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Wireless Sensor Networks: Minimum-energy communicationPowerPoint Presentation

Wireless Sensor Networks: Minimum-energy communication

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Wireless Sensor Networks

- Large number of heterogeneous sensor devices
- Ad Hoc Network

- Sophisticated sensor devices
- communication, processing, memory capabilities

Wireless Sensor Networks: Minimum-energy communication

Project Goals

- Devise a set communication mechanisms s.t. they
- Minimize energy consumption
- Maximize network nodes’ lifetimes
- Distribute energy load evenly throughout a network
- Are scalable (distributed)

Wireless Sensor Networks: Minimum-energy communication

Wireless Sensor Networks: Minimum-energy communication

Unicast communication model

- Link-based model
- each link weighed
- how to chose a weight?

- Power-Aware Metric [Chang00]
- Maximize nodes’ lifetimes
- include remaining battery energy (Ei)

- Maximize nodes’ lifetimes

Wireless Sensor Networks: Minimum-energy communication

Unicast problem description

- Definitions
- undirected graph G = (N, L)
- links are weighed by costs
- the path A-B-C-D is a minimum cost path from node A to node D, which is the one-hop neighbour of the sink node
- minimum costs at node A are total costs aggregated along minimum cost paths

- Minimum cost topology
- Minimum Energy Networks [Rodoplu99]
- optimal spanning tree rooted at one-hop neighbors of the sink node
- each node considers only its closest neighbors - minimum neighborhood

D

C

B

A

Wireless Sensor Networks: Minimum-energy communication

Building minimum cost topology

- Minimum neighborhood
- notation: - minimum neighborhood of node
- P1: minimum number of nodes enough to ensure connectivity
- P2: no node falls into the relay space of any other node

- Finding a minimum neighborhood
- nodes maintain a matrix of mutual link costs among neighboring nodes (cost matrix)
- the cost matrix defines a subgraph H on the network graph G

C

A

B

Wireless Sensor Networks: Minimum-energy communication

Finding minimum neighborhood

- We apply shortest path algorithmto find optimal spanning tree rooted at the given node
- Theorem 1: The nodes that immediately follow the root node constitute the minimum neighborhood of the root node
- Theorem 2: The minimum costroutes are contained in the minimum neighborhood
- Each node considers just its min. neighborhood

subgraph H

Wireless Sensor Networks: Minimum-energy communication

Distributed algorithm

- Each node maintains forwarding table
- E.g. [originator ¦ next hop ¦ cost ¦ distance]

- Phase 1:
- find minimum neighborhood

- Phase 2:
- each node sends its minimumcost to it neighbors
- upon receiving min. costupdate forwarding table

- Eventually the minimum cost topology is built

Wireless Sensor Networks: Minimum-energy communication

An example of data routing

- Different routing policies
- different packet priorities
- nuglets [Butt01]
- packets flow toward nodes with
lower costs

- Properties
- energy efficiency
- scalability
- increased fault-tolerance

Wireless Sensor Networks: Minimum-energy communication

Wireless Sensor Networks: Minimum-energy communication

- Every node j is assigned abroadcast cost

- Omnidirectional antennas
- By transmitting at the power level max{Eab,Eac} node a can reach both node b and node c by a single transmission
- Wireless Multicast Advantage (WMA) [Wieselthier et al.]

b

Eab

Ebc

Eac

a

c

- Trade-off between the spent energy and the number of newly reached nodes

- Power-aware metric
- include remaining battery energy (Ei)
- embed WMA (ej/Nj)

Wireless Sensor Networks: Minimum-energy communication

C1={S1, S2, S3}

C2={S3, S4, S5}

C*=

- BCP

Greedy algorithm:

at each iteration add the set Sj that minimizes ratio cost(Sj)/(#newly covered nodes)

Broadcast cover problem (BCP)- Set cover problem

Wireless Sensor Networks: Minimum-energy communication

Distributed algorithm for BCP

- Phase 1:
- learn neighborhoods (overlapping sets)

- Phase 2:(upon receiving a bcast msg)
1: if neighbors covered HALT

2: recalculate the broadcast cost

3: wait for a random time before re-broadcast

4: if receive duplicate msg in the mean time goto 1:

- Random time calculation
- random number distributed uniformly between 0 and

Wireless Sensor Networks: Minimum-energy communication

Simulations

- GloMoSim [UCLA]
- scalable simulation environment for wireless and wired networks

average node degree ~ 6

average node degree ~ 12

Wireless Sensor Networks: Minimum-energy communication

Simulation results (1/2)

Wireless Sensor Networks: Minimum-energy communication

Simulation results (2/2)

Wireless Sensor Networks: Minimum-energy communication

Conclusion and future work

- Power-Aware Metrics
- trade-off between residual battery capacity and transmission power are necessary

- Scalability
- each node executes a simple localized algorithm

- Unicast communication
- link based model

- Broadcast communication
- node based model
- Can we do better by exploiting WMA properly?

Wireless Sensor Networks: Minimum-energy communication

if (Pac – Pab < Pbc) thentransmit atPac

Minimum-energy broadcast- Propagation model:
- Omnidirectional antennas
- Wireless Multicast Advantage (WMA) [Wieselthier et al.]

b

Pab

Pbc

Pac

a

c

- Challenges:
- As the number of destination increases the complexity of this formulation increases rapidly.
- Requirement for distributed algorithm.

- What are good criteria for selecting forwarding nodes?
- Broadcast Incremental Power (BIP) [Wieselthier et al.]
- Add a node at minimum additional cost
- Centralized
- Cost (BIP) <= Cost (MST)

- Improvements?
- Take MST as a reference
- Branch exchange heuristic…
- … to embed WMA in MST

Wireless Sensor Networks: Minimum-energy communication

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