nJ/bit. The plot shows that
LEACH achieves between 7x and 8x reduction in energy compared
with direct communication and between 4x and 8x reduction in
energy compared with MTE routing.
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2005 Prof. I Stavrakakis 42
LEACH
LEACH’sLEACH’s strengthsstrengths•• Localised coordination of clustersLocalised coordination of clusters•• Randomized rotation of the Randomized rotation of the
clusterheadsclusterheads•• Scalable due to clustering hierarchyScalable due to clustering hierarchy•• EnergyEnergy--efficient due to the combination efficient due to the combination
of data compression and routingof data compression and routing
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2005 Prof. I Stavrakakis 43
LEACH
LEACH’sLEACH’s weaknessesweaknesses•• Presence of a Presence of a hot spothot spot can deplete can deplete the power of nodes in its vicinity the power of nodes in its vicinity very quicklyvery quickly
•• Some sensors may not be able to Some sensors may not be able to power down due to their assigned power down due to their assigned taskstasks
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2005 Prof. I Stavrakakis 44
SPINAdaptive Protocols for Information Adaptive Protocols for Information Dissemination in Wireless Sensor NetworksDissemination in Wireless Sensor Networks
Family of adaptive protocols called SPIN for efficient dissemination of information in energy-constrained wireless sensor network
SPIN characteristicsSPIN characteristicsIntroduction of high-level data descriptors (use of meta-data)Use of meta-data negotiation to eliminate transmission of redundant informationNodes base communication decisions upon application-specific knowledge and knowledge of the resources that are available to them
We present a family of adaptive protocols, called SPIN (Sensor
Protocols for Information via Negotiation), that efficiently disseminates
information among sensors in an energy-constrained wireless sensor
network. Nodes running a SPIN communication protocol name their
data using high-level data descriptors, called meta-data. They use
meta-data negotiations to eliminate the transmission of redundant
data throughout the network. In addition, SPIN nodes can base their
communication decisions both upon application-specific knowledge of
the data and upon knowledge of the resources that are available to
them. This allows the sensors to efficiently distribute data given a
limited energy supply. We simulate and analyze the performance of
two specific SPIN protocols, comparing them to other possible
approaches and a theoretically optimal protocol.
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2005 Prof. I Stavrakakis 45
SPINAnalysis of problems characterizing Analysis of problems characterizing conventional protocols for data conventional protocols for data dissemination in a sensor network:dissemination in a sensor network:
1. Implosion2. Overlap3. Resource blindness
SPIN solutions:SPIN solutions:1. Negotiation2. Resource adaptation
The design of SPIN grew out of an analysis of the different strengths and limitations
of conventional classic flooding protocols for disseminating data in a sensor network.
In classic flooding, each node keeps a record containing a list of all the data that it has
sent to its neighbours. The protocol begins when a source node sends its data to all of
its neighbours. Upon receiving a piece of data, each node stores the data and checks
the record to see whether it has already forwarded the data to its neighbour. If not, it
forwards a copy of the data to all of its neighbours and updates the record. This is
therefore a straightforward protocol requiring only a small amount of protocol state at
any node, and it disseminates data quickly in a network where bandwidth is not scarce
and links are not loss-prone.
The SPIN family of protocols incorporates two key innovations that
overcome these deficiencies: negotiation and resource-adaptation.
To overcome the problems of implosion and overlap, SPIN nodes
negotiate with each other before transmitting data. Negotiation helps
ensure that only useful information will be transferred. To negotiate
successfully, however, nodes must be able to describe or name the
data they observe. We refer to the descriptors used in SPIN
negotiations as meta-data. In SPIN, nodes poll their resources before
data transmission. Each sensor node has its own resource manager
that keeps track of resource consumption; applications probe the
manager before transmitting or processing data. This allows sensors
to cut back on certain activities when energy is low, e.g., by being
more prudent in forwarding third-party data.
Together, these features overcome the three deficiencies of classic
flooding. The negotiation process that precedes actual data
transmission eliminates implosion because it eliminates transmission
of redundant data messages. The use of meta-data descriptors
eliminates the possibility of overlap because it allows nodes to name
the portion of the data that they are interested in obtaining. Being
aware of local energy resources allows sensors to cut back on
activities whenever their energy resources are low, thereby extending
longevity.
