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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]

Three-dimensional wireless sensor networks (3D WSNs) are communication networks consisting of numerous wireless sensor nodes. Aiming at the node deployment problem of 3D WSNs, a solution based on the two-layer chaotic particle swarm optimization (TCPSO) algorithm is proposed. TCPSO is utilized to optimize the deployment of nodes and improve the spatial coverage of the network. The TCPSO algorithm divides the population into elite and ordinary subpopulations based on nonlinear classification coefficients, adopting different velocity and position update formulas for iterative optimization. TCPSO introduces a decreasing inertia weight based on Logistic chaotic mapping to control the local exploitation of the algorithm. To avoid premature convergence, levy flight strategy is introduced to enhance the global search ability of the algorithm. Simulation experiments are conducted in three-dimensional grid space to verify the ability of TCPSO and particle swarm optimization (PSO) algorithms to solve the node deployment problem of 3D WSNs. Three sets of experiments are conducted with different numbers of nodes, communication radii, and population sizes, respectively. The control variable method is adopted to observe the performance of TCPSO under different conditions. The node deployment solutions proposed by TCPSO in all experiments are significantly superior to those of PSO. The experimental results indicate that the proposed scheme can effectively improve the spatial coverage of 3D WSNs, reduce the cost of network construction, and provide strong support for practical applications. [ABSTRACT FROM AUTHOR]

Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Blockchain technology has emerged as a promising solution for addressing trust and security concerns in the Internet of Things (IoT), which is a network of interconnected devices. By deploying blockchain technology at the node level of the IoT, a mutually trusted transaction network can be established between devices, promoting a shift from platform-based to device-based information exchange. However, the implementation of blockchain in wireless IoT scenarios can pose many challenges due to its high resource consumption, including computing power, memory space, and energy consumption. To address these issues, we propose a low-cost algorithm that minimizes the number of blockchain nodes required to meet IoT throughput requirements while ensuring the proper functioning of blockchain, such as guaranteeing consensus success rates in the presence of Byzantine nodes. Our numerical simulation results demonstrate the effectiveness of our proposed scheme in reducing operating costs and promoting the deployment of blockchain in the IoT. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for deploying wireless sensor nodes in the Industrial Internet of Things (IIoT) to address the safety and efficiency challenges in underground coal mining. The methodology is intended to support long-term planning on mitigating the risks in occupational health and safety policies. To ensure realistic and accurate deployment, we propose a software tool that generates mine models based on geolocation data or blueprints in image format, allowing precise adaptation to the specific conditions of each mine. Furthermore, the process is based on sensing and communication range values obtained through simulations and on-site experiments. The deployment strategy is articulated in two complementary steps: a deterministic deployment, where nodes are strategically placed according to the structure of the tunnels, followed by a random stage to include additional nodes that ensure optimal coverage and connectivity inside the mine by comparing different methodologies for deploying sensor networks using coverage density as a performance metric. We analyze coverage and connectivity based on the three probability density functions (PDFs) for the random deployment of nodes: uniform, normal, and exponential, evaluating both the degree of coverage (k-coverage) and the degree of connectivity (k-connectivity). The results show that our proposed methodology stands out for its lower density of sensors per square meter, which translates into a reduction of between 20.81% and 23.46% for uniform and exponential PDFs, respectively, concerning the number of sensors compared to the analyzed methodologies. In this way, it is possible to determine which distribution is suitable to cover the elongated area with the smallest number of nodes, considering the coverage and connectivity requirements, to reduce the deployment cost. The uniform PDF minimizes the number of sensors needed by 44.70% in small mines and 46.27% in medium ones compared to the exponential PDF. These findings provide valuable information to optimize node deployment regarding cost and efficiency; a uniform function is a good option depending on prices. The exponential distribution reached the highest values of k-coverage and k-connectivity for small and medium-sized mines; in addition, it has greater robustness and tolerance to faults like signal network intermittence. This methodology not only improves the collection of critical information for the mining operation but also plays a vital role in reducing the risks to the health and safety of workers by providing a more robust and adaptive monitoring system. The approach can be used to plan IIoT systems based on Wireless Sensor Networks (WSN) for underground mining exploitation, offering a more reliable and adaptable strategy for monitoring and managing complex work environments. [ABSTRACT FROM AUTHOR]

