Algorithm For Sensor Networks Research Articles

A Time-Critical Low Complexity Distributed Self Organizing Hierarchical Particle Swarm Optimization Algorithm for Wireless Sensor Networks

A Time-Critical Low Complexity Distributed Self Organizing Hierarchical Particle Swarm Optimization Algorithm for Wireless Sensor Networks

An enhanced DV-Hop localization algorithm in wireless sensor networks with variable velocity strategy and human conception optimization

An enhanced DV-Hop localization algorithm in wireless sensor networks with variable velocity strategy and human conception optimization

Deep neural networks in low energy algorithms for wireless sensor networks

Deep neural networks in low energy algorithms for wireless sensor networks

An energy-efficient routing algorithm for dual-energy harvesting-assisted wireless sensor networks based on whale optimization strategy

An energy-efficient routing algorithm for dual-energy harvesting-assisted wireless sensor networks based on whale optimization strategy

Energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization

Aiming at the problems of large number of data transmission node deaths and large transmission energy consumption output in energy-saving clustering routing communication of wireless sensor networks, an energy-saving clustering routing algorithm for heterogeneous wireless sensor networks based on energy iteration model and bee colony optimization is proposed. Firstly, a network communication energy consumption model is constructed, and the network node distribution is optimized in time with the goal of reducing energy consumption combined with differential bee colony algorithm; then, based on the network node distribution optimization results, an energy-saving clustering method for heterogeneous wireless sensor networks is formulated, and the cluster head is determined by using the energy iteration cluster selection method, and the cluster head radius is obtained to complete the energy-saving clustering of communication nodes in heterogeneous wireless sensor networks; finally, the distance between the communication cluster head node and the base station is set, the routing level of the node communication is determined, and the energy-saving routing communication of the heterogeneous wireless sensor network is realized by combining the multi-hop routing communication mode. The experimental results show that when the method is used for network energy-saving clustering routing communication, the number of data transmission nodes that die is at most 22, and the maximum energy consumption of node transmission is 21 nJ/bit , indicating that the node communication energy-saving effect of this method is good.

ATD-300 an AI empowered miniature acoustic device for automatic gunshot attacks detection and image capture

Built on over 30 years of acoustic source recognition expertise in transient impulsives signature, recently highly empowered by neural networks, and wave propagation model driven algorithms, ACOEM has developed the ATD-300, an acoustic sensor for automatic gunshot detection, identification and localization. What sets this sensor apart from the competition is its reliability in terms of detection rate over false alarm and robust high resolution shooter pointing capability. Therefore it can be used directly by police departments without third-party human validation. Resulting from the design effort to distribute intelligence among a collaborative sensor network, processing and artificial intelligence algorithms are fully embedded locally, enabling an alert to be raised in less than three seconds and a camera to be pointed at the shooter with only one sensor. In addition, as the system is already installed in several American cities, we will show how a device-to-server distributed artificial intelligence approach continuously improves the performance of the solution, fed by real field data.

Pantograph fault prediction of urban rail transit vehicles based on edge feature extraction and detection

Nowadays, as an indispensable part of urban infrastructure, urban rail transit (URT) vehicles have also developed rapidly. A large amount of manpower, material resources, and financial resources need to be invested in the construction process of URT. For URT vehicles, research on more accurate fault prediction methods can save a lot of maintenance costs and improve the reliability of URT construction. As an important electrical equipment for urban rail transit vehicles to obtain electric energy from the catenary, the operation of rail transit vehicles puts forward higher performance requirements for the pantograph. For solving the problems of low accuracy of fault prediction, over reliance on practical experience and high cost of fault prediction in the application of traditional URT vehicle pantograph fault prediction model. Combining sensor network and artificial intelligence algorithm, this paper analyzed the traditional rail transit vehicle pantograph fault prediction model, and verified it through comparative experiments. Through the comparative analysis of the experimental results, this paper can draw a conclusion that compared with the traditional rail transit vehicle pantograph fault prediction model, the rail transit vehicle pantograph fault prediction model has higher fault prediction accuracy, less model response time, lower risk of pantograph failure, higher model application satisfaction, and the accuracy of fault prediction increased by about 6.6%. The rail transit vehicle pantograph fault prediction model can effectively improve the accuracy of vehicle pantograph fault prediction, which can greatly promote the safety of URT and promote the intelligent process of URT.

A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

Threshold-driven K-means sector clustering algorithm for wireless sensor networks

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND.

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

An enhanced localization algorithm for 3D wireless sensor networks using group learning optimization

Combined Sweeping and Jumping Method to Enhance Node Insertion Algorithm for Wi-Fi Sensor Networks

Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist.

