Coverage In Wireless Sensor Networks Research Articles

A family system based evolutionary algorithm for obstacle-evasion minimal exposure path problem in Internet of Things

Barrier coverage in wireless sensor networks (WSNs) is a well-known model for military security applications in IoTs, in which sensors are deployed to detect every movement over the predefined border. The fundamental sub-problem of barrier coverage in WSNs is the minimal exposure path (MEP) problem. The MEP refers to the worst-case coverage path where an intruder can move through the sensing field with the lowest capability to be detected. Knowledge about MEP is useful for network designers to identify the worst coverage in WSNs. Most prior research focused on this problem with the assumption that the WSN has an ideal deployment environment without obstacles, causing existing gaps between theoretical and practical WSNs systems. To overcome this drawback, we investigate a systematic and generic MEP problem under real-world environment networks by presenting obstacles called Obstacle-Evasion-MEP (hereinafter OE-MEP). We propose an algorithm to create several types of arbitrary-shaped obstacles inside the deployment area of WSNs. The OE-MEP problem is an NP-Hard with high dimension, non-differentiation, non-linearity, and constraints. Based upon its characteristics, we then devise an elite algorithm namely Family System based Evolutionary Algorithm (FEA) with our newly-proposed concepts of Family System, tailored to efficiently solve the OE-MEP. We also build an extension to a custom-made simulation environment to integrate a variety of network topologies as well as obstacles. Experimental results on numerous instances indicate that the proposed algorithm is suitable for the converted OE-MEP problem and performs better in solution accuracy than existing approaches.

Mitigation of coverage and connectivity issues in wireless sensor network by multi-objective randomized grasshopper optimization based selective activation scheme

Connectivity and coverage play a major role in the proper functioning and performance of Wireless Sensor Network (WSN). Network connectivity indicates how well the sensory data can be communicated by the sensor nodes to the sink. Coverage specifies how well a Point of Interest (POI) is being monitored by the WSN. The sensors have limited energy source, sensing, and communication range, so proper activation of the sensors have a significant influence on WSN. Therefore, the main challenge is to ensure that a small number of sensors are activated to provide coverage and connectivity in the POI. To overcome these challenges, this paper proposes a multi-objective randomized Grasshopper Optimization Algorithm-based Selective Activation (MORGOA-SA) method to sustain the expected connectivity and coverage in the network. Initially, the fitness is evaluated with five main objective functions such as (i) maximizing the coverage, (ii) Ensuring connectivity, (iii) minimizing the coverage overlap, (iv) selection of nodes with higher residual energy, and (v) minimizing active sensor nodes. Then the nodes with the best fitness function are activated, whereas the rest undergo sleep mode. Multi-objective randomized GOA-SA reduces the energy consumption of the network and prevents the failure of sensor nodes. The simulations and comparative analysis justify that the proposed MORGOA-SA scheme has attained better results than the existing schemes in terms of minimum energy consumption, minimum activation of nodes, high throughput, high connectivity, quick convergence, and increased network lifetime.

MSQAC: Maximizing the Surveillance Quality of Area Coverage in Wireless Sensor Networks

In wireless sensor networks (WSNs), area coverage is an important issue that aims to schedule a minimal number of sensors to cover the given monitoring area without any hole. Many of the existing strategies investigated that sensors are battery powered and considered Boolean Sensing Model (BSM). However, BSM affects the actual sensing quality since it did not consider the physical characteristics of the sensor. This study proposed an area coverage algorithm called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {MSQAC}$ </tex-math></inline-formula> , which considers the rechargeable sensors and utilizes solar-powered energy. The proposed mechanism aims to maximize the surveillance quality for a given monitoring area by applying the Probabilistic Sensing Model (PSM). Two issues have been investigated while designing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {MSQAC}$ </tex-math></inline-formula> algorithm. The first one is the energy discharging rate of each sensor is larger than the energy recharging rate. Thus, how to better utilize the recharged energy by scheduling the right sensor in the right working time is a big challenge. The second challenge is the contribution of each sensor with or without cooperation is different by considering the PSM. If the neighboring sensors are scheduled at work in the same time slot, they can be treated as the combination set of neighboring sensors. It is another big challenge to find the best combination set of sensors for each space point at each time slot such that the minimal monitoring quality can be maximized. Through extensive simulations, it is shown that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {MSQAC}$ </tex-math></inline-formula> yields the best performance in terms of monitoring quality ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit {QoM}$ </tex-math></inline-formula> ), coverage ratio and fairness index.

