Energy‐efficient compressive sensing for multi‐target tracking in wireless visual sensor networks

Abstract

Summary Wireless visual sensor networks (WVSN) have vital roles in surveillance applications. In these networks, wireless visual sensors include camera and transceiver module and collect visual information. However, energy consumption and coverage of the tracked targets are important challenges in WVSNs since increasing the coverage leads to increasing energy consumption. Therefore, energy optimization and satisfying the quality of experience (QoE) of the tracked targets are essential issues in these networks. In fact, the appropriate focal length setting leads to increasing the target coverage and quality of the captured targets' images and energy consumption. Therefore, selection of the suitable visual sensors and setting their focal length can overcome the energy consumption challenges and improve QoE of the tracked targets. In this case, compressive sensing is also expected to overcome the battery constraints of the WVSN resources. In this paper, the problem is to minimize the energy consumption of the multi‐target tracking with high reliability, while the coverage and also the quality of the received image of the targets are satisfied by selection of the proper visual sensors and the focal length adjustment. The convex optimization method is used to solve the problem. Also, based on the Karush‐Kuhn‐Tucker conditions, the optimal solution for the problem is obtained. Simulation results validate the efficiency of the proposed method in comparison with the other bench mark algorithms.

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

“…Of which:𝑋 ̃is the set of localized target states in the sensor network model, containing variables such as the number of tracked targets and state parameters, and in case of multi-target tracking [26], then, the𝑋 ̃= {𝑋 ̃1, 𝑋 ̃𝑚}, denotes the existence of multiple goals, and the state variables of the goals satisfy𝑋 ̃1 ≠ 𝑋 ̃𝑚；𝑍 𝑡 indicates the target observations acquired by all sensor nodes at the time𝑡, the𝑍 (𝑡) indicates all observations prior to the time 𝑡 , i.e., the 𝑍 (𝑡) = 𝑍 1 , ⋯ , 𝑍 𝑡 ； 𝐿 𝑍,𝑡 (𝑋 ̃) = 𝑓 𝑦 (𝑍|𝑋 ̃) is the observed likelihood function; the 𝑓(𝑋 ̃|𝑞) 𝑡+1|𝑡 is the multiobjective state set Markov state transfer density; the𝑓(𝑋 ̃|𝑍 (𝑡) ) 𝑡 |𝑡 is the posterior probability density of the set of multi-objective states at the moment𝑘; the𝜆is the normalization parameter, which is calculated as follows:…”

Recipe for Fabricating Optimized Solid‐State Electrochromic Devices and Its Know‐How: Challenges and Future

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