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Wireless networks provide small sensing, machine and wireless networking nodes. Different designs and implementation techniques were built based on the device requirements for wireless network sensors (WSN). Sensor networks are used in various applications, such as environmental monitoring, home automation, military applications, etc. In this study introduce an architectural survey and deployment of nodes in the Wi-Fi Sensor network in this article. The environmental features that can be added to the sensor networks are given. The program relies on the node installed in the WSN and is deterministic or random. But the biggest issue in both cases is the coverage of the region involved. Researcher also describe WSN routing protocols. In this paper, a new technique to deployment problem is proposed based on the artificial bee colony (ABC) algorithm which is enhanced for the deployment of sensor networks to gain better performance by trying to increase the coverage area of the network and energy consumption. The good performance of the proposed EABC algorithm shows that it can be utilized in the deployment of WSN.

In many-to-one patterns of wireless sensor networks, the energy hole problem is a crucial issue that ultimately causes to reduce the network lifetime considerably. Since the closer nodes locating to the base station/sink spend more energy than the nodes in the outer regions, as a result, these nodes will die soon and fast. During the last decades, several schemes have been proposed to overcome the energy holes and enhance the network lifetime. Based on previous related works in uniform node deployment strategies, the innermost layer is prone to energy holes due to high traffic load compared with outer layers. However, recognition of the critical region (the region with the most energy holes in the network) is an important issue that has not been addressed in non-uniform node deployment strategies yet. Likewise, complete energy balancing has not been taken into consideration in most prior studies. Therefore, in this paper, first, we propose an equation to find the critical region in many-to-one networks; meanwhile, we present equations to gain the required initial energy in these networks in order to maximize the network lifetime and remove the energy hole problem by completely balancing energy consumption and with no extra nodes. Afterwards, the proposed model is compared to the other previous strategies based on two scenarios. The final results indicate that the proposed method can maximize the network lifetime. Besides, it requires less total energy than other previous works, including Ferng and q-switch strategies.

The growth of Wireless Sensor Networks (WSN) becomes the backbone of all smart IoT applications. Deploying reliable WSNs is particularly significant for critical Internet of Things (IoT) applications, such as health monitoring, industrial and military applications. In such applications, the WSN’s inability to perform its necessary tasks and degrading QoS can have profound consequences and can not be tolerated. Thus, deploying reliable WSNs to achieve better Quality of Service (QoS) support is a relatively new topic gaining more interest. Consequently, deploying a large number of nodes while simultaneously optimizing various measures is regarded as an NP-hard problem. In this paper, a Grey wolf-based optimization technique is used for node deployment that guarantees a given set of QoS metrics, namely maximizing coverage, connectivity and minimizing the overall cost of the network. The aim is to find the optimum number of appropriate positions for sensor nodes deployment under various p-coverage and q-connectivity configurations. The proposed approach offers an efficient wolf representation scheme and formulates a novel multi-objective fitness function. A rigorous simulation and statistical analysis are performed to prove the proposed scheme’s efficiency. Also, a comparative analysis is being carried with existing state-of-the-art algorithms, namely PSO, GA, and Greedy approach, and the efficiency of the proposed method improved by more than 11%, 14%, and 20%, respectively, in selecting appropriate positions with desired coverage and connectivity.

Wireless sensor networks (WSNs) are used in industrial applications and focused on target coverage and node connectivity based WSNs. The set of sensors and targets is placed in optimal position the target coverage and node connectivity achieving maximum with limited senor nodes. To resolve this problem, the proposed hybrid genetic algorithm combined with lifting wavelet multi-resolution principles for recognizing optimal position for sensors to cover entire targets present in the fields. The hybrid genetic algorithm randomly identifies each sensor position and 2D Haar lifting wavelet transform to improve the quality of target coverage by adjusting node position. The 2D Haar lifting decomposes the population matrix into the optimal position of sensors. Experimental results show the performance of the proposed hybrid genetic algorithm and fast local search method compared with available algorithms improves the target coverage and the number of nodes with varying and fixed sensing ranges with a different region.