Construction of an Innovative Development Model of Intelligent Media under the Coverage of a Wireless Sensor Network

A distributed network composed of sensor nodes with limited computing and communication capabilities is a wireless sensor network, which is a system that can autonomously and intelligently complete target tasks according to the surrounding environment. With its wide application in the fields of smart medical care, smart home, networks of vehicles, and smart media, its communication performance requirements are also increasing. According to the needs of different application scenarios, the first key problem to be solved in wireless sensor networks is what optimization coverage strategy to adopt, which will have a direct impact on the optimal allocation of very limited resources such as node energy, communication bandwidth, computing power, and other network services’ quality. Nodes adopt a probability-based joint perception model, and its algorithm is a node scheduling mechanism based on the connected dominating set to construct a tree. Each node sets the waiting time and becomes a candidate node priority according to the remaining energy and the distance from the parent node. In this paper, the issue of energy fairness is considered in heterogeneous wireless sensor networks. In order to prevent the occurrence of “energy holes,” this paper proposes a node deployment model similar to cellular networks. By deploying heterogeneous sensor nodes in this way, energy saving can be achieved. Based on this model, game theory is used to simulate the data packet transmission process between sensor nodes, an energy consumption mechanism suitable for each stage of data transmission is proposed, and Nash equilibrium is finally obtained by rationally designing the measurement function. The node deployment scheme proposed in this paper not only balances the energy consumption of the network but also prolongs the life cycle of the network. Aiming at the three-dimensional (3D) space environment, this paper studies a node deployment mechanism that guarantees connectivity and has a perceptual coverage degree of k with the help of the Reuleaux tetrahedron model. In this paper, a node deployment strategy is proposed and compared with the node deployment algorithm for 3D sensor network based on truncated octahedron. The simulation experiment results show that the wireless sensor network with artificial intelligence can not only optimize the production process of intelligent media but also improve each link of the media industry chain and further catalyze the development of new formats in the media industry. The experimental results also show that the algorithm can effectively meet the requirements of sensing coverage and connectivity coverage, the number of working nodes is small, the network life cycle is significantly prolonged, and the overall energy consumption of the network is reduced. The study results of this paper provide a reference for follow-up research on the construction of innovative development model of intelligent media under the coverage of wireless sensor network.

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review.

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

Research on Channel Modeling and Communication Coverage of Wireless Sensor Networks in Barrier Area of Nuclear Power Plants

In view of the multimetal barrier environment of nuclear power plant, by considering the factors such as transmission power, transmission position, and multipath interference, based on the simulation of metal pipes and equipment, this paper carries out the barrier area channel modeling in logarithmic fading mode and makes quantitative analysis on the channel transmission, path loss, channel power characteristics, and so on under the metal barrier environment. Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. The simulation results show that the improved teaching and learning algorithm can optimize the network coverage of the obstacle area well, and under the four obstacle modules, 14 nodes can cover the whole area by more than 99%. This provides a solution to the problem of network coverage in the practical application of wireless sensor networks.

Modified Parallel Tunicate Swarm Algorithm and Application in 3D WSNs Coverage Optimization

&lt;p&gt;As the application of Wireless Sensor Networks (WSNs) in today’s society becomes more and more extensive, and the status is getting higher and higher, the node layout of sensors has also begun to attract social attention. In reality, the coverage of WSNs in 3D space is particularly important. Therefore, it is worth investigating an efficient way to find out the maximum coverage of WSNs. In this paper, a Modified Parallel Tunicate Swarm Algorithm (MPTSA) is proposed based on modified parallelism, which can improve the convergence of the algorithm and optimal global solution. Next, the proposed MPTSA is implemented and tested on 23 benchmark functions to verify the algorithm performance. Finally, a WSNs network layout scheme based on MPTSA is proposed to improve the coverage of the whole network. Experimental results show that, compared with the traditional PSO (Particle Swarm Optimization), improved PSO (PPSO and APSO), GBMO (Gases Brownian Motion Optimization) and traditional TSA, MPTSA family algorithms show better performance in WSNs network layout.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks.