As it is one of the main means of insuring food security, agriculture has employed different technologies such as the Internet of Things and wireless sensor networks (WSN), to improve the quality and quantity of agricultural products, while preserving natural resources. But unfortunately, in agricultural plots where we have large surfaces of interest, the optimization of node deployment in a WSN remains among the major problems to be solved. In this work, we proposed a WSN node deployment optimization model for an agricultural application according to classical constraints of coverage, over-coverage, connectivity and nodes number, in addition to the nodes separating distance constraint which affects the quality of physical parameters models. We have applied two variants of Multi-Objective Genetic Algorithms, the Non Sorting Genetic Algorithm II (NS-GA II) and the Strength Pareto Evolutionary Algorithm II (SPEA II). As a result, for a 100 m2 plot, both algorithms ensured a communication rate of 100% while SPEA 2 presented a lower sensor number and over-coverage rates with a smaller separating distance, and the execution time of NSGA II was shorter with 11 s. Besides, both of them were greedy in terms of computation time with the increase in the size of the plots.

In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.

In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data. [ABSTRACT FROM AUTHOR]

Deployment of sensor nodes is an essential issue in wireless sensor networks (WSNs). The first step involves effective deployment of sensor nodes in the target area based on predetermined location to ensure guaranteed quality of surveillance. One of the hardest tasks in a WSN is figuring out how to route data from source to destination. Sensors gather data from the sensor network area and transmit it to various locations, and a large number of nodes must be deployed in a proper way. There are numerous deployment techniques available to enhance localization success rates and accuracy. The paper examines significant research findings related to sensor deployment, while also outlining key findings with examples of supporting applications. Finally, the unique characteristics and requirements of these deployment techniques for sensor nodes in 5G-integrated traditional WSNs are discussed. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the number of sensors that detect targets and the connections between them play an important role in ensuring data transmission to the base station. To ensure reliable data transmission in wireless sensor networks, our paper proposes an optimization algorithm that uses a minimum number of nodes to deploy such that at least k sensors detect each target and at least m connections are made between sensors for data transmission. The proposed optimization algorithm uses a tabu search algorithm to solve the optimization problem. The proposed tabu search algorithm is designed to deploy the minimum number of nodes for a given objective by using an efficient solution structure and neighborhood generation method. The performance of the proposed tabu search algorithm is evaluated in terms of the number of nodes to detect all targets and the algorithm execution time under various network conditions. The evaluation results show that the proposed tabu search algorithm outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

For efficient monitoring of marine environments using underwater wireless sensor networks (UWSNs), a fundamental issue is to maximize network coverage as well as connectivity by appropriate deployment of the nodes within a given sensing area. Most of the existing node deployment schemes proposed for UWSNs, assume that sensor nodes are static and their location after deployment are fixed. However, due to ocean current and the contact of the underwater creatures, these deployed static sensor node moves from its original locations to other locations, which disrupts the network connectivity with the base stations as well as most of the targets remain uncovered. To find a solution to this issue, authors propose a coverage and connectivity aware deployment scheme for optimal placement of a set of autonomous underwater vehicles (AUVs) within UWSNs. The proposed scheme uses an improved Non-dominated Sorting Genetic Algorithm-II based metaheuristic technique with a novel fitness function which contains three parameters namely coverage quality, connected cost and network lifetime. Further, proposed scheme applies an effective encoding scheme for the population representation and devises a novel fitness function for upgrading the quantity of the AUVs as well as their location. Performance estimation of the proposed scheme and its comparison with the existing schemes with respect to convergence rate, coverage quality, connected cost, average energy consumption and network lifetime are discussed in detail. The simulation outcome confirm that the proposed approach upgrades the coverage of the network. [ABSTRACT FROM AUTHOR]

We study a heterogeneous two-tier wireless sensor network in which $N$ heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to $M$ heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.