1.2. The solution
The SPIN family of protocols incorporates two key innovations that overcome these
deficiencies: negotiation and resource-adaptation. To overcome the problems of
implosion and overlap, SPIN nodes negotiate with each other before transmitting data.
Negotiation helps ensure that only useful information will be transferred. To negotiate
successfully, however, nodes must be able to describe or name the data they observe.
We refer to the descriptors used in SPIN negotiations as meta-data.
In SPIN, nodes poll their resources before data transmission. Each sensor node has its
own resource manager that keeps track of resource consumption; applications probe
the manager before transmitting or processing data. This allows sensors to cut back on
certain activities when energy is low, e.g., by being more prudent in forwarding third-
party data.
It also allows sensors to take resource tradeoffs into account when making decisions.
For example, a SPIN node may decide to send a piece of data unconditionally,
without any negotiation, if it believes that the associated costs of sending the data are
less than the costs of negotiating for it. Together, these features can help SPIN nodes
overcome the three deficiencies of classic flooding. The negotiation process that
precedes actual data transmission eliminates implosion because it eliminates
transmission of redundant data messages. The use of meta-data descriptors eliminates
the possibility of overlap because it allows nodes to name the portion of the data that
they are interested in obtaining. Being aware of local energy resources allows sensors
to make prudent decisions about using these resources, thereby extending longevity.
Exchanging sensor data may be an expensive network operation, but exchanging data
about sensor data need not be. Second, nodes in a network must monitor and adapt to
changes in their own energy resources to extend the operating lifetime of the system.
This section presents the individual features that make up the SPIN family of
protocols.
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2005 Prof. I Stavrakakis 46
SPINImplosion problem Overlap problem
Figure 1 Figure 2
Figure 1. The implosion problem. In this graph, node A starts by flooding its data to
all of its neighbors. Two copies of the data eventually arrive at node D. The system
wastes energy and bandwidth in one unnecessary send and receive.
1. Implosion. In classic flooding, a node always sends data to its neighbours,
regardless of whether or not the neighbour has already received the data from another
source. This leads to the implosion problem.
Here, node A starts out by flooding data to its two neighbours, B and C. These nodes
store the data from A and send a copy of it on to their neighbour D. The protocol,
thus, wastes resources by sending two copies of the data to D. It is easy to see that
implosion is linear in the degree of any node.
Figure 2. The overlap problem. Two sensors cover an overlapping geographic region.
When these sensors flood their data to node C, C receives two copies of the data
marked r.
2. Overlap. Sensor nodes often cover overlapping geographic areas, and nodes often
gather overlapping pieces of sensor data. Figure 2 illustrates what happens when two
nodes (Aand B) gather such overlapping data and then flood the data to their common
neighbor (C). Again, the algorithm wastes energy and bandwidth sending two copies
of a piece of data to the same node. Overlap is a harder problem to solve than the
implosion problem – implosion is a function only of network topology, whereas
overlap is a function of both topology and the mapping of observed data to sensor
nodes.
3. Resource blindness. In classic flooding, nodes do not modify their activities based
on the amount of energy available to them at a given time. A network of embedded
sensors can be “resource-aware” and adapt its communication and computation to the
state of its energy resources.
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2005 Prof. I Stavrakakis 47
SPIN: Sensor Protocol for Information via Negotiation
Two basic ideas:Two basic ideas:1. sensor applications need to communicate with
each other about the data that they already have and the data they still need to obtain
2. nodes in a network must monitor and adaptto changes in their own energy resources to extend the operating lifetime of the system
MetaMeta--data:data:If x is the meta-data descriptor for sensor data X, then size of x < size of X for SPIN to be efficient
The SPIN family of protocols rests upon two basic ideas. First, to operate efficiently and to
conserve energy, sensor applications need to communicate with each other about the data
that they already have and the data they still need to obtain. Exchanging sensor data may be
an expensive network operation, but exchanging data about sensor data need not be. Second,
nodes in a network must monitor and adapt to changes in their own energy resources to
extend the operating lifetime of the system.
