Optimal Sensor Network Research Articles

Optimal relay nodes placement with game theory optimization for Wireless Sensor Networks

Wireless Sensor Networks (WSN) play a major role in the wide variety of applications like underground pipeline and leaks monitoring, temperature distribution monitoring in industrial cyber systems, military, forest life monitoring, and environmental and geographical monitoring. Sensors are widely used in these different applications. The number of sensors and the application concerned mainly decides the energy consumption, network lifetime. In this process relay nodes may help the sensors as backbone to connect with sink node or base stations. In this paper, we introduce a new approach for relay node selection in WSN to minimize the energy consumption of the network. It uses channel aware relay selection technique using game theory optimization and act as a virtual backbone in connecting to the base station. However, the relay nodes are varied to check the optimal number of relays required for the small, medium and large number of nodes deployed in the network. Simulations are carried out using Network Simulator NS-2.35 and network is analyzed in wide variety of scenarios. Results show that the proposed relay node selection algorithm reduces energy consumption, improves lifetime, throughput of the network.

Outage performance and energy efficiency optimization of wireless-powered millimeter-wave sensor networks

AbstractWith the widespread use of wireless sensor networks, one of the most pressing concerns is extending the lifetime of the sensors. By deploying directional antenna arrays, millimeter wave (mmWave) is a possible candidate for wireless energy transfer (WPT). This paper investigates a beneficial combination of WPT and data transmission in a typical mmWave sensor network with Rayleigh channels, where a transmission interval can be divided into two sub-intervals. During the first sub-interval, one hybrid access point (HAP) employs beamforming techniques to transfer energy for serving multiple sensors within the service sector. The sensors then transmit their individual signal in turn to the HAP based on time division multiple address (TDMA) strategy by using the whole harvested energy. According to stochastic geometry, the exact and approximate expressions of beam outage probability for the considered system are determined, respectively. The optimal time allocation of energy harvesting and data transmission for sensors is examined in order to maximize the energy efficiency of the system. The optimization problem can be translated into corresponding parametric form, and the resulting optimization problem can be solved using the Lagrange dual method with Karush–Kuhn–Tucker (KKT) conditions. The numerical results show the variation trend of the beam outage probability under various parameters and verify the accuracy of the theoretical analyses. Furthermore, the simulation results illustrate that the proposed optimal time allocation strategy can significantly enhance the overall energy efficiency of the system compared with a similar scheme.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

An Indoor Positioning Algorithm Based on Particle Filter and Neighbor-Guided Particle Optimization for Wireless Sensor Networks

An Indoor Positioning Algorithm Based on Particle Filter and Neighbor-Guided Particle Optimization for Wireless Sensor Networks

Adaptive Age of Information Optimization in Rateless Coding-Based Multicast-Enabled Sensor Networks

Adaptive Age of Information Optimization in Rateless Coding-Based Multicast-Enabled Sensor Networks

Deployment Optimization of Intelligent Wireless Sensor Network Using Graph Similarity Based on Multi-Granularity Cross Representation and Matching

Deployment Optimization of Intelligent Wireless Sensor Network Using Graph Similarity Based on Multi-Granularity Cross Representation and Matching

An intelligent algorithm for energy efficiency optimization in software-defined wireless sensor networks for 5G communications.

Wireless communications have lately experienced substantial exploitation because they provide a lot of flexibility for data delivery. It provides connection and mobility by using air as a medium. Wireless sensor networks (WSN) are now the most popular wireless technologies. They need a communication infrastructure that is both energy and computationally efficient, which is made feasible by developing the best communication protocol algorithms. The internet of things (IoT) paradigm is anticipated to be heavily reliant on a networking architecture that is currently in development and dubbed software-defined WSN. Energy-efficient routing design is a key objective for WSNs. Cluster routing is one of the most commonly used routing techniques for extending network life. This research proposes a novel approach for increasing the energy effectiveness and longevity of software-defined WSNs. The major goal is to reduce the energy consumption of the cluster routing protocol using the firefly algorithm and high-efficiency entropy. According to the findings of the simulation, the suggested method outperforms existing algorithms in terms of system performance under various operating conditions. The number of alive nodes determined by the proposed algorithm is about 42.06% higher than Distributed Energy-Efficient Clustering with firefly algorithm (DEEC-FA) and 13.95% higher than Improved Firefly Clustering IFCEER and 12.05% higher than another referenced algorithm.

