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The study of continuous natural or industrial phenomena in time and space requires the emergence of new wireless sensor networks. A virtual sensor network (VSN) is a wireless camera network that appears to overcome the limitations of traditional wireless sensor networks in terms of the ability to store, process, and communicate data in a 3D region of interest. In this paper, we proposed a Starlings Model-based virtual Sensor Network for Coverage, Connectivity, and Data Communication (SM-VSN-3C) to ensure 3D coverage of temporally and spatially continuous 3D phenomena. Starlings are a good example of a VSN in nature. We therefore simulate the 3D movement of the stars in the sky, ensure the associated permanent coverage, and communicate with the Renault model. Use the behavioral model proposed by Reynolds to simulate herd movement. We've demonstrated the efficiency of the proposed network (SM-VSN-3C) in terms of communication and continuous coverage in time and space (3D). When simulating large and dense VSNs, there are two challenges in terms of coverage and communication: How to efficiently track a set of VSN-Starlings (VSN-Birds) in terms of coverage? In such a dense environment, how can a single Starling be tracked in terms of communication and data routing? [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are used in an ever-increasing range of applications, thanks to their ability to monitor and transmit data related to ambient conditions in almost any area of interest. The optimization of coverage and the assurance of connectivity are fundamental for the efficiency and consistency of Wireless Sensor Networks. Optimal coverage guarantees that all points in the field of interest are monitored, while the assurance of the connectivity of the network nodes assures that the gathered data are reliably transferred among the nodes and the base station. In this research article, a novel algorithm based on Particle Swarm Optimization is proposed to ensure coverage and connectivity in Wireless Sensor Networks. The objective function is derived from energy function minimization methodologies commonly applied in bounded space circle packing problems. The performance of the novel algorithm is not only evaluated through both simulation and statistical tests that demonstrate the efficacy of the proposed methodology but also compared against that of relative algorithms. Finally, concluding remarks are drawn on the potential extensibility and actual use of the algorithm in real-world scenarios. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have paved the way for a wide array of applications, forming the backbone of systems like smart cities. These systems support various functions, including healthcare, environmental monitoring, traffic management, and infrastructure monitoring. WSNs consist of multiple interconnected sensor nodes and a base station, creating a network whose performance is heavily influenced by the placement of sensor nodes. Proper deployment is crucial as it maximizes coverage and minimizes unnecessary energy consumption. Ensuring effective sensor node deployment for optimal coverage and energy efficiency remains a significant research gap in WSNs. This review article focuses on optimization strategies for WSN deployment, addressing key research questions related to coverage maximization and energy-efficient algorithms. A common limitation of existing single-objective algorithms is their focus on optimizing either coverage or energy efficiency, but not both. To address this, the article explores a dual-objective optimization approach, formulated as maximizing coverage Max ∑(i = 1) ^ N Ci and minimizing energy consumption Min ∑(i = 1) ^ N Ei for the sensor nodes, to balance both objectives. The review analyses recent algorithms for WSN deployment, evaluates their performance, and provides a comprehensive comparative analysis, offering directions for future research and making a unique contribution to the literature. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are essential in environmental monitoring, healthcare, and industrial automation. Persistent connectivity and coverage challenges in WSN stem from intermittent node connectivity due to obstacles, signal interference, node failures, and compromised data reliability. Existing solutions, while useful, exhibit limitations in fully addressing these concerns. To confront these challenges, a proposed system introduces the Hybrid Grey Optimizer–Harmony Search Algorithm (Hybrid GWO‐HSA), merging adaptive routing protocols and efficient deployment techniques. The Hybrid GWO‐HSA system conducts an initial environmental analysis to pinpoint factors affecting node communication. It strategically deploys additional nodes to bridge coverage gaps, using the Grey Wolf Algorithm's capabilities to optimize node placement. Moreover, it employs the Harmony Search Algorithm to dynamically adjust communication paths based on real‐time network conditions, ensuring robust data transmission. The system workflow involves an environmental assessment followed by node deployment guided by the Grey Wolf Algorithm. Subsequently, the Harmony Search adapts communication paths to enhance connectivity. Simulations and practical experiments across diverse environments validate the Hybrid GWO‐HSA system's effectiveness. Results showcase substantial improvements: network lifetime of 13,200 s, a network delay of 37 ms, a coverage rate of 0.88, and an energy consumption of 590 J. This Hybrid GWO‐HSA‐based system establishes a resilient and efficient WSN infrastructure vital for reliable data collection and transmission in challenging settings. The Hybrid GWO‐HSA system offers a comprehensive approach to WSN connectivity and coverage issues by leveraging firefly and genetic algorithms, and it significantly enhances WSN performance and reliability across multifaceted application domains. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are vital for applications such as environmental monitoring, surveillance, and healthcare, where comprehensive network coverage is essential for accurate data collection. However, achieving full coverage in WSNs presents significant challenges due to resource constraints, such as limited battery life, processing capabilities, and environmental factors like terrain and obstacles. To address these issues, coverage optimization techniques are employed to maximize spatial coverage while minimizing energy consumption and deployment costs. This paper provides a thorough overview of these coverage optimization techniques, categorizing them based on different deployment strategies, including static and dynamic sensor placement. It explores their respective advantages, limitations, and application scenarios, offering valuable insights for researchers and practitioners. The study is motivated by the need to better understand how to improve WSN coverage efficiency and ensure reliable data collection in diverse environments. The research aims to synthesize existing knowledge on WSN coverage optimization, identify gaps in current strategies, and guide future studies in this field. Key findings emphasize the effectiveness of various techniques in enhancing coverage, such as mobility-based approaches and energy-aware algorithms, while also addressing practical challenges like sensor redundancy and environmental unpredictability. Ultimately, this paper contributes to the ongoing efforts to develop more adaptive, scalable, and energy-efficient solutions for WSN coverage optimization. [ABSTRACT FROM AUTHOR]