Deploying edge computing nodes on the demand side can effectively reduce the pressure of data transmission and storage of the power network and improve the quality of electric power service. At present, the deployment location of edge nodes is mostly determined from the dimension of grid topology, and the service scope of nodes is defined by the grid method. The working process among nodes is independent. Therefore, the flexibility of the location and capacitydetermination of edge nodes is low, and it may cause waste of equipment computing resources.Therefore, an edge computing node deployment method considering task migration is proposed in this paper. Firstly, an edge computing architecture considering task migration is proposed based on the characteristics of edge devices and the spatial characteristics of residential areas. Secondly, feature data is formed according to spatial information and load characteristics of residential nodes. Then the number, address and service scope of nodes are determined by using the improved density peak analysis algorithm. Finally, a heuristic algorithm is designed to realize the task migration among the edge nodes to ensure the balanced utilization of computing resources of nodes and improve the stability of the system. A residential area in Nanjing is taken as an example to design simulation experiments. The results show that the proposed edge nodes deployment method can effectively reduce the data transmission costs of residential nodes. Also, the task migration algorithm can effectively balance the computing resources among edge devices and improve execution efficiency of edge computing services.

Researchers have achieved significant progress on the problem of Sensor Node Deployment (SND) in Underwater Acoustic Sensor Networks (UASNs) in terms of enhancing underwater communication. However, most studies focus on enhancing coverage, connectivity, selective network performance metrics, and/or deployment expenses at the cost of other critical factors. Given the limited resources and difficulties with using renewable energy sources to recharge UASN batteries, it is necessary to minimize Energy Consumption (EC) through the node deployment mechanism to extend network lifetime. Therefore, in this paper, the author proposes the Distributed Deployment Optimization algorithm using Grid-based Depth Adjustable (DDOGDA) based on grid node deployment for 3D network architecture. With this model, this paper endeavors to monitor the underwater environment with the minimum number of underwater nodes while meeting the QoS requirements of a tsunami-monitoring application in the Solomon Islands. The proposed algorithm considers Geographic Information System (GIS) data, non-environmental factors, and the unique characteristics of underwater sensors. These factors were used to provide guidance on how to place nodes properly to achieve certain objectives. Herein, the proposed algorithm is compared to six other node deployment algorithms. Simulation results indicate that our proposed algorithm surpasses random, tetrahedron, cuboid, triangular, pipeline, and grid node deployment in terms of End-to-End Delay (E2ED), EC, and Packet Delivery Ratio (PDR) by 266%, 183%, and 22%, respectively.

This paper presents a novel methodology for deploying wireless sensor nodes in the Industrial Internet of Things (IIoT) to address the safety and efficiency challenges in underground coal mining. The methodology is intended to support long-term planning on mitigating the risks in occupational health and safety policies. To ensure realistic and accurate deployment, we propose a software tool that generates mine models based on geolocation data or blueprints in image format, allowing precise adaptation to the specific conditions of each mine. Furthermore, the process is based on sensing and communication range values obtained through simulations and on-site experiments. The deployment strategy is articulated in two complementary steps: a deterministic deployment, where nodes are strategically placed according to the structure of the tunnels, followed by a random stage to include additional nodes that ensure optimal coverage and connectivity inside the mine by comparing different methodologies for deploying sensor networks using coverage density as a performance metric. We analyze coverage and connectivity based on the three probability density functions (PDFs) for the random deployment of nodes: uniform, normal, and exponential, evaluating both the degree of coverage (k-coverage) and the degree of connectivity (k-connectivity). The results show that our proposed methodology stands out for its lower density of sensors per square meter, which translates into a reduction of between 20.81% and 23.46% for uniform and exponential PDFs, respectively, concerning the number of sensors compared to the analyzed methodologies. In this way, it is possible to determine which distribution is suitable to cover the elongated area with the smallest number of nodes, considering the coverage and connectivity requirements, to reduce the deployment cost. The uniform PDF minimizes the number of sensors needed by 44.70% in small mines and 46.27% in medium ones compared to the exponential PDF. These findings provide valuable information to optimize node deployment regarding cost and efficiency; a uniform function is a good option depending on prices. The exponential distribution reached the highest values of k-coverage and k-connectivity for small and medium-sized mines; in addition, it has greater robustness and tolerance to faults like signal network intermittence. This methodology not only improves the collection of critical information for the mining operation but also plays a vital role in reducing the risks to the health and safety of workers by providing a more robust and adaptive monitoring system. The approach can be used to plan IIoT systems based on Wireless Sensor Networks (WSN) for underground mining exploitation, offering a more reliable and adaptable strategy for monitoring and managing complex work environments.