Our design of the SPIN protocols is motivated in part by the principle of Application Level
Framing (ALF). With ALF, network protocols must choose transmission units that are
meaningful to applications, i.e., packetization is best done in terms of Application Data Units
(ADUs). One of the important components of ALF-based protocols is the common data naming
between the transmission protocol and application, which we follow in the design of our meta-
data. We take ALF-like ideas one step further by arguing that routing decisions are also best
made in application-controlled and application-specific ways, using knowledge of not just
network topology but application data layout and the state of resources at each node. We
believe that such integrated approaches to naming and routing are attractive to a large range
of network situations, especially in mobile and wireless networks of devices and sensors.
Sensors use meta-data to succinctly and completely describe the data that they collect. If x is
the meta-data descriptor for sensor data X, then the size of x in bytes must be shorter than
the size of X, for SPIN to be beneficial. If two pieces of actual data are distinguishable, then
their corresponding meta-data should be distinguishable. Likewise, two pieces of
indistinguishable data should share the same meta-data
representation.
SPIN does not specify a format for meta-data; this format is application-specific. Sensors that
cover disjoint geogTaphic regions may simply use their own unique IDS as meta-data. The
meta-data x would then stand for “all the data gathered by sensor x”. A camera sensor, in
contrast, might use (x, y, 4) as meta-data, where (z, y) is a geographic coordinate and C$ is an
orientation. Because each application’s
meta-data format may be different, SPIN relies on each application to interpret and synthesize
its own metadata. There are costs associated with the storage, retrieval, and general
management of meta-data, but the benefit of having a succinct representation for large data
messages in SPIN far outweighs these costs.
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2005 Prof. I Stavrakakis 48
SPINSPIN messages:SPIN messages:
1. ADV: New Data Advertisement (meta-data)Nodes that have data to share send advertisement messages containing meta data
2. REQ: Request for Data (meta-data)Nodes wishing to receive some data, send request messages to inform the source node
3. DATA: Data message (data)This message type contains actual sensor data with a meta-data header
SPIN Messages
SPIN nodes use three types of messages to communicate:
ADV - new data advertisement. When a SPIN node has data to share,
it can advertise this fact by transmitting an ADV message containing
meta-data.
REQ - request for data. A SPIN node sends an REQ message when it
wishes to receive some actual data.
DATA - data message. DATA messages contain actual sensor data
with a meta-data header.
Because ADV and REQ messages contain only meta data, they are
smaller, and cheaper to send and receive, than their corresponding
DATA messages.
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SPIN-1: A 3-stage Handshake Protocol
1. ADV stageNew Data AdCheck for DataData Request
2. REQ stageData TransmissionData FusionNew Data Ad
3. DATA stageData RequestData Transmission
SPIN-l: A 3-Stage Handshake Protocol
The SPIN-l protocol is a simple handshake protocol for disseminating
data through a lossless network. It works in three stages (ADV-REQ-
DATA), with each stage corresponding to one of the messages
described above. The protocol starts when a node obtains new data
that it is willing to disseminate. It does this by sending an ADV
message to its neighbors, naming the new data (ADV stage). Upon
receiving
an ADV, the neighboring node checks to see whether it has already
received or requested the advertised data. If not, it responds by
sending an REQ message for the missing data back to the sender
(REQ stage). The protocol completes when the initiator of the protocol
responds to the REQ with a DATA message, containing the missing
data (DATA stage).
There are several important things to note about this example. First, if
node B had its own data, it could aggregate this with the data of node
A and send advertisements of the aggregated data to all of its
neighbors (d). Second, nodes are not required to respond to every
message in the protocol. In this example, one neighbor does not send
an REQ packet back to node B (e). This would occur if that node
already possessed the data being advertised.
Though this protocol has been designed for lossless networks, it can
easily be adapted to work in lossy or mobile networks. Here, nodes
could compensate for lost ADV messages by re-advertising these
messages periodically. Nodes can compensate for lost REQ and DATA
messages by rerequesting data items that do not arrive within a fixed
time period. For mobile networks, changes in the local topology can
trigger updates to a node’s neighbor list. If a node notices that its
neighbor list has changed, it can spontaneously re-advertise all of its
data.