The movement assisted sensor deployment is the most common design issue in mobile design. Various models, assumptions, goals, and shortcomings are identified, and formulas are mentioned. A taxonomy of motion-assisted sensor deployment algorithms that captures fundamental variations between current solutions is introduced. Six approaches are identified; they use a particular theory to shift the node from the original position to the target position. The comparison of the self-deployment algorithm and classes is discussed in detail in this paper.

At present, wireless sensor networks are developing rapidly with the support of the Internet of things. Wireless sensor networks can deliver the information people need at any time, free from the constraints of time and space. Wireless sensor network is widely used, which lays a solid foundation for the development of Internet of things. As the node deployment environment of wireless sensor networks is usually very complex, it is necessary to study the security of wireless sensor networks so as to reduce security threats and network attacks. In this paper, the security and application of the wireless sensor network are emphasized.

In smart city wireless network infrastructure, network node deployment directly affects network service quality. This problem can be attributed to deploying a suitable ordinary AP node as a wireless terminal access node on a given geometric plane, and deploying a special node as a gateway to aggregate. Traffic from ordinary nodes is to the wired network. In this paper, Pareto multi-objective optimization strategy is introduced into the wireless sensor network node security deployment, and an improved multi-objective particle swarm coverage algorithm based on secure connection is designed. Firstly, based on the mathematical model of Pareto multi-objective optimization, the multi-target node security deployment model is established, and the security connectivity and node network coverage are taken as the objective functions, and the problems of wireless sensor network security and network coverage quality are considered. The multi-objective particle swarm optimization algorithm is improved by adaptively adjusting the inertia weight and particle velocity update. At the same time, the elite archive strategy is used to dynamically maintain the optimal solution set. The high-frequency simulation software Matlab and simulation platform data interaction are used to realize the automatic modeling, simulation analysis, parameter prediction and iterative optimization of wireless network node deployment in smart city based on adaptive particle swarm optimization. Under the premise of meeting the performance requirements of wireless network nodes in smart cities, the experimental results show that although the proposed algorithm could not achieve the accuracy of using only particle swarm optimization algorithm to optimize the parameters of wireless network nodes in smart cities, the algorithm is completed. The antenna parameter optimization process takes less time and the optimization efficiency is higher.

The deployment of acoustic sensor nodes in 3-D underwater acoustic wireless sensor networks (UAWSN) is a difficult task due to various aquatic conditions and physical obstacles. This work proposes multi-agent-based acoustic sensor node deployment (MASD) to deploy the acoustic nodes at ideal positions to enhance coverage and seamless connectivity. The proposed scheme works is threefold: 1) AUV initiates random walk in the network to gather the information and prospective common reference points; 2) the base station gets this information through surface buoys and computes the routing path, feasible locations for deploying new nodes; and 3) AUV collects this information and follows the path to deploy nodes with the help of agents. The multi-agent-enabled deployment framework (MADF) is proposed to support the deployment process at each level of the proposed MASD scheme. The performance of propagation loss, coverage, and overhead tradeoffs are analyzed to validate the proposed scheme. Mobility issues can be further re-investigated in shallow water as a future direction to the MASD scheme.

Node deployment is a matter of considerable concern in designing wireless sensor networks (WSNs). This paper studies this issue in the context of rechargeable WSNs (RWSNs). We propose an efficient algorithm, namely node deployment for target coverage in RWSNs (NDTCR), which determines the number and positions of installed sensors in two phases. The first phase applies genetic algorithm to construct a mesh over a subset of positions. The mentioned mesh covers the targets and connects them to the sink. In the second phase of NDTCR, we propose an integer linear programming (ILP) model to install some sensors at each position of the mesh. The advantage of applying the first phase is that it prunes the solution space considerably. Therefore, the proposed ILP model can be solved in a reasonable time. The experimental results demonstrate that NDTCR requires 29% fewer sensors on average in comparison with the previous approaches.

