Coverage In Wireless Sensor Networks Research Articles

An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization

Simulation of entertainment interactive robots based on intelligent algorithms in personalized fitness training systems

In the current context of rapid development of information technology and AI, entertainment interactive robots can play an auxiliary role in fitness training. Intelligent technical services can provide athletes with timely training course information and gamified sports training. Various data will also be uploaded to technical platforms for analysis. This article studies the design of a personalized fitness training system for entertainment interactive robots based on intelligent algorithms. Firstly, this article analyzes the main technologies of photon integrated biosensors, establishes a linear photon integrated biosensor imaging model. Choose to use intelligent algorithms to optimize coverage for wireless sensor networks. This problem is transformed into a mathematical function model, and then the optimal solution is sought through algorithms, effectively reducing the energy consumption of the network and extending its lifespan. The system improves interaction with humans by collecting training action videos, automatically recognizes the types of human training actions, and automatically extracts key postures from training actions for comparison and analysis with standard postures. The practice and research of human–computer interaction have shown that in addition to the actual performance of social robots, the emotional interaction of social robots can also affect people's trust in their abilities.

A new hybrid optimization algorithm for maximizing area coverage in wireless sensor networks

ABSTRACT The rapid advancements in technology influence develop our lives day-by-day. However, the area coverage issue exhibited by sensor nodes in WSN still seems a complicated issue even if the area coverage enhancement models are developed. This research work is developed on the basis of implementing an efficient area coverage maximising approach with an advanced optimisation model, Coati Modernized Beluga Whale Optimisation (CMBWO). The whole process of the area coverage maximising approach revolves in two important steps: (i) Sensor node coverage model and (ii) Area coverage optimisation. In the first step, a 2D WSN monitoring area set-up is considered and in which target monitoring points are assumed to solve the coverage issue. Most importantly, the distance between the sensor node and the target monitoring point is calculated using weighted Manhattan distance evaluation instead of conventional Euclidean distance evaluation. This improvement is introduced to calculate the precise position of the target monitoring point from the position of the sensor node. The next, area coverage optimisation step is handled by the CMBWO algorithm for maximising area coverage in WSN. This hybridisation is possible by modernising BWO via COA. This efficient hybridisation led to maximising the area coverage of sensor nodes in WSN.

Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimization

Hybrid remora crayfish optimization for engineering and wireless sensor network coverage optimization

Coverage optimization of wireless sensor networks based on bottle sea sheath optimization algorithm

To tackle the problem of inadequate placement of wireless sensor networks’ sensor nodes, a proposed algorithm aims to enhance network coverage through a more optimal approach. Recognizing limitations in the Salp Swarm Algorithm (SSA) optimization, specifically its insufficient local search capability and susceptibility to local extreme values, a hybrid strategy is introduced in the search step. This strategy involves adaptive weight factor adjustments and incorporates Levy flight, achieving a better balance between global search, convergence speed, and computational accuracy. Consequently, this refinement contributes to improved effective network coverage, enhancing the overall network coverage in wireless sensor networks and facilitating faster coverage of the nodes.

A heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networks

A heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networks

An Improved Grey Wolf Optimizer with Multi-Strategies Coverage in Wireless Sensor Networks