With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN’s monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.

A novel approach to partial coverage in wireless sensor networks via the roman dominating set

One major challenge in deploying wireless sensor networks (WSN) in real-world applications is minimising the energy consumption by the sensors while maintaining the coverage of the monitored field. However, many applications do not need full coverage of the monitored area all the time, which can help us reduce the network's energy consumption. One approach to exploit this property is to set up a sleep/wake-up schedule for each node such that no redundant nodes are active in an area of coverage simultaneously. This will allow the existence of monitoring holes in a controlled manner, which the authors call partial coverage. In this study, the partial coverage condition is imposed by constructing a Roman Dominating Set of awake nodes in the network. Roman domination is a method for colouring a graph's vertices with three labels (0, 1, 2), such that all the vertices with label 0 have an adjacent vertex with label 2, while the sum of the labels of nodes is minimised. Based on this formulation, a simple greedy algorithm is proposed to construct such a structure supported by three theorems. Furthermore, the performance of the authors’ proposal in different scenarios is evaluated.

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks.

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

A hybrid-strategy-improved butterfly optimization algorithm applied to the node coverage problem of wireless sensor networks.

To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to initialize the population to ensure a more uniform search space. Second, a new inertial weight modified from the Sigmoid function is introduced to balance the global and local search capacities. Third, we comprehensively use elite-fusion and elite-oriented local mutation strategies to raise the population diversity. Then, we introduce a perturbation based on the standard normal distribution to reduce the possibility of the algorithm falling into premature. Finally, the simulated annealing process is introduced to evaluate the solution's quality and improve the algorithm's ability, which is helpful to jump out of the local optimal value. Through numerous experiments of the international benchmark functions, the results show the performance of H-BOA has been significantly raised. We apply it to the WSN nodes coverage problem. The results show that H-BOA improves the WSN maximum coverage and it is far more than other optimization algorithms.

Forest fire detection system using barrier coverage in wireless sensor networks

Forest fires are one of the main reasons for various types of pollution and disturb an ecology in a very impactful way. Technological applications to detect, monitor, and avoid forest fires have helped reduce the damage caused by these fires but are not effective in many situations. Wireless sensor networks have played a crucial role in many of these methods. Barrier coverage-based sensor networks are the solution. This paper puts forth a system that helps in the early detection of forest fires and helps authorities to get into action to avoid them as soon as possible. Sensor nodes in this system are drones that hover over the area under surveillance. These drones are equipped with the necessary setup to detect forest fires and to transfer data to other nodes so that the information reaches the appropriate authorities. These drones form a set of barriers so that fire starting at any point in the area is detected. A base station is considered as the final destination of data and communication is done through the X-bee module protocol. Data is hopped from one node to another until it reaches the base station.

Performance Evaluation of Onboard Wi-Fi Module Antennas in Terms of Orientation and Position for IoT Applications

With ever increasing demand of IoT based sensor systems, there is a need to assess the performance of wireless sensor networks especially in indoor environment. In these networks, antenna plays an important role. The performance of onboard antenna of the sensor module with respect to its height and orientation are examined in this paper. Several experiments were carried out mostly in indoor environment by changing orientation and height of the antennas. The performance is assessed on the basis of Received Signal Strength (RSS) and its modelling using linear, logarithmic and rational polynomial regression techniques which will characterize the channel in a particular environment. Out of all the combinations in terms of height of the antennas and their orientation, it is found for a given indoor environment, with transmitting antenna at a medium height facing upwards and receiving antenna with an inclination of 700 towards transmitter resulted in better performance with R2poly value of 86.81% and RMSE of 4dB. Therefore, this combination is suggested for wireless sensor networks in indoor environment for achieving the of cost-effective energy-efficient green IoT. The analysis would be useful for improving the efficiency and coverage of wireless sensor networks.