WSNs (Wireless sensor networks) are used in multiple applications including IoT (Internet of Things) applications like intelligent control, prediction, tracking, and other communication network services (Internet-of-Things). Due to their limited design for two-dimensional space, high computing costs, or sensitivity to measurement errors, the typical localization frameworks might not perform well in real-world settings. Location information of deployed sensor nodes in their surrounding environments is important for algorithmic three-dimensional localizations. But there are drawbacks to current 3D localization algorithms in many parameters including complexity, positional precisions, and excessive energy consumption. Hence, this work proposes Enhanced 3D-DV-Hop (3D-Distance Vector Hop) localizations based on PSO (Particle Swarm Optimization) and GAs (Genetic algorithms) for the aforementioned issues. To further increase the diversity and accuracy of the DV-Hop outputs, a learning technique is employed. The learning technique leads to an improvement in search effectiveness, convergence speed, and result in quality. The simulation results demonstrate that the suggested strategy can improve positioning coverage while maintaining positioning accuracy. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have paved the way for a wide array of applications, forming the backbone of systems like smart cities. These systems support various functions, including healthcare, environmental monitoring, traffic management, and infrastructure monitoring. WSNs consist of multiple interconnected sensor nodes and a base station, creating a network whose performance is heavily influenced by the placement of sensor nodes. Proper deployment is crucial as it maximizes coverage and minimizes unnecessary energy consumption. Ensuring effective sensor node deployment for optimal coverage and energy efficiency remains a significant research gap in WSNs. This review article focuses on optimization strategies for WSN deployment, addressing key research questions related to coverage maximization and energy-efficient algorithms. A common limitation of existing single-objective algorithms is their focus on optimizing either coverage or energy efficiency, but not both. To address this, the article explores a dual-objective optimization approach, formulated as maximizing coverage Max ∑(i = 1) ^ N Ci and minimizing energy consumption Min ∑(i = 1) ^ N Ei for the sensor nodes, to balance both objectives. The review analyses recent algorithms for WSN deployment, evaluates their performance, and provides a comprehensive comparative analysis, offering directions for future research and making a unique contribution to the literature.

We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications, including military, environment monitoring, and surveillance and body area sensor networks. A wireless sensor network builds the core part for IoT. Besides this, lifetime maximization is the biggest challenge in the wireless sensor network. Also, In a wireless sensor network, it is difficult to find an optimal node deployment approach that would minimize costs, be robust to node failures, decrease computing overhead and communication, and maintain a high degree of coverage and network connectivity. There is numerous literature addressed this challenge which is discussed in this paper; still there are lot many challenges yet to be addressed. Considering this scenario, in this paper, we propose a scheduled-based node deployment algorithm using Firefly Optimization (FA) to offer a circumstance where we have a group of target points that satisfy p-coverage and sensor nodes that satisfy q-connectivity, with subject to the selection of the optimal number of a sensor node that has the highest energy and minimum distance. The multiple parameters as no. of sensor nodes, distance, survivability factors, coverage, and connectivity of the sensor nodes are considered for designing the fitness function. A comprehensive statistical analysis is done using the simulation results to prove the proposed scheme's efficiency with other existing state-of-the-art methods under various p-coverage and q-connectivity configurations. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been extensively explored due to their incredible capabilities and ever-growing field of applications. In cluster-based WSNs, cluster heads (CHs) deplete their energy quickly due to the extra workload of data aggregation and data forwarding as relay nodes (RNs) from the member sensor nodes (SNs). While placing the RNs in the application areas to form the clusters, coverage of all the SNs and connectivity amongst the RNs are essential for the proper function of the networks. Moreover, reducing the intra-cluster distances between the RNs and SNs is important to save the transmission energy of the SNs. Moreover, the problem of placement of RNs in cluster-based WSNs is known as NP-Hard. To address the challenge of deploying RNs while managing multiple conflicting objectives, we present a range of evolutionary algorithms (EAs) as potential solutions. These include techniques such as differential evolution (DE), particle swarm optimization (PSO), multi-objective DE (MODE), and multi-objective PSO (MOPSO). First, a mathematical formulation of the problem is given. The solution vectors are efficiently encoded. All the objective functions are efficiently derived to evaluate the solution vectors. An extensive simulation is performed over the proposed algorithms. The results are analyzed to determine the robust algorithm to be recommended for the studied problem. The simulated results claimed that the MODE is comparably better than others for the studied problem. The analysis of variance (ANOVA) is also performed, followed by a post-hoc analysis using the least significant difference (LSD) method. [ABSTRACT FROM AUTHOR]