PurposeWith the rapid growth of wireless sensor networks (WSNs), they have become an integral and substantial part of people's life. As such WSN stands as an assuring outlook, but because of sensor's resource limitations and other prerequisites, optimal dual route discovery becomes an issue of concern. WSN along with central sink node is capable of handling wireless transmission, thus optimizing the network's lifetime by selecting the dual path. The major problem confronted in the application of security mechanisms in WSNs is resolving the issues amid reducing consumption of resources and increases security.Design/methodology/approachAccording to the proposed system, two metrics, namely, path length and packets delivery ratio are incorporated for identifying dual routes amid the source and destination. Thereafter by making use of the distance metric, the optimal dual route is chosen and data transmission is carried out amid the nodes. With the usage of the recommended routing protocol high packet delivery ratio is achieved with reduced routing overhead and low average end to end delay. It is clearly portrayed in the simulation output that the proposed on demand dual path routing protocol surpasses the prevailing routing protocol. Moreover, security is achieved make use of in accord the data compression reduces the size of the data. With the help of dual path, mathematical model of Finite Automata Theory is derived to transmit data from source to destination. Finite Automata Theory comprises Deterministic Finite Automata (DFA) that is being utilized for Dual Path Selection. In addition, data transition functions are defined for each input stage. In this proposed work, another mathematical model is 10; introduced to efficiently choose an alternate path between a receiver and transmitter for data transfer with qualified node as relay node using RR Algorithm. It also includes Dynamic Mathematical Model for Node Localization to improve the precision in location estimation using Node Localization Algorithm. As a result a simulator is built and various scenarios are elaborated for comparing the performance of the recommended dual path routing protocol with respect to the prevailing ones.FindingsReliability and fault-tolerance: The actual motive in utilizing the approach of multipath routing in sensor network was to offer path resilience in case of a node or link failures thus ascertaining reliable transmission of data. Usually in a fault tolerant domain, when the sensor node is unable to forward the data packets to the sink, alternative paths can be utilized for recovering its data packets during the failure of any link/node. Load balancing: Load balancing involves equalizing energy consumption of all the existing nodes, thereby degrading them together. Load balancing via clustering improves network scalability. The network's lifetime as well as reliability can be extended if varied energy level's nodes exist in sensor node. Quality of service (QoS): Improvement backing of quality of service with respect to the data delivery ratio, network throughput and end-to-end latency stands very significant in building multipath routing protocols for various network types. Reduced delay: There is a reduced delay in multipath routing since the backup routes are determined at the time of route discovery. Bandwidth aggregation: By dividing the data toward the same destination into multiple streams (by routing all to a separate path) can aggregate the effective bandwidth. The benefit being that, in case a node possesses many links with low bandwidth, it can acquire a bandwidth which is more compared to the individual link.Research limitations/implicationsFew more new algorithms can be used to compare the QoS parameters.Practical implicationsProposed mechanism with feedback ascertains improvised delivery ratio compared to the single path protocol since in case of link failure, the protocol has alternative route. In case there are 50 nodes in the network, the detection mechanism yields packet delivery of 95% and in case there are 100 nodes, the packet delivery is lowered to 89%. It is observed that the packet rate in the network is more for small node range. When the node count is 200, the packet ratio is low, which is lowered to 85%. With a node count of 400, the curve depicts the value of 87%. Hence, even with a decrease in value, it is superior than the existing protocols. The average end-to-end delay represents the transmission delay of the data packets that have been successfully delivered as depicted in Figure 6 and Table 3. The recommended system presents the queue as well as the propagation delay from the source to destination. The figure depicts that when compared to the single path protocol, the end-to-end delay can be reduced via route switching. End-to-end delay signifies the time acquired for the delay in the receival of the the retransmitted packet by each node. The comparison reveals that the delay was lower compared to the existing ones in the WSN. Proposed protocol aids in reducing consumption of energy in transmitter, receiver and various sensors. Comparative analysis of energy consumptions of the sensor in regard to the recommended system must exhibit reduced energy than the prevailing systems.Originality/valueOn demand dual path routing protocol. Hence it is verified that the on demand routing protocol comprises DFA algorithms determines dual path. Here mathematical model for routing between two nodes with relay node is derived using RR algorithm to determine alternate path and thus reduce energy consumption. Another dynamic mathematical model for node localization is derived using localization algorithm. For transmitting data with a secure and promising QoS in the WSNs, the routing optimization technique has been introduced. The simulation software environment follows the DFA. The simulation yields in improvised performance with respect to packet delivery ratio, throughput, average end-to-end delay and routing overhead. So, it is proved that the DFA possesses the capability of optimizing the routing algorithms which facilitates the multimedia applications over WSNs.

