Wireless sensor networks minimum energy communication
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Wireless Sensor Networks: Minimum-energy communication. Wireless Sensor Networks. Large number of heterogeneous sensor devices Ad Hoc Network Sophisticated sensor devices communication , processing , memory capabilities. Project Goals. Devise a set communication mechanisms s.t. they

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Wireless sensor networks minimum energy communication

Wireless Sensor Networks: Minimum-energy communication


Wireless sensor networks

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

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

Minimum-energy unicast

Wireless Sensor Networks: Minimum-energy communication


Unicast communication model

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)

Wireless Sensor Networks: Minimum-energy communication


Unicast problem description

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

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

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

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

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

Minimum-energy broadcast

Wireless Sensor Networks: Minimum-energy communication


Broadcast communication model

  • Every node j is assigned abroadcast cost

Broadcast communication model

  • 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


Broadcast cover problem bcp

Example:

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

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

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

Simulation results (1/2)

Wireless Sensor Networks: Minimum-energy communication


Simulation results 2 2

Simulation results (2/2)

Wireless Sensor Networks: Minimum-energy communication


Conclusion and future work

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


Minimum energy broadcast

  • Minimum-energy broadcast:

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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