The energy-harvesting sensor network is a new network architecture to further prolong the lifetime of sensor networks and enhance the quality of IoT services. Due to the inherent problems of energy-harvesting sensor networks, it is really hard to collect fresh and useful sensory data. To solve the above problems, we investigate the data collection scheme in edge-assisted energy-harvesting sensor networks and try to collect fresh and useful sensory data from such networks. Enlightened by the concept of the age of information, we define a new metric, the age of useful information (AoUI), to measure the usefulness and freshness of the sensory data. Furthermore, we define the Minimizing the Maximum Age of Useful Information problem (Min-AoUI) to construct a sensory data collection method to minimize the AoUI of the sensory data. We prove that the Min-AoUI problem is NP-Hard, and approximation algorithms are proposed to solve this problem. The time complexity and the approximation ratio of this algorithm are analyzed. The performance of the algorithm is also verified by extensive experimental results. [ABSTRACT FROM AUTHOR]

With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the space model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed. [ABSTRACT FROM AUTHOR]

Increasing coverage and reducing the energy consumed in wireless sensor networks is an interesting field for researchers since the discovery of wireless sensor networks and it is an open problem. Activating the lowest connectivity range for each node individually depending on its remaining power and the place it covers, while maximizing coverage by placing it in the optimal place will reduce the energy consumed and increase the lifetime of wireless sensor networks. In this paper, we find an approach to increase coverage by optimizing correlation and residual energy using genetic algorithms by placing each node in the optimal position to maximize coverage, assuming that each node has a different energy from the other nodes. We will use drones to carry and move sensors to optimal position. The aim of the proposed work is to cover the largest area of a given region by using the least range of connection and increasing the Lifetime. We ran a succession of simulations and found the proposed model better than the strategies we found in literature. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is widely used in a variety of practical applications. WSN may be used to sense objects, gather information, analyze it, and then transmit it again. The significance of optimization techniques is crucial for the accurate and reliable estimation of the sensor nodes' location. The positioning accuracy of traditional distance vector hop (DV-Hop) localization algorithm is not entirely satisfactory instead of it is quite simple, stabilized, feasible, and requires less hardware. Thus to enhance the positioning accuracy without increasing the hardware cost of a sensor node, this article provides an improved distance vector hop (IDV-Hop) localization algorithm using human conception optimization. The proposed method adds a parameter to alter the anchor nodes' hop size. Furthermore, it is analyzed with traditional DV-Hop, IDV-Hop algorithm, DV-Hop based particle swarm optimization, and DV-Hop based class topper optimization. The simulation results support the conclusion that, the proposed algorithm performs better than the competing algorithms by minimizing the localization error, localization error variance, and the localization accuracy with varying the number of anchor nodes, total number of nodes, and the communication range. [ABSTRACT FROM AUTHOR]

The diversity of applications supported by Underwater Sensor Networks (UWSNs) explains the success of this type of network and the increasing interest in exploiting and monitoring seas and oceans. One of the most important research fields is network deployment, since this deployment will affect all other research aspects in the UWSNs. Moreover, the initial random deployment resulting from scattering underwater sensor nodes on the network area's surface does not ensure this area's coverage and network connectivity. In this research, we propose a self-adjustment redeployment protocol that enhances network coverage and connectivity while reducing the energy consumed during network deployment. This protocol takes into account the peculiar dynamism of the underwater environment due to the water currents. First, we study the impact of these water currents on network deployment. Then, we exploit these water currents to adjust the nodes' positions to achieve total area coverage and reduce the energy consumed during the deployment by reducing the total distance traveled by the underwater sensor nodes. Simulation results show that the proposed protocol achieves a very high coverage rate (97%) and reduces the distance traveled by nodes during the deployment by 41%. [ABSTRACT FROM AUTHOR]

Maintaining coverage, power consumption, and network lifetime are the most fundamental challenges for wireless sensor networks. Since it is impossible to replace or recharge the battery of the sensor nodes, discharging the battery will end the sensor node's life. With the death of some sensor nodes and disconnecting, the network coverage is also violated. This paper presents a method for detecting and recovering coverage holes in the wireless sensor network. In the proposed method, the network is cellulated first, and a node is selected as an agent for each cell. Then, the degree of overlap of each node's sensing area by its neighbors is calculated to schedule sensor nodes. Based on the node overlap information, the cell agent determines cell coverage and detects holes. Finally, mobile nodes and the grasshopper optimization algorithm are used to recover the holes. The simulation results reveal that the proposed method leads to a decrease in the network's energy consumption, an increase in network lifetime, and improved coverage in the network. [ABSTRACT FROM AUTHOR]

