Abstract
Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.
Highlights
In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles
Wireless Sensor Networks (WSN) are an emerging technology that promises an ability to monitor the physical world via spatially distributed networks of small and inexpensive wireless sensor nodes that have the ability to self-organize into a well-connected network
A joint sparsity model for multi-signal ensembles with both intra- and inter-signal correlation captures the essence of real physical scenarios
Summary
Wireless Sensor Networks (WSN) are an emerging technology that promises an ability to monitor the physical world via spatially distributed networks of small and inexpensive wireless sensor nodes that have the ability to self-organize into a well-connected network. In order to guarantee perfect reconstruction, each node has to transmit enough measurements This means that DCS only utilizes the inter-signal correlations in jointly decoding processes, but not in jointly encoding processes. We design a mobile-agent-based Adaptive Data Fusion (ADF) algorithm for multiple signal ensembles which is inspired by DCS. According to the sparse property of single signal and the joint sparsity of multiple signal ensembles under the results of data fusion, each node can determine the minimum number of measurements needed to transmit to Sink and effectively reduces the transmission cost.
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