Wireless sensor networks (WSNs) play a prominent role in the world of computer networks. WSNs rely on deployment as a basic requirement and an effective factor on the basic network services. In deployment, creating a balance between conflicting optimisation factors, e.g. connectivity and coverage, is a challenging and sophisticated issue, so that deployment turns into an NP-complete problem. The majority of existing researches has attempted to tackle this problem by applying classic single-objective metaheuristic algorithms in 2D small-scale uniform environments. In this study, a new hybrid multi-objective optimisation algorithm, which is constructed by the combination of multi-objective bee algorithms and Levy flight (LF) random walk is proposed to deal with the deployment problem in WSNs. For this purpose, two of the most important criteria, connectivity and coverage, have been considered as objectives. A series of experiments are carried out in large-scale non-uniform 3D environments, despite the fact that most of the present methods are applicable in small-scale uniform 2D environments. This study completely takes into account the stochastic behaviour of swarms, something that other papers do not consider. The evaluation results show that the multi-objective LF bee algorithm, in most cases, surpasses NSGAII, IBEA and SPEA2 algorithms.

Node deployment has an influence on energy dissipation of cooperative transmission scheme (CTS) in wireless sensor networks. In order to reduce the energy dissipation in single-hop wireless sensor networks, the problem of node deployment for cooperative beamforming (CBF) based CTS is studied in this paper. First, the energy dissipation for CTS based on CBF is studied, then the node’s position and the modulation size is optimized through two-step optimization to minimize energy dissipation. Numerical results show that the optimized node deployment and CBF scheme can greatly reduce the energy dissipation.

In Wireless Sensor Network (WSN) - Internet of Things (IoT) the coverage of underground becomes an essential challenge and issue in today’s era due to the unpredicted natural disaster occurred in t...

As deployment process is one of the major tasks in underwater sensor network due to its constraint like: acoustic communication, energy, processing speed, cost and memory and dynamic nature of water. As many researchers have proposed many algorithms for the deployment of nodes in underwater sensor network. It was always a great issue in WSN as well as underwater sensor networks. This work proposes a node deployment technique based on depth. This work consists the following major components: (i) sensor nodes to sense the phenomena in underwater sensor networks, (ii) multiple surface station on the water surface. Use of multiple surface station provides better area coverage and connectivity in the networks. This work is divided into three phase like: initialization where nodes are randomly deployed at water surface and from 2D network topology, second phase is depth calculation for all the nodes and third is to distribute the depth to each node and send them to their designated depth to expand the 2D network into the 3D network. The proposed technique is simulated on Matlab for the analysis of area coverage and connectivity. Simulation results show better performance in terms of area coverage and connectivity as compared to ADAN-BC.

The key to the study of node deployment in Wireless Sensor Networks (WSN) is to find the appropriate location of the WSN nodes and reduce the cost of network deployment while meeting the monitoring requirements in the covered area. This paper proposes a WSN node deployment algorithm based on real 3D terrain, which provides an effective solution to the surface-covering problem. First of all, actual geographic elevation data is adopted to conduct surface modeling. The model can vividly reflect the real terrain characteristics of the area to be deployed and make the deployment plan more visible and easy to adjust. Secondly, a probabilistic coverage model based on DEM (Digital Elevation Model) data is proposed. Based on the traditional spherical coverage model, the influence of signal attenuation and terrain occlusion on the coverage model is added to make the deployment model closer to reality. Finally, the Greedy algorithm based on grid scanning is used to deploy nodes. Simulation results show that the proposed algorithm can effectively improve the coverage rate, reduce the deployment cost, and reduce the time and space complexity in solving the WSN node deployment problem under the complex 3D land surface model, which verifies the effectiveness of the proposed algorithm.