Wireless ad hoc sensor networks have recently emerged as a premier research topic. They have great long-term economic potential and ability to transform our lives and pose many new system building challenges. Sensor networks also pose a number of new conceptual and optimization problems. Most of researches in wireless sensor networks are focused in obtaining better target coverage in order to reduce energy and cost of the network. The problem of planar target analysis is one of the crucial problems that should be considered while studying coverage problem of sensor networks. By combining computational geometry and graph theoretic techniques, specifically the Voronoi diagram and graph search algorithms, this paper introduces a novel sensor network coverage model that deals with plane target problem based on Clifford algebra which is a powerful tool that is coordinate free. Also, the calculations of the node coverage rate for the plane target in the sensor network using Clifford algebra are presented. Then, the maximum clearance path (worst-case coverage) of the sensor network for a plane target is proposed. The optimality and reliability of the proposed algorithm have been proved using simulation. Also, a comparison between the breach weight of the point target and the plane target is provided. [ABSTRACT FROM AUTHOR]

One of the important research issues in wireless sensor networks (WSNs) is the optimal layout designing for the deployment of sensor nodes. It directly affects the quality of monitoring, cost, and detection capability of WSNs. Layout optimization is an NP-hard combinatorial problem, which requires optimization of multiple competing objectives like cost, coverage, connectivity, lifetime, load balancing, and energy consumption of sensor nodes. In the last decade, several meta-heuristic optimization techniques have been proposed to solve this problem, such as genetic algorithms (GA) and particle swarm optimization (PSO). However, these approaches either provided computationally expensive solutions or covered a limited number of objectives, which are combinations of area coverage, the number of sensor nodes, energy consumption, and lifetime. In this study, a meta-heuristic multi-objective firefly algorithm (MOFA) is presented to solve the layout optimization problem. Here, the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs, which includes coverage, connectivity, lifetime, energy consumption and the number of sensor nodes. Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions, in comparison to multi-objective genetic algorithms (IBEA, NSGA-II) and particle swarm optimizers (OMOPSO, SMOPSO). Therefore, MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring. [ABSTRACT FROM AUTHOR]

The positions of sensor nodes in wireless sensor networks (WSNs) plays important role in order to achieve optimum values of various parameters, such as coverage, localization, etc. WSNs are used in every field such as in agriculture, military etc. and to achieve optimum performance of WSNs in these fields the sensor nodes are required to deploy at optimized positions. In this paper the jaya-sine cosine optimization algorithm (Jaya-SCOA) is proposed to deploy the sensor nodes at optimum positions and to increase the coverage of sensor nodes. Further the sensor nodes are deployed in three different topologies such as C shape, outer boundary and random topology and coverage rate is computed for all topologies. The major goal of the paper is to find the best topology for deploying the sensor nodes in WSNs and to enhance the coverage of nodes. The simulation results of proposed Jaya-SCOA is compared with fruit fly optimization algorithm (FFOA) and bat optimization algorithm (BOA) for all topologies in terms of maximum and minimum coverage, standard deviation and mean coverage rate. The simulation results demonstrate that for all topologies the coverage rate of Jaya-SCOA is higher than that of FFOA and BOA and average coverage rate of Jaya-SCOA for random topology is increased by 14.55% and 27.04% as compared to outer boundary and C shape topology, respectively. Therefore, the random topology is the best topology as compared to other topologies and Jaya-SCOA is effective than BOA and FFOA for all topologies in terms of coverage and stability. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area. [ABSTRACT FROM AUTHOR]

Although researchers have investigated multiple facets of fault tolerance, majority of them have overlooked fault tolerance in face structured WSNs. Motivated by this, we propose a Fault-Tolerant Coverage Preserving Strategy for Face Topology-based WSNs (FtCFt). Unlike existing methods of recovering failures by merging the adjacent faces, we propose a coverage-aware node replacement method to replace the failing node with a suitable alternate node. This is significant because a mobile target will go undetected, and no evidence of it can be acquired until it leaves the hole region and is sensed by a node. FtCFt offers fault tolerance by incorporating node self-check and link-check strategies that works in conjunction with one of its mobile target tracking applications. Unlike existing works, the proposed restoration algorithm effectively repairs and restores the face structure to ensure network coverage and connectivity. Simulation results reveal that FtCFt improves coverage, quality of service and WSN liferime. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC's data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an effective MC trajectory. Much attention has been received to MC's path planning through SPs in a static environment where the path is determined during the initial phase, but they do not emphasize the nodes' coverage rate and cannot be adapted to network topology changes. In this context, we propose an Efficient Trajectory Planning method for Coverage Enhanced Data collection in WSN (ETP-CED). We introduce an enhanced method based on integrated Particle Swarm Optimization and Ant Colony Optimization for selecting the best set of SPs and planning efficient MC trajectory. ETP-CED is adaptive to node failures, allowing the nodes to reposition themselves to patch up coverage holes in the network. MC readjusts its planned path when there are less nodes in the network due to node failures, thereby shortening the trajectory length and speeding up data delivery. The results show that ETP-CED outperforms existing methods in the aspects of nodes' coverage and data collection efficiency. [ABSTRACT FROM AUTHOR]

