Node Deployment Research Articles (Page 1)

Abstract In researching wireless sensor networks (WSNs), node deployment for three-dimensional (3D) curved scenarios is challenging. This study aims to develop a node deployment strategy for 3D curved surface WSNs with high coverage, good connectivity, low energy consumption, and long lifespan. Firstly, a 3D curved surface (mountain) model and a sensing model with the attenuation of perception ability when encountering obstacles are established. Secondly, a joint deployment scheme for sensor and relay nodes is proposed to reduce the likelihood of energy-hole occurrence and the complexity of the entire network deployment. A multi-objective node deployment (MOND) model with connectivity ratio as a constraint and maximum coverage and minimum energy consumption as optimization objectives are constructed. Finally, to address this MOND problem, this article proposes two algorithms based on Pareto optimal and hunter-prey optimization: Multi-objective Hunter-Prey Optimization Algorithm (MOHPO) and Multi-objective Hunter-Prey Optimization Algorithm with Fuzzy C-Means Clustering (MOHPO/FCM). Simulation experiments were conducted in both multi-peak and multi-depression terrain scenarios to evaluate the node deployment performance of the proposed algorithm. Simulation results show that MOHPO and MOHPO/FCM exhibit superior performance in improving network coverage, enhancing connectivity, reducing energy consumption, and extending network lifespan compared to the comparison algorithms.

Dynamic deployment of mobile sensor nodes to optimize barrier coverage

Efficient deployment of Wireless Sensor Networks (WSNs) is critical to ensuring optimal performance across key metrics such as coverage, connectivity, energy efficiency, and network longevity. Traditional deployment strategies such as random, grid-based, and deterministic placements often fail to accommodate heterogeneous node capabilities or adapt to dynamic environmental conditions. This study proposes a Particle Swarm Optimization (PSO)-based deployment framework tailored for heterogeneous WSNs. The framework incorporates intelligent role assignment, metaheuristic optimization, and adaptive maintenance phases to balance coverage quality, minimize energy consumption, and enhance fault tolerance. A comprehensive simulation-based evaluation involving nine deployment strategies demonstrates that the proposed PSO-based approach achieves superior performance, including an average coverage of 91.4 ± 1.8%, extended operational lifetime exceeding 3,400 rounds, and improved connectivity robustness. The framework also exhibits strong scalability and resilience in the presence of node failures. These findings establish the proposed deployment model as a viable solution for real-world, large-scale, and energy-constrained WSN applications.

Internet of Trees (IoTr) technology enables a novel approach to real-time remote monitoring, particularly of trees and forest ecosystems. However, existing literature focuses on soil moisture monitoring of trees and lacks the contextual intelligence and adaptive sensing capabilities necessary for smart tree monitoring. This paper proposes a novel layered architecture for IoTr and the I-TREES (IoTr-based Tree Routing for Energy-Efficient Systems) framework for smart sensing and context-driven monitoring. I-TREES integrates embedded IoTr environmental sensors, LPWAN communication, edge processing, and cloud-based reasoning to monitor physiological and ecological stress parameters in vulnerable tree species. The proposed architecture enables selective sensing, where sensors adapt their behaviour based on real time environmental conditions to reduce energy consumption and avoid redundant transmissions. We evaluate I-TREES using Network Simulator 3 (NS-3) simulations across 700 node deployments under forest-like conditions and compare them with benchmark routing protocols. Results show that I-TREES achieves a superior packet delivery ratio approximately 97%, reduced latency, and lower energy consumption than existing schemes. I-TREES offers a robust solution for sustainable forest ecosystem monitoring and early disease detection by combining scalable sensing with intelligent context awareness.

Edge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence and integrates graph-based learning to propose a Cross-Layer Controller Tasking Scheme (CLCTS). The scheme operates through two primary phases: task grouping assignment and cross-layer control. In the first phase, controller nodes executing similar tasks are grouped based on task timing to achieve monotonic and synchronized completions. The second phase governs controller re-tasking both within and across these groups. Graph structures connect the groups to facilitate concurrent tasking and completion. A learning model is trained on inverse outcomes from the first phase to mitigate task acceptance errors (TAEs), while the second phase focuses on task migration learning to reduce task prolongation. Edge nodes interlink the groups and synchronize tasking, migration, and re-tasking operations across IIoT layers within unified completion periods. Departing from simulation-based approaches, this study presents a fully implemented framework that combines learning-driven scheduling with coordinated cross-layer control. The proposed CLCTS achieves an 8.67% reduction in overhead, a 7.36% decrease in task processing time, and a 17.41% reduction in TAEs while enhancing the completion ratio by 13.19% under maximum edge node deployment.

