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Consider a set of sensors having varying capabilities and respectively located to maximize an intruder's minimal expected exposure to traverse a defended border region. Given two subsets of the sensors that have been respectively incapacitated or degraded, we formulate a multi-objective, bilevel optimization model to relocate surviving sensors to maximize an intruder's minimal expected exposure to traverse a defended border region, minimize the maximum sensor relocation time, and minimize the total number of sensors requiring relocation. Our formulation also allows the defender to specify minimum preferential coverage requirements for high-value asset locations and emplaced sensors. Adopting the ε-constraint method for multi-objective optimization, we subsequently develop a single-level reformulation that enables the identification of non-inferior solutions on the Pareto frontier and, consequently, identifies trade-offs between the competing objectives. We demonstrate the aforementioned model and solution procedure for a scenario in which a defender is relocating surviving air defense assets to inhibit intrusion by a fixed-wing aircraft. [ABSTRACT FROM AUTHOR]

Barrier coverage in wireless sensor networks (WSNs) is a well-known model for military security applications in IoTs, in which sensors are deployed to detect every movement over the predefined border. The fundamental sub-problem of barrier coverage in WSNs is the minimal exposure path (MEP) problem. The MEP refers to the worst-case coverage path where an intruder can move through the sensing field with the lowest capability to be detected. Knowledge about MEP is useful for network designers to identify the worst coverage in WSNs. Most prior research focused on this problem with the assumption that the WSN has an ideal deployment environment without obstacles, causing existing gaps between theoretical and practical WSNs systems. To overcome this drawback, we investigate a systematic and generic MEP problem under real-world environment networks by presenting obstacles called Obstacle-Evasion-MEP (hereinafter OE-MEP). We propose an algorithm to create several types of arbitrary-shaped obstacles inside the deployment area of WSNs. The OE-MEP problem is an NP-Hard with high dimension, non-differentiation, non-linearity, and constraints. Based upon its characteristics, we then devise an elite algorithm namely Family System based Evolutionary Algorithm (FEA) with our newly-proposed concepts of Family System, tailored to efficiently solve the OE-MEP. We also build an extension to a custom-made simulation environment to integrate a variety of network topologies as well as obstacles. Experimental results on numerous instances indicate that the proposed algorithm is suitable for the converted OE-MEP problem and performs better in solution accuracy than existing approaches. • Formulate the obstacles-evade minimal exposure path problem in WSNs. • Model and devise a method to randomly generate obstacles in different forms. • Propose the Family System based on Evolutionary Algorithm for solving this problem. • Conduct a number of systematic simulations and analyze the experimental results. [ABSTRACT FROM AUTHOR]

Mobile wireless sensor networks (MWSNs), a sub-class of wireless sensor networks (WSNs), have recently been a growing concern among the academic community. MWSNs can improve network coverage quality which reflects how well a region of interest is monitored or tracked by sensors. To evaluate the coverage quality of WSNs, we frequently use the minimal exposure path (MEP) in the sensing field as an effective measurement. MEP refers to the worst covered path along which an intruder can go through the sensor network with the lowest possibility of being detected. It is greatly valuable for network designers to recognize the vulnerabilities of WSNs and to make necessary improvements. Most prior studies focused on this problem under a static sensor network, which may suffer from several drawbacks; i.e., failure in sensor position causes coverage holes in the network. This paper investigates the problem of finding the minimal exposure paths in MWSNs (hereinafter MMEP). First, we formulate the MMEP problem. Then the MMEP problem is converted into a numerical functional extreme problem with high dimensionality, non-differentiation and non-linearity. To efficiently cope with these characteristics, we propose HPSO-MMEP algorithm, which is an integration of genetic algorithm into particle swarm optimization. Besides, we also create a variety of custom-made topologies of MWSNs for experimental simulations. The experimental results indicate that HPSO-MMEP is suitable for the converted MMEP problem and performs much better than existing algorithms. [ABSTRACT FROM AUTHOR]