This protocol’s strength is its simplicity. Each node in the network
performs little decision making when it receives new data, and
therefore wastes little energy in computation. Furthermore, each node
only needs to know about its single-hop network neighbors. The fact
that no other topology information is required to run the algorithm
has some important consequences. First, SPIN-l can be run in a
completely unconfigured network with a small, startup cost to
determine nearest neighbors. Second, if the topology of the network
changes frequently, these changes only have to travel one hop before
the nodes can continue running the algorithm.
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SPIN: Limited-energy simulations
DetermineDetermine how effectively how effectively each protocol uses its each protocol uses its available energyavailable energy
SPIN-1 distributes 68%SPIN-2 is able to distribute 73%the ideal protocol distributes 85%flooding distributes 53%gossiping distributes only 38%
For this experiment, we limited the total energy in the system to 1.6
Joules to determine how effectively each protocol uses its available
energy. Figure shows the data acquisition rate for the SPIN-l, SPIN-2,
flooding, gossiping, and ideal protocols. This figure shows that SPIN-2
puts its available energy to best use and comes close to distributing
the same amount of data as the ideal protocol. SPIN-2 is able to
distribute 73% of the total data as compared with the ideal protocol
which distributes 85%. We note that SPIN-1 distributes 68%, flooding
distributes 53%, and gossiping distributes only 38%.
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SPINOverall assessmentOverall assessment
Focus on efficient dissemination of sensor data to data sinks and energy conservation at the sensorsEmploys two key innovations: negotiation and resource-adaptationIntroduces meta-data as descriptors for negotiationsEach sensor has a resource manager for monitoring resourcesExchanging meta-data is more efficient than exchanging dataPolling the resource manager allows for extensive energy savings of sensors
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Directed Diffusion for WSNMotivationMotivation for algorithm designfor algorithm design
1. Robustness of communication2. Scaling for high nubmers of nodes3. Energy efficienct network operation
•• Example of operation:Example of operation:• “How many pedestrians do you observe in the
geographical region X?”
• “In what direction is that vehicle in region Y moving?”
Advances in processor, memory, and radio technology will enable
small and cheap nodes capable of sensing, communication, and
computation. Networks of such nodes can coordinate to perform
distributed sensing of environmental phenomena. In this paper, we
explore the directed-diffusion paradigm for such coordination. Directed
diffusion is data-centric in that all communication is for named data.
All nodes in a directed-diffusion- based network are application aware.
This enables diffusion to achieve energy savings by selecting
empirically good paths and by caching and processing data in-
network (e.g., data aggregation). We explore and evaluate the use of
directed diffusion for a simple remote-surveillance sensor network
analytically and experimentally. Our evaluation indicates that directed
diffusion can achieve significant energy savings and can outperform
idealized traditional schemes (e.g., omniscient multicast) under the
investigated scenarios.
Motivated by robustness, scaling and energy efficiency requirements we will
examine a new data dissemination paradigm for sensor networks. This
paradigm, directed diffusion, is data-centric. Data generated by sensor nodes
is named by attribute-value pairs. A node requests data by sending interests
for named data. Data matching the interest is then ‘drawn’ down towards that
node. Intermediate nodes can cache or transform data and may direct
interests based on previously cached data.
To motivate, consider this simplified model of how such a sensor
network will work. One or more human operators pose, to any node in
the network, questions of the form: “How many pedestrians do you
observe in the geographical region X?” or “In what direction is that
vehicle in region Y moving?” These queries result in sensors within the
specified region being tasked to start collecting information. Once
individual nodes detect pedestrians or vehicle movements, they might
collaborate with neighboring nodes to disambiguate pedestrian
location or vehicle movement direction. One of these nodes might then
report the result back to the human operator.
