a survey on energy-efficient techniques and a study on ...€¦ · directed diffusion for wireless...

A Survey on Energy-Efficient Techniques and a study on data dissemination protocol

Mohammad Khoshkholgh

A Survey on Energy-Efficient Routing Techniques with QoS Assurances for Wireless Multimedia Sensor Networks, IEEE Communications Survey & tutorials.

Directed Diffusion for Wireless Sensor Networking, IEEE/ACM Transactions on Networking

Routing in WSNs 2

1. Wireless Sensor Networks (WSN)

2. Routing in WSNs

3. Example of Routing in WSNs: Directed Diffusion for WS Networking

4. Wireless Multimedia Sensor Networks (WMSN)

5. Design Requirements for WMSN

6. Routing Techniques for WMSNs

7. Challenges and Open Issues in WMSNs

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1. Wireless Sensor Networks (WSN) 2. Routing in WSNs






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Small and Low-Prices Sensor Nodes

Promising technologies

Advances in embedded microprocessors, low-power analog and digital electronics, and radio communications

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Randomly Scattered Large number or irreplaceable, battery-powered SNs

Sensing and Gathering data from surrounding environment

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Multi-hop and self-organize

Cooperative sensing, collection, process

Sensor nodes are prone to failures

Topology is highly dynamic

Environment might be harsh

Generally broadcast rather than point-to-point

Nodes often lack global identification

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WSN is dense but sinks are sparse, thus efficient routing is required considering energy efficiency as the main concern.

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2. Routing in WSNs 3. Example of Routing in WSNs: Directed

Diffusion for WS Networking





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Flooding

Gossiping

Hierarchical Routing

Location-Based Routing

Network Flow Protocol

Data-Centric Routing

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Flooding

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Flooding 1. Does not need sophisticated routing protocol

2. Does not need topology maintenance

3. Energy inefficient

4. Is not scalable

5. Resource blindness

6. overlap

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Flooding

Gossiping





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Gossiping: A version of Flooding in which receiving node sends the packet to a randomly selected neighbor

Gossiping ◦ Network connectivity and latency

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Flooding

Gossiping





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1. Minimizing energy consumption by clustering

2. AGNs gather and compress sensed data and directly pass to the sinks

3. Usually selecting the cluster-heads (AGNs) is random

4. Increasing overhead signaling especially in large networks

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LEACH (Low Energy Adaptive Clustering Hierarchy)

PEGASIS (Power-Efficient Gathering in Sensor Information Systems)

TEEN(APTEEN) (Threshold-Sensitive Energy Efficient Protocols)

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Flooding

Gossiping





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Location-Based Protocols: using location information to improve efficiency

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Location-Based Protocols 1. Location is derived from GPS, infrastructure based

localization systems (distributed algorithms that enables a set of n nodes to determine the RF propagation delays between every pair of nodes, hence the spatial topology can be readily determined.)

2. Enables single hop communication (with nearest nod)

3. Fast response to dynamic topology change

4. Reduction in energy consumption and latency

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Location-Based Protocols

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Flooding

Gossiping





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(Cost, remained Energy)

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Flooding

Gossiping





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◦ Sources and destinations are less significant than data

◦ Queries are posted for specific data rather than data from particular node

◦ Routing is done based on negotiation to find interested nodes for data dissemination

◦ Examples: SPIN, directed diffusion, rumor routing, and gradient-based routing

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A family of adaptive protocols called Sensor Protocols for Information via Negotiation (SPIN)

1. assign a high-level name to completely describe their collected data (called meta-data)

2. Use thee types of messages ADV (advertisement), REQ (request) and DATA

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SPIN

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SPIN ◦ More efficient than Gossiping/Flooding

◦ May not guarantee stringent QoS requirement

◦ May not guarantee end-to-end delivery of data (if uninterested nodes)

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LEACH (Low Energy Adaptive Clustering Hierarchy) (Hierarchical Routing) Routing in WSNs 33

In general hierarchical routing are outperform Data-Centric Routing

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Propose an application-aware paradigm to facilitate efficient aggregation, and delivery of sensed data to inquiring destination

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An example of Data-centric and query-driven protocol ◦ Human operator’s query (task) is diffused ◦ Sensors begin collecting information about query ◦ Information returns along the reverse path ◦ Intermediate nodes aggregate the data

Combing reports from sensors

Challenges ◦ Scalability ◦ Energy efficiency ◦ Robustness / Fault tolerance in outdoor areas ◦ Efficient routing

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Data-centric: using interest to specify named data

