A Class of Cross‐Layer Optimization Design for Congestion and Energy Efficiency with Compressed Sensing in Wireless Sensing Networks. Issue 2 (26th June 2013)

• Record Type: Journal Article
• Authors: Li, Mingwei; Jing, Yuanwei; Li, Chengtie
• Is Part Of: Asian journal of control. Volume 16:Issue 2(2014:Mar.)
• Page Start: 565
• Page End: 573
• Publication Date: 2013-06-26
• Subjects: Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.743
• Languages: English
• ISSNs: 1561-8625

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>In wireless sensor networks (WSNs), the congestion problem not only causes packet loss, but also leads to an increase in delays and energy consumption. The actual performance of wireless sensor networks (WSNs) can be severely influenced by the quality of the communication channel and the bit in transmission. In this paper, the distributed protocols, which attain global optimum control for signals by the compressed sensing technique and achieve fair channel allocation by the scheduling algorithm, are proposed for WSNs. We take into account the congestion problem by robust optimization with congestion ratio for two classic aspects in energy limited WSNs: minimum transmission rate and maximum transmitted information. To achieve the goal, three protocols are developed. In the first protocol, the desired control input is designed based on the compressed sensing technique. A minimal bit of signal is provided to reduce the transmission flow for the congestion model. The second protocol is resource allocation. The resources can be allocated increasingly to the channel in order to avoid more severe congestion. This can also avoid conservative reduction of resource allocation for eliminating congestion. Channel selection abides by the fair resource allocation principle. The above protocols separately are implemented through a congestion ratio at network layer, transport layer, and MAC layer. Simulation results demonstrate that the proposed algorithm effectively relieves congestion, and achieves higher throughput and lower energy consumption.</p> </abstract>