Connectivity and area coverage are two essential criteria for improving the Quality of Service (QoS) in Wireless Sensor Networks (WSNs). These criteria strongly affect lifetime and performance of the WSNs. Furthermore, connectivity and full area coverage between the sensors ensure that the collected data will be transferred to the base station. In this paper, a new algorithm is proposed to improve the connectivity and full area coverage by utilizing some of the mobile robots to change the initial topology and the position of the sensors. Therefore, a grid-based model is first used to divide the environment into several cells and megacells. In order to change the position of the sensors by mobile robots, the robot path planning algorithm is also introduced in two steps. In the local phase, the Analytic Hierarchy Process (AHP) is utilized to determine the source point and destination point. An improved evolutionary programming algorithm is then used to find the optimal path between the source point and destination point, thereby changing the position of the sensors which cover the environment. In the global phase, the A* algorithm is used to cover the part of the environment which is not covered in the local phase. Finally, we perform comprehensive experiments to validate the performance of the proposed method. Compared with existing approaches, the proposed algorithm demonstrates clear improvements in the connectivity and area coverage, showing the superiority of our model. [ABSTRACT FROM AUTHOR]

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and environmental protection and can also avoid invasive damage caused by traditional data acquisition methods. An adaptive cooperative optimization seagull algorithm for optimal coverage of heterogeneous sensor networks is proposed in order to address the issue of coverage blind zone and coverage redundancy in the initial random deployment of heterogeneous sensor network nodes in the sensing layer of the Internet of Things. Calculate the individual fitness value according to the total number of nodes, coverage radius, and area edge length, select the initial population, and aim at the maximum coverage rate to determine the position of the current optimal solution. After continuous updating, when the number of iterations is maximum, the global output is output. The optimal solution is the node's mobile position. A scaling factor is introduced to dynamically adjust the relative displacement between the current seagull individual and the optimal individual, which improves the exploration and development ability of the algorithm. Finally, the optimal seagull individual position is fine-tuned by random opposite learning, leading the whole seagull to move to the correct position in the given search space, improving the ability to jump out of the local optimum, and further increasing the optimization accuracy. The experimental simulation results demonstrate that, compared with the coverage and network energy consumption of the PSO algorithm, the GWO algorithm, and the basic SOA algorithm, the coverage of the PSO-SOA algorithm proposed in this paper is 6.1%, 4.8%, and 1.2% higher than them, respectively, and the energy consumption of the network is reduced by 86.8%, 68.4%, and 52.6%, respectively. The optimal deployment method based on the adaptive cooperative optimization seagull algorithm can improve the network coverage and reduce the network cost, and effectively avoid the coverage blind zone and coverage redundancy in the network. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have various sensors which are wide spread and also equipped with supplies. For the deployment sensor nodes are used for capturing the information, the region of interest is selected and the nodes are deployed. Lower sensing power degrades the DC supply reducing the life of wireless sensor networks, this can also be due to improper sensor deployment. Based on the above various wireless sensor network algorithms are available to compute and implement the required optimal figures. Harris hawk is among the one such algorithm used in wireless sensors. It works on the principle of the bird Harris hawk which catches the prey from a very high altitude, resemblance to this and many other features it is implemented in wireless sensors. Various wireless sensor characteristics can be found, figured and tabulated which are essential in this domain. The characteristics like coverage, connectivity, location, energy, and can be estimated. In the work the coverage and energy fitness is estimated using Harris hawk algorithm and its results are being illustrated. [ABSTRACT FROM AUTHOR]

Scheduling of sensor nodes in an energy-efficient manner is one of the most effective ways for extending the lifetime of wireless sensor networks (WSNs). In energy-efficient scheduling, only a subset of the deployed sensor nodes is enabled to monitor the targets. As sensor nodes have restricted communication and sensing range, coverage and network connectivity should be considered while scheduling with fewer sensor nodes. For guaranteed transmission of sensing data from every target point to the base station, connectivity and coverage are the most pivotal issues in the scheduling of sensor nodes. In this research article, we have presented the energy-efficient active/sleep scheduling of the sensor nodes using a genetic algorithm (GA) with Dither Creeping mutation in which only few sensors are activated that ensures the coverage to all targets as well as communication with the sensor nodes and base station (BS). The novelty of the proposed GA with crossover and Dither Creeping mutation (GACDCM) is that the mutation probability is generated randomly rather than the fixed value for each string. As a result, for the same generation, the various strings of the proposed algorithm will be subjected to various creeping mutation probabilities and the same string is subjected to various creeping mutation probabilities at successive generations. The proposed algorithm replaces the traditional bitwise mutation. For exploring the search space in case of extremely constrained problems, Dither Creeping Mutation is more efficient than bitwise mutation. We have simulated the proposed algorithm extensively with several WSN scenarios. the simulation results are analyzed with the existent algorithms to validate the efficiency of the presented algorithm. The experimental result showed that the lifetime of the suggested GACDCM is increased by 53.27% than traditional GA, 27.93% than GANCDCM, 13.23% than NSGA-II, and 4% than algorithm proposed by Harizan and Kuila (Wireless Netw 25(4):1995–2011, 2019). [ABSTRACT FROM AUTHOR]