In Internet of Things (IoT), intrusion detection plays an important role in many applications for detecting malicious intruders. The intruder can be, an unexpected physically moving entity, invading an area under surveillance, or an adversary in a battlefield. Node deployment strategy plays a crucial role in determining the intrusion detection capability of an IoT network. With uniform deployment, the detection probability is the same for any location in the network area. Nevertheless, different applications may need diverse levels of detection probability at key areas within the network. For example, a battlefield surveillance application needs improved detection probability around the headquarter. On the contrary, a Gaussian deployment strategy provides improved detection probability to the key areas due to differentiated node density. However, it is neither energy-efficient nor provides a quick detection of the physical intruder. In this work, we introduce a novel deployment strategy to overcome the above said limitations of both uniform and Gaussian deployments for energy-efficient and quick detection. Initially, we investigate the problem of physical intrusion detection in our introduced deployment strategy considering a realistic sensing model. Furthermore, we examine the effects of different network parameters on the detection probability in details. We also derive the relationship between the different network parameters and connectivity to ensure fast detection. We perform exhaustive experiments on real datasets, primarily, in order to validate the correctness of modeling and analyses. Next, we examine the effects of different network parameters on the detection probability. The results clearly demonstrate that our approach improves the detection probability by more than 25% when compared to two well-known deployment strategies under various network parameters.

Urban water distribution networks (WDNs) are usually threatened by leakage, reflux, infiltration and internal pollution. To ensure the safety of water supply, it is essential to properly monitor the WDNs. However, it is not easy to design a fast, fine-grained and comprehensive monitoring solution under limited cost, due to the large scale and high complexity of WDNs. In recent years, mobile sensor networks have been widely utilized to monitor WDNs with data uploaded by static nodes or receiver nodes. However, existing solutions haven't addressed the impact of duplicated uploading caused by the topology of WDNs. Hence, the coverage rate is unsatisfactory. In addition, the mobile sensor nodes could be potentially far away from the receiver nodes, which might introduce long data acquisition delay. In this work, we propose a low delay and high coverage WDNs monitoring solution, with the constraint of limited number of mobile and receiver nodes. The proposed solution can find the optimal deployment strategy of mobile and receiver nodes by constructing a probability dependent model between mobile and receiver nodes, and coordinating the trade-off between low-delay and high-coverage under multi-objective optimization model. We also analyzed the theoretical lower bound of the algorithm, by utilizing the submodular function characteristics of the objective. Extensive simulation proved that the proposed algorithm can obtain higher coverage with lower delay.

With recent advances in electronics and wireless communication technologies, the Wireless Sensor Networks (WSNs) are being used in wide range of applications of military, ecological, health related areas. Due to their resource constraints such as, sensor node’s size, memory, and processing capabilities, the scale of deployment of WSNs requires careful decisions with respect to various performance measures. In the last few years, there has been a tremendous interest on development of large-scale wireless sensor networks as a basic issue to be addressed as it can influence the performance metrics of WSNs such as, coverage, connectivity, resilience and scalability requirements. Many deployment schemes have been proposed for wireless sensor networks. In this paper, we survey six deployment models random, rectangular-grid, square-grid, triangular-grid, hexagonal-grid, and hybrid deployments schemes and analyze their implications on network area coverage in WSNs. More generally, under some deployment assumptions on an irregular geographical map for a defense monitoring, we show the analytical and simulation-based results of a WSN made up of mica2 motes using the deployment knowledge to motivate the use of these emerging paradigms in order to achieve higher network area coverage. To apply deployment schemes on irregular geographical target area, we propose to include the concept of voronoi diagram based approach in WSNs, to provide a way of dividing an irregular geographical area into a number of regular regions. We have been configured the sensor node parameters such as sensing, temperature, energy capabilities using mannasim based on NS-2.34.

