Deployment Of Sensor Networks Research Articles

Optimisation of spatiotemporal context-constrained full-view area coverage deployment in camera sensor networks via quantum annealing

Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP’s complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced.

ML Assisted Foot Step Power Generation using Piezoelectric Sensors

In our rapidly evolving world, the escalating demand for energy coupled with the finite nature of traditional resources underscores the imperative for sustainable, pollution-free, and inexhaustible energy solutions. This paper introduces a pioneering approach to harnessing the kinetic energy expended during human locomotion through the innovative use of piezoelectric sensors. Leveraging the piezoelectric effect, these sensors efficiently convert mechanical energy generated by footstep pressure into electrical energy, thereby mitigating wastage and addressing the increasing energy needs. Our model advocates for the deployment of an extensive sensor network along footpaths, complemented by an RFID-based mobile charging system for enhanced convenience and functionality. Moreover, we introduce an innovative method integrating Machine Learning (ML) techniques to enhance power generation efficiency through intelligent modulation of piezoelectric element resistance. Additionally, we leverage ML algorithms to enhance the requisite daily footstep count necessary to fulfill the energy demands of specific areas. This proactive approach ensures optimal deployment of footstep power generation devices based on actual foot traffic patterns. Our research underscores the significance of this technology in the context of urban energy sustainability, particularly in densely populated regions like China and India, where foot traffic is abundant. By harnessing mechanical energy and leveraging advanced ML algorithms, our approach promises to revolutionize energy harvesting paradigms, paving the way for greener and more efficient power generation systems

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Enhancing Resolution and Fault Tolerance of Barrier Coverage with Unmanned Aerial Vehicles

Securing the borders of a protected region using sensor network deployment is termed “barrier coverage.” Unmanned aerial vehicles (UAVs) with cameras pointed downward can serve as mobile sensors to achieve barrier coverage of a protected region. The resolution of the camera, in addition to the extent of coverage, is a crucial parameter used to evaluate the quality of barrier coverage of a region. This paper presents a cost function that measures the resolution of a barrier coverage network, which can be used to improve the quality of an already established barrier-covered network. An optimization problem is proposed to find the barrier coverage while adhering to an overlapping constraint for UAVs that are placed arbitrarily in the belt. The approach is also demonstrated to be applicable for borders of any shape by utilizing multiple rectangular belts in combination. Furthermore, a fault tolerance model is proposed to ensure continuous barrier coverage even in the presence of faulty UAVs. This model utilizes nearby functional UAVs to compensate for any gaps and preserve the overlap constraint. Specifically, the model identifies neighboring functional UAVs for each faulty UAV and uses them to maintain barrier coverage.

“Clustering Protocol Based on Territorial Energy for Random Deployment of Wireless Sensor Networks"

Wireless Sensor Networks (WSNs) have become increasingly essential for monitoring and gathering data in diverse environments. The efficient utilization of energy resources is crucial for prolonging the network lifetime in WSNs. This paper presents a comparative analysis of three prominent clustering protocols: Low Energy Adaptive Clustering Hierarchy (LEACH), Residual Energy Aware Clustering (REAC), and Territorial Energy-Driven Clustering Protocol (TEDC). TEDC introduces a novel approach where cluster heads are elected based on territorial dominance and energy levels, aiming to enhance energy efficiency and prolong network lifetime. The performance of LEACH, REAC, and TEDC is evaluated under various scenarios, including node density, network size, and communication range. Key performance metrics such as energy consumption, network lifetime, and packet delivery ratio are analyzed to assess the effectiveness of each protocol. The results provide valuable insights into the comparative strengths and weaknesses of these protocols, aiding network designers and researchers in selecting the most suitable protocol for specific WSN deployments. Additionally, the paper discusses potential optimizations and future research directions to further enhance the performance of clustering protocols in WSNs.

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

Optimization method for underwater sensor networks based on a virtual force‐oriented enhanced whale optimization algorithm

AbstractThis paper presents an enhanced whale optimization algorithm based on virtual force to optimize coverage and address the problem of uneven coverage during the deployment of underwater sensor networks. This method is guided by the Lévy flight and virtual force algorithms and adopts a differential mutation strategy based on random walks. This improves the fitness of the initial population and population richness of the algorithm. The effectiveness of this method was verified through simulation.

Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment

Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment

Retraction Note: Quality analysis of multi-sensor intrusion detection node deployment in homogeneous wireless sensor networks

Retraction Note: Quality analysis of multi-sensor intrusion detection node deployment in homogeneous wireless sensor networks

Outage-Aware Deployment in Heterogeneous Rayleigh Fading Wireless Sensor Networks

Outage-Aware Deployment in Heterogeneous Rayleigh Fading Wireless Sensor Networks

Secure and scalable data aggregation techniques for healthcare monitoring in WSN

Wireless Sensor Networks (WSN) are crucial in the healthcare field as they allow for the immediate monitoring and collection of data from medical sensors. The growing incorporation of WSN in healthcare requires the creation of authentication protocols that are both strong and energy-efficient. This study presents Lightweight Authentication Protocol for Cloud-based Health-care Systems (LAPCHS) using BLAKE2 designed specifically for WSN in medical settings for secure and scalable data aggregation. The proposed method tackles the difficulties presented by limited resources in sensor nodes, with the goal of achieving a careful equilibrium between strong security and effective energy usage. LAPCHS utilizes innovative techniques to improve the energy efficiency in WSN. The system employs the BLAKE2 cryptographic hash algorithm to establish robust mutual authentication and key agreement between the tag and the cloud server. The protocol is characterized by its low weight and high efficiency, rendering it appropriate for deployment in medical sensor networks with limited resources, which minimizes the need for global key distribution and decreases communication overhead. The proposed method is useful to meet the distinct needs of medical sensor networks, where energy preservation is of utmost importance to ensure extended device scalability. The findings demonstrate that LAPCHS surpasses current approaches in terms of energy efficiency and latency, while simultaneously upholding a robust level of security. The protocol’s novel strategy for managing encryption keys and selecting cryptographic algorithms makes it a promising solution for securing medical sensor networks while maintaining energy sustainability. This study makes a valuable contribution to the developing field of WSN security by proposing a practical solution for ensuring strong and energy-efficient authentication in healthcare applications.

Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Vibrational Genetic Algorithm-Based Deployment of Wireless Sensor Networks With Heterogeneous Nodes in Irregularly Shaped Areas

Adaptive Tasmanian Devil Optimizer for Global Optimization and Application in Wireless Sensor Network Deployment

Adaptive Tasmanian Devil Optimizer for Global Optimization and Application in Wireless Sensor Network Deployment

A Coverage Model of FMCW Radar for Optimizing Sensor Network Deployment

A Coverage Model of FMCW Radar for Optimizing Sensor Network Deployment

Wireless sensor network deployment optimization for a smart farming application: comparison of two Multi-Objective Evolutionary Algorithms

As it is one of the main means of insuring food security, agriculture has employed different technologies such as the Internet of Things and wireless sensor networks (WSN), to improve the quality and quantity of agricultural products, while preserving natural resources. But unfortunately, in agricultural plots where we have large surfaces of interest, the optimization of node deployment in a WSN remains among the major problems to be solved. In this work, we proposed a WSN node deployment optimization model for an agricultural application according to classical constraints of coverage, over-coverage, connectivity and nodes number, in addition to the nodes separating distance constraint which affects the quality of physical parameters models. We have applied two variants of Multi-Objective Genetic Algorithms, the Non Sorting Genetic Algorithm II (NS-GA II) and the Strength Pareto Evolutionary Algorithm II (SPEA II). As a result, for a 100 m2 plot, both algorithms ensured a communication rate of 100% while SPEA 2 presented a lower sensor number and over-coverage rates with a smaller separating distance, and the execution time of NSGA II was shorter with 11 s. Besides, both of them were greedy in terms of computation time with the increase in the size of the plots.