Due to the evolving variations in government and political systems both domestically and internationally, along with the imposition of high tariffs at borders, these borders have become vulnerable points for terrorism and smuggling activities. Consequently, each country endeavors to develop its own protection systems, with the technologies employed varying depending on the severity and significance of the installations to be safeguarded. While some of these technologies are costly and redundant, others offer effective and adaptable levels of efficiency. Therefore, designing a surveillance system capable of monitoring and controlling access has become essential. In this context, the present work holds significant strategic and geopolitical importance. It integrates established alarm and monitoring methods with innovative Internet of Things (IoT) applications, including Wireless Sensor Networks (WSN) and Optical Fiber Sensors (OFS). This article introduces the deployment of wireless radar nodes in conjunction with Bragg fiber sensors to identify each approaching intruding vehicle in the monitored zone, enabling the determination of its speed, weight, and wheelbase distance. Our system's results demonstrated acceptable levels of intruder detection and classification. We verify that the wavelength-based pressure detection error rate is small, approximately 0.05kg/cm2. The wheelbase distance estimate had an error rate of around 0.012m, while the weight calculation from the pressure had an error rate of about 12kg. This value is negligible and cannot skew the outcome. In our case, we apply WSN, radar and optical fiber sensors to detect vehicles in the border area. However, several works in literature.

Wireless sensor networks (WSNs) are becoming more prevalent in a wide range of uses, including tracking the environment, medical care, and military monitoring. Yet, security, energy efficiency, and overall efficiency are important concerns for these networks. Ensuring the security of data transmission while maintaining energy efficiency is critical for the longevity and reliability of WSNs. This paper proposes a novel method to enhance both security and efficiency in WSNs done the integration of metaheuristic optimization algorithms. The proposed method leverages advanced optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) to dynamically optimize key parameters, including node deployment, routing protocols, and encryption schemes. By optimizing the network topology, routing paths, and energy consumption strategies, the algorithm significantly improves data security, minimizes energy usage, and enhances network lifespan. The simulation results show that the suggested methodology beats standard methods in terms of throughput, security resilience and energy efficiency. The findings highlight the potential of metaheuristic optimization algorithms as a robust tool for improving WSN security and operational efficiency in real-world applications

Positioning, coverage, and energy efficiency are essential for developing next-generation intelligent sensor networks. In wireless sensor networks (WSNs), the random deployment of sensor nodes (SNs) frequently results in suboptimal area coverage and excessive energy consumption, primarily due to overlapping sensing regions and redundant data transmissions. This research presents a Particle Swarm Optimization (PSO) algorithm to optimize the deployment of electronic information sensing nodes. The focus is on maximizing the monitored area while minimizing energy usage. A Scalable coverage-based particle swarm optimization (SCPSO) algorithm integrates a probabilistic coverage model based on Euclidean distance to detect coverage gaps and guide the optimal positioning of nodes, ensuring that each target within the region of interest is covered by at least one sensor. Data preprocessing, including Z-score normalization and Independent Component Analysis (ICA), ensures feature scaling and dimensionality reduction for improved model performance, enabling effective optimization. Experimental results under different key metrics included coverage rate (CR) for various numbers of nodes (0.9971) with 50 nodes, deployment (99.95%) with the best coverage, and computation time (0.008s), indicating significant performance improvements under optimized deployment configurations. These results highlight the effectiveness of swarm intelligence methods in enabling energy-efficient, performance-optimized deployment of electronic information sensing systems in intelligent WSNs.

