Probabilistic Dynamic Deployment of Wireless Sensor Networks by Artificial Bee Colony Algorithm

The optimization for deployment of wireless sensor networks is helpful to improve the sensor coverage and the reliability of system. In this work, an enhanced fireworks algorithm is applied to the dynamic deployment of mobile sensor networks. Compared with the existing mainstream swarm intelligence algorithms, this proposed approach has the advantages of good convergence rate, fast convergence and better coverage, which is closer to the theoretical coverage value. Experimental results show that the enhanced fireworks algorithm is able to get the optimal deployment quickly and effectively in the actual scene.

When detecting a target or monitoring a physical phenomenon in a region, the deployment problem is fundamental in these applications. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. It is well known that the mobility of sensor nodes can improve the coverage and the probability of the detecting, so we deal with the problem of detecting a target using hybrid sensor networks which contain both stationary sensors and mobile sensors. In this paper, to begin with, we prove that the node deployment problem is NP-complete. Then, one polynomial algorithm for node deployment in hybrid wireless sensor networks is proposed, which aims at minimizing the number of all sensors to reduce the cost. The simulations verify the efficiency of our algorithm.

The recent advances in embedded software/hardware design have enabled large-scale and cost-effective deployment of Wireless Sensor Networks (WSNs). Such a network consists of many small sensor nodes with sensing, control, data processing, communications, and networking capabilities. The wireless sensor networks have a broad spectrum applications ranging from wild life monitoring and battlefield surveillance to border control and disaster relief and have attracted significant interests from both academy and industry. A wireless sensor node generally has limited storage and computation capabilities, as well as severely constrained power supplies, and the networks often operate in harsh unattended environments. Successful design and deployment of wireless sensor networks thus call for technology advances and integrations in diverse fields including embedded hardware manufacturing and signal processing as well as wireless communications and networking across all layers. We have seen the initial and incremental deployment of real sensor networks in the past decade, for example, the ZebraNet for wildlife tracking, the CitySense for weather and air pollutants reporting, and the Sensormap portal for generic monitoring services, to name but a few; yet the full potentials of such networks in the real world remain to be explored and demonstrated, which involves numerous practical challenges in diverse aspects. This special issue aims to summarize the latest development in the design, implementation, and evaluation of wireless sensor systems. We received a total of XX papers by the deadline of February 2010, and through a rigorous review process, we have selected 12 papers from them. The first paper “Ambient data collection with wireless sensor networks” presents a general survey of the current data collection designs for wireless sensor networks. It is divided into data gathering and message dissemination, which reflects on the many-to-one data collection pattern and the one-to-many control dissemination pattern. We classify the remaining papers into two categories: (1) system implementation, prototyping, and deployment; and (2) individual module/protocol design and optimization.

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.

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

Scenarios where the sensor nodes themselves are mobile, introduces a new dimension of problems in sensor network. This paper presents a mobility vector based neighbour selection for a mixed deployment of mobile and static Wireless Sensor Network. The proposed approach uses the velocity of the mobile node and the residual energy of the static node to select a neighbour. Multichannel based MAC protocol is adopted for an interference free communication. The protocol was simulated and evaluated against MA-MAC protocol. Simulation results indicate that the proposed approach performs better. Proof of Concept of proposed approach was done using Berkley motes.

This research highlights the need for more accurate wireless sensor network deployments (WSN) models to support decision-making. Specifically, it presents the effects of terrain variations on network connectivity in Wireless Sensor Networks (WSN) deployments. Through use of statistical hypothesis testing, visualization, and Analysis of Variance (ANOVA), results support significant difference in network connectivity when comparing deployment performance under ideal versus more practical or irregular terrains. These results point to the inadequacy of current simulation environments for supporting deployment decision-making, and point to the need for future research that that improves WSN performance via better deployment techniques.

The dynamic deployments of Wireless Sensor Networks refer to the process of determining the location of the networking sensors in the region of interest after initial deployment. In this process, the fact that the dynamic deployments of sensors is effectively made plays a significant role in increasing the coverage rates of WSNs. The optimization of the coverage rates of Wireless Sensor Networks indicates that the targets in the region of interest are optimally covered by deployed mobile sensors and ultimately these targets can be monitored by the sensors. Therefore, the monitoring of the whole area in Wireless Sensor Network's military and civilian applications is possible by the optimum placement of randomly deployed sensors in the region of interest. In this study, a new dynamic deployment approach was developed based on the current meta-heuristic Whale Optimization Algorithm to find a solution to the problem of dynamic deployment of sensors. In order to solve the area coverage problem of Wireless Sensor Networks, the dynamic deployments of mobile nodes, the initial deployment of which was made randomly, was made by the developed approach using the Binary Detection Model. This approach was compared with the Maximum Area Detection Algorithm based on Electromagnetism-Like in the literature, and the performance of Wireless Sensor Network at coverage rates was measured. Simulation results have demonstrated that the approach developed for the area coverage problem is more effective and can be suggested with respect to the number of mobile sensors deployed and the reached coverage rates of the network.

It is a recent trend to consider wireless sensor networks in harsh industrial environments. With actual deployment of wireless sensor networks, it would be desirable to make a concrete deployment plan regarding connectivities and to place sensors by grouping them according to the planned deployment points, even more in case of targeting multiple objects to be sensed and monitored in harsh environments. The connectedness of groups as well as individual sensors is important specifically for real-time data acquisitions and even more if there are no external communication links among these groups. In this paper, we focus on the connectivity of sensor groups, rather than the individual sensors only, and propose a novel <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">group connectivity model</i> so as to analyze group connectivity and to make a concrete deployment plan of sensor groups with regard to the internal distribution of sensors and group positions. We believe that the proposed model should be useful in planning the deployment of wireless sensor networks in harsh industrial environments where running wires is less practical and also prohibitively expensive.

The penetration of Wireless Sensor Networks (WSNs) in various applications poses a high demand on reliable data storage, especially considering sensor networks are usually deployed in harsh environment. In this paper, we introduce Heterogeneous Wireless Sensor Networks where robust storage nodes are deployed in sensor networks and data redundancy is utilized through coding techniques, in order to improve the reliability of data storage. Taking into account the cost of both data delivery and storage, we propose an algorithm to jointly optimize data routing and storage node deployment. This problem is a binary non-linear combinatorial optimization, and it is highly non-trivial to design efficient algorithms due to its NP-hardness. By levering the Markov approximation framework, we elaborately deign a Continuous Time Markov Chain (CTMC) based scheduling algorithm to drive the storage node deployment and the corresponding routing strategy. Extensive simulations are performed to verify the efficacy of our algorithm.

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.

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.

This paper addresses the issue of Wireless Sensor Network (WSN) deployment. We investigate this problem in the case where the monitored area is characterized by a geographical irregularity of the sensed events. Precisely, we consider that each point of the deployment area requires a minimum threshold guarantee on the event detection probability. Our proposed scalable deployment method, named Potential Field-based Deployment Algorithm (PFDA), is based on the Potential Field and the Virtual Force approaches. Our proposal is able to (1) satisfy the required event detection probability threshold for each point, in a large-scale area, while minimizing the number of deployed sensors and (2) to ensure the network connectivity. The results and evaluation analysis show that PFDA outperforms the other strategies proposed in literature.

This paper proposes soft computing technique Big Bang-Big Crunch (BB-BC) to address the main issue of deployment of wireless sensor networks. Deployment is the main factor that significantly affects the performance of the wireless sensor network. This approach maximizes the coverage area of the given set of sensors. We implemented our approach in MATLAB and compared it with ABC approach and found that the proposed approach is much better than the said approach.