When using wireless sensor networks (WSNs) for data transmission, some critical respects should be considered. These respects are limited computational power, storage capability and energy consumption. To save the energy in WSNs and prolong the network lifetime, we design for the signal control input, routing selection and capacity allocation by the optimization model based on compressed sensing (CS) framework. The reasonable optimization model is decomposed into three subsections for three layers in WSNs: congestion control in transport layer, scheduling in link layer and routing algorithm in network layer, respectively. These three functions interact and are regulated by congestion ratio so as to achieve a global optimality. Congestion control can be robust and stable by CS theory that a relatively small number of the projections for a sparse signal contain most of its salient information. Routing selection is abided by fair resource allocation principle. The resources can be allocated more and more to the channel in the case of not causing more severe congestion, which can avoid conservatively reducing resources allocation for eliminating congestion. Simulation results show the stability of our algorithm, the accurate ratio of CS, the throughput, as well as the necessity of considering congestion in WSNs.
Cross layer congestion control algorithm based on compressed sensing (CS) is developed and designed in order to relieve congestion in Wireless Sensor Networks (WSNs).The main idea of this paper is to reduce congestion of sensor nodes by compressed transmission signal and allocated channel. In order to make an ideal distribution for sensor node, original signal is compressed at the network bottleneck, prevented high levels of data flow. The channel weights and the maximum effective set of containing channel least are calculated, and we allocate the appropriate channel in order to avoid channel contention and balance the network loading among the sensor nodes. Simulation results indicate the superior performance of our proposed algorithm to strike the appropriate performance in the congestion control, energy consumption and network lifetime for the wireless sensor networks.
In this paper, we address the problems of joint design for channel selection, medium access control (MAC), signal input control, and power control with cooperative communication, which can achieve tradeoff between optimal signal control and power control in wireless sensor networks (WSNs). The problems are solved in two steps. Firstly, congestion control and link allocation are separately provided at transport layer and network layer, by supply and demand based on compressed sensing (CS). Secondly, we propose the cross-layer scheme to minimize the power cost of the whole network by a linear optimization problem. Channel selection and power control scheme, using the minimum power cost, are presented at MAC layer and physical layer, respectively. These functions interact through and are regulated by congestion rate so as to achieve a global optimality. Simulation results demonstrate the validity and high performance of the proposed algorithm.
We provide a joint scheme for rate control, scheduling, routing, and power control protocol for wireless sensor networks based on compressive sensing. Using a network utility maximization formulation, we present cross-layer optimization solutions using Lagrangian multipliers in the transport, network, media access control, and physical layers. Inspired by compressive sensing, we focus on the construction of utility functions based on the constraints of the link capacity, rate, routing, and power to decrease the computational cost, accelerate the convergence rate, and degrade the error ratio. The optimization solutions are developed by solving the optimization model of network utility maximization. We prove the effectiveness by the theory analysis at the stability of the transmission rate and error ratio. Finally, simulation results demonstrate the performance in terms of stability of the error ratio of compressive sensing, energy consumption, and transmission delay in wireless sensor networks.
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