Wireless sensor networks (WSNs) are widely used in various applications requiring efficient coverage and minimal resource utilization. This paper presents an enhanced hybrid particle swarm optimization (EHPSO) algorithm that incorporates a spatial position encoding (SPE) strategy to optimize coverage while dynamically adjusting the number of sensors deployed in WSNs. The proposed approach leverages the strengths of particle swarm optimization (PSO) by integrating it with the SPE mechanism, which effectively guides the search process towards high-quality solutions. The EHPSO algorithm is designed to balance exploration and exploitation capabilities, enabling dynamic node adjustment and ensuring robust performance across different network configurations and environmental conditions. Extensive simulations are conducted to evaluate the performance of the proposed method against state-of-the-art algorithms in terms of coverage quality and node count. A multi-objective optimization model is also established, further illustrating the algorithm’s performance and its effectiveness in balancing the number of sensors and coverage rate. Results demonstrate improvements in coverage optimization and reduction of node deployment compared to existing methods. This research contributes to more efficient and cost-effective deployment strategies for WSNs, particularly in scenarios where resources are limited and optimal coverage is critical.

The rise of Wireless Sensor Networks (WSN) has redefined the modern digital infrastructure by enabling real-time sensing, decision making, and automation across diverse sectors. However, this rapid evolution has introduced unprecedented security challenges due to constrained computational resources, heterogeneous device environments, and wide-scale deployment of IoT nodes. This research provides a comprehensive review of lightweight and scalable security mechanisms tailored for wireless IoT systems, with a focus on practical deployment realities. It begins by outlining the security requirements and architectural constraints specific to IoT devices and then evaluates the security capabilities and vulnerabilities of commonly used wireless communication protocols. Emphasis is placed on the limitations of current implementations and protocol-level security inconsistencies. To address these gaps, the paper explores lightweight cryptographic techniques, particularly the NISTapproved Ascon algorithm suite, assessing its adaptability to resource-constrained environments. The discussion extends into scalable key management mechanisms and then investigates the challenges of large-scale deployment. It concludes by identifying future research areas that integrates security within broader system goals.

The exponential growth of Internet of Things (IoT) ecosystems has resulted in a vast deployment of battery-powered sensor nodes across diverse application domains, including healthcare, smart cities, agriculture, and environmental monitoring. These devices often operate in energy-constrained and inaccessible settings, where battery replacement or maintenance is impractical. Addressing this critical challenge, this paper introduces EAMLOF (Energy-Aware Multi-Layer Optimization Framework), a novel cross-layer framework designed to optimize energy utilization in battery-powered IoT networks. Unlike conventional protocols such as PEGASIS and APTEEN, which offer limited adaptability, EAMLOF integrates adaptive sleep/wake scheduling, intelligent data filtering, energy-aware routing, and context-sensitive sampling. The framework employs lightweight edge intelligence to assess node energy levels, data variability, and environmental conditions, enabling dynamic adjustment of communication strategies. This significantly reduces redundant transmissions and energy waste. Simulation results conducted on a network of 1000 nodes using the First-Order Radio Model demonstrate that EAMLOF consistently outperforms existing benchmarks in terms of energy efficiency, network lifetime, packet delivery, and node survivability, showcasing its suitability for long-term and large-scale deployments. EAMLOF presents a scalable, intelligent, and sustainable approach to energy conservation in IoT systems, contributing to the realization of robust, green, and autonomous smart environments.

ABSTRACTIn the Internet of things–aided wireless sensor networks, the deployment of several nodes on a large scale presents distinctive complexities different from conventional WSNs, which leads to varied issues and challenges. Generally, the sensor nodes operate on restricted energy resources, and they play a vital role in the networks. Designing a protocol that is both resilient and energy efficient to enhance the network's prolonged existence poses an important issue. Thus, a novel metaheuristic optimization algorithm is introduced in this paper, which is aimed at addressing cluster head selection and routing in IoT‐based WSNs. Generally, selecting cluster heads is considered essential for enhancing the performance of clustering routing protocols. To address this, a novel approach for optimization called the Artificial Hummingbird Cheetah Optimizer Algorithm is presented, which is the combination of both the Artificial Hummingbird and Cheetah Optimizer Algorithm. Additionally, multiobjective prediction is conducted by employing a convolutional neural network model, which is trained with a cat‐and‐mouse optimizer model. Then, CHS is performed using a fuzzy system, by considering predicted energy levels, lifetime of the sensor nodes, trust, and quality of service. Routing is next executed by employing the AHbCOA model. Finally, an experimentation analysis is performed by evaluating the performance of the presented approach with several optimization models. The performance of these approaches is evaluated by employing various metrics such as energy consumption, throughput, QoS, trust, and LLT. The overall analysis reveals that the proposed model outperforms others, achieving values of 0.933 (J) for energy, 0.592 (s) for LLT, 0.938 for trust, 0.791 (Mbps) for QoS, and 0.934 (kbps) for throughput.