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Example of operation:Example of operation:The operator’s query will be transformed into an interest that is diffused toward nodes in regions X or Y (broadcast, geographical routing)Nodes activate their sensors which begin collecting information about pedestriansInformation returns along the reverse path of interest propagationIntermediate nodes might aggregate the data
Directed Diffusion for WSN
Using this communication paradigm, our example might be
implemented as follows. The human operator’s query would be
transformed into an interest that is diffused (e.g., broadcasted,
geographically routed) toward nodes in regions X or Y. When a node in
that region receives an interest, it activates its sensors which begin
collecting information about pedestrians. When the sensors report the
presence of pedestrians, this information returns along the reverse
path of interest propagation. Intermediate nodes might aggregate the
data, e.g., more accurately pinpoint
the pedestrian’s location by combining reports from several sensors.
An important feature of directed diffusion is that interest and data
propagation and aggregation are determined by localized interactions
(message exchanges between neighbors or nodes within some vicinity).
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Directed Diffusion for WSNDirected Diffusion elements:
Algorithm based on InterestsInterestsData messagesData messagesGradientsGradientsReinforcementsReinforcements
Sinks request data by sending interest messagesinterest messagesEach interest contains a description of a sensing a description of a sensing tasktask for acquiring dataData is a collection of eventscollection of events or processed processed informationinformation of a physical phenomenon
Directed diffusion consists of several elements: interests, data
messages, gradients, and reinforcements. An interest message is a
query or an interrogation which specifies what a user wants. Each
interest contains a description of a sensing task that is supported by a
sensor network for acquiring data. Typically, data in sensor networks
is the collected or processed information of a physical phenomenon.
Such data can be an event, which is a short description of the sensed
phenomenon.
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Directed Diffusion elementsDirected Diffusion elements:Data is named using attributeattribute--value pairsvalue pairsThe interest dissemination sets up gradients gradients within the networkwithin the network designed to “draw” eventsA gradient direction stateA gradient direction state is created in each node that receives an interestEvents start flowingstart flowing towardtoward the originators of interests along multiple gradient pathsThe sensor network reinforces onereinforces one or a small a small numbernumber of these paths
Directed Diffusion for WSN
In directed diffusion, data is named using attribute-value pairs. A
sensing task (or a subtask thereof) is disseminated throughout the
sensor network as an interest for named data. This dissemination sets
up gradients within the network designed to “draw” events (i.e., data
matching the interest). Specifically, a gradient direction state is
created in each node that receives an interest. The gradient direction
is set toward the neighboring node from which the interest is received.
Events start flowing toward the originators of interests along multiple
gradient paths. The sensor network reinforces one or a small number
of these paths.
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Directed Diffusion for WSN
Key Key featuresfeatures1.1. Interests Interests
disseminationdissemination2.2. Gradients setupGradients setup3.3. Reinforcement of Reinforcement of
one or more one or more gradient pathsgradient paths
2. Reinforcement
1. Low data rate
3. High data rate
In Directed Diffusion, a query would be transformed into an interest that is diffused or
flooded towards nodes in the interested region. When a sensor node in that region
receives the interest, it activates its sensors and begins to monitor the interested event.
The sensed data is then returned in the reverse path(s) of the interest propagation. The
intermediate nodes might aggregate the data based on their name and attribute-value
pairs. The propagation and aggregation procedures in Directed Diffusion are all based
on local information gained by localized interactions.
Directed Diffusion employs reinforcement to choose a particular path to sending
events. To do that, the interest is initially diffused with a longer interval (low-rate
events). Then low-rate events might reach the sink via multiple paths. After the sink
receives these low rate events, it reinforces one particular neighbor in order to “draw
down” real data. To do that, the sink re-sends the original interest message but with a
smaller interval. When the neighboring node receives the interest, it notices its
gradient to this neighbor and the new interval in the interest. In this case, if the new
interval is smaller than any existing gradient, it must reinforce at least one neighbor.
As this process is repeated, a path will be eventually established between the source
and the sink.
Since Directed Diffusion networks are application aware, they can achieve energy
saving by selecting good paths empirically by caching and processing data in-
network.