Maintaining prolonged service lifetime and adequate quality of sensing coverage are the key challenges in constructing Wireless Sensor Network (WSN) based applications. As such networks usually operate in inhospitable and hostile environment, failures are ineludible and providing resilience is a necessity. However, it is challenging to satisfy the conflicting problems of enhancing energy efficiency and fault tolerance simultaneously. Fault-tolerance is a significant requirement while designing WSN. It is crucial to detect the failures in advance and take necessary measures to maintain durable and efficient functioning of the network. Generally, in the existing face structured WSNs, node faults and failures can induce the formation of coverage holes, disrupt the face structure and consequently curtail the application performance. The coverage quality will affect the monitoring effectiveness of tracking applications, e.g., a moving target tracking. Moreover, node failures can cause the network to be partitioned, further reducing the accuracy in tracking. In this paper, we propose a robust fault-tolerance scheme with coverage preservation using a face structured WSN topology ( F CAFT ). The key objective of the proposed F CAFT scheme is to sustain the performance of the network by timely healing the faults in the network, to enhance the durability and reliability of the WSN. The results of simulation and comparison with existing methods reveal that F CAFT is efficacious in enhancing the service lifetime of WSN by about 14% and sustains about 96% of coverage even when the failure rate is more than 20%, which is a necessity for critical monitoring and tracking applications of WSNs. [ABSTRACT FROM AUTHOR]

Data collection is an important task in many mobile wireless sensor network (MWSN) applications. The energy of sensor nodes around the sink depletes rapidly due to transmitting large amounts of data from neighboring nodes. This problem can be mitigated through the use of intelligent mobile vehicles to collect the data. While traditional data collection methods focus on maximizing data acquisition or reducing network energy consumption, they do not take into account the actual sensor nodes' coverage of the region of interest (ROI). To the best of our knowledge, most research on data collection focuses on path planning for the mobile collector in a static environment. During the lifetime of the network, coverage holes may appear due to node energy depletion. We propose a coverage aware topology adaptive path planning algorithm (CAPP) for path planning for WSNs where all sensor nodes are coverage aware and respond by moving to better locations to improve coverage of the network and compensate for the failed nodes. First, the path planning algorithm determines the number of Stop Points (SPs) where it will stop to gather data. Then, Particle Swarm Optimization is used to find the best location for these SPs. Finally, the shortest path through these SPs is determined by Ant Colony Optimization. Through extensive simulation, we show that CAPP performs efficiently in data collection while also allowing the nodes to move for coverage hole repair. The result shows improvement in area coverage and reduced delay in data collection, with no increase in energy consumption. [ABSTRACT FROM AUTHOR]

Wireless sensor network found immense uses in the daily life. Also, the random deployment of nodes is a preferable option in many applications such as earthquake observation, military applications, and forest fire detection. It is expected that deployed nodes should be able to monitor the field of interest (FoI) with the optimum capacity. In order to maximize the coverage of area, each node should be repositioned to an optimal position inside the FoI. A modified particle swarm optimization (PSO) algorithm has been proposed to achieve optimize coverage while keeping the number of nodes minimum. It introduces the concept of negative velocity in order to avoid premature convergence of the algorithm. This paper describes a way to tackle the two dimensional obstacles present inside the FoI. The simulated results show a significant improvement in the performance with compared to the standard PSO in presence of obstacles. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is built with the wireless interconnection of Sensor Nodes (SNs) generally deployed to monitor the changes within the environment of hostile, rugged, and unreachable target regions. The optimal placement of SNs is very important for the efficient and effective operation of any WSN. Unlike small and reachable regions, the deployment of the SNs in large-scale regions (e.g., forest regions, nuclear radiation affected regions, international border regions, natural calamity affected regions, etc.) is substantially challenging. Present paper deals with an autonomous air-bone scheme for the precise placement of SNs in such large-scale regions. It uses an Omni-directional Circular Glider (OCG) per SN. After being aerially dropped, SN pilots the OCG to glide itself to the predetermined locations (PL) within a target region. The major advantage of using OCG is its capability to quickly update the direction, during the flight (with turning radius = 0) toward its PL. The proposed uses a recursive path correction model to maintain the orientation of the gliding SN towards the PL. The simulation results, and the hardware implementation, indicate that the proposed model is effectively operational in the environmental winds. It is time-efficient and more accurate in the deployment of the SNs in comparison to existing state of art SN deployment models. [ABSTRACT FROM AUTHOR]

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN concerning Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, from 2010 to 2021. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for for research community in identifying the most adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper by stating the open research issues and new directions for future research.

Micro-structured sensors that can perceive and communicate at the same time have emerged as a result of the quick growth of microelectronics technology, wireless communication technology, and sensor technology. This sensor has the ability to sense many types of environmental data, gather it at the sink node, and then send it to the data centre. In the civic, industrial, agricultural, military, and other domains, wireless sensor networks are frequently employed. According to the needs of curved surface ecological monitoring such as grasslands, wetlands, deserts, coastal beaches, a virtual force model of moving coverage of curved surface ecological monitoring network is presented, and a moving coverage algorithm of curved surface ecological monitoring network is given. The moving coverage algorithm of curved surface ecology monitoring network, by the virtual force between sensor nodes in the ecological monitoring network, push the sensor nodes to the uncovered area on the monitored surface, and repairs the monitoring blind zone on the monitored surface. To confirm the effectiveness of moving coverage algorithm of curved surface ecological monitoring network, the moving coverage process of the moving coverage algorithm of the ecological monitoring network is simulated. The simulation results show that the moving coverage algorithm proposed in this paper can accurately locate the monitoring blind zone of the ecological monitoring network and push the sensor nodes to the monitoring blind zone for coverage, and effectively improve the coverage of the ecological monitoring network on the monitored surface. The coverage ratio of node deployment phase can reach 85%-90%, and the final coverage ratio is more than 95%. [ABSTRACT FROM AUTHOR]