Heuristic Forest Fire Detection Using the Deep Learning Model with Optimized Cluster Head Selection Technique

Disaster prediction systems enable authorities and communities to identify and understand the risks associated with various natural and man‐made disasters. Disaster prediction systems are essential tools for enhancing public safety, reducing the impact of disasters, and enabling more informed and strategic decision‐making across various sectors. Their development and implementation represent a crucial aspect of modern disaster risk management and resilience systems. This novel technique introduces a modern approach to forest fire prediction by integrating a deep learning model with an optimized cluster head selection technique. The major goal is to augment the accuracy and efficiency of forest fire prediction, leveraging the capabilities of advanced machine learning algorithms and optimized sensor network management. The proposed system comprises two core components: a deep learning model for predictive analysis and an optimized selection process for cluster heads in sensor networks. The deep learning model utilizes various environmental data parameters such as humidity, wind speed, temperature and former fire incidents. These parameters are processed through a sophisticated neural network architecture designed to identify patterns and correlations that signify the likelihood of a forest fire. The model is trained on historical data to improve its predictive accuracy, and its performance is continuously evaluated against new data. Simultaneously, the optimized cluster head selection using the cat‐mouse optimization technique plays a crucial role in efficiently managing sensor networks deployed in forests. The integration of these two components results in a robust system capable of predicting forest fires with high precision. The system not only assists in early detection and timely alerts but also contributes to the strategic planning of firefighting and resource allocation efforts. This approach has the prospective to significantly lessen the impact of forest fires, thereby protecting ecosystems and communities.

Intrusion Detection System Fog Security Model for the Smart Cities and Urban Sensing

In smart cities, Intrusion Detection Systems (IDS) play a critical role in enhancing cybersecurity and safeguarding the interconnected network of devices and systems. Smart cities leverage various technologies, such as the Internet of Things (IoT), sensors, and communication networks, to optimize urban services and improve overall efficiency. However, this increased connectivity also introduces vulnerabilities that could be exploited by malicious actors. This paper presents a thorough exploration of contemporary developments in the domains of security and optimization within the context of smart cities. The rapid integration of Internet of Things (IoT) devices and technologies in urban environments necessitates robust security frameworks and efficient resource management strategies. The paper begins by scrutinizing Intrusion Detection Systems (IDS) tailored for smart city networks, evaluating their efficacy in mitigating diverse cyber threats. Novel approaches, such as Anomaly Detection and Firewall systems, are analyzed alongside traditional IDS, providing a comprehensive overview of the security landscape. In parallel, the paper introduces innovative clustering techniques, with a focus on the proposed Flora Optimization Weighted Clustering (FOWC) model. Inspired by biological growth mechanisms, FOWC presents a unique paradigm for optimizing sensor placements in smart cities. A comparative analysis with existing clustering algorithms like LEACH and Genetic Algorithm underscores FOWC’s superior performance in terms of Detection Rate, Precision, Recall, and F1 Score, positioning it as a promising solution for urban sensor network optimization. Beyond security and optimization, the paper addresses broader challenges in the realm of smart cities, including secure IoT applications, intrusion detection for IoT-enabled environments, and the integration of artificial intelligence for enhanced cyber defense. The research studies presented collectively contribute to a comprehensive understanding of the evolving landscape of smart cities, offering insights with practical implications for policymakers, urban planners, and technologists. Keywords: Smart cities; internet of things (iot); intrusion detection system (ids); fog computing; security

Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

K-means Clustering and PSO Algorithm for Wireless Sensor Networks Optimization

K-means Clustering and PSO Algorithm for Wireless Sensor Networks Optimization

Towards energy balancing optimization in wireless sensor networks: A novel quantum inspired genetic algorithm based sinks deployment approach

A Wireless Sensor Network (WSN) with multiple sinks alleviates the bottleneck problem of imbalanced energy consumption and leads to improved scalability and convenience in data collection for diverse range of large-scale applications. However, introducing more than one sink also brings a new set of challenges to overcome. To minimize the uneven energy consumption among sensors for longevity of the WSN, it is required to find optimal locations of the sinks that will permit most of the sensors to forward their data using minimum hops and to determine the optimal route for each sink with consistent route length. Routing in large networks is an NP-Hard problem. Previous schemes have solved this problem using multiple sinks deployment either in planned ways or random manner without simultaneously considering the possibilities of network topological changes, edge cases of sensors, and problem of uneven path length. In this context, this paper proposes a quantum inspired genetic algorithm based multiple sinks deployment approach (Q-GEMS) for energy consumption balancing among sensors to extend the network lifetime. Specially, the exiting approaches have focused on either minimizing energy consumption or reducing the path length. In addition to addressing these constraints, the proposed Q-GEMS is designed to be resilient which adapts to any changes in the planned topologies of deployed sinks in anticipation of sensor failures and external influences. Also to achieve greater energy consumption balance among sensors by equalizing the path length and bring down the cost of WSN by minimizing the number of sinks, novel heuristics have been proposed for positioning the sinks, binding the sensors to sinks, evaluating the quality of solutions and updating the Q-bit population. The experimental results show that the proposed Q-GEMS performs comparatively better than state of the art approaches and achieving network lifetime 5.1%, 21%, and 24% longer than that of EEMS, EESS, and DPSO+PSO respectively.

Convex optimization approach to design sensor networks using information theoretic measures

AbstractAccurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims at developing convex optimization formulations for designing the optimal sensor network using information‐theoretic measures in linear steady‐state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback–Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic‐based optimal sensor selection problems are shown to be integer semi‐definite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.