Mobile sink problems in wireless sensor networks (WSNs) have gained increasing worldwide attention in recent years because of a great number of applications and advantages. In this paper, we proposed a problem model consisting of a WSN with a mobile sink (MS) and a set of fixed targets which the MS needs to collect information. In this case, the MS just receives information from targets by transmitting it through sensors. The objective of the problem is to determine the location and trajectory of the MS to optimize the number of sensors that need to be set, thereby reducing the cost to build WSNs in practice. We proposed a heuristic algorithm to solve the problem with the set of targets that form a convex polygon on the 2-dimensional plane and consider this heuristic algorithm in the case of a random set of targets on the plane and in some special cases.

In order to offer the post-disaster management services discussed in the last two chapters, the existence of a post-disaster opportunistic network framework becomes crucial. A post-disaster opportunistic network framework comprises two entities, such as opportunistic network architecture and energy-efficient post-disaster routing protocols. The post-disaster opportunistic network architecture typically includes suitable network architecture and node deployment plans which are elaborated in this chapter.

The chapter focuses on the recent development in the field of the sensor node deployment in the UWSN (under water wireless sensor network). In the chapter, the technical challenges during the node deployment of the sensor nodes in the UWSN (under water wireless sensor network) are represented with prefacing the background. The chapter focuses on the different methods of node deployment and presents a generalized model for ensure the reliability. A view of analyzing the deployment of sensor nodes is also shown in the example by following the recent researches in the domain. Finally, the future scope and conclusion is represented with the idea of new paradigms in the deployment of sensor nodes in the UWSN.

Due to widespread applications and an orientation towards dimensionality enhancement, the underwater sensor networks (UWSNs) are going to be in demand for today's researchers. The core idea to design a strong and reliable build under the water revolves around the master plan of sensor deployment. The quality parameters like coverage, connectivity, and network lifespan, etc. are considered as main pillars for these deployment plans. There is always a trade-off between these quality parameters. So, it is quite necessary to maintain a balance among these parameters in order to establish a successful communication system. In this chapter, the authors have categorized the node deployment plans on the basis of the above-stated quality parameters. During this categorization, the view of dimensionality enhancement from two dimensions to three dimensions is also kept in the mind.

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., arXiv admin note: substantial text overlap with arXiv:1901.06742

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.

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector&rsquo, s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.

This article presents prototype for deployment of a wireless sensor network by a mobile robot. Its motivation arises from the need of solutions that allow for monitoring and exploration of areas of difficult access to humans in disaster situations or rescue operations. Our testbed proposes a scenario where a robot moves from a point A to B deploying Intermediate Sensor Nodes at a given threshold of received signal strength indication. Preliminary testing was conducted to determine the ideal value for this threshold while maintaining the connectivity of the robot to the network.

Author(s): Guo, Jun; Karimi-Bidhendi, Saeed; Jafarkhani, Hamid; IEEE | Abstract: We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.

Aiming at the problems of the low coverage and poor connectivity of traditional wireless sensor networks, a design method of an efficient and optimized wireless sensor network for dairy farming is proposed. The coverage and connectivity of wireless sensor networks are two key issues to be considered in the deployment of network nodes. Particle swarm optimization is used to optimize the node deployment of wireless sensor networks. The experimental results show that the proposed method can quickly and effectively realize the global optimization of wireless sensor network layout, overcome the influence of fixed sensor nodes on layout optimization, and achieve fast convergence speed and improve the effective coverage of wireless sensor networks. At the same time, the particle swarm optimization algorithm can effectively find the optimal nodes that can be added in the network, and connect the network into a whole by adding a small number of nodes to make the network stable and effective.

