Voronoi, Genetic Algorithms and Their Tandem Application in Wireless Sensor Network Deployment

Wireless sensor networks consist of small nodes with sensing, computation and wireless communication capabilities. Various architectures and node deployment strategies have been developed for wireless sensor network, depending upon the requirement of application. Sensor networks are used in different applications e.g. environmental monitoring, habitat monitoring, home automation, military application etc. In this paper we present survey of state-of-the-art of architecture and node deployment in wireless sensor network. We present the characteristics of the environment in which the sensor networks may deploy. Node deployment in wireless sensor network is application dependent and can be either deterministic or randomized. But in both the cases coverage of interested area is the main issue. We also explain the routing protocols for wireless sensor network.

Wireless Sensor Network (WSN) is the main element of modern Industrial Internet of Things system in event monitoring. However, WSN is unable to respond for longer life due to the limited battery backup. So, it is hard to monitor the event of interest for longer span of time with deployed WSN. This can be resolved by utilizing energy efficient design approach for data communication. Cluster based routing scheme could be the most prominent design approach for the said option. Nevertheless, on the cost of certain design constraints. Although, most of the real-life applications are designed based on clustering scheme. But, it utilizes random node deployment strategy to support assumptions of prethrown nodes in test field. Similarly, uniform node deployment is also being demanded in some specific applications. Thus, there is a high need to design and implement clustering protocol that can work satisfactorily independent of the node deployment. This paper presents an innovative design approach with Clustering Algorithm for Deployment of Independent Heterogeneous Wireless Sensor Network. In proposed approach cluster head (CH) is selected based on index modeling with available node attributes. Proposed design ensures selected CH offers better node degree and intra-connectivity inside the network. Here, we try to minimize the efforts of CH and cluster member such that energy efficiency of each individual node get extend to a high level. Hence, it improves stability period, lifetime and throughput of the network. Simulation of proposed approach outperforms the existing LEACH, SEP and DEEC by more than 1.3 times.

Random deployment enables rapid installation of a large number of sensors in locations of difficult access, which is important for many infrastructure-less cyber physical systems (CPS), e.g., for disaster response, battle field and extraterrestrial operations. However, random deployment results in heterogeneous sensor postures with varying antenna orientations, a source of network performance degradation. Although much is known about the effect of antenna orientations in 802.15-based wireless sensor networks (WSNs), most works assume no physical disturbance to antennas. However, heterogeneous sensor postures (e.g., overturned sensors) may cause significant disruption to the functionalities of antennas in addition to the reported variation of received signal strength (RSSI). In this paper, we provide empirical characterization of node-to-node communication in random deployment and present the first radio model that effectively captures the distinctive characteristics. Through simulations using our model, we provide a quantitative analysis of the aggregate effect of random antenna orientations of a large number of sensors with heterogeneous postures. Furthermore, we introduce a prototype sensor package that can be used to reduce the impact of random deployment.

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.

The quality of service (QoS) and lifetime of wireless sensor networks (WSNs) are severely degraded by coverage holes generated by random deployment or battery exhaustion of sensors. This work firstly introduces a novel confident information coverage CIC) model to dramatically reduce the density of sensor nodes and accurately detect confident information coverage holes (CICHs). Then, the problem of repairing confident information coverage holes (RCICHs) for big data collection in a large-scale heterogeneous WSN (LS-HWSN) which widely spreads over a geographic area with thousands of stationary sensor nodes and mobile sensor nodes is formulated, called as RCICH problem, which is to effectively repair CICHs considering that the transmitted data velocity of sensor nodes is different. Furthermore, we prove it to be NP-completeness. The target of the problem is to find a subset of mobile sensor nodes from all mobile sensor nodes while minimizing the amount of lost throughputs LTs) of all dispatched mobile sensor nodes or maximizing the amount of repairing transmission times (RTTs) for all dispatched mobile sensor nodes, with different objectives. Finally, based on the CIC model and the data-centric perspective, two heuristic schemes including a centralized dispatch scheme and a distributed dispatch scheme are proposed to effectively solve the RCICH problem. Simulation results show that the proposed schemes effectively repair CICHs while increasing the QoS and lifetime of the LS-HWSN with the topology control of a fan-shaped clustering (FSC) protocol.

