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This report discusses distributed source coding (DSC) as a data compression technique to reduce redundancy in wireless sensor networks (WSNs). It focuses on the use of the Slepian-Wolf method and investigates the performance of a DSC scheme in a slotted ALOHA WSN. The goal is to minimize decoding latency at the fusion center.
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後卓越進度報告 蔡育仁老師實驗室 2007/03/19
Distributed source coding is a data compression technique to reduce the redundancy w/o information exchanges among sensor nodes. Sensors individually encode their received information. E.g., Slepian-Wolf method is a widely used distributed source coding algorithm. : received signal … … … Distribute Source Coding (DSC) in WSNs : Sensor : Sink Decoding Order
DSC with Slotted ALOHA Random Access • Performance of a DSC scheme in a slotted ALOHA WSN has been investigated. Assumptions: • Node itransmit in a slot with a constant probabilityPi. • The packet of node i can be reconstructed if the packet of node 1 to node i–1 was received successfully. Goal: • Analyze and try to minimize the decoding latency at fusion center Results: • The analytical result and an approx. result were obtained. • Several probability assignment strategies were compared. Prolong decoding latency
DSC with Slotted ALOHA — Adaptive Tx. Probabilities • Goal • Design an adaptive criterion for nodes’ transmit probabilities (TxPr) to reduce the decoding latency. • Transmission probability adaptation • Initial total traffic load: • Initial TxPr-weighting of node i : • The average reduced traffic in the k-th slot: • Adaptation procedure of i-th node’s Tx. prob. in k-th slot
P1[0] P2[0] P1[0] …… P2[0] …… PN-1[0] PN-1[0] PN[0] PN[0] …… …… TxPr Adaptation with Linear TxPr Weighting • For linear TxPr-weighting, we define …… …… Node N N 1 2 3 4 N-1 1 2 3 4 N-1 Node 3’s packet is received
20 18 16 14 12 10 8 6 Analytical Simulation 4 2 0 0 50 100 150 Analytical & Simulation Results — 20 Nodes (Linear TxPr Weighting), ΔP = 0.004 Adaptive Transmit Probability Fixed Transmit Probability Average no. of reconstructed pkts Time slot
20 18 16 14 12 10 8 6 Analytical Simulation 4 2 0 0 10 20 30 40 50 60 70 80 90 100 Linear Assignment of Nodes’ Initial TxPr Weighting—with 20 Nodes, Initial Traffic G = 0.8 Average no. of reconstructed pkts Time slot
Future Work • Evaluate the average energy consumption for information reconstruction. • Take packet lengths into consideration to allocate the optimum Pi for packets with different lengths. • Take other random access techniques into considerations.