The deployment of network nodes is essential to ensure the wireless sensor network's regular operation and affects the multiple network performance metrics, such as connectivity, coverage, lifetime, and cost. This paper focuses on the problem of minimizing network costs while meeting network requirements, and proposes a corona-based deployment method by using the variable transmission distance sensor. Based on the analysis of node energy consumption and network cost, an optimization model to minimize Cost Per Unit Area is given. The transmission distances and initial energy of the sensors are obtained by solving the model. The optimization model is improved to ensure the energy consumption balance of nodes in the same corona. Based on these parameters, the process of network node deployment is given. Deploying the network through this method will greatly reduce network costs.

In this paper, a proficient node deployment mechanism is proposed which covers all the target points using adjustable sensing ranges in grid-based wireless sensor networks. In the beginning, the sink and the target points are randomly deployed in a grid-based environment where locations are arbitrarily selected. At first, the ants are positioned at the sink location (grid point) from where the deployment of sensors starts. An ant can move in one of the four possible directions, i.e., {1 (left); 2 (right); 3 (top); 4 (bottom)} from the sink location depending on the maximum number of uncovered target points. In case there is no path to move forward, an existing sensor is selected as the next location corresponding to that next sensor point where a deployed sensor can cover maximum number of uncovered targets in one of the four possible directions. Our method deploys minimum number of sensors to cover all the targets with least number of iterations. It also provides full coverage and connectivity with minimum deployment cost.

Wireless sensor networks (WSNs) have been widely adopted to fulfil the imperative requirement of real-time monitoring and/or long-term surveillance of the field-of-interest. However, due to the limited battery capacity, energy is the most critical constraint for improving the sustainability of a WSN. Hence, conserving energy and extending battery life are important in designing a sustainable WSN. Fortunately, the emerging energy harvest techniques provide us with a semi-permanent energy resource to power WSNs. In this article, we introduce a novel energy-aware hierarchical two-tier (HTT) energy harvesting-aided WSNs deployment scenario. More precisely, we consider two types of nodes in the system: one is the regular battery-powered sensor node (RSN), and the other is the energy harvesting-aided data relaying node (EHN). The objective is to use only RSNs to monitor FoI, while EHNs focus on collecting the sensed data from RSNs and forwarding the gathered data to the data sink. The minimum number of EHNs is deployed based on a newly designed probability density function to minimize the energy consumption of RSNs. This, in turn, extends the lifetime of the deployed WSN. The simulation results indicate that the proposed scheme outperforms some well-known techniques in the network lifetime, while enhancing the total throughput.

The deployment of mobile ad hoc network communication nodes is similar to the game process of go black and white. Using the deep reinforcement learning technology in AlphaZero algorithm, the deployment process of mobile ad hoc network nodes is abstracted as a chess game, and the deployment process is regarded as a direct game process between communication nodes and users. The difficulty and difference are that the chess board of the chess game does not change, while the grid application of mobile ad hoc network. The geographical environment changes with different application scenarios, and affects the deployment of communication nodes. This paper studies the deep reinforcement learning technology framework based on AlphaZero algorithm, and proposes that the relationship between geographical environment and communication node deployment can be implicitly deduced through the relationship reasoning technology, so as to solve the problem of node deployment in different terrain scenarios.

Wireless sensor networks are composed of a series of sensors deployed in a certain location, which have been widely used in industrial, environmental, military, and health fields in recent years. The node deployment of sensors provides coverage for the area of interest and ensures the connection and communication of the wireless network. The coverage problem was investigated thoroughly in the literature and several algorithms and techniques were proposed. This paper summarizes the status of the research, different types of coverage, common sensing models and optimization algorithm used in coverage problems.