For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific problems of high node deployment costs and insufficient effective coverage in WSNs, this paper proposes a WSN coverage optimization method based on the improved grey wolf optimizer with multi-strategies (IGWO-MS). As far as IGWO-MS is concerned, first of all, it uses Sobol sequences to initialize the population so that the initial values of the population are evenly distributed in the search space, ensuring high ergodicity and diversity. Secondly, it introduces a search space strategy to increase the search range of the population, avoid premature convergence, and improve search accuracy. And then, it combines reverse learning and mirror mapping to expand the population richness. Finally, it adds Levy flight to increase the disturbance and improve the probability of the algorithm jumping out of the local optimum. To verify the performance of IGWO-MS in WSN coverage optimization, this paper rasterizes the coverage area of the WSN into multiple grids of the same size and symmetry with each other, thereby transforming the node coverage problem into a single-objective optimization problem. In the simulation experiment, not only was IGWO-MS selected, but four other algorithms were also selected for comparison, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), grey wolf optimization based on drunk walk (DGWO), and grey wolf optimization led by two-headed wolves (GWO-THW). The experimental results demonstrate that when the number of nodes for WSN coverage optimization is 20 and 30, the optimal coverage rate and average coverage rate using IGWO-MS are both improved compared to the other four comparison algorithms. To make this clear, in the case of 20 nodes, the optimal coverage rate of IGWO-MS is increased by 13.19%, 1.68%, 4.92%, and 3.62%, respectively, compared with PSO, GWO, DGWO, and GWO-THW; while IGWO-MS performs even better in terms of average coverage rate, which is 16.45%, 3.13%, 11.25%, and 6.19% higher than that of PSO, GWO, DGWO, and GWO-THW, respectively. Similarly, in the case of 30 nodes, compared with PSO, GWO, DGWO, and GWO-THW, the optimal coverage rate of the IGWO-MS is increased by 15.23%, 1.36%, 5.55%, and 3.66%; the average coverage rate is increased by 16.78%, 1.56%, 10.91%, and 8.55%. Therefore, it can be concluded that IGWO-MS has certain advantages in solving WSN coverage problems, which is reflected in that not only can it effectively improve the coverage quality of network nodes, but it also has good stability.

Multi-objective Evolutionary Algorithms for Coverage and Connectivity Aware Relay Node Placement in Cluster-Based Wireless Sensor Networks

Multi-objective Evolutionary Algorithms for Coverage and Connectivity Aware Relay Node Placement in Cluster-Based Wireless Sensor Networks

ESND-FA: An Energy-Efficient Scheduled Based Node Deployment Approach Using Firefly Algorithm for Target Coverage in Wireless Sensor Networks

ESND-FA: An Energy-Efficient Scheduled Based Node Deployment Approach Using Firefly Algorithm for Target Coverage in Wireless Sensor Networks

A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks

Mobile charging provides a new energy replenishment technology for Wireless Rechargeable Sensor Network (WRSN), where the Mobile Charger (MC) is employed for charging nodes sequentially according to the mobile charging scheduling result, using node charging timeliness and quality of sensing coverage as the scheduling criteria. Sensing coverage is a critical network property and has received more interest in recent research studies in mobile charging scheduling in WRSN. As the network environment is usually uncertain and the charging demands may change dynamically from time to time, online mobile charging scheduling is crucial, but existing online approaches are mostly based on specific network models, which are difficult to obtain in practical applications. In this paper, we propose a novel model-free deep reinforcement learning algorithm for the Online Mobile Charging Scheduling with optimal Quality of Sensing Coverage (OMCS-QSC) problem in WRSN, Multistage Exploration Deep Q-Network (MEDQN), where MC is designed as an agent to explore the online charging schedules via a new multistage exploration strategy for maximizing the network QSC according to the real-time network state. In addition, we also design a novel reward function to evaluate the MC charging action via the real-time sensing coverage contributions of the nodes. Extensive simulations show that MEDQN can reach the convergence state stably and is superior to existing online algorithms, especially in large-scale WRSNs.

Optimizing Coverage in Wireless Sensor Networks: A Binary Ant Colony Algorithm with Hill Climbing

Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage.

Coverage and Routing Optimization of Wireless Sensor Networks Using Improved Cuckoo Algorithm

Coverage and Routing Optimization of Wireless Sensor Networks Using Improved Cuckoo Algorithm

An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm.

