Self Organizing and Network Coding in WSNs

Rennie Archibald

Contents

• Brief description of Wireless Sensor Networks• Protocols For Self Organization of a Wireless

Sensor Network• Network Information Flow– Intro to Network Coding – Network Coding for Efficient Communication in

Extreme Networks• Conclusion

Wireless Sensor Networks

• Wireless Sensor Network (WSN) are a wireless network made up of distinct nodes distributed over some area and most often take readings of their environment (e.g. temp, precipitation, radiation, etc…)

• WSN Characteristics– Expected to scale to thousands of nodes– Changing network topology– Robust to failure– Maintenance free

Characteristics of Wireless Sensor Nodes

• limited power, computation, and communication capabilities.

• Interact with the physical environment– Must be robust to their environment

• Maintenance free• Cheap

WSN example

Courtesy of University of California, Berkeley, and Wireless Sensor Networks Lab

Other Wireless Networks• Mobile ad hoc networks (MANETs)

– Nodes interact with one another– QoS oriented– Networks of up to 100s of nodes

• Cellular Networks– Nodes interact only with base station which has high computational capabilities and

virtually unlimited power– Organization is limited to tower handoffs due to

• Distance• Chipping code

• Bluetooth– 10 m range– Requires a Master-Slave relationship (1-7)

• HomeRF– Short range (home)– 802.11 standard communication

Protocols for Self-Organization of a WSN[1]

• Wireless networking considerations specific to WSNs– Low power nodes– Failure of neighboring node(s)

• These issues effect the design of– Physical layer– Channel coding and access methods (e.g. CDMA/CA)– What data is transmitted (raw or processed)

• Tradeoff in power consumed.

• To deal with these issues a protocol must:– Be energy efficient– Distributive– Adapt to changes in topology

Protocols for Self-Organization of a WSN

• Paper proposes a suite of algorithms to deal with the networking aspect of WSN. – Self-Organizing Medium Access Control for Sensor

Networks (SMACS)• The initial self-organization portion.

– Eavesdrop-And-Register (EAR) • For mobile agents

– Sequential Assignment Routing (SAR)• Routing protocol

Link Layer and SMACS

• Issues to address with in Link Layer1. Formation of a topology2. Regulation of channel access among the nodes

• The proposed SMACS has the following benefits– Distributed algorithm– Deals with both issues simultaneously, reducing

the communication between nodes– Simple and somewhat scalable.

SMACS protocol1. Wake and listen for a designated amount of time2. If no message is received at the end of it’s listening stage a node i will

transmit a TYPE 1 message (essentially a “HELLO” msg)3. Listening nodes not already connected to nodei will respond with a

TYPE 2 message4. Nodei will send out a TYPE 3 message with the id of the node j whose

response was received first, along with it’s schedule for communication

5. Nodej will respond with a mutually open frequency if one exists

**Presumably if any collisions occur during this process the nodes will sleep for a random amount of time and then restart the process.**

SMACS exampleNode A Node B Node C Node D

wake

Listen

wake

Listen

wake

Listen

wake

Listen

winner

Type 1

Type 2

Type 3

Type

EAR algorithm

• The EAR algorithm seeks to allow mobile sensor connection to the overall WSN

• Algorithm similar to SMACS:1. Stationary node invites and listens with a Broadcast Invite (BI)

message2. Mobile node chooses stationary node with best signal-to-

noise-ratio (SNR) and sends Mobile Invite (MI) message.3. Stationary node determines if link is possible and responds

with an Mobile Response (MR) message detailing when to communicate

4. The mobile node cuts communication with a Mobile Disconnect (MD) message.

SAR algorithm

• Table-driven multipath approach– Goal is to have paths to the sink that are disjoint. Thus

robust to failure to the degree of their disjointedness (say k)– Assume that the bottleneck in the network will occur at the

one-hop neighbors to the sink.• Achieved by rooting trees at each of the one-hop

neighbors of the sink and selecting which path based on the path’s energy resources and QoS.

• Issues:– How much computation is a node doing EVERY time it sends

a packet(s)?

a) Initial Network– 45 nodes– 100 frequencies– = 0.04 nodes/m2

b) Initial connected graph– Avg degree = 2.13– 31% of nodes have

multiple paths– Issue: What about final

connected graph?

c) Positions of the mobile node and its connections

d-f) Spanning trees connecting the sensor to the mobile node

Some comments/questions on the “Protocols for Self-Organization in WSNs”

• These protocols are a security nightmare• Continuously make the assumption of random node placement

– When would random node placement occur?• Ocean, air dropped, …

• By taking the first message received you are giving precedence to local connections.– This could lead to the problem addressed by David; The existence of well connected

subgraphs that are connected by very few links.• Claim that protocols must work on thousands of nodes but only do tests on 45• Also Their assumption on the number of available channels is optimistic at

best. Instead of 2600 channels 16 would be a better estimate.• Is a “Best Effort” approach good enough for the scenarios they mentioned

WSNs could be used?– Surveillance, environmental sampling, security, health monitoring.

Network Coding Motivation

• In a computer network a broadcast message originates at some node origin node v and is propagated throughout the connected network (assuming there is no limit on hop count).

• Multicasting is a directed broadcast such that the information is only sent from the origin once but reaches only certain nodes.

• The commercial application for multicasting comes mainly from streaming multimedia (such as IPTV)

Network Coding

• In a given network a max-flow min-cut algorithm will yield the maximum amount of data that can be transferred over the network.