Underwater sensor networks have important application value in the fields of water environment data collection, marine environment monitoring and so on. It has some characteristics such as low available bandwidth, large propagation delays and limited energy, which bring new challenges to the current researches. The research on coverage control of underwater sensor networks is the basis of other related researches. A good sensor node coverage control method can effectively improve the quality of water environment monitoring. Aiming at the problem of high dynamics and uncertainty of monitoring targets, the random events level are divided into serious events and general events. The sensors are set to sense different levels of events and make different responses. Then, an event-driven optimization algorithm for determining sensor target location based on self-organization map is proposed. Aiming at the problem of limited energy of underwater sensor nodes, considering the moving distance, coverage redundancy and residual energy of sensor nodes, an underwater sensor movement control algorithm based on residual energy probability is proposed. The simulation results show that compared with the simple movement algorithm, the proposed algorithm can effectively improve the coverage and life cycle of the sensor networks, and realize real-time monitoring of the water environment. [ABSTRACT FROM AUTHOR]

Due to complex terrain and harsh environments, sensor nodes are often randomly scattered in the monitoring area, which may cause coverage holes or network disconnection. Current works move some sensor nodes to certain places to address this problem. However, these works cannot guarantee the coverage and connectivity simultaneously and have larger moving cost in energy. In this paper, we propose a coverage optimization strategy based on the flower pollination algorithm (FPA). First, to solve the shortcomings of the classical FPA in convergence and accuracy, an improved FPA is proposed. Then, the network deployment optimization problem is modeled as a multi-objective optimization problem that guarantees the coverage of target points and the connectivity of the network while minimizing the energy consumption of sensor nodes' moving. The sensor nodes are selected and moved to the proper position by utilizing the improved FPA to minimize the energy consumed by the sensors' motion and guarantee the coverage and connectivity. Test results show that the improved FPA has good convergence speed and accuracy compared with other evolutionary algorithms. Simulation results demonstrate that the proposed algorithm can guarantee network connectivity and satisfy the coverage requirement while minimizing the energy consumption of the sensor movement. Consequently, more energy of the sensor node can be used to collect and transmit sensed data. These results indicate that our algorithm can prolong network lifetime and improve monitoring quality in fields such as forest monitoring. [ABSTRACT FROM AUTHOR]

Wireless recharging using a mobile charger has been widely discussed in recent years. Most of them considered that all sensors were equally important and aimed to maximize the number of recharged sensors. The purpose of energy recharging is to extend the lifetime of sensors whose major work is to maximize the surveillance quality. In a randomly deployed wireless rechargeable sensor network, the surveillance quality highly depends on the contributions of coverage and network connectivity of each sensor. Instead of considering maximizing the number of recharged sensors, this article further takes into consideration the contributions of coverage and network connectivity of each sensor when making the decision of recharging schedule, aiming to maximize the surveillance quality and improve the number of data collected from sensors to the sink node. This article proposes an energy recharging mechanism, called an energy recharging mechanism for maximizing the surveillance quality of a given WRSNs (ERSQ), which partitions the monitoring region into several equal-sized grids and considers the important factors, including coverage contribution, network connectivity contribution, the remaining energy as well as the path length cost of each grid, aiming to maximize surveillance quality for a given wireless sensor network. Performance studies reveal that the proposed ERSQ outperforms existing recharging mechanisms in terms of the coverage, the number of working sensors as well as the effectiveness index of working sensors. [ABSTRACT FROM AUTHOR]

A wireless sensor network requires judicious utilization of its limited energy resources. As a result, the energy efficient algorithms have become a pre-requisite for healthy and prolonged functioning of these networks. Consequently, the present work minimizes the energy expenditure, maximizes the node utilization, incurs less state switching, and prolongs the network lifetime by proposing a partly centralized and partly distributed sleep wake scheduling for the overlapped dominating sets. The concepts of such a hybrid scheduling and overlapped dominating sets have not been explored to their full potential for continuous sensing. Therefore, the research work presented in this paper shall be a stepping stone to further explore the suggested concept for the purpose of reducing energy consumption in wireless sensor networks. It is envisioned that the proposed approach shall be beneficial in battlefield monitoring, security surveillance, environmental monitoring etc., which necessitate long-term continuous/non-periodic coverage of the target region. The proposed sleep wake scheduling algorithm is hybrid in nature and possesses the advantages of both distributed and centralized approaches. The superiority and significance of the results have been established by a comparative analysis with other existing techniques mentioned in the literature. [ABSTRACT FROM AUTHOR]