Neighbor-to-neighbor communication: ant colonies that are scalable and extremely robust

Sensors do not need to have global address or identification, only being distinguishable among neighbors is enough

Each node is able to cache, aggregate, and process message: useful to coordinate sensing close to sensed phenomena

Reactive routing: routes are established based on demand

Generally useful for location tracking in remote surveillance sensor networks

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Data Naming

Interests and Gradient

Data Propagation

Reinforcement

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Interest propagation Data delivery along reinforced path Initial gradients setup

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Data Naming


Data Propagation

Reinforcement

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Data Naming ◦ A list of attribute-value pairs that describe a task

Data Reply ◦ Using attribute-value pairs

Type = Wheeled vehicle // detect vehicle location

Interval = 20 ms // send events every 20ms

Duration = 10 s // Send for next 10 s

Field = [x1, y1, x2, y2] // from sensors in this area

Type = Wheeled vehicle // type of vehicle seen

Instance = truck // instance of this type

Intensity = 0.6 // signal amplitude measure

Confidence = 0.85 // confidence in the match

Timestamp = 01:20:34 // event generation time

Field = [x1, y1, x2, y2] // from sensors in this area

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Data Naming


Data Propagation

Reinforcement

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Interest Propagation

◦ Sink diffuses exploratory interest (Exp)

◦ Sink periodically refreshes Exp: resending Exp with increasing timestamp to improve robustness (tradeoff between overhead and robustness)

◦ Rout between sink and sources will be discovered

◦ Neighbors update interest-cache and forwards Exp: each item in cash corresponds to distinct interest

◦ Interest entries only contains information about previous hop not sink: improve scalability

◦ Interest aggregation: similar interests can be represented by a single interest

◦ No way of knowing differentiating new interests from repeated

Type = Wheeled vehicle

Interval = 1 s

Field = [x1, y1, x2, y2]

timestamp=01:20:40

expiresAt=01:30:40

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Interest Propagation: Without considering which nodes are able to satisfy the interest

Save the energy

Immobile sensor network

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Gradient Establishment

◦ Gradient direction is set toward the neighboring nodes from which the interest is received

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Data Naming


Data Propagation

Reinforcement

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Data Propagation: Exploratory event


Instance =unicycle

Location=[125,220]

Intensity=0.6

Confidence=0.85

timestamp=01:20:40

By highest requested event rate among all its outgoing gradients=1 event per sec

Check to find matching interest entry in its cache:

No: drop data message Yes: check the data cache for recently seen data items

Yes: drop data message No: add data to cache and resent to the neighbors by down converting data rate based on appropriate gradient

Loop prevention

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Data Naming


Data Propagation

Reinforcement

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Data Propagation: Reinforcement to single path delivery

Then low-delay path will be selected One path that delivers event faster than others Choosing neighbor from which most events have been received Choosing neighbor which consistently sends events before other

neighbors


Interval = 10 ms

Field = [x1, y1, x2, y2]

Timestamp=01:22:35

expireAt=01:30:40

Sending original interest with higher data rate (smaller interval) by sink

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Data Propagation: Path establishment for multiple sources: sink is able to receive data streams from both sources

Path establishment for multiple sinks: sink Y has already reinforced a path, identical interest at X can use the reinforcement without waiting for data

Local repair for failed paths (energy depletion or obstacles): intermediate nodes on previously reinforced path can apply the reinforcement rules

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Data Propagation: negative reinforcement

◦ Soft state: time out all data gradients in the network

unless A

◦ Sink sends interest with lower data rate to B to degrade the path

Sink needs to negatively reinforce path through B (lossy or high-delay path)

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Simulation results ◦ Comparing with Flooding and Omniscient multicast

◦ Metrics:

Average Dissipate Energy: average work done by a node in delivering useful tracking information

per node energy dissipation / # events seen by sinks

Average Delay: temporal accuracy of location estimation

latency of event transmission to reception at sink

Distinct-event delivery ratio:

# of distinct events received / # of events originally sent

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Flooding

Omniscient Multicast

Directed Diffusion

aggragation reduces Diffusion redundancy - Flooding is poor because of multiple paths from source to sink

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Flooding

Omniscient Multicast

Directed Diffusion

Diffusion finds least delay paths

- Flooding incurs latency due to high MAC contention, collision

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Delivery ration degrades with more nodes failures

- Graceful degradation indicates efficient negative reinforcement

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Disadvantages ◦ Design doesn’t deal with congestion or loss

◦ Periodic broadcasts of interest reduces network lifetime

◦ Nodes within range of human operator may die quickly

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Extracting more realistic and precise information