In response to the problem of coverage redundancy and coverage holes caused by the random deployment of nodes in wireless sensor networks (WSN), a WSN coverage optimization method called GARWOA is proposed, which combines the genetic algorithm (GA) and reinforced whale optimization algorithm (RWOA) to balance global search and local development performance. First, the population is initialized using sine map and piecewise linear chaotic map (SPM) to distribute it more evenly in the search space. Secondly, a non-linear improvement is made to the linear control factor 'a' in the whale optimization algorithm (WOA) to enhance the efficiency of algorithm exploration and development. Finally, a Levy flight mechanism is introduced to improve the algorithm's tendency to fall into local optima and premature convergence phenomena. Simulation experiments indicate that among the 10 standard test functions, GARWOA outperforms other algorithms with better optimization ability. In three coverage experiments, the coverage ratio of GARWOA is 95.73, 98.15, and 99.34%, which is 3.27, 2.32 and 0.87% higher than mutant grey wolf optimizer (MuGWO), respectively.

Editorial

Dear Readers,  At the end of this year, it gives me great pleasure to announce the eleventh regular issue of 2023. In this issue, various topical aspects of computer science are covered in 6 articles by 18 authors from 7 countries (Argentina, Brazil, China, France, Germany, Spain, and Turkey). I would like to thank all the authors for their sound research and the editorial board for the highly valuable review effort and comments for improvement. These contributions, together with the generous support of the consortium members, sustain the quality of our journal. I am looking forward to continuing my work as Managing Editor-in-Chief with the J.UCS community.  In an ongoing effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. Please consider yourself and encourage your colleagues to submit high-quality articles or special issue proposals for our journal. Finally, we are looking for further financial support from potential consortium members to cover the costs of publishing the journal next year.  In this regular issue, I am very pleased to introduce the following 6 accepted articles: F. Kebire Bardak, M. Nuri Seyman, and Feyzullah Temurtaş from Turkey present in their research a hybrid algorithm for emotion classification based on electroencephalogram signals, which is composed of a radial basis function neural network and a probabilistic neural network. In a collaborative research effort between Spain and France, Anita Herrera, Ángel Arroyo, Alfredo Jiménez and Álvaro Herrero conduct a comprehensive review of the different techniques and models with regard to Artificial Intelligence when applied to the tourism industry. In a collaboration between researchers from Brazil and Germany, Ana Cristina Alves de Oliveira, Marco Aurélio Spohn, Christof Fetzer, Le Quoc Do, and André Martin aim to address the cost problem of DaaS by developing a model that optimizes the cost of querying distributed data sources over virtual machines spread across multisite data centers. Herminia Beatriz Parra and Marcela Vegetti from Argentina present in their article OntoFoCE, Ontology for Electronic Mail Forensics, which is a specific ontology for the forensic analysis of emails to help the computer expert in validating an email presented as judicial evidence. Valdicélio Santos and Michel S. Soares from Brazil propose a framework, and then the design and further evaluation of a web-based application to support software architects in using the activities and tasks of the architecture conceptualization clause based on the ISO/IEC/IEEE 42020 framework. Last but not least, Yunwu Xu and Yan Li from China contribute to the improvement of an existing wireless sensor network coverage optimization method which is based on the pigeon-inspired optimization algorithm.  Season greetings to all of you, relaxing holidays and ‘Enjoy Reading’!  Cordially,  Christian Gütl, Managing Editor  Graz University of Technology, Graz, Austria

Wireless Sensor Network Coverage Optimization for Internet of Things

The objective of this work is to improve the existing Wireless Sensor Network coverage optimization method. The pigeon-inspired optimization algorithm was first evaluated, and its shortcomings were noted. The pigeon-inspired optimization method was then enhanced with the good point set, Yin-Yang optimization algorithm, and opposition-based learning. To test the improved algorithm, five representative standard functions were chosen: sphere function (f1), Rosenbrock function (f2), Levy function (f3), Schwefel function (f4), and Levy function N.13 (f5). The algorithm's speed of convergence may be determined by the first two functions, which are unimodal. The final three functions, which are multimodal, can extract several local optimal values from the local optimum. In comparison with other known algorithms, the improved Yin-Yang PIO algorithm showed the highest optimization accuracy and stability. Three sets of experiments were performed to optimize the WSN coverage with different parameters. The first series of experiments suggest that Yin–Yang PIO has the best optimization effect, with a coverage rate of 99.51% (10.22% higher with PIO and 6.41% higher compared with PSO). The second and third series of experiments show that Yin-Yang PIO significantly increased the WSN coverage ratio, up to 99.9%. The algorithm can be applied to optimize WSN coverage in various environments. Future research can extend the research scope to include other optimization problems in IoT. 

