Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

Abstract

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.

Research on video sensor networks coverage optimization method in obstacle scene

“…In order to verify the performance of the proposed method, the influence of the video sensor node parameters and the proposed algorithm parameters on the node coverage is studied, and the existing coverage enhancement algorithms PCMOD [7] , VFA-PSO [9] and Genetic Algorithm [10] , respectively, to evaluate the coverage performance of the video sensor networks through coverage.…”

“…However, the conditions above are too ideal, considering the effects of obstacles. Aiming at the presence of obstacles in the monitoring area, an optimization strategy based on the VF algorithm is proposed [7] . Overlapping and obstruction occlusion areas in the network are gradually eliminated through the interaction of virtual forces.…”

Iterative Forward-Backward Pursuit Algorithm for Compressed Sensing

“…They mainly include Matching Pursuit (MP) algorithm [3], Orthogonal Matching Pursuit (OMP) [4], Subspace Pursuit (SP) [5], Compressive Sampling MP (CoSaMP) [6], Look Ahead OMP (LAOMP) [7], and Forward-Backward Pursuit (FBP) [8]. CS has been widely used in many fields, such as wireless sensor network [9,10] and magnetic resonance imaging (MRI) [11][12][13][14].…”