Obtaining the desired signals in wireless sensor networks can be challenging due to various constraints on sensor placement or deployment. Retrieving the information accurately from sensors placed at non-uniform locations, is a problem of sensor communication and signal interpolation. In this research, the optimal recovery (OR) method, a deterministic framework that can use a priori bandwidth or spectral shape information, is used to interpolate from the given non-uniformly spaced samples. The error to be minimized is the maximum possible norm difference over a set of feasible signals. In the OR problem formulation and solution, the role of worst case feasible signals can be recognized but these signals are very difficult to find analytically. Computer simulations of feasible signals can help to produce estimates of the theoretical minimal worst-case error bounds. In this paper, monitoring of OR error bounds serves to assess sensor deployment configuration quality and to optimize the placement of additional sensors. Starting from an initial configuration of sensors, optimal deployment of additional sensors is clearly a more powerful option than random deployment of such sensors. These two approaches are compared and contrasted to show the improvement that is possible using the OR framework for one-dimensional and two-dimensional signals.

Wireless Sensor Network (WSN) most popular area of research where lots of work done are in this field. Energy efficiency is one of the most focusing areas because the lifetime of the network is the most common issue. Many algorithms work for cluster and these algorithms form cluster either distance or neighbor node. In our study, we focus on node deployment approach and after that, we focus on routing algorithm. In the Wireless Sensor Network, the node placement is the essential part for the proper communication between the sensor nodes and Base Station (BS). For better communication nodes should be aware of their own or neighbor node’s location. Better optimization of resources and performance improvement are the main concern for the Wireless Sensor Network. Optimal techniques should be utilized to place the nodes at the best possible locations for achieving the desired goal. For node placement, flower pollination optimization is used to generate the better result. Base Station is responsible for the communication of nodes with each other and it should be reachable to nodes. For this Region of Interest (RoI) is helpful to choose the best location. Placement of Base Station in the middle is the suitable place for the static nodes deployment and there should be another strategy for the dynamic environment. Nodes should be connected to each other for the transmission of data from the source to Base Station properly. From the MATLAB simulation, it has been shown that the proposed methodology improves the network performance in terms of dead nodes, energy and the various packets sent to Base Station.

One of the main design aspects of Wireless Sensor Networks (WSNs) is the deployment strategy of the sensors. In general, WSN deployment methods fall under two categories: planned deployment and random deployment. In this paper, we focus on planned deployment which is defined as selectively deciding the locations of the sensors to optimize one or more design objectives of the WSN under some given constraints. There have been a large number of studies which proposed algorithms for solving the planned deployment problem. In this paper, we present a novel classification of the algorithms proposed in the literature for planned deployment of WSNs, based on the mathematical approach used for modeling and solving the deployment problem. Four distinct mathematical approaches are presented: Genetic Algorithms, Computational Geometry, Artificial Potential Fields and Particle Swarm Optimization. For each approach, we provide a discussion of its background and basic mathematical foundation. We then review the algorithms which belong to each approach and provide a comparison between them in terms of their objectives, assumptions and performance. Based on our extensive survey, we discuss the strengths and limitations of the four approaches and compare them in terms of the different WSN design factors.

Soil salinization is one form of soil degradation. In this paper, the formation of physical topology known as node deployment in wireless sensor networks is studied based on the characteristics of wireless sensor networks. After calculating the value of the edge length of the grid, the minimum number nodes achieved according to the relationship between the radius of node sensing and radius of node communication. For data transmission, a mobile sink energy efficient routing algorithm based on the shortest path was applied to reduce the energy consumption.

Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms

In hybrid wireless sensor networks composed of both static and mobile sensor nodes, the random deployment of stationary nodes may cause coverage holes in the sensing field. Hence, mobile sensor nodes are added after the initial deployment to overcome the coverage holes problem. To achieve optimal coverage, an efficient algorithm should be employed to find the best positions of the additional mobile nodes. This paper presents a genetic algorithm that searches for an optimal or near optimal solution to the coverage holes problem. The proposed algorithm determines the minimum number and the best locations of the mobile nodes that need to be added after the initial deployment of the stationary nodes. The performance of the genetic algorithm was evaluated using several metrics, and the simulation results demonstrated that the proposed algorithm can optimize the network coverage in terms of the overall coverage ratio and the number of additional mobile nodes.

Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D

Metaheuristic algorithms are widely used to address complex real-world optimization problems, but many existing algorithms face challenges such as slow convergence, low accuracy, and susceptibility to local optima. The newly proposed Crested Porcupine Optimizer (CPO), while demonstrating effectiveness across domains, still suffers from these limitations in practical applications, restricting its performance in solving complex optimization tasks like wireless sensor network (WSN) deployment. To address these shortcomings, this study aims to enhance CPO’s overall performance by developing an improved version called the Enhanced Crested Porcupine Optimizer (ECPO). The methodology of ECPO integrates four key enhancement strategies: (1) Sobol sequences for population initialization, ensuring uniform distribution of initial solutions to boost global search capability; (2) a guided search strategy based on the global optimal solution, directing the algorithm toward optimal regions to reduce ineffective exploration; (3) an adaptive Lévy flight search strategy, maintaining population diversity and improving convergence accuracy; and (4) a centroid-based reverse learning strategy for population updates, expanding search space coverage and accelerating convergence. The performance of ECPO was validated on four authoritative benchmark suites (CEC2014, CEC2017, CEC2020, CEC2022) by comparing it with classical algorithms (e.g., PSO, DE), recently proposed algorithms (e.g., BKA, SBOA), and CEC-winning algorithms (e.g., LSHADE, AGSK). Statistical results show ECPO outperformed most comparison algorithms in 93.81%, 93.33%, 75.71%, and 86.90% of tests on the four benchmarks, respectively, exhibiting significant advantages in convergence speed, accuracy, and stability. Additionally, when applied to WSN node deployment optimization, ECPO achieved a higher coverage rate (average 84.95%) and better robustness than competing algorithms, with more rational node distribution and minimal resource waste. These findings confirm that ECPO effectively overcomes the limitations of the original CPO and outperforms many state-of-the-art optimizers. As a high-performance metaheuristic algorithm, ECPO not only excels in numerical optimization but also demonstrates broad applicability in practical engineering problems like WSN deployment, providing a reliable tool for solving complex optimization challenges.

Sensor deployment is one of the key topics addressed in Wireless Sensor Network (WSN). This paper proposes a new deployment technique of sensor nodes for WSN called as Quasi Random Deployment (QRD). The novel approach to deploy sensor nodes in QRD fashion is to improve the energy efficiency of the WSN in order to increase the network life time and coverage. The QRD produces highly uniform coordinates and it systematically fills the specified area. Along with Random Deployment (RD) pattern of wireless sensor node QRD is analysed in this study. The network is simulated using NS-2 simulator. The efficiency of this deployment is evaluated assuming three routing protocols used for the adhoc networks. They are AODV, DSDV, and DSR. The protocol assumed at transport layer is UDP. Application layer generates the CBR traffic. The performance of these three routing techniques is compared based on total energy consumption, coverage area. The simulation results show that the conventional routing protocols like DSR have a best performance for both RD and QRD of the sensor nodes when there is no mobility of the sensor nodes as compared to AODV and DSDV. Among AODV and DSDV, AODV performs better as compared to DSDV.

Data acquisition from multi-hop large-scale outdoor wireless sensor network (WSN) deployments for environmental monitoring is full of challenges. This is because of the severe resource constraints on tiny battery-operated motes (e.g., bandwidth, memory, power, and computing capacity), the data acquisition volume from large-scale WSNs, and the highly dynamic wireless link conditions in outdoor harsh communication environments. We present a novel compressed sensing approach, which can recover the sensing data at the sink with high fidelity when a very few data packets need to be collected, leading to a significant reduction of the network transmissions and thus an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is both efficient and simple to implement on the resource-constrained motes without motes’ storing of any part of the random projection matrix, as opposed to other existing compressed sensing-based schemes. We further propose a systematic method via machine learning to find a suitable representation basis, for any given WSN deployment and data field, which is both sparse and incoherent with the random projection matrix in compressed sensing for data collection. We validate our approach and evaluate its performance using a real-world outdoor multi-hop WSN testbed deployment in situ . The results demonstrate that our approach significantly outperforms existing compressed sensing approaches by reducing data recovery errors by an order of magnitude for the entire WSN observation field while drastically reducing wireless communication costs at the same time.