A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm

Abstract

Energy-efficiency and reliability are vital metrics of the robustness of Wireless Sensor Networks (WSNs). Various data reduction techniques are used to improve them, among them compressive sensing (CS) is a data reduction technique used to recover extensive data from fewer samples in case of sparse representation of sensor-readings. Unfortunately, energy-efficiency and accuracy are contradictory metrics, as increased accuracy requires a large number of measurements, and data transmissions. Therefore,, in this paper, a CS-based algorithm is proposed for efficient data transfer through WSNs, which uses multiple objective genetic algorithms (MOGA) to optimize the number of measurements, transmission range, and the sensing matrix. The algorithm aims at striking the right balance between energy-efficiency and accuracy. It constructs a path in a multi hop manner based on the optimized values. Numerical simulations and experiments show that Paretofront, which is the output of MOGA, helps the user to select the right combination of the number of measurements and the transmission range fitting the application at hand, and to strike a good balance between energy efficiency and accuracy. The results also demonstrate the existence of measurement matrices which lower mutual coherency improve the accuracy of CS.

Modeling and Optimization of Ship Waste Heat Utilization System Based on Genetic Algorithm and Sensing

“…e power turbine and steam turbine are built on the same chassis and connected to the generator through a reduction gearbox. e system can meet the total power demand of the ship in a variety of circumstances, significantly reduce the fuel cost of the ship, and recover about 8-11% of the energy [13,14]. When evaluating whether a waste heat utilization system should be installed in a new ship project, the payback period of system installation is always involved.…”

Fuzzy spider monkey optimization routing protocol to balance energy consumption in heterogeneous wireless sensor networks

“…Each node has two different shortest paths to the sink to reduce the traffic load in the network. Pandey et al [19] and Al-Mazaideh and Levendovszky [20] described a novel technique for clustering the HWSNs approach that efficiently selected the head of the cluster nodes, the degree of sensor nodes, and the remaining energy. Here, the chaining technique is collected and sent to the information package.…”

Design of Non-Orthogonal Sequences Using a Two-Stage Genetic Algorithm for Grant-Free Massive Connectivity

“…Thanks to the fast convergence to local optima, GA has attracted much attention in machine learning and data mining [69]− [71]. Recently, GA has expanded its application to other areas, e.g., channel coding [72]− [75], spreading code design [76], [77], CS recovery [78]− [80] and matrix optimization [63]− [65], etc.…”

“…Recently, the genetic algorithm (GA) [62] has been applied for sensing matrix optimization in specific applications, e.g., reducing the complexity of radar imaging [63], allocating an optimized pilot pattern for channel estimation [64], and maximizing the energy efficiency of wireless sensor networks (WSN) [65]. In particular, GA has been used to find subsampling patterns to optimize partial Fourier matrices with specific parameters [63], [64].…”