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]

To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power-efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We consider a large-scale wireless path-loss model with a realistic angle-dependent radiation pattern for the directional antennas. Based on such a model, we determine the optimal 3-D deployment of N UAVs to minimize the average transmit-power consumption of the users in a given target area. The users are assumed to have identical transmitters with ideal omnidirectional antennas and the UAVs have identical directional antennas with given half-power beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For uniformly distributed ground users, we show that the UAVs have to share a common flight height in an optimal power-efficient deployment, by simulations. We also derive in closed-form the asymptotic optimal common flight height of N UAVs in terms of the area size, data-rate, bandwidth, HPBW, and path-loss exponent.

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gatherdata from the surrounding environment and transmit their sensed information to$M$ fusion centers (FCs) via multi-hop wireless communications. This nodedeployment problem is formulated as an optimization problem to make a trade-offbetween the sensing uncertainty and energy consumption of the network. Ourprimary goal is to find an optimal deployment of sensors and FCs to minimize aLagrange combination of the sensing uncertainty and energy consumption. Tosupport arbitrary routing protocols in WASNs, the routing-dependent necessaryconditions for the optimal deployment are explored. Based on these necessaryconditions, we propose a routing-aware Lloyd algorithm to optimize nodedeployment. Simulation results show that, on average, the proposed algorithmoutperforms the existing deployment algorithms.

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The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations. [ABSTRACT FROM AUTHOR]

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi diagrams as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd-like algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average.

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi diagrams as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd-like algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average.

Deployment of sensor nodes in three dimensional areas with sufficient coverage of sensor nodes is one of the major challenges in wireless sensor network. Coverage is main concern in node deployment because it influences the performance of wireless sensor network. For better performance of wireless sensor network it is essential to increase the coverage of nodes by locating the nodes at optimum positions with help of efficient optimization algorithm. In this paper the sensor nodes are located by using Hybrid fruit fly optimization algorithm and bat optimization algorithm in three dimensional environment. The exploration feature of fruit fly optimization algorithm is combined with exploitation characteristics of bat optimization algorithm in proposed algorithm. The grid points covered once by sensor node are removed from entire grid points for remaining nodes. With removal of grid points the overload on sensor nodes is reduced in proposed algorithm. The simulation results of Hybrid fruit fly optimization algorithm and bat optimization algorithm are compared in terms of variance, standard deviation, coverage rate and coefficient of dispersion with fruit fly optimization algorithm and bat optimization algorithm. Moreover, to verify the efficiency of proposed algorithm the results are also compared with other optimization algorithms such as artificial bee colony algorithm with dynamic search strategy, grey wolf optimization algorithm, enhanced grey wolf optimization algorithm, whale optimization algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm, differential evolution algorithm, shuffled frog leaping algorithm, hybrid shuffled frog leaping algorithm and whale optimization algorithm based on differential evolution respectively. The simulation result signifies that proposed Hybrid fruit fly optimization algorithm and bat optimization algorithm is efficient than above stated existing optimization algorithm in terms of average coverage rate. The simulation results also demonstrate that proposed algorithm has attained maximum coverage about 99.25% which is higher as compared to existing algorithms. The standard deviation of proposed algorithm is least i.e. 0.0012 which means proposed algorithm is more reliable as compared to existing algorithms for deploying sensor nodes in three dimensional wireless sensor network. [ABSTRACT FROM AUTHOR]

When deploying network nodes, there are many redundant nodes, low network coverage and high energy consumption of network nodes, the node deployment method of wireless sensor networks(WSN) based on mobile edge computing is studied. WSN nodes are divided into anchor nodes and unknown nodes. Taking the location information of anchor nodes as a reference, the specific location of unknown nodes is obtained by trilateral measurement. Minimizing the node distance error is taken as the objective function, and the cuckoo search algorithm is used to solve it to obtain the final location result of the node. The mobile edge computing method is used to design the node deployment method of WSN to complete the node deployment. Simulation results show that the number of redundant nodes in this method is 3, maximum network coverage is 89%, maximum energy consumption of network nodes is 34.3J. [ABSTRACT FROM AUTHOR]