Growing pace of technological demand

Availability of low-cost and miniature camera and microphone

WMSN: capable of capturing video, images, and sensor data

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Architectures: for sending data to sinks

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All nodes (WMSN) have the same capabilities unless multimedia processing hubs

Composed of WMSNs and WSNs

First layer: WSNs for sensing data, Second layer: WMSNs for gathering multimedia data, Third layer: WMSN for performing complex tasks

WMSN stretches WSN application WMSN: ◦ Real-time mission critical and monitoring

application ◦ Security surveillance ◦ Traffic and environment monitoring ◦ Wild animal tracking ◦ Disaster management ◦ Patient monitoring

Energy consumption is still the main issue like WSN, but routing techniques are required to be improved or re-invented

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5. Design Requirements for WMSN 6. Routing Techniques for WMSNs


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QoS requirements

Energy Efficiency

Architectural Issues

Hole Detection and Bypassing

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Latency: physical events must be reported within a certain period of time ◦ Intruder tracking, wild-fare monitoring, medical

care, and structural health diagnosis ◦ Two classes for end-to-end delay guarantee:

Deterministic (hard-latency): system failure

Predictive (soft-latency): probabilistic guarantee

Bandwidth: multimedia application requires high amount of bandwidth

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Reliability: source-destination communication with minimum packet loss ◦ Based on the application different reliability is

required

◦ Loss is mainly due to interference and congestion: Sending different copies on several paths

◦ Both latency and reliability: for volcanic eruption, toxic gases, intruder detection, and rescue services

◦ Appropriate routing protocol is required

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Jitter: causes glitches, discontinuity, and errors in video/audio

Synchronization is necessary

Buffering

QoS requirements

Energy Efficiency



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Energy consumption is prime concern ◦ Especially for environment and habitat monitoring

◦ WMSN consumes much higher energy than WSN due to applications and requirements

◦ Energy consumption is WSN is due to communication functionalities, but it is not true in WMSN

◦ Energy-aware routing protocol conducting all forms of energy consumption

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QoS requirements

Energy Efficiency



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Network dynamics: nodes, sinks, and target mobility ◦ Routing is very challenging

◦ Impose signaling overhead

◦ Routing should be scalable and flexible to topological changes

Data Delivery Model: routing protocols: continuous, event-driven, and query-driven ◦ Continuous is not suitable for WMSN

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Architectural Configuration: heterogeneous set of nodes raises many technical issues ◦ Different class of nodes report events with different

rates ◦ Their energy, memory, bandwidth requirements are

different ◦ Routing should consider these issues

Channel Capacity: ◦ Capacity and delay is location dependent and vary

continuously ◦ Multimedia is bandwidth hungry, delay intolerable,

and bursty in nature

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QoS requirements

Energy Efficiency



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Dynamic holes: bursty, bandwidth-hungry multimedia data makes some paths get exhausted

Bypassing holes and establishing new routes by balancing energy usage

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2. Routing in WSNs




6. Routing Techniques for WMSNs 7. Challenges and Open Issues in WMSNs

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Data-Centric Protocols: ◦ Negotiation based: not suitable for WMSN

Meta-data description depletes battery in multimedia data

ADV, REQ, and DATA flooding mechanism at each node is not appropriate for multimedia QoS

End-to-end delivery is not guaranteed

◦ Generally Swift depletion of a single path

In-network processing overhead which results in early network breakdown

Directed diffusion is not applicable for real-time application

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Hierarchical Routing: not suitable for WMSN ◦ Randomly selection of cluster heads ◦ Direct communication between cluster heads and

sinks

Location-Based Protocols: ◦ Reduction in latency and energy consumption ◦ If QoS requirements of multimedia application

meet, this is a suitable routing for WMSN

Network-Flow: not suitable for WMSN ◦ Longer paths may be chosen (reducing energy

consumption) without considering end-to-end latency

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1. Wireless Sensor Networks (WSN) 2. Routing in WSNs 3. Example of Routing in WSNs: Directed

Diffusion for WS Networking 4. Wireless Multimedia Sensor Networks

(WMSN) 5. Design Requirements for WMSN 6. Routing Techniques for WMSNs


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Balance between energy efficiency and QoS requirements

Almost all routing protocols in WMSN do not incorporate mobility

Multiple sources and sinks

Secure routing protocols

Cross-layer awareness: physical, data link, and network layers

Multichannel routing

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a survey on energy-efficient techniques and a study on ...€¦ · directed diffusion for wireless...

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