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Naming for a vehicle tracking exampleNaming for a vehicle tracking example
Directed Diffusion for WSN
Interest Naming
{type = wheeled vehicle;
interval = 20 ms;
duration = 10 s;
rect = [-100, 100, 200, 400] }
Data Naming{type = wheeled vehicle;interval = truck;location = [125; 220];intensity = 0:6;confidence = 0:85;timestamp = 01 : 20 : 40}
In directed diffusion, task descriptions are named by, for example, a list of
attribute-value pairs that describe a task. A vehicle-tracking task might be described
as:
type = wheeled vehicle // detect vehicle location
interval = 20 ms // send events every 20 ms
duration = 10 s // for the next 10 s
rect = [-100, 100, 200, 400] // from sensors within rectangle
For ease of exposition, we choose the subregion representation to be a rectangle
defined on some coordinate system; in practice, this might be based on GPS
coordinates. Intuitively, the task description specifies an interest for data matching
the attributes. For this reason, such a task description is called an interest. The
data sent in response to interests are also named using a similar naming scheme.
Thus, for example, a sensor that detects a wheeled vehicle might generate the
following data:
type = wheeled vehicle // type of vehicle seen
interval = truck // instance of this type
location = [125; 220] // node location
intensity = 0:6 // signal amplitude measure
confidence = 0:85 // confidence in the match
timestamp = 01 : 20 : 40 // event generation timestamp
For our sensor network, we have chosen a simple attribute-value based interest and
data naming scheme. In general, each attribute has an associated value range. For
example, the range of the attribute is the set of codebook values representing mobile
(vehicles, animal, humans). The value of an attribute can be any subset of its range.
In our example, the value of the attribute in the interest is that corresponding to
wheeled vehicles.
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An example of path ReinforcementAn example of path Reinforcementinitial interest: { type = wheeled vehicle; interval = 1 s; rect = [-100, 200, 200, 400]; timestamp = 01 : 20 : 40; expiresAt = 01 : 30 : 40}A possible rule: Reinforce any neighbor from which a node receives a previously unseen eventthe sink resends the original interest: { type = wheeled vehicles; interval = 10 ms; rect = [-100, 200, 200, 400]; timestamp = 01 : 22 : 35; expiresAt = 01 : 30 : 40}
Directed Diffusion for WSN
An interest is usually injected into the network at some (possibly arbitrary) node in the network. We use
the term sink to denote this node.
Interest Propagation: Given our choice of naming scheme, we now describe how interests are diffused
through the sensor network. Suppose that a task, with a specified type and rect, a dureation of 10 min
and an interval of 10 ms, is instantiated at a particular node in the network. The interval parameter
specifies an event data rate; thus, in our example, the specified data rate is 100 events per second. This
sink node records the task; the task state is purged from the node after the time indicated by the
duration attribute. For each active task, the sink periodically broadcasts an interest message to each of
its neighbors. This initial interest contains the specified rect and duration attributes, but contains a
much larger interval attribute. Intuitively, this initial interest may be thought of as exploratory; it tries to
determine if there indeed are any sensor nodes that detect the wheeled vehicle. To do this, the initial
exploratory interest specifies a low data rate (in our example, one event per second)
Then, the initial interest takes the following form:
type = wheeled vehicle
interval = 1 s
rect = [-100, 200, 200, 400]
timestamp = 01 : 20 : 40
expiresAt = 01 : 30 : 40
When a node receives an interest, it checks to see if the interest exists in the cache. If no matching entry
exists (where a match is determined by the definition of distinct interests specified above), the node
creates an interest entry. The parameters of the interest entry are instantiated from the received interest.
This entry has a single gradient toward the neighbor from which the interest was received, with the
specified event data rate. In our example, a neighbor of the sink will set up an interest entry with a
gradient of one event per second toward the sink.
Gradient Establishment: Previous figures shows the gradients established in the case where interests are
flooded through a sensor field. Note that for our sensor network, a gradient specifies both a data rate and
a direction in which to send events. More generally, a gradient specifies a value and a direction. In
summary, interest propagation sets up state in the network (or parts thereof) to facilitate “pulling down”
data toward the sink. The interest propagation rules are local and bear some resemblance to join
propagation in some Internet multicast routing protocols.