The deployment strategy for wireless sensor networks (WSNs) affects the quality of service (QoS). Adopting a reasonable deployment strategy can improve the QoS of WSNs. In this paper, the problem regarding sensor node deployment for WSNs on three-dimensional (3D) terrain is modeled as a multi-objective optimization problem. The coverage rate of the WSNs, their unbalanced energy consumption, and the number of sensor nodes are used as fitness functions for the optimization problem. We propose a non-dominated sorting genetic algorithm based on a Voronoi diagram (VNSGA) for solving the wireless sensor network deployment issue and improving the QoS of WSNs on 3D terrain. The proposed algorithm applies the Voronoi diagram to obtain the node-sensing radius and communication radius, which are suitable for 3D terrain with respect to calculating the fitness function of the optimization problem. The Pareto optimal solution is obtained by retaining the solution close to the reference point. The experiments compare the proposed algorithm with the Multi-Objective Particle Swarm Algorithm (MOPSO) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) on two terrains with different ranges. The experimental results show that the proposed algorithm outperforms the comparison algorithm on both terrains with different range sizes. The proposed algorithm can improve the coverage to 97.95% and reduce the imbalance in energy consumption to 9.13% on large range terrain. [ABSTRACT FROM AUTHOR]

In recent years, WSNs have become one of the fastest emerging networks. It enables a larger variety of applications in the real-time as well as automation industries. WSN applications are made up of a large count of sensor nodes that are distributed as per the application's requirements. Sensor nodes, depending on its manufacturing rationale, monitor, sense, receive, record, and transfer any type of data. Sensors are inexpensive, tiny, and have limited energy efficiency. Inefficient methods of utilizing this scarce battery power results in the death of nodes which consequently affects the lifetime of the entire network. The failure of nodes because of inadequate routing strategies reduces the network's lifespan and overall quality. Numerous previous research methodologies were applied to improve network lifespan and node connection together with communication dependability. Most of the solutions failed to deliver ideal performance in terms of improving overall QoS, which is a collective characteristic. In this research, a novel WBC approach for data gathering, node clustering, and load balancing in WSN is proposed. The functioning of the proposed model relies on the effective assignment of nodes to the communication task based on their weighted function computed based on the performance characteristic. Load balancing as well as data aggregation, are the two attributes effectively considered in this research work. The performance of the suggested WBC is compared to traditional benchmark techniques using the NS2 program. Multiple measures have been calculated and studied, and in every case, the suggested WBC outperforms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and Big Data technologies are becoming increasingly significant parts of national defense and the military, as well as in the civilian usage. The proper deployment of large-scale wireless sensor network (WSN) provides the foundation for these advanced technologies. Based on the Fruchterman–Reingold graph layout, we propose the Fruchterman–Reingold Hexagon (FR-HEX) algorithm for the deployment of WSNs. By allocating edges of hexagonal topology to sensor nodes, the network forms hexagonal network topology. A comprehensive evaluation of 50 simulations is conducted, which utilizes three evaluation metrics: average moving distance, pair correlation diversion (PCD), and system coverage rate. The FR-HEX algorithm performs consistently, the WSN topologies are properly regulated, the PCD values are below 0.05, and the WSN system coverage rate reaches 94%. Simulations involving obstacles and failed nodes are carried out to explore the practical applicability of the FR-HEX algorithm. In general, the FR-HEX algorithm can take full advantage of sensors' hardware capabilities in the deployment. It may be a viable option for some IoT and Big Data applications in the near future. [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.