Machine learning for coverage optimization in wireless sensor networks: a comprehensive review

Machine learning for coverage optimization in wireless sensor networks: a comprehensive review

Secure clustering and routing based on multi Objective – trust aware average inertia weighted cat swarm optimization for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a group of devices/sensors which are connected as a network for transferring and receiving the data observed from the environment through intermediate links. Energy efficiency and security during data broadcasting are considered challenging tasks in the WSN. These challenging tasks are considered as a motivation of this research and the Multi-Objective - Trust Aware Average Inertia Weighted Cat Swarm Optimization (MO-TAIWCSO) is proposed for achieving secure reliable transmission over the WSN. Due to an effective velocity update of searching process, the AIWCSO is selected for discovering an optimal solutions. The developed MO-TAIWCSO is optimized by using the trust, energy ratio, communication cost, and degree of SCH. This MO-TAIWCSO performs optimal Secure Cluster Head (SCH) and secure path discovery for the secure transmission of data under malicious attacks. The main objective of this MO-TAIWCSO is to improve the data delivery while minimizing the energy usage of the nodes. The performance of the MO-TAIWCSO method is analyzed by using the throughput, Packet Delivery Ratio (PDR), energy consumption, network lifetime, Normalized Routing Load (NRL) and End to end delay (EED). The existing researches namely ETOR and TBSEER are used to evaluate the MO-TAIWCSO. The PDR of MO-TAIWCSO for 100 nodes is 99.97%, which is high when compared to the ETOR and TBSEER.

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

An opportunistic energy‐efficient dynamic self‐configuration clustering algorithm in WSN‐based IoT networks

SummaryThe demand for the Internet of Things (IoT) has significantly increased in the current scenario; specific sectors that require IoT include industrial automation, home control, health care applications, military and surveillance applications, habitat monitoring, and nanoscopic sensor applications. The use of optimal wireless sensor networks (WSNs) in transmission techniques has resulted in their involvement. A WSN is made up of thousands of randomly distributed sensor nodes that sense and transmit environmental data such as temperature, pressure, humidity, light, and sound. One of the most important requirements when using these sensor nodes is energy. As a result, it has become a major area of research in recent years; additionally, several design techniques and protocols have been presented in the last decade, particularly for IoT‐based applications. As a result, the systemization of an energy‐optimized WSN in dynamic functional conditions with automatic self‐configuration of sensor nodes is a critical goal. This paper proposed an opportunistic energy‐efficient dynamic self‐configuration routing (OEDSR) algorithm for IoT‐based applications. Initially, the optimal route to the base station (BS) is calculated by using the residual energy and mobility factor of the sensor nodes obtained through a routing tree model based on graph theory. To reduce the number of connections, an optimal path is determined based on dynamic cluster generation through a hierarchical tree architecture. Finally, the network‐related parameters, such as throughput, delay and packet delivery ratio (PDR), are compared with the peer existing routing protocols to depict the efficiency of the OEDSR protocol.

Understanding the Role of Sensor Optimisation in Complex Systems.

Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective.

High traffic communication congestion control for wireless sensor networks based on harmony search optimization

In wireless sensor networks (WSNs), communication between the wireless nodes requires minimum response delay, minimum congestion and communication reliability. A wide variety of sensors produces a mixture of heterogeneous traffics with different reliability requirements. The article focuses on high traffic congestion which affects communication and produces latency. In the existing approaches, the congestion was controlled and the optimization was done during the time of node deployment. In the proposed method, high traffic congestion was controlled by a hop-by-hop approach which was applied in the statically deployed sensor nodes, the optimization was performed at the time of communication. To provide a uninterrupted communication to the WSNs the proposed approach analyses the occupancy ratio of the buffer and evaluates the downstream node congestion level. Here, the Harmony Search Algorithm is considered for design the optimal sensor network with Support Vector Machine (SVM). The experimental result shows the effectiveness and feasibility of the HSA-SVM environment. Also, it significantly enhances communication in diverse traffic conditions, specifically in heavy traffic areas with limited data.

An AoTI-Driven Joint Sampling Frequency and Access Selection Optimization for Industrial Wireless Sensor Networks

The freshness of system information has attracted extensive attention and has become one of the most critical Quality of Service (QoS) indicators in industrial wireless sensor networks (IWSNs). For practical IWSNs composed of numerous components, the system freshness for a specific task is usually related to multiple and multitype sensing data. For example, in a chemical plant fire alarm system, the system controller (SC) cannot determine the fire notification until analyzing multiple types of ambient information, such as temperature, flame, carbon dioxide, and smoke. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. Therefore, to measure the system freshness accurately in IWSNs, we propose the Age of Task-oriented Information (AoTI) for industrial tasks. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to an industrial task. Considering the time-varying and wireless environments, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access selections and sampling frequencies for all sensors. We first formulate this problem as a Mixed Integer Nonlinear Programming (MINLP) problem and then transform it into a Constrained Markov Decision Process (CMDP), which is further relaxed as an MDP using the Lagrangian method. Finally, we develop a Learning-based Access selection and Sampling frequency Control (LASC) algorithm and verify its superiority through extensive simulations.