Wearable Devices based on Wireless Sensor Network and Speech Synchronization Overlay Algorithm Application in Sports Training Data Simulation

Wearable Devices based on Wireless Sensor Network and Speech Synchronization Overlay Algorithm Application in Sports Training Data Simulation

Wearable Optical Imaging Devices Based on Wireless Sensor Networks and Fuzzy Image Restoration Algorithms for Sports Image Analysis

Wearable Optical Imaging Devices Based on Wireless Sensor Networks and Fuzzy Image Restoration Algorithms for Sports Image Analysis

Retraction Note: Enhancement of security using optimized DoS (denial-of-service) detection algorithm for wireless sensor network

Retraction Note: Enhancement of security using optimized DoS (denial-of-service) detection algorithm for wireless sensor network

A DRL-based Partial Charging Algorithm for Wireless Rechargeable Sensor Networks

Breakthroughs in Wireless Energy Transfer technologies have revitalized Wireless Rechargeable Sensor Networks. However, how to schedule mobile chargers rationally has been quite a tricky problem. Most of the current work does not consider the variability of scenarios and how many mobile chargers should be scheduled as the most appropriate for each dispatch. At the same time, the focus of most work on the mobile charger scheduling problem has always been on reducing the number of dead nodes, and the most critical metric of network performance, packet arrival rate, is relatively neglected. In this article, we develop a DRL-based Partial Charging algorithm. Based on the number and urgency of charging requests, we classify charging requests into four scenarios. And for each scenario, we design a corresponding request allocation algorithm. Then, a Deep Reinforcement Learning algorithm is employed to train a decision model using environmental information to select which request allocation algorithm is optimal for the current scenario. After the allocation of charging requests is confirmed, to improve the Quality of Service, i.e., the packet arrival rate of the entire network, a partial charging scheduling algorithm is designed to maximize the total charging duration of nodes in the ideal state while ensuring that all charging requests are completed. In addition, we analyze the traffic information of the nodes and use the Analytic Hierarchy Process to determine the importance of the nodes to compensate for the inaccurate estimation of the node’s remaining lifetime in realistic scenarios. Simulation results show that our proposed algorithm outperforms the existing algorithms regarding the number of alive nodes and packet arrival rate.

Energy efficient scheme for improving network lifetime using BAT algorithm in wireless sensor network

SummaryWireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently.

Self-adaptive bifold-objective rate optimization algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) is a set of several sensor nodes that are used for monitoring heterogeneous physical objects. In WSNs, irregular and bursty traffic Leads to the congestion problem, which incites a decrease in Packet Delivery Ratio (PDR) and increases packet loss as well as end-to-end delay. In the recent era, manifold efforts have been carried out to reduce network congestion however, these solutions have slow and premature optimization. To address optimization issues, this paper presents a self-adaptive source-sending rate optimization algorithm, which is a hybrid version of Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Bifold-objective Proportional Integral Derivative (BPID) called N3-BPID. These techniques play a significant role in optimizing source rates to reduce network congestion. NSGA-III is a reference-based evolutionary approach, which dynamically configures the PID coefficients to get an optimal response. Furthermore, a novel bifold-objective fitness function is designed that balances the trade-offs between two PIDs performance indexes such as the Integral of Absolute Error and the Integral of Square Error. Due to simplicity and efficiency, an identically weighted aggregation mechanism is applied to ensemble both objectives into a single one. The proposed work is implemented to demonstrate a smart border surveillance application using Network Simulator v3 and compared with the state-of-the-art congestion control model Cuckoo Fuzzy PID (CFPID). The experimental result reveals that the proposed algorithm has significantly outperformed existing schemes in terms of PDR by 6.82%, packet loss by 24.52%, end-to-end delay by 15.31%, and queue length deviation by 8.93%.

Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm in Cognitive Radio Wireless Sensor Networks (CRWSNs)

Unequal Clustering Energy Hole Avoidance (UCEHA) algorithm in Cognitive Radio Wireless Sensor Networks (CRWSNs)

A Review on Energy and Latency Efficient Routing Protocol Design for Wireless Sensor Networks

Wireless sensor networks (WSNs) have been utilized by various industries, such as disaster management, the chemical and heavy industries, marine research, and space exploration. One of the primary challenges faced by wireless sensor networks is their limited network lifetime. Several techniques have been devised to extend the duration of network operation. This proposed method introduces a threshold-based algorithm to minimize the amount of data that needs to be transferred. In this approach, the selection of cluster heads and their sizes is not fixed. Instead, they are dynamically determined at the beginning of each iteration. The design of routing algorithms for wireless sensor networks faces three main challenges: lowering the rate at which the average node energy declines, minimizing the number of dead nodes, and eventually prolonging the network's lifetime. This paper provides a thorough examination of efficient routing protocols for wireless sensor networks with the goal of improving network longevity. Keywords: Efficient Routing, WSN Clustering, Network Latency, Network Lifetime.

HBO-SROA: Honey Badger optimization based clustering with secured remora optimization based routing algorithm in wireless sensor networks

HBO-SROA: Honey Badger optimization based clustering with secured remora optimization based routing algorithm in wireless sensor networks