High-performance, Wind-driven Triboelectric-Electromagnetic Hybrid Nanogenerator for Self-powered Wireless Transmission and Wind Speed Sensing in the Internet of Things

Wind energy is becoming more crucial in addressing the scarcity of fossil fuel due to the development of promising technologies like Electromagnetic Generators (EGs), Triboelectric Nanogenerators (TNs), and their hybrid nanogenerators. However, only a portion of wind energy can be efficiently converted into electricity. By integrating 5G technology with the Internet of Things (IoT) and expanding the deployment of wireless sensor networks, vulnerable areas may be effectively monitored for potential disasters. Nevertheless, substituting conventional chemical batteries with new ones in IoT devices poses a substantial difficulty, particularly in distant regions. This article presents a hybrid energy harvester explicitly designed for wind energy harvesting. This work introduces the High Performance, Wind-driven Triboelectric-Electromagnetic Hybrid Nanogenerator (HPW-TEHN) as a solution for self-powered Wireless Transmission (WT) and Wind Speed Sensing (WSS) in the Internet of Things (IoT). Here, we present a High-Performance Wind Turbine with a wide operational range of wind speeds, allowing for efficient harnessing of wind energy across different levels, including gentle breezes and moderate and severe winds. The gadget comprises a spinning body and a sliding body. The power-producing mechanism consists of the EGs in the rotating body and the TNs in the sliding body. All created units are fully enclosed within the device box, effectively insulated from challenging environmental conditions. This research comprehensively analyzes the impact of dielectric material kinds and sizes on the output performance of TNs. Additionally, it investigates the output performance of this device under various wind speeds. The performance of HPW-TEHN improves as the wind speed increases. At a wind speed of 10 m/s, the TN, EG, and HPW-TEHN generate an output power of 0.19 mW, 0.38 mW, and 1.4 mW, respectively.

Promoting quality in low-cost gas sensor devices for real-world applications

Recent advancements in metal oxide semiconductor sensors enable system integrators to make sensor devices without the traditional complications of operating barebone sensor components. Anyone, so it seems, can put together multi-sensor-systems by combining an integrated sensor subsystem with a small piece of digital infrastructure, resulting in low-cost sensor systems or sensor networks for all kinds of applications. With low energy consumption, the deployment of close meshed sensor networks is becoming a reality, promising high density data for big data models. However, data quality is not necessarily a feature of such devices, since the device output signals are heavily processed and the insight into the actual operating technique is black-boxed by the respective sensor manufacturer. High volume production of a sensor model requires widely applicable output information, yet this mainstreaming negatively impacts quality control efforts on the system level as well as niche applications that require specialized operation modes. This article exemplarily examines the measurement chain of typical metal oxide semiconductor sensor applications and deduces requirements for a technically sound advancement of the subject for research and commercial purposes. Equivalent considerations can be made for any low-cost sensor principle and their respective challenges. The conclusion urges all stakeholders participating in the development and marketing of sensor devices to advance a scientifically valid state of knowledge and to educate their customers and even the general public accordingly.

Additively manufactured magic cube platforms for fully integrated wireless sensing nodes for Internet of Things applications

Wireless sensor networks for environmental monitoring are a key feature in developing the Internet of Things. Although there has been much research in developing components for wireless sensing nodes, advances in creating fully integrated sensing nodes is limited. Furthermore, because most sensing nodes that have been developed are intended to perform a fixed task, each new effort to design an integrated sensing node with different functionality must start from scratch. Here we introduce a broadly applicable platform for the development and production of fully integrated wireless sensing nodes. The platform is an additively manufactured cube that has different subsystems occupying separate faces of the 3D structure. While both additively manufactured sensors and cube-shaped wireless sensing nodes have been previously reported, these two approaches have yet to be combined. A key technology that enables this is the use of additively manufactured, nonplanar bent microstrips. This realization offers a “plug-and-play” approach to sensor node design, as the subsystems are considered modular and can be swapped to alter the function of the device. Implementing this concept enables the rapid development and deployment of wireless sensor networks.

Energy-Aware Framework for Underwater Mine Detection System Using Underwater Acoustic Wireless Sensor Network

Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. However, the use of these self-powered sensors for intensive data sensing and wireless communication is often energy-scaring and might call into question the viability of their application. One attractive solution to extend the underwater wireless sensor network will be the adoption of cluster-based communication, since data processing and communication loads are distributed in a timely manner over the members of the cluster. In this context, this study proposes an energy-efficient solution for high-accuracy underwater mine detection based on the adequate clustering approach. The proposed scheme uses a processing approach based on wavelet transformation to extract relevant features to efficiently distinguish mines from other objects using the Naïve Bayes algorithm for classification. The main novelty of this approach is the design of a new low-complexity scheme for efficient sensor-based acoustic object detection that outperforms most of the existing solutions. It consumes a low amount of energy, while ensuring 95.12% target detection accuracy.