Aiming at the problems of uneven distribution and low coverage when nodes are randomly deployed in wireless sensor network (WSN), a hybrid particle swarm-butterfly algorithm (HPSBA) is proposed for node deployment optimization. Firstly, logistic mapping and adaptive adjustment strategies are designed to control parameter values, so that the optimization speed, convergence accuracy and global search capability of HPSBA are improved. Then, four benchmark functions are used to analyze the performance of HPSBA. The simulation results show that HPSBA has higher optimization accuracy, faster optimization speed, and better stability. Finally, HPSBA is used in WSN node deployment optimization and compared with other six typical algorithms such as PSO, BOA, IGWO and so on. The results show that HPSBSA has higher coverage rate, which can effectively reduce the redundancy of nodes and prolong the survival time of WSN. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor network (WSN) has been widely used in many fields. Network coverage is the basis of providing perception services and collecting location information and has become one of the hot topics. For node deployment, this paper proposes two algorithms. One is an improved virtual force (VF) algorithm. The virtual forces of nodes include repulsive force between nodes and repulsive force at the boundary. The improved VF algorithm sets the virtual force threshold. The other is the resampling particle swarm optimization algorithm embedded with virtual force (RPSO-DV). The algorithm combines the advantages of three algorithms, including resampling particle swarm optimization (RPSO) algorithm, particle swarm optimization algorithm based on coefficient adjustment (PSO-D) and improved VF algorithm. In this paper, the two proposed algorithms and reference algorithms in the pieces of literature and are simulated and compared. Firstly, this paper compares the impact of different node numbers and deployment modes on coverage performance in the improved VF algorithm. The simulation shows that the improved VF algorithm can make the network reach a stable state quickly and achieve a high coverage rate. Secondly, this paper lists the confidence intervals for the coverage rate of multiple algorithms at the significance level of 0.05. At the same time, we analyze the specific coverage rate curves and deployment diagrams. The simulation results show that our proposed RPSO-DV algorithm improves the diversity of the population and speeds up the convergence speed. Compared with other reference algorithms, the RPSO-DV algorithm has the highest coverage rate. Finally, this paper analyzes the sensitivity of the parameters of the proposed RPSO-DV algorithm. According to the orthogonal experiment design method, we design 64 sets of experiments. The simulation results show that the algorithm has a certain tolerance and robustness to parameter values. [ABSTRACT FROM AUTHOR]

Coverage of the bounded region gets importance in Wireless Sensor Network (WSN). Area coverage is based on effective surface coverage with a minimum number of sensor nodes. Most of the researchers contemplate the coverage region of interest as a square and manifest the radio ranges as a circle. The area of a circle is much higher than the area of a square because of the perimeter. To utilize the advantage of the circle, the coverage region of interest is presumed as a circle for sensor node deployment. This paper proposes a novel coverage improved disc shape deployment strategy. Comparative analysis has been observed between circle and square regions of interest based on the cumulative number of sensor nodes required to cover the entire region. A new strategy named as disc shape deployment strategy is also proposed. Traditional hexagon and strip-based deployment strategies are compared with the disc shape deployment strategy. The simulation result shows that the circle shape coverage region of interest extremely reduces the required number of sensor nodes. The proposed deployment strategy provides desirable coverage, and it requires few more sensor nodes than hexagon shape deployment strategy. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a combination of autonomous sensor nodes and used to monitor various physical conditions. Static and mobile are two types of WSN. In static WSN the sensor nodes do not alter their positions after deployment. Due to the security constraints, WSN is vulnerable to various types of attacks. In that, Clone attack is the most dangerous attack on WSN. In Clone attack an adversary physically captures a sensor node. It reprograms the detained node and generates many clone nodes. The real nodes and clone nodes are installed in the sensor field, so clone node identification is a difficult task. The new protocol proposed to identify the clone nodes in the static WSN called as Node Based Clone Attack Detection (NBCAD) protocol. This protocol detects the clones in a efficient manner compare with SET and RED protocols. The advantage of proposed NBCAD protocol include high clone detection ratio, minimum communication overhead, minimum end-to-end delay, minimum latency time and high throughput.

