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One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches. [ABSTRACT FROM AUTHOR]

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]

Abstract A well-known method for evaluating the coverage quality of Wireless Sensor Networks (WSNs) is using exposure as a measure, especially in barrier coverage problems. Among all studies related to exposure, discussions regarding the Minimal Exposure Path (MEP) problem have dominated research in recent years. The problem aims to find a path on which an intruder can penetrate through the sensing field with the lowest probability of being detected. This path along with its exposure value enables network infrastructure designers to identify the worst-case coverage of the WSN and make necessary improvements. Most prior research worked on the MEP problem under the assumption that there are no environmental factors such as vibration, temperature, etc., which causes errors in practical WSN systems. To overcome this drawback, we first formulate the MEP problem based on Probabilistic Coverage Model with noise (hereinafter PM-based-MEP) and introduce a new definition of the exposure metric for this model. The PM-based-MEP is then converted into a numerically functional extreme with high dimension, non-differentially and non-linearity. Adapting to these characteristics, we propose two approximation methods, GB-MEP and GA-MEP, for solving the converted problem. GB-MEP is based on the traditional grid-based method which is fine-tuned by several tweaks, and GA-MEP is formed by the genetic algorithm with a featured individual representation and an effective combination of genetic operators. Experimental results on numerous instances indicate that the proposed algorithms are suitable for the converted PM-based-MEP problem and perform well regarding both solution accuracy and computation time compared with existing approaches. Highlights • Formulated a minimal exposure path problem under the probabilistic coverage model with noise in a wireless sensor networks, called PM-based-MEP. • A new definition of exposure measure for this model is also introduced. • Converted the PM-based-MEP into an optimization problem with an objective function and constraints which permits the use of mathematical optimization methods to solve. • Proposed a new genetic algorithm and a grid-based method incorporated with several improvements to solve PM-based-MEP problem. • Conducted simulations and analyzed experimental results to give insights into the effectiveness of each proposed algorithm. [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.

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]

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.

The fundamental objective of deploying wireless sensors is to monitor a physical condition(s) within the desired region and relay the measured data to the interested parties through a gateway. However, achieving maximum coverage over the desired area after the initial random deployment of nodes is likely impossible. Arbitrary paths become exposed, uncovered by any of the sensor nodes questioning the quality of service of the network. In our proposed coverage maximization scheme, exposure profiles of sensor nodes were used by the sink node to compute the minimal exposure path. A combination of the computed minimal exposed path and the ratio of covered to uncovered grids were used as the function fitness in our particle swarm optimization algorithm. Bearing in mind the energy constraints of sensor nodes, randomly deployed nodes were only repositioned in the optimization process when their energy levels are above the predefined threshold. The accuracy and efficiency of our scheme are established via simulations. We compared the results of our scheme with a similar optimization scheme. The results showed our scheme achieved a higher coverage degree with the same number of deployed nodes over a region of interest of the same size. [ABSTRACT FROM AUTHOR]