Energy-Efficient Signal Processing and Communication Algorithms for Scalable Distributed Fusion

1. Energy-Efficient Signal Processing and Communication Algorithms for Scalable Distributed Fusion

2. Wireless Sensor Networks for Fusion Sensor networks for • surveillance and reconnaissance • target detection and tracking • environmental applications

3. 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

4. Objectives Desired 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

5. 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

6. Ad-hoc Networks • Setting • 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

7. 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)

8. Computation of Global Maximum Objective • 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

9. 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

10. 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