Abstract In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Underwater Wireless Sensor Networks (UWSNs) play a crucial role in underwater communication system, in which node deployment is a complex problem. Most of the previous researches are based on all-direction underwater acoustic sensors. However, most of the sensors are in limited direction in practical systems. The deployment of directional sensors not only needs to consider its position adjustment, but also needs to accurately adjust its direction according to the sensing angle, sensing distance and other parameters in its sensing model, so it is difficult to achieve optimization with traditional algorithms. In this paper, we propose the Adaptive Heuristic Algorithm for UWSNs (AHAU) to solve this problem in underwater communication systems. AHAU features the addition of inner layer of adaptive parameter iteration to outer layer of global iteration. AHAU can more precisely adjust the direction of the sensor to achieve higher target coverage rate. The effectiveness and reliability of the proposed algorithm are verified by designing experiments. The results of experiments show that AHAU can achieve better adjustment effect under different parameter conditions to ensure the spatial coverage of UWSNs. [ABSTRACT FROM AUTHOR]

A Body Node Coordinator (BNC) is responsible for coordinating and receiving the data transmissions of bio-sensor nodes within a wireless body area network (WBAN). Hence, it is necessary to place BNC at an optimal location in WBAN to minimize network energy consumption in data transmission. This paper presents a complete data routing strategy for WBAN that implements a multi-objective particle swarm optimization (MO-PSO) based BNC placement scheme along with a cluster routing protocol. The proposed scheme uses a three-variable particle structure where the initial two variables specify the BNC coordinates, and the third variable determines the cluster head node. A multi-objective fitness evaluation operator estimates the fitness of MO-PSO particles in terms of network energy consumption and average bit error rate (BER) observed during a single round of data transmission. The developed model evolves into an optimal BNC location that minimizes network energy consumption and average BER simultaneously. A reduced BER further brings about a significant increase in network throughput rate. Simulation results depict that the proposed scheme achieves 67.1% energy efficiency with a 94% network throughput rate. The proposed scheme meets the requirement of a short operational period apart from minimal involvement of sensor nodes. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are widely used in many applications like forest fire monitoring, environmental monitoring, search and rescue operations and weather monitoring etc. The WSNs are organized into clusters in order to conserve energy. A high level of reliability is necessary for using WSNs particularly in mission-critical applications like battlefield surveillance, disaster management etc. The node deployment is an essential feature of WSN. It is of two types- random and deterministic. An efficient node deployment scheme not only reduces the energy cost but also enhances the network lifetime. The paper proposes four deterministic WSN cluster deployment topology models – the square, the pentagonal, the hexagonal and the 3 × 3 grid model for fixed sensor deployment. The performance of the four proposed models are first analyzed using a Reliability Block Diagram (RBD) where the reliability value for each of the proposed models is calculated using the Sum of Disjoint Products (SDP) method. Then, the proposed models are compared through simulation for different performance metrics. The paper also proposes a Reliable Neuro-Fuzzy Optimization Model (RNFOM) data aggregation technique which combines the power of an ANFIS estimator for intra-cluster and inter-cluster fault detection in WSNs for different fault types with Gorti’s Enhanced Homomorphic Cryptosystem (EHC) for imparting security to the network. Both the analytical and the simulation results confirm that the 3 × 3 WSN grid cluster deployment model powered with the proposed RNFOM data aggregation technique gives comparatively better performance and a longer network lifetime than the other proposed models for any clustered WSN.

Abstract This paper mainly studies the deployment of wireless sensor network nodes in the warehouse environment monitoring system, discusses the deployment algorithm of wireless sensor network nodes in the warehouse environment, and finds out the node deployment scheme with better network performance through comparison. Wireless sensor network node deployment is the basis of wireless sensor network application in storage environment monitoring system. It affects the performance of the whole network and is the primary problem to be solved in network application. This paper discusses the advantages of wireless sensor network in the monitoring of storage environment, especially the deployment and simulation analysis of sensor nodes in the warehouse environment. Aiming at the influence of sensor perception model on the effectiveness of the node deployment plan, this paper proposes a node deployment collaborative perception model based on 0-1 perception model and exponential model. The sensor node deployment problem is transformed into a three-dimensional node deployment problem. Finally, the algorithm is applied to tobacco storage environment. In order to verify the effectiveness of the proposed algorithm, the scheme obtained by the proposed algorithm is compared with that obtained by the corresponding deployment algorithm in this paper. The comparison results show that the overall performance of the algorithm is better than that of the usual scheme.