Energy-Efficient Signal Processing and Communication Algorithms for

Scalable Distributed Fusion

Wireless Sensor Networks for Fusion

Sensor networks for

• surveillance and reconnaissance

• target detection and tracking

environmental applications

Signal Processing and Communication Challenges

System constraints• limited energy (and bandwidth) resources per sensor

• need for power-efficient processing algorithms and communication protocols

• limitations in sensing and on-board processing equipment• limitations in type and rate of processing

• interested in ensembles of measurements (e.g., maximum, average)• need for algorithms that obtain the fusion objective, not all individual measurements

• large networks; changes in network topology• real-time knowledge of global topology impractical locally constructed algorithms

Additional requirements• scalability

• fault-tolerance• algorithms that can compute (reduced) fusion objective over reduced topologies

ObjectivesDesired Tasks

• computation of statistics of the measurements over very large networks of wireless sensors

• maximum, average, locally-averaged (in space) signal estimates

Algorithmic Objectives

• algorithms for data processing and relaying across the network locally constructed, yet reliable exploit compression benefits via distributed local fusion designed for energy-efficient on-board processing

Hierarchical Networks

Setting

hierarchical protocol for data communication and fusion

Advantages

bandwidth efficient

readily scalable hierarchy

Disadvantages

unequal distribution of resources

often power usage inefficiency

sensitivity to fusion node failures robustness asymmetry

Ad-hoc NetworksSetting

two-way local communication between closely located (“connected”) sensors

each sensor node receives messages send by connected nodes

each sensor broadcasts messages to connected nodes

Advantages

robust, readily scalable

space-uniform resource usage

transmit power efficient

Issues

need for networking

Ad hoc Networks for Fusion

Related work ad hoc networks, amorphous computing

Distinct features in fusion problem

interested in underlying signal in data (e.g, target location), not data

info about signal “spread” over many nodes

multiple destinations

Remarks Advantages: data compression in fusion, inherent scalability

Key problem: communication loops (contamination of information)

Computation of Global MaximumObjective

Compute maximum among measurements

Approach

sequence of local maximum computations

Sensor State=current maximum estimate

communication step:

Each node broadcasts its state

fusion step:

New state at its node= maximum of all received states

Result:

Each node state converges to the global maximum (in finite number of steps) provided network is connected

Computation of Weighted Averages

Remarks

not all local averaging rules yield global average (data contamination)

Approach

Locally constructed fusion rules can be designed [Scher03] which asymptotically compute

weighted averages of functions of the individual measurements (e.g. average, power, variance of measurements)

Advantages

distributed, robust, readily scalable

address non-contributing node problem in distributed fashion

Project Objectives

Remarks

Finite delays and limitations in available energy and on-board processing

finite-time approximate computations

Analysis and Optimization

Design energy-efficient methods for approximate computation of maxima, averages and other measurement statistics

Determine trade-offs on-board processing and communication power, delays and quality of computation

Non-contributing nodes need for power-efficient data relaying