• While this optimal transfer is desired it was shown that traditional routers are not capable of achieving it.

• Instead the concept of Network Coding is introduced in [2].– Instead of simply routing information nodes are

capable of processing.

Network Coding example

• This example, which uses simple linear coding, from [3] shows that the max-flow min-cut algorithm will show that max-flow to each sink is 2.

• However, simple routing cannot achieve the max-flow to each sink.• The max-flow is achievable through the use of network coding shown in

the figure on the right. By using an xor it is possible to encode the data and then recover it at each sink.

• Note that there is no unique solution to network coding.

Limitations and Issues

• Limitations– Single source.– This particular protocol can’t be done on the fly.• Assumes nodes have knowledge on how to code and

decode

• Issue:– Multisource multicasting is in general a hard

problem.

Network Coding for Efficient Communication in Extreme Networks

• Given a delay tolerant network, such as a WSN or some other ad-hoc network. Widmer et al outline a routing protocol incorporating– Distributed Network Coding [4]– Probabilistic Routing– Generations– Information Aging

The Distributed Network Coding Concept

• Additions from original work on Network Encoding– The model used in this paper was actually proposed by Chou et al in [4].

The original network coding algorithm required that decoders had knowledge of the code sent to them.

– Here the encoding vector is sent with each message, thus there is overhead in the scheme (we will see how much later)

• Messages are recovered from the decoding matrix Gv (distinct to each node v), which has 2-tuple rows (encoding vector, information vector)– The matrix bounded by m(m+M)– At the latest the message can be decoded when m original source vectors

are received.• Only store innovative packets

– An innovative is a packet that increases the rank of the matrix

Efficient Network Coding Terminology

• y(ei) – The message received on link i– Note that there must be h of these messages to

successfully decode the message.

• y(ei) can be broken into two parts.1. g(ei) – the encoding vector received link i.

2. xi – The actual data, originally encoded

Network Coding Example

• For this example we look at the node t2

• Incoming links:– y(e1) = [1 0 | b1]

– y(e3) = [1 1 | b1 + b2]

– Let b1 = b2 = 1

• Remember that b1 could be a string of bits (say 1400 in an IP packet.

Probabilistic routing

• In order to minimize redundant traffic Probabilistic routing is used

• Instead of broadcasting each packet it receives, a node broadcasts the current packet with a certain probability.

• This avoids the scenario shown on the right. Where all nodes broadcast with probability 1. – Clearly there are redundant

packet transmissions here.

4

1

3

2

Forwarding Factor

• Instead of simply broadcasting a packet on reception with some probability Widmer et al take advantage of the network coding by using a so-called “forwarding factor” d

• When an innovative packet is received, d vectors will be generated from the decoding matrix and broadcast, an additional vector is generated and broadcast with probability d - d

Generations• A Generation corresponds to a particular decoding

matrix Gv at node v, – upper bound of m(m+M) on the size of the matrix

• It may be beneficial to force a smaller generation (fewer rows) due to memory constraints.– However to low a size decreases the efficiency of

Network Coding• The authors chose to use a hash function over the

sender’s address and the packet identifier to determine the generation

Gain Generation Size

• The graph to the left shows that there is an increase in efficiency as generation sizes grow.

• What must be balanced is the overhead incurred by larger generation sizes. An example of the increasing overhead is shown in the table.

Information Aging

• Due to limited memory capacity on nodes information aging is employed to decrease the size of a generation

• This process occurs after a node v has a complete decoding matrix Gv

• As space is needed two rows can be linearly combined. Thus information that is feasibly innovative to a nodes neighbors is not lost.

Dense Network Simulations

• On the left we see the performance for network coding on a static network.– Network Coding allows for 100% PDR with a forwarding factor of d = 0.25– Probabilistic routing requires nearly triple the amount of forwarding to

reach 100% PDR.• The graph on the right gives the results of a network in which

mobility is present.

Information on the Neighborhood

• The proceeding tests on sparse networks were done with additional variable, the amount of knowledge a node has about it’s neighbors– Without beacons: No information on neighbors– Normal beacons: Only send a packet if neighbors

exist– Intelligent beacons: announce the presence of a

neighbor and all information currently in Gv

• Prevents redundant information transfer less overhead

Performance in Sparser Networks

Questions on usefulness of WSNs

• Once the battery is drained what happens?– Just leave a toxic item lying in a field? If not how do you collect

them?• Questionable usage in an agricultural environment where the sensor may

become difficult to find but must be removed to avoid destroying crops.

• What kind of QoS can we gurauntee?– Attacks this Network Coding algorithm allows

• Replay• Blackhole• Homing

• Attacker has physical access– An attack such as the one George discussed on Tuesday would

succeed.

Network Coding• [1] K. Sohrobi et al., "Protocols for Self-Orgonizotion of o Wireless

Sensor Network,'' IEEE Personal Communication. vol. 7, no. 5, Oct. 2000, pp. 16-27.

• [2] R. Ahlswede, N. Cai, S. R. Li, and R. W. Yeung. Network Information flow. IEEE Transactions on Information Theory, July 2000.

• [3] P.A. Chou, Y. Wu, and K. Jain, “Practical Network Coding,” Proc. 51st Ann. Allerton Conf. Comm., Control, and Computing, Oct. 2003.

• [4] Widmer, J. and Le Boudec, J. “Network coding for efficient communication in extreme networks” In Proc of the 2005 ACM SIGCOMM Workshop on Delay-Tolerant Networking August, 2005.