Reliable and accurate position information is of great importance for many mass-market and emerging applications. Network localization and navigation (NLN) is a promising paradigm to provide such information ubiquitously, where a network of nodes is used to aid in localizing its members. This paper explores various network operation strategies, which play an essential role in NLN as they determine the network lifetime and localization accuracy. Efficient network operation requires several functionalities, including node prioritization, node activation, and node deployment. The roles of these functionalities are described and different techniques for implementing respective functionalities via algorithmic modules are introduced. Some important concepts such as cooperative operation, robustness guarantee, and distributed design in the development of the network operation strategies are also introduced. Finally, numerical results are provided to demonstrate the localization performance improvement attributed to the optimized network operation strategies.

Multiple sensor nodes are required to gather the information and exchange the information in the direction of the sink node which makes a network. The static common node (NC) deployment has been work towards the coverage of deterministic territory. At that point, the coordinates of each regular node have been determined with the assistance of geometry of coverage locale. Among those basic nodes, on the off chance that one of the nodes gets fail, at that point, the coverage hole is made. To solve this issue, a viable alternate node (NA) deployment method has been presented for supplanting the damaged node. And furthermore developed quadrant based neighbor to sink and neighbor to source (Q-(NS)2) routing protocol for lessening the superfluous flooding of ‘RREQ’ message to the majority of its neighbor while route discovery. A viable comparison has been done between this other node deployment procedure and references. The performance comparison has been done between Quadrant based Direct routing protocol (Q-DIR), Angle routing protocol (ARP) and Q-(NS)2 routing protocol. Therefore, Q-(NS)2 routing protocol decreases the pointless flooding of ‘RREQ’ to the greater part of its neighbor which implies it devours less energy for data packet delivery and no redundant node in NA deployment.

This paper studies how to deploy relay nodes into traditional wireless sensor networks with constraint aiming to simultaneously optimize two important factors; average energy consumption and average network reliability. We consider tackling this multi-objective (MO) optimization problem with three metaheuristics, which employ greatly different evolutional strategies, and aim at an in-depth analysis of different performances of these metaheuristics to our problem. For this purpose, a statistical procedure is employed to analyse the results for confidence, in consideration of two MO quality metrics; hypervolume and coverage of two sets. After comprehensive analysis of the results, it is concluded that NSGA-II provides the best performance.

Wireless sensor networks on mountain roads will form winding and long transmission paths and accordingly result in centralized energy dissipation near the sink and large transmission delay. In this letter, we propose a node deployment scheme to settle the above-mentioned thorny issues. The core idea is to construct extra paths to the sink and achieve data diversion. Specifically, the places with relatively large transmission hops to the sink and also comparatively small distance away from it are selected as the starting points of the extra paths. This selection mechanism contributes to alleviating the energy holes and reducing the transmission delay. Finally, experimental simulations show the effectiveness and superiority of our deployment scheme.

Energy efficiency has always remained a pressing matter in the world of Wireless Sensor Networks. Irrespective of the number of routing protocols that exist for Wireless Sensor Networks, only a handful can be named as efficient. Yet above all these routing protocols stands the emblematic one, the LEACH protocol. This research work is aimed at bringing forth a new routing strategy based on the LEACH protocol, which aims at improving the energy efficiency in Wireless Sensor Networks and applying the given clustering technique, in randomly deployed and fixed sensor network simulation environment using MATLAB. In depth simulations have proven that the proposed clustering strategy gives better performance compared to LEACH based on the lifetime of the Nodes. A comparative analysis of the rate of energy consumed on various node deployment strategies has also been carried out.

Sensor nodes are always considered in wireless sensor networks. So deployments of these sensor nodes are considerable, but proper deployment can decrease the complication of problems in wireless sensor networks. During such communication, data routing must be done efficiently in order to reduce the complexity. In addition, it minimizes energy consumption and thus extends the lifetime of Network. An attempt is made using Virtual Force and Particle Swarm Optimization for effective node deployment. First step, Virtual Force Algorithm is used for placement of nodes. Secondly, the result is provided to Particle Swarm Optimization to optimize the best fit between the neighbor nodes. The result depicts the proper deployment of nodes done in wireless sensor networks and improves the efficiency by minimal energy consumption.