The optimal design of aperiodic/irregular clustered phased arrays (PAs) for base stations (BSs) in multi-user (UE) multiple-input–multiple-output (MU-MIMO) communication systems is addressed. This article proposes an ad hoc synthesis method aimed at maximizing the UEs traffic capacity within the cell served by the BS while guaranteeing a sufficient level of the signal at the terminals. Toward this end, the search of the optimal aperiodic clustering is carried out through a customized tiling technique able to consider both single and multiple tile shapes and assure the complete coverage of the antenna aperture for the maximization of the directivity. Representative results, from a wide set of numerical examples concerned with realistic antenna models and benchmark Third Generation Partnership Project (3GPP) scenarios, are reported to assess the advantages of the irregular array architectures in comparison with regular/periodic layouts proposed by the standard development organizations as well. [ABSTRACT FROM AUTHOR]

An emergent solution to overcome the limitations of traditional multi-hop routing in wireless sensor networks (WSNs) is to use mobile collectors (MCs) for data gathering, thereby reducing energy consumed in internode communications. Most of the existing data collection approaches emphasize data gathering or network lifetime extension, without taking into account sensor node area coverage or how to handle sensor node failures through node mobility. It is desirable to utilize node mobility as a key functionality for WSN coverage optimization. We propose a robust coverage-aware multiple path-planning algorithm (CAMP) for WSN data gathering using MCs. CAMP works in tandem with any coverage hole-repair algorithm to heal coverage holes created by dying nodes, if any, and can plan efficient paths for MCs. CAMP initially selects polling points using Particle Swarm Optimization, and then divides the area into radial sections based on the number of available MCs. The size of subsection is adjusted to balance the estimated trip times within an acceptable margin and each MC traverses its assigned section following the shortest path determined by Ant Colony Optimization. Performance is analyzed in terms of coverage, energy consumption, data delivery delay, and network lifetime. Results reveal that CAMP provides above 90% coverage of nodes. Moreover, it is robust to failures and covers over 70% of the area even when more than half of the nodes fail. CAMP also saves a considerable amount of nodes' communication energy, and the network lifetime is increased by 2.5 times when compared to a similar state-of-the-art algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSN) are usually characterized by dense deployment of energy constrained node. In such a network, more active nodes consume more energy and shorten the network lifetime. An energyefficient approach is nodes management, mainly switched off or deactivating some redundant nodes during some periods of time. The redundant nodes are those that disabling them does not affect the overall performance of the network such as full connectivity and coverage. This paper presents two methods for identifying redundant nodes in large-scale WSN networks. The proposed methods can identify more redundant nodes, especially lateral redundant nodes, based on heuristic graph theories in the network graph. The simulation results showed that the proposed methods work well both in dense and non-dense WSNs and reduces the overall network energy consumption better than the previous method. [ABSTRACT FROM AUTHOR]

Energy recharging in wireless rechargeable sensor networks (WRSNs) has acquired much attention in recent years. In literature, many recharging path construction algorithms have been proposed. Most of them considered that all sensors are equally important and designed algorithms to increase the number of recharged sensors or decrease the path length of the mobile charger. However, different sensors have different coverage contributions. Recharging the sensors with larger coverage contribution can achieve better surveillance quality. The proposed recharging scheduling algorithm is divided into three phases, including the Initialization, Recharging Scheduling and Path Construction Phases. In the second phase, this paper proposed two recharging scheduling algorithms, namely the Cost-Effective (CE) algorithm and Cost-Effective with Considerations of Coverage and Fairness ( $C^{2}F$ ) algorithm. The proposed two algorithms construct paths for the mobile charger and select the recharging sensors based on the higher weight in terms of larger coverage contribution and smaller path cost. Performance results show that the CE and $C^{2}F$ algorithms yield better performance in terms of the fairness of recharging, recharging stability and coverage ratio, as compared with the existing studies.

The successful operation of a wireless sensor network depends on the proper coverage of the environment, which in turn is affected by the number and location of sensors. In general, when the sensors are deployed randomly, the initial coverage is not high. One of the major challenges for network design is to determine the placement strategy of the sensors so that the deployed nodes can cover as many regions as possible. On the other hand, the power supply of each sensor node is a non-rechargeable battery. Therefore, the objective of this study is to solve the coverage problem in such a way that the energy consumption of the nodes is minimal, too. The proposed approach uses division and detection of uncovered regions. Then a greedy method based on the topology and properties of the nodes and the network deployment region is presented to select the optimal nodes and cover the region. The proposed approach is simulated and the evaluation results show a decrease in the displacement of the sensors for more coverage and a reduction in energy consumption compared to similar works. [ABSTRACT FROM AUTHOR]

In this paper, an efficient connectivity and coverage management module for wireless sensor networks is proposed. The proposed approach is composed of centralized-based algorithms that depend on software defined network architecture. The focus in this paper is on coverage and connectivity problem under the restrictive assumptions that the controller has sufficient information about the sensor nodes in the area of interest such as nodes IDs, locations, and residential energy. This information is used to determine the active, the connection and the sleep set of nodes via two main algorithms. First, coverage algorithm uses location, residential energy and other parameters to choose coverage set which will be the active nodes in the sensing field. Second, a shortest path algorithm uses the active set produced from the coverage algorithm in order to choose the connectivity set of nodes that ensures connectivity of the entire network. The proposed approach has been compared with another related approach. Simulations results have showed that the proposed algorithm outperforms the other approach in terms of the network lifetime, coverage, and connectivity performance. [ABSTRACT FROM AUTHOR]