Multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints

Abstract

Wireless sensor networks have emerged as a promising way to develop high security systems. This paper presents the optimizations of a space-based reconfigurable sensor network under hard constraints by employing an efficient multi-objective evolutionary algorithm (MOEA). First, a system model is proposed for cluster-based space wireless sensor networks. Second, the statement of multi-objective optimization problems is mathematically formulated under hard constraints. Third, the MOEA is used to find multi-criteria solutions in the sense of Pareto optimality. Finally, simulation results are provided to illustrate the effectiveness of applying the MOEA to the multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints

Overview of Jamming Technology for Satellite Navigation

“…The effect diagram of optimized deployment of suppression jamming is shown in Figure 7. In the literature [43][44][45], the genetic algorithm is used to solve the problem of maximum coverage of the target area via the minimum number of suppression jamming sources. Yan et al [46] pointed out that the jamming sources of the above methods did not achieve coordination, and the utilization rate was not high.…”

Optimal Mobile Sensor Scheduling for a Guaranteed Coverage Ratio in Hybrid Wireless Sensor Networks

“…Wireless sensor networks (WSNs) have received intensive research interest in recent years due to their potential applications as computing platforms for monitoring and controlling various environments [1]. Hybrid WSNs consisting of static and mobile sensors are more robust and fault tolerant to the changing environment conditions than conventional WSNs which only contain static resource constrained sensors [2, 3]. By incorporating mobile sensors to WSNs, the network capability can be further improved in many aspects, such as automatic sensor deployment [2–12], dynamic event coverage [13, 14], mobiles target tracking [15–17], flexible topology adjustment [18, 19], and efficient data collection and processing [20–22].…”

Software Architecture Optimization Methods: A Systematic Literature Review

“…[153], [155], SOO(9) [17], [28], [68], [116], [193], [194], [211], [212], [240] COST(1) [72], GENERAL(1) [194], MAPPING(1) [153], MEMORY(1) [153], NOT PRESENTED(1) [206], PATH LOSS(1) [237], PERFORMANCE(8) [17], [28], [67], [68], [116], [193], [211], [212], PHYSICAL(3) [155], [237], [240], REDUNDANCY LEVEL(1) [165], STRUCTURAL(2) [84], [188], TIMING(1) [240] Weight (5) ES(4) [137], [215]- [217], GENERAL(1) [147] DT(5) [137], [147], [215]- [217] AVAILABILITY(1) [217], COST(5) [137], [147], [215]- [217], RELIABILITY(4) [137], [147], …”

“…A summary of the solution-specific aspects that are extracted from the set of papers included in the literature Performance (84) ES(32) [32], [45], [67]-COST(1) [33], GENERAL(10) [71], [79], [86], [113], [138], [139], [145], [205], [224], [243], MAPPING(3) [5], [95], [153], MEMORY(2) [5], [153], NOT PRE-SENTED(7) [21], [36], [91], [162], [202], [219], [242], PER-FORMANCE(1) [33], STRUCTURAL(1) [150] Energy(18) ES(17) [17], [28], [67], [68], [72], [84], [116], [153], [155], [165], [188], [193], [194], [211], [212], [237], [240], GENERAL(1) [206] DT(9) [67...…”

“…Evolutionary Algorithms (EAs) [18], [33], [37], [38], [41], [57]- [60], [71], [73], [76], [88], [92], [102], [108], [118], [128], [148], [149], [157]- [159], [161], [175], [196], [216], [221], [223], [227]- [229], [231], [237] are some of the most commonly used approximate methods in architecture optimization. EAs are seen as robust algorithms that exhibit approximately similar performance over a wide range of problems [98], hence their popularity in the software engineering domain.…”