Abstract
Reliability and energy efficiency are two key considerations when designing a compressive sensing (CS)-based data-gathering scheme. Most researchers assume there is no packets loss, thus, they focus only on reducing the energy consumption in wireless sensor networks (WSNs) while setting reliability concerns aside. To balance the performance–energy trade-off in lossy WSNs, a distributed data storage (DDS) and gathering scheme based on CS (CS-DDSG) is introduced, which combines CS and DDS. CS-DDSG utilizes broadcast properties to resist the impact of packet loss rates. Neighboring nodes receive packets with process constraints imposed to decrease the volume of both transmissions and receptions. The mobile sink randomly queries nodes and constructs a measurement matrix based on received data with the purpose of avoiding measuring the lossy nodes. Additionally, we demonstrate how this measurement matrix satisfies the restricted isometry property. To analyze the efficiency of the proposed scheme, an expression that reflects the total number of transmissions and receptions is formulated via random geometric graph theory. Simulation results indicate that our scheme achieves high precision for unreliable links and reduces the number of transmissions, receptions and fusions. Thus, our proposed CS-DDSG approach effectively balances energy consumption and reconstruction accuracy.
Highlights
As the perceptual layer of the Internet of Things (IoT) [1,2], wireless sensor networks (WSNs) [3]are widely deployed for purposes such as environment monitoring [4], industry automation [5] and military reconnaissance [6]
Compressive sensing (CS)-DDSG is resistant to the packet loss rate
We present the performance comparations of CS-DDSG, Compressive Sensing Data storage (CStorage) [20], Improved CStorage (ICStorage) [21], Compressed Network Coding based
Summary
As the perceptual layer of the Internet of Things (IoT) [1,2], wireless sensor networks (WSNs) [3]. It is imperative to investigate effective ways to apply DDS for data gathering with the dual purposes of resisting packet loss and reducing the number of transmissions To address this problem, many studies have been carried out on this topic. Broadcasting data consumes large amount of reception energy, the received data are rarely merged None of these studies consider the problem of packet loss; instead, they make the unrealistic assumption that the wireless links are completely reliable. The second problem is related to reducing the impact of lossy links (namely, the packet loss rate) on data reconstruction To solve these two challenges, a distributed data storage and gathering algorithm based on compressive sensing (CS-DDSG) is proposed utilizing CS and DDS.
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