Underwater Acoustic Sensor Networks (UASNs) are gaining increasing interest from researchers due to their promising applications in areas like oil spill monitoring, maritime surveillance, deep-sea archaeology, and marine environment monitoring. With approximately 70% of the Earth's surface covered by water, accessing valuable data from the seafloor is challenging without the aid of specialized technology. Sensor nodes are used in UWSNs to monitor the underwater environment. Once data is collected, it is sent to a sink node, which forwards it to a base station for additional processing. However, sensor node deployment in UWSNs is challenging due to the harsh underwater circumstances, and issues like high energy consumption and restricted communication range make data routing more complicated. UASNs are vulnerable to attacks from malicious nodes, including wormhole, black hole, and Sybil attacks. Lightweight cryptography, which focuses on algorithms that consume less memory, processing power, and energy, is ideal for resource-constrained devices like smartphones, sensors, and IoT devices. One such method that has been used is the Hénon map, a chaotic system, which is particularly useful for text and image encryption. It offers a balance between security and resource efficiency by generating random sequences that drive encryption techniques on devices with limited processing power.

Extensive research into maintaining coverage over time has been spurred by the growing need for wireless sensor networks to monitor certain regions. Coverage gaps brought on either haphazard node placement or failures pose the biggest threat to this objective. In order to identify and fix coverage gaps, this study suggests an algorithm based on swarm intelligence. Using both local and relative information, the swarm of agents navigates a potential field toward the nearest hole and activates in reaction to holes found. In order to spread out effectively and speed up healing, the agents quantize their perceptions and approach holes from various angles. The need for wireless sensor networks to monitor certain areas has grown, leading to many studies on maintaining coverage over time. Random node deployment or failures create coverage gaps, which pose the biggest threat to this objective. A swarm intelligence-based approach is proposed in this paper to identify and fix coverage deficiencies. Even with Their encouraging performance and operational quality, WSNs are susceptible to various security threats. The security of WSNs is seriously threatened by sinkhole attacks, one of these. In this research, a detection strategy against sinkhole attacks is proposed and developed using the Swarm Intelligence (SI) optimization algorithm. MATLAB has been used to implement the proposed work, and comprehensive Models have been run to assess its effectiveness in terms of energy consumption, packet overhead, convergence speed, detection accuracy, and detection time. The findings demonstrate that the mechanism we have suggested is effective and reliable in identifying sinkhole attacks with a high rate of detection accuracy.

Abstract The Internet of Things has led to a surge in data generation and network complexity, especially in edge environments with dynamic topologies and moving objects. Traditional clustering methods in edge computing often fail to address these challenges, such as efficient data aggregation and computational management. DynaClusterNet, a novel framework, introduces three protocols: Adaptive Cluster‐Based Deployment Protocol (ACDP), Dynamic Algae Spider Protocol (DASP), and Deep Q Routing Protocol (DQRP). The ACDP uses Voronoi diagrams for optimal node deployment and cluster formation, while the DASP uses Artificial Algae and Black Widow Algorithms to dynamically select cluster heads and optimize data transmission. The DQRP uses deep reinforcement learning to determine efficient routing paths, adapting to environmental changes, node mobility, and evolving network topologies. DynaClusterNet significantly outperforms existing protocols in terms of end‐to‐end delay, energy consumption, and Packet Delivery Ratio. It ensures a robust, efficient, and adaptable network performance with a minimal end‐to‐end delay of approximately 0.05 s and significantly lower energy consumption profile than competing protocols.

ABSTRACTIn Wireless Sensor Networks (WSNs), localization is extensively wielded for determining the current location of the Sensor Nodes (SNs). But, localization is a challenging task. The localization problems in WSN can be addressed by the existing localization algorithms with the consequences of cost, size, as well as energy problems only. Thus, an efficient location signature generation framework is presented by using the Flexible Ligands Oriented on Grid (FLOG) localization algorithm in WSN. Primarily, the regions are identified and the nodes are randomly deployed. Then, the signal strength is collected. The sensor field is divided into equal triangles in which edge set, vertex set, and coverage value are extracted after the deployment of all the nodes. Then, by using Moore Graph 1‐Separation Formula Kemeny Constant, a planar graph is plotted. After taking all the edge nodes, the exact location of the nodes is discovered by using the Trilateration‐based Gorilla Troop Optimization along with the SAT‐3 algorithm (T‐GTO‐SAT‐3). Then, by using True‐True Matching, the location results are matched with the original node deployment status. Moreover, by using FLOG, the location signature is generated and the nodes are localized. Next, by using Eulerian Learning (EL), the dissemination line setup is done. Lastly, based on the threshold values, the network lifetime is calculated. Hence, the results proved that the proposed model obtained a Mean Localization Error (MLE) of 0.01, which outperformed traditional approaches.