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

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

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Nowadays, countless applications benefit from the services provided by wireless sensor networks (WSNs) in remote surveillance and data gathering. Coverage is considered an important quality of service (QoS) criterion for many of these applications. To ensure that the required QoS is achieved, the sensors must be placed in locations that optimise the area coverage and eliminate the coverage holes, especially when deploying sensors with heterogeneous sensing ranges. In this paper, a novel sensor deployment scheme based on the improved bees algorithm (IBA) for optimising the area coverage in both homogeneous and heterogeneous WSNs is proposed. The IBA includes a neighbourhood shrinking procedure that aims to improve the local search efficiency and a site abandonment procedure for preventing the algorithm from the entrapment in a local optimum. Experiments show that the IBA delivers high-quality solutions and optimises the area coverage effectively compared with four state-of-the-art methods in homogeneous and heterogeneous WSNs.

A software-defined multi-modal wireless sensor network for ocean monitoring

The software-defined networking paradigm enables wireless sensor networks as a programmable and reconfigurable network to improve network management and efficiency. However, several challenges arise when implementing the concept of software-defined networking in maritime wireless sensor networks, as the networks operate in harsh ocean environments, and the dominant underwater acoustic systems are with limited bandwidth and high latency, which render the implementation of software-defined networking central-control difficult. To cope with the problems and meet demand for high-speed data transmission, we propose a radio frequency–acoustic software-defined networking-based multi-modal wireless sensor network which leverages benefits of both radio frequency and acoustic communication systems for ocean monitoring. We first present the software-defined networking-based multi-modal network architecture, and then explore two examples of applications with this architecture: network deployment and coverage for intrusion detection with both grid-based and random deployment scenarios, and a novel underwater testbed design by incorporating radio frequency–acoustic multi-modal techniques to facilitate marine sensor network experiments. Finally, we evaluate the performance of deployment and coverage of software-defined networking-based multi-modal wireless sensor network through simulations with several scenarios to verify the effectiveness of the network.

Novel Scheme to Resolve Coverage and Connectivity Issue in Wireless Sensor Networks

Novel Scheme to Resolve Coverage and Connectivity Issue in Wireless Sensor Networks

Novel Scheme to Resolve Coverage and Connectivity Issue in Wireless Sensor Networks

Novel Scheme to Resolve Coverage and Connectivity Issue in Wireless Sensor Networks

The VF-PSO optimization algorithm for coverage and deployment of underwater wireless sensor network

Coverage is a factor to reflect the network service quality of the Underwater Wireless Sensor Network (UWSN). Existing UWSN has problems of void-hole and low coverage, which is reducing UWSN lifetime and ability to monitor deployment areas. To improve network coverage and network lifetime, a coverage optimization method based on virtual force and particle swarm optimization (VF-PSO) is proposed in this article. By action of virtual force, the underwater mobile nodes would move to a better position to improve network coverage in this method. For the VF-PSO algorithm, the virtual force can guide the optimization of particles and accelerate the convergence of particles to the global optimal solution. This algorithm could not only optimize the movement trend of nodes to maximize the coverage ratio but also adjust the node distance threshold to reduce the network coverage redundancy. Simulation presents that compared with other typical algorithms, VF-PSO can improve the network connectivity and coverage of the UWSN area, and effectively avoid the network void-hole problem.

Data Reconstruction Coverage Based on Graph Signal Processing for Wireless Sensor Networks

Sensing coverage is a crucial metric for the quality of service of Wireless Sensor Networks (WSNs). Coverage models have a great impact on sensing coverage of WSNs. However, existing coverage models are simple but inefficient, like the most frequently used disk coverage model, in which a covered point is within the fixed sensing radius of at least one sensor node. Thus, how to develop an efficient coverage model is an essential problem. To this end, in this letter, we propose a novel coverage model without the limitation of the sensor’s sensing radius, namely, Data Reconstruction Coverage (DRC). Based on the theory of graph signal processing, the model can jointly reconstruct missing data at unsampled points (which are not covered by any sensors) by using our proposed centralized data reconstruction coverage algorithm which fully exploits the smoothness of temporal difference signals and the graph Laplacian matrix, without increasing the number of sensors. Simulation results based on real-world datasets show that the proposed DRC model has better coverage performance of WSNs compared with the disk coverage model and confident information coverage model typically used in WSNs.