Coverage Optimization Strategy Research Articles

Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks

Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks

Research of Coverage Optimization Strategy Based on Improved Northern Goshawk Algorithm

Research of Coverage Optimization Strategy Based on Improved Northern Goshawk Algorithm

Novel WSN Coverage Optimization Strategy Via Monarch Butterfly Algorithm and Particle Swarm Optimization

Novel WSN Coverage Optimization Strategy Via Monarch Butterfly Algorithm and Particle Swarm Optimization

Coverage Optimization Strategy of UWSNs Based on Improved Sparrow Search Algorithm

Randomly distributed nodes lead to low coverage rate and low connectivity in the underwater wireless sensor networks (UWSNs). An UWSNs node coverage optimization strategy based on the Improved Sparrow Search Algorithm (ISSA) is designed to improve coverage rate and connectivity. The ISSA algorithm effectively balances the global and local optimization capabilities of nodes by introducing nonlinear inertia weights and Levy flight strategy, thereby avoiding the algorithm from getting trapped in local optima. Simulation experiments demonstrate that ISSA achieves optimal deployment results in a short time. It not only improves network coverage but also maintains good network connectivity and reduces node redundancy, thereby e ffectively reducing network energy consumption. Compared to other common algorithms , ISSA can increase network coverage up to 8.4% and improve network utilization up to11.8%, and meets the deployment requirements.

Coverage Optimization Strategy for 3-D Wireless Sensor Networks Based on Improved Sparrow Search Algorithm

Coverage Optimization Strategy for 3-D Wireless Sensor Networks Based on Improved Sparrow Search Algorithm

An Improved Honey Badger Algorithm for Coverage Optimization in Wireless Sensor Network

<p>One of the critical indicators for a significant impact on the quality of service of wireless sensor networks (WSN) is coverage that directly determines the monitoring capability of the target monitoring area. This paper suggests a node coverage optimization strategy solution with an improved Honey-badger algorithm (called IHBA) to address the problems of WSN nodes’ uneven distribution and low coverage in the random deployment. The honey-badger algorithm (HBA) is a recently developed metaheuristic algorithm with several advantages, e.g., robust process and ease of implementation; still, HBA has limitations in avoiding the local optimum trap when dealing with complicated node coverage optimization situations. The IHBA is implemented by modifying and updating equations with elite reverse learning and multi-direction strategies to prevent its original algorithm drawbacks. The obtained results on testing the benchmark function and the optimal WSN node coverage of the IHBA compared with the other algorithms in the literature. Compared results show that the IHBA algorithm provides effective optimal performance, convergence speed, and increasing feasible coverage. Significantly, the coverage rate archived of the IHBA is 87%, but the other methods are only below or equally 84% in the same comparison conditions.</p> <p> </p>

A Coverage Optimization Algorithm for the Wireless Sensor Network with Random Deployment by Using an Improved Flower Pollination Algorithm

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.

Research on Coverage Optimization in a WSN Based on an Improved COOT Bird Algorithm.

To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems.

Continuous Use Intention of Mobile Social Network Information Service Based on User Behavior Perception.

In order to improve the willingness of continuous use of mobile social network information services, this study combines user behavior perception to analyze the continuous use of mobile social network information services and proposes a data coverage optimization strategy based on service quality perception. Furthermore, this study measures participants' regional preferences based on the duration of participants in the perceptual region and the number of historical perceptual tasks completed on the perceptual region. In addition, this study designs a perceptual data coverage optimization algorithm to optimize the perceptual data coverage and ensure the real-time validity of the perceptual data. Through algorithm research and systematic evaluation, it can be seen that the continuous use willingness system of mobile social network information service based on user behavior perception can basically meet the actual needs.