To tackle the challenges of extensive data transmission and high redundancy in wireless sensor networks (WSNs), this study proposes a novel data collection scheme based on expected network coverage and clustered compressive sensing (CS). First, the K-medoids clustering algorithm organizes nodes within the WSN coverage area into clusters. Combined with an optimized network coverage algorithm, a node scheduling strategy is introduced to focus on critical observation areas while minimizing overall energy consumption. Next, by analyzing the relationship between network clustering and node deployment, a weakly correlated observation matrix is designed to mitigate the impact of packet loss on data collection. Finally, the sparrow search algorithm is employed to enhance the accuracy of CS data reconstruction at the cluster head. Simulation results demonstrate that, compared to existing data collection schemes, the proposed approach significantly reduces WSN transmission overhead, ensures accurate recovery of raw data, decreases data reconstruction error, and extends network lifetime.

In wireless sensor networks, evolutionary algorithms have emerged as pivotal tools for addressing complex localization challenges inherent in non-convex and nonlinear maximum likelihood estimation problems associated with received signal strength (RSS) measurements. While differential evolution (DE) has demonstrated notable efficacy in optimizing multimodal cost functions, conventional implementations often grapple with suboptimal convergence rates and susceptibility to local optima. To overcome these limitations, this paper proposes a novel enhancement of DE by integrating opposition-based learning (OBL) principles. The proposed method introduces an adaptive scaling factor that dynamically balances global exploration and local exploitation during the evolutionary process, coupled with a penalty-augmented cost function to effectively utilize boundary information while eliminating explicit constraint handling. Comparative evaluations against state-of-the-art techniques—including semidefinite programming, linear least squares, and simulated annealing—reveal significant improvements in both convergence speed and positioning precision. Experimental results under diverse noise conditions and network configurations further validate the robustness and superiority of the proposed approach, particularly in scenarios characterized by high environmental uncertainty or sparse anchor node deployments.

Electric vehicles with charging stations are being rapidly deployed. Traditional methods for detecting anomalies and attacks on charging stations merely employ local data, resulting in lower detection accuracy. Intuitively, centralized federated learning approaches impose heavy computational loads on the central node. When the central node experiences abnormalities or failures, the charging station network may encounter data isolation. This paper starts from individual charging stations and utilizes a transformer for anomaly and attack detection. Based on this, a distributed privacy-preserving data aggregation scheme is proposed. More specifically, the transformer effectively eliminates the influence of abnormal data fluctuations on detection accuracy. The proposed scheme dynamically aggregates data through a distributed federated learning framework without requiring any prior knowledge or central node deployment. In addition, considering the statistical characteristics of the model parameters, an effective model parameter updating method is proposed to reduce the communication bandwidth requirement in distributed federation learning and enhance the algorithm’s robustness. According to the results, the suggested system may safely detect anomalous activity or possible threats at EV charging stations while safeguarding user privacy.

Modern public blockchains like Ethereum rely on p2p networks to run distributed and censorship-resistant applications. With its wide adoption, it operates as a highly critical public ledger. On its transition to become more scalable and sustainable, shifting to PoS without sacrificing the security and resilience of PoW, Ethereum offers a range of consensus client implementations to participate in the network. In this paper, we present a methodology to measure the performance of the consensus clients based on the latency to receive messages from the p2p network. The paper includes a study that identifies the incentives and limitations that the network experiences, presenting insights about the latency impact derived from running the different consensus implementations at different locations. Our study highlights the need for a holistic approach to node deployment, where hardware, software, and geographic factors have to be carefully considered. Properly dimensioned hardware is essential to mitigate latency-related performance issues and ensure the reliable operation of beacon nodes, especially in geographically distant locations.