Reinforcement for Path Establishment: In the scheme we have described so far, the sink initially and
repeatedly diffuses an interest for a low-rate event notification. We call these exploratory events, since
they are intended for path setup and repair. We call the gradients set up for exploratory
events exploratory gradients. Once a source detects a matching target, it sends exploratory events,
possibly along multiple paths, toward the sink. After the sink starts receiving these exploratory events, it
reinforces one particular neighbor in order to “draw down” real data (i.e., events at a higher data rate that
allow high quality tracking of targets). We call the gradients set up for receiving high-quality tracking
events data gradients.
In general, reinforcememnt feature of directed diffusion is achieved by data driven local rules. One
example of such a rule is to reinforce any neighbor from which a node receives a previously unseen event.
To reinforce this neighbor, the sink resends the original interest message but with a smaller interval
(higher data rate), as follows:
type = wheeled vehicles
interval = 10 ms
rect = [-100, 200, 200, 400]
timestamp = 01 : 22 : 35
expiresAt = 01 : 30 : 40
When the neighboring node receives this interest, it notices that it already has a gradient toward this
neighbor. Furthermore, it notices that the sender’s interest specifies a higher data rate than before. If this
new data rate is also higher than that of any existing gradient (intuitively, if the “outflow” from this node
has increased), the node must also reinforce at least one neighbor. The node uses its data cache for this
purpose. Again, the same local rule choices apply. For example, this node might choose that neighbor
from whom it first received the latest event matching the interest. Alternatively, it might choose all
neighbors from which new events were recently received. This implies that we reinforce that neighbor only
if it is sending exploratory events. Obviously, we do not need to reinforce neighbors that are already
sending traffic at the higher data rate.
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2005 Prof. I Stavrakakis 59
Differences w.r.t. IPDifferences w.r.t. IP--based networksbased networksdiffusion is datadiffusion is data--centriccentricall communication in diffusion is neighborneighbor--toto--neighborneighbor (not end-to-end)sensor nodes do not need to have globally globally unique identifiersunique identifiers (no IP address required)every node can cachecache, aggregateaggregate, and more generally, process messagesprocess messages (no servers for performing such tasks)
Directed Diffusion for WSN
Our description points out several key features of diffusion and how it
differs from traditional networking. First, diffusion is data-centric; all
communication in a diffusion-based sensor network uses interests to
specify named data. Second, all communication in diffusion is
neighbor-to-neighbor, unlike the end-to-end communication in
traditional data networks. In other words, every node is an “end” in a
sensor network. In the
sense, there are no “routers” in a sensor network. Each sensor node
can interpret data and interest messages. This design choice is
justified by the task specificity of sensor networks. Sensor networks
are not general-purpose communication networks. Third, sensor
nodes do not need to have globally unique identifiers or globally
unique addresses. Nodes, however, do need to distinguish among
neighbors. Finally, in an IP-based
sensor network, for example, sensor data collection and processing
might be performed by a collection of specialized servers which may,
in general, be far removed from the sensed phenomena. In our sensor
network, because every node can cache, aggregate, and more
generally, process messages, it is generally desirable to perform
coordinated sensing close to the sensed phenomena. Diffusion is
clearly related to traditional network data-routing algorithms. In some
sense, it is a reactive routing technique, since “routes” are established
on demand. However, it differs from other ad hoc reactive routing
techniques in several way. First, no attemptis made to find one loop-
free path between source and sink before data transmission
commences. Instead, constrained or directional flooding is used to set
up a multiplicity of paths and data messages are initially sent
redundantly along these paths. Second, soon thereafter, reinforcement
attempts to reduce this multiplicity of paths to a small number, based
on empirically observed path performance. Finally, a message cache is
used to perform loop avoidance. The interest and gradient setup
mechanisms themselves do not guarantee loop-free paths between
source and sink
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Directed Diffusion for WSNDirected Diffusion characteristicsDirected Diffusion characteristics
All communication is for named dataData is named by attribute-value pairsIntermediate nodes may aggregate dataThus achieving significant energy-savingsPropagation and aggregation procedures are based on local information, gained by localized interactions
DD is capable of realizing robust, multi-path, energy-efficient data delivery in WSNs
Directed Diffusion can be used to realize robust multi-path delivery. It empirically
adapts to a small subset of network paths, and therefore achieves energy saving when
intermediate nodes aggregate responses to the previous queries.