Localized Techniques for Power Minimization and Information Gathering in Sensor Networks

EE249 Final Presentation

David Tong NguyenAbhijit Davare

Mentor: Farinaz Koushanfar

Outline

Introduction Assumptions Project Goals Problem Formulations Related Work 1. Node coordination for power minimization 2. Network traversal algorithm 3. Generation of optimal solution Experimental Results Conclusions Future Work

Introduction

Ad-Hoc wireless sensor networks Unattended autonomous operation Limited energy sources Idle power consumption Not just point to point routing, but

gathering information using only local information Uncertainty about node status (active,

standby)

Assumptions for the sensor network

Unit Disk Communication Model Nodes can communicate iff (Euclidean

distance Rc), where Rc is fixed communication range

ECommunication >> EComputation

Eidle ~ ECommunication (While radio is on) The algorithms run above the MAC layer protocol Node Information includes its ID, geographic

position and status (active/standby) Each node has information about its neighbors

Project Goals

Efficient localized node coordination for extending the network lifetime

Power efficient information gathering method Gathers the queries from all of the nodes within

a predefined area in the deployment field Attempts to visit as few nodes as possible,

minimizing communication energy consumption

Problem Formulations

1 – Localized power efficient coordination: Objective: Maximize the number of nodes in standby mode

using only local information. Constraints: Global network connectivity should be

preserved, i.e. A node cannot go into standby if it disconnects the network.

2 – Localized efficient information gathering: Objective: Minimize the number of communications

required for gathering complete information from a network, where some nodes are in standby.

3 – Generation of optimal solution for network traversal Objective: Find a network and an optimal traversal path

through that network that minimizes the number of nodes visited while gathering data from each node

Related Work Power aware MAC layer

PAMAS [Kravets et al. 2000], [Woo et al. 2001], S-MAC [Ye et. al. 2002]

Coordination power saving strategies Span [Chen et al. 2001], GAF [Xu et al. 2001] Ascent [Cerpa et.

al., 2002] They do not state necessary and sufficient conditions for putting a

node in standby & have less power savings. Network discovery

Birthday protocols [McGlynn et al. 2001], TopDisk [Deb et. al. 2002], ad-hoc routing survey [Stojmenovic et al. 2002] We also consider the network shape & regions of low density.

Perimeter routing Guaranteed delivery [Bose et al. 2001], GSPR [Karp et al. 2001]

They did not consider perimeter routing for studying the shape of the network and has just used it for coming out of local minima in routing.

1 - Efficient Node Coordination for Power Minimization

We guarantee that enough nodes stay active to maintain network connectivity Necessary and sufficient condition for

putting a node into standby is to ensure an alternate path exists between any two of its neighbors

Fair power saving method Only local information used

1 - Initial Phase: Token Assignments

Token defines the current active node that has the control of the flow of procedure

Distributed local computation multiple tokens required

Handshaking between tokens is done through a semaphore-like mechanism

During the initial phase, tokens are assigned to the nodes Such that every node has a token Tokens act in a localized area

1 - Node Selection for Standby Mode

Each token uses updated information from its local area to make a decision.

Token considers itself and its neighbors. Each token “locks” the nodes it is considering. Token chooses node whose neighbors will be

able to communicate for the longest time if the node stays in standby mode.

Each node sleeps for Ts interval, dependent on the energy in its local area

Token is then passed to node which gone the largest amount of time without obtaining the token (“miss me?”)

1 - Parameter Tuning

Flexibility in choosing: Ts vs. density Number of tokens Ts vs. number of tokens

2 - Information Gathering: What is new?

While there exist many point-to-point routing algorithms, no major contribution for complete area traversal.

Guarantee complete information gathering Graph theoretic and geometric abstraction

of the network area: Perimeter (shape) of the network Ranking w.r.t connectivity

Completely localized traversal procedure

Find the perimeter of the network using method similar to Right-Hand Rule.

2 - Perimeter Routing

Starting Node

Problem: If edges cross in the network, right-hand rule fails

2 - Perimeter Routing - Planarizing

Solution: Planarize the Graph

u vw • To include the edge (u, v) in the graph,

the shaded circle must not contain any node w. (Gabriel graph planarization)

2 - Partitioning the graph

While traversing the perimeter, find partitioning points of the planarized graph.

Starting Node

Partitioning Point

2 - Traversal Method

Network traversal begins at a perimeter node

Next node is determined locally according to: Rank – Distance from perimeter Parity – Even or odd rank Section – Prefer unvisited nodes in same

section Novelty – # of unvisited neighbors

3 - Generating Instances with Known Solutions

To accurately evaluate the quality of network traversal heuristic, must know the optimal solution.

However, given a network, generation of optimal network traversal is NP-complete.

Alternative: Generate an optimal solution first, then generate network around it. A path through the network is the optimal if each

node on the path has at least one unique neighbor, and no other nodes have unique neighbors

3 - Generating Instances with Known Solutions (cont’d)

1. Place the initial node randomly.

2. Choose a unique neighbor node within rc.

3. Choose a second node that is in range of the first node, but not of the unique node

4. Iterate until path is required length

5. Filler nodes can be added that are in range of the path nodes

Experimental Results: Coordination

Extension of Network Lifetime with Standby Strategy

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Standby Strategy

Baseline

• As number of nodes in the network increases, standby strategy becomes more effective

Experimental Results: Network Traversal Algorithm

#Nodes visited in network traversal for different network shapes and sizes

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a/b = 1

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Conclusions

Tremendous energy savings using a localized standby strategy

Necessary and sufficient conditions to maintain the network connectivity

Energy efficient information gathering, which uses both geometric and graph theoretic information

Future Work

Find efficient information dissemination methods

Integrate other power saving strategies into the simulations