A Novel Coverage Optimization Strategy for Heterogeneous Wireless Sensor Networks Based on Connectivity and Reliability

To overcome the problems of coverage blind areas and coverage redundancy when sensor nodes are deployed randomly in heterogeneous wireless sensor networks (HWSNs). An optimal coverage method for HWSNs based on an improved social spider optimization (SSO) algorithm is proposed, which can reduce the energy consumption and improve the network coverage. First, a mathematical model of HWSN coverage is established, which is a complex combinatorial optimization problem. To improve the global convergence speed of the proposed algorithm, a chaotic initialization method is used to generate the initial population. In addition, the SSO algorithm has a poor convergence speed and search ability, which is enhanced by improving the neighborhood search, global search, and matching radius. In the iterative optimization process, the optimal solution is ultimately obtained by simulating the movement law of the spider colony, i.e., according to the cooperation, mutual attraction, and mating process of female and male spiders. An improved SSO algorithm based on chaos, namely the CSSO algorithm, is proposed to apply to the optimal deployment of sensory nodes in HWSNs. On this basis, the optimization goals are to improve the network coverage and reduce network costs. The optimal deployment plan of nodes is searched via the proposed CSSO algorithm, which effectively prevents coverage blind spots and coverage redundancy in the network.

A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks.

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization

As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes “external dispersion method” to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes “free-particle swarm optimization” to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage.

Local Coverage Optimization Strategy Based on Voronoi for Directional Sensor Networks.

In this paper, we study the area coverage of directional sensor networks (DSNs) with random node distribution. The coverage of DSNs depends on the sensor’s locations, the sensing radiuses, and the working directions, as well as the angle of view (AoV), which is challenging to analyze. We transform the network area coverage problem into cell coverage problems by exploiting the Voronoi diagram, which only needs to optimize local coverage for each cell in a decentralized way. To address the cell coverage problem, we propose three local coverage optimization algorithms to improve the cell coverage, namely Move Inside Cell Algorithm (MIC), Rotate Working Direction Algorithm (RWD) and Rotation based on boundary (RB), respectively. Extensive simulations are performed to prove the effectiveness of our proposed algorithms in terms of the coverage ratio.

Coverage Optimization Strategy for WSN based on Energy-aware

In order to optimize the wireless sensor network coverage, this paper designs a coverage optimization strategy for wireless sensor network (EACS) based on energy-aware. Under the assumption that the geographic positions of sensor nodes are available, the proposed strategy consists of energy-aware and network coverage adjustment. It is restricted to conditions such as path loss, residual capacity and monitored area and according to awareness ability of sensors, it would adjust the monitored area, repair network hole and kick out the redundant coverage. The purpose is to balance the energy distribution of working nodes, reduce the number of “dead” nodes and balance network energy consumption. As a result, the network lifetime is expanded. Simulation results show that: EACS effectively reduces the number of working nodes, improves network coverage, lowers network energy consumption while ensuring the wireless sensor network coverage and connectivity, so as to balance network energy consumption.

Inverse lighting design using a coverage optimization strategy

Lighting design is an essential process in computer cinematography, games, architectural design and various other applications for correctly illuminating or highlighting parts of a scene and enhancing storytelling. When targeting specific illumination goals and constraints, this process can be tedious and counter-intuitive even for experienced users and thus automatic, goal-driven methods have emerged for the estimation of a lighting configuration to match the desired result. We present a general automatic approach to such an inverse lighting design problem, where the number of light sources along with their position and emittance are computed given a set of user-specified lighting goals. To this end, we employ a special hierarchical light clustering that operates in the lighting goal coverage domain and overcomes limitations of previous approaches in environments with high occlusion or structural complexity. Our approach is independent of the underlying light transport model and can quickly converge to usable solutions. We validate our results and provide comparative evaluation with the current state of the art.

WSN Coverage Optimization Strategy Based on Improved Artificial Fish Swarm Algorithm

WSN Coverage Optimization Strategy Based on Improved Artificial Fish Swarm Algorithm

Optimization of Multi-Objective Coverage Strategy Based on Multiple Particle Swarm Coevolutionary Algorithm for Water Environment Monitoring System

This paper built a multi-objective optimization model and proposed an improved multi-objective particle swarm optimization algorithm called MPS2O ,which is based on Multiple Particle Swarm Co-evolutionary. The MPS2O algorithm has considerable potential for solving multi-objective optimization problems. Mathematical benchmark functions also shows that the proposed algorithm is an excellent Alternative for solving multi-objective optimization problems. Making full use of the research findings home and abroad, MPS2O has been chosen to be the coverage optimization strategy of the wireless sensor networks in Water Environment Monitoring System. Simulation results demonstrate that the MPS2O algorithm is more efficient than the PSO algorithm in solving this real-world problem.