Q-learning based vegetation evolution for numerical optimization and wireless sensor network coverage optimization

Vegetation evolution (VEGE) is a newly proposed meta-heuristic algorithm (MA) with excellent exploitation but relatively weak exploration capacity. We thus focus on further balancing the exploitation and the exploration of VEGE well to improve the overall optimization performance. This paper proposes an improved Q-learning based VEGE, and we design an exploitation archive and an exploration archive to provide a variety of search strategies, each archive contains four efficient and easy-implemented search strategies. In addition, online Q-Learning, as well as ε-greedy scheme, are employed as the decision-maker role to learn the knowledge from the past optimization process and determine the search strategy for each individual automatically and intelligently. In numerical experiments, we compare our proposed QVEGE with eight state-of-the-art MAs including the original VEGE on CEC2020 benchmark functions, twelve engineering optimization problems, and wireless sensor networks (WSN) coverage optimization problems. Experimental and statistical results confirm that the proposed QVEGE demonstrates significant enhancements and stands as a strong competitor among existing algorithms. The source code of QVEGE is publicly available at https://github.com/RuiZhong961230/QVEGE.

Three-Dimensional Iterative Enhancement for Coverage Hole Recovery in Underwater Wireless Sensor Networks

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.

Adaptive traffic aware clustering and routing model for wireless sensor networks

<span>Designing effective clustering algorithms plays a very important role in improving network lifetime target coverage (NLTC) in heterogeneous wireless sensor networks (HWSNs). However, the current clustering mechanism failed to address hotspot problem resulting in poor coverage and network lifetime performance. Recently, unequal clustering has been used for addressing hotspot problem in HWSNs. However, when performing inter-cluster routing, load balancing is not efficient and the energy degradation is high, which affects the overall network performance. This work introduces an effective model namely adaptive traffic aware clustering and routing (ATACR) model to address hotspot and load balancing issues. The ATACR introduces a novel unequal clustering and ideal distribution of load in different cluster levels. The ATACR model improves network lifetime, reduces control channel overhead, and attains better throughput in comparison with existing routing models.</span>

Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy

Because of the low convergence accuracy of the basic Harris Hawks algorithm, which quickly falls into the local optimal, a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy (TDHHO) is proposed. The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development. The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed. The difference variation strategy is used to mutate the individual position and calculate the fitness, and the fitness of the original individual position is compared. The greedy technique is used to select the one with better fitness of the objective function, which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value. The test function tests the TDHHO algorithm, and compared with other optimization algorithms, the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved. Finally, the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks (WSN) coverage optimization problems, and the feasibility of the TDHHO algorithm in practical application is further verified.

Node Deployment Strategy Based on Improved Particle Swarm Algorithm in Three-dimensional Underwater Sensor Networks

To enhance the monitoring quality of the underwater three-dimensional target area and address issues such as uneven node distribution, inadequate coverage, and poor connectivity in traditional deployment strategies, a new node deployment strategy based on grid deployment with a variable weight particle swarm algorithm has been proposed in this research paper. Its goal is to minimize the quantity of deployed nodes while maintaining the coverage and connectivity of the three-dimensional wireless sensor networks (UWSNs). Simulation results demonstrate that this deployment strategy leads to good performance of the sensor network, and our approach ensures maximum coverage with the same number of nodes.