Large-scale Wireless Sensor Networks Research Articles

EBPT-CRA: A clustering and routing algorithm based on energy-balanced path tree for wireless sensor networks

With the rapid development of internet of things (IoT), the scale of wireless sensor networks (WSNs) is gradually expanding, and it is necessary to study the energy saving and balancing algorithm for WSNs in multi-sensor nodes and large deployment range scenarios. A clustering and routing algorithm based on an energy-balanced path tree (EBPT-CRA) is proposed for large-scale WSNs. First, the energy-balanced path tree (EBPT) is constructed, and then the aggregation betweenness of the nodes are calculated to obtain the cluster head competition coefficients of the nodes. Second, an iterative process is executed to select the cluster heads and build the cluster trees to complete the clustering and intra-cluster routing. Finally, the inter-cluster routing is determined based on the node residual energy and the communication energy. The simulation results demonstrate that the EBPT-CRA can better save and balance the node energy, while improve the network lifetime, throughput and service capability in large-scale WSNs.

Retraction Note: An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Retraction Note: An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Performance of Interconnected Hybrid ZigBee-Optic for Extended Wireless Sensor Networks

Wireless sensor networks (WSNs) are widely used to monitor remote areas far away from the monitoring center. For large-scale or high-capacity WSNs, when they contain many sensor nodes, a transmission system with low latency and large bandwidth is required. In order to extend the network range, the use of optical communication is one of the alternatives to provide more capacity and a longer range. This study discusses the performance of a range-extended WSN utilizing a hybrid of ZigBee and optic transmission. The performance of the proposed method was evaluated by analyzing the throughput, delay per meter, package loss, and error, which were then compared to a ZigBee-Wifi based system. The experimental results showed that the throughput of the hybrid ZigBee-Fiber Optic (ZigBee FO) system was about 12% greater than that of the ZigBee-Wifi system, and it transmitted the sensor data with a significantly lower delay, reduced by 83%, compared to the ZigBee-Wifi. The package loss and error of ZigBee-FO was 35.7% lower than that of ZigBee-Wifi. Based on these results, the ZigBee-FO WSN has the advantage of significantly improving network performance by reducing the transmission delay, therefore it is beneficial in extending the WSN range.

Double firefly based efficient clustering for large-scale wireless sensor networks

Double firefly based efficient clustering for large-scale wireless sensor networks

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks.

False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.

Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks

Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation. EFL with Cloud Integration uses ensemble methods and federated learning to enhance detection accuracy and data privacy. OAD-EE with Cloud-based Model Aggregation uses online learning and energy-efficient techniques to conserve energy on resource-constrained sensor nodes. By combining EFL and OAD-EE, a comprehensive and efficient framework for anomaly detection in WSNs can be created. Experimental results show that EFL with Cloud Integration achieves the highest detection accuracy, while OAD-EE with Cloud-based Model Aggregation has the lowest energy consumption and fastest detection time among all algorithms, making it suitable for real-time applications. The unified algorithm contributes to the system's overall efficiency, scalability, and real-time response. By integrating cloud computing, this algorithm opens new avenues for advanced WSN applications. These promising approaches for anomaly detection in resource constrained and large-scale WSNs are beneficial for industrial applications.

Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization

<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to construct a distance cost term, a cost function model is built based on these cost terms, and graph optimization is adopted to minimize this function. Then, the node position is estimated to minimize the overall observation error. Simulation results indicate that the proposed algorithm has higher ranging and localization accuracy than existing ranging and localization algorithms, and it can meet the requirements of node localization in large-scale WSN.</p> <p> </p>

Retracted: Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs

Retracted: Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs

An Improved Coverage Hole Finding System for Critical Applications Based on Computational Geometric Techniques

Wireless Sensor Networks (WSNs) contain coverage holes caused by both random node sensor deployment and malfunctioning nodes. Because fixing the battery is challenging, collaborative discovery and assessment of coverage shortfalls, as well as getting rid of these holes, has been recognized as critical in WSNs. While placing nodes for sensors in a large-scale WSN is challenging. This research provides a cost-effective coverage hole detection approach based on collaborative distributed point placement. Create a polygon first by employing an angle estimate approach and neighbor data. Following that, a based on points hole identification technique is used to assess if a coverage issue appears in a large-scale WSN's supplied ROI. Furthermore, the region of the coverage hole is estimated using computational geometry-based polygonal triangulation methods. The accuracy of the method is tested here using statistical data. The results show that it outperforms earlier coverage hole-detecting algorithms. In particular, the method improves coverage rate by 75% when compared to conventional methodologies. It also lowers energy usage by 90%, adding to increased network lifetime. The quantitative favourable results demonstrate the effectiveness of the collaborative distributed point placement technique in detecting and successfully resolving coverage gaps in WSNs. In regards to coverage rate, energy consumption, and network longevity, the system being proposed beats previous coverage hole-detecting techniques.

Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks

Performance Analysis of Gossip Algorithms for Large Scale Wireless Sensor Networks

A Novel Topology-scale-adaptive and Energy-efficient Clustering Scheme for Energy Sustainable Large-Scale SWIPT-enabled WSNs

A Novel Topology-scale-adaptive and Energy-efficient Clustering Scheme for Energy Sustainable Large-Scale SWIPT-enabled WSNs

Large-Scale WSNs Resource Scheduling Algorithm in Smart Transportation Monitoring Based on Differential Ion Coevolution and Multi-Objective Decomposition

Large-Scale WSNs Resource Scheduling Algorithm in Smart Transportation Monitoring Based on Differential Ion Coevolution and Multi-Objective Decomposition

Approximate dynamic programming for condition-based node deployment in a wireless sensor network

The flexibility of deployment strategies combined with the low cost of individual sensor nodes allow wireless sensor networks (WSNs) to be integrated into a variety of applications. Network operations degrade over time as sensors consume a finite power supply and begin to fail. In this work we address the selective maintenance of a WSN through a condition-based deployment policy (CBDP) in which sensors are deployed over a series of missions. The main contribution is a Markov decision process (MDP) model to maintain a reliable WSN with respect to region coverage. Due to the resulting high dimensional state and outcome space, we explore approximate dynamic programming (ADP) methodology in the search for high quality CBDPs. Our model is one of the first related to the selective maintenance of a large-scale WSN through the repeated deployment of new sensor nodes with a reliability objective, and one of the first ADP applications for the maintenance of a complex WSN. Additionally, our methodology incorporates a destruction spectrum reliability estimate which has received significant attention with respect to network reliability, but its value in a maintenance setting has not been widely explored. We conclude with a discussion on CBDPs in a range of test instances, and compare the performance to alternative deployment strategies.

Adaptive squirrel coyote optimization-based secured energy efficient routing technique for large scale WSN with multiple sink nodes

In general, Wireless Sensor Networks (WSNs) require secure routing approaches for delivering the data packets to their sinks or destinations. Most of the WSNs identify particular events in their explicit platforms. However, several WSNs may examine multiple events using numerous sensors in a similar place. Multi-sink and multi-hop WSNs include the ability to offer network efficiency by securing effective data exchanges. The group of nodes in the multi-sink scenario is described through a distance vector. Though, the efficiency of multi-sink WSNs is considerably impacted by the routing of data packets and sink node placement in the cluster. In addition, many WSNs for diverse reasons existed in the similar geographical region. Hence, in this task, a secured energy-efficient routing technique is designed for a Wireless sensor network with Large-scale and multiple sink nodes. Here, the concept of an improved meta-heuristic algorithm termed Adaptive Squirrel Coyote Search Optimization (ASCSO) is implemented for selecting the accurate selection of cluster head. The fitness function regarding residual distance, security risk, energy, delay, trust, and Quality of Service (QoS) is used for rating the optimal solutions. The consumption of energy can be reduced by measuring the mean length along with the cluster head and multiple sink nodes. The latest two heuristic algorithms such as Coyote Optimization Algorithm (COA) and Squirrel Search Algorithm (SSA) are integrated for suggesting a new hybrid heuristic technique. Finally, the offered work is validated and evaluated by comparing it with several optimization algorithms regarding different evaluation metrics between the sensor and sink node.

ECAR: Energy efficient cluster based adaptive routing for large scale WSN

Wireless sensor networks consist of a large number of randomly distributed nodes in a given area. WSN nodes are battery-powered, so they lose all their energy after a certain period and this energy constraint affects the network lifetime. This study aims to maximize network lifetime while minimizing overall energy use. In this study, a novel Energy Efficient Cluster based Adaptive Routing (ECAR) approach has been proposed for large-scale WSNs. Initially, the Genetic Bee Colony algorithm (GBCA) is introduced, which provides an effective way for selecting cluster heads based on node degrees, node centralities, distances to neighbors, and residual energy. Consequently, the Quantum Inspired African Vulture Optimization algorithm (QIAVO) is utilized to find a routing path between the source and the destination over the cluster heads. To optimize the network performance, QIAVO considers multiple objectives, including residual energy, distance, and node degree. The proposed method is evaluated based on average packet delivery ratios, energy consumption, and average end-to-end delays. According to simulation results, the proposed protocol successfully balances the energy consumption of all sensor nodes and increases network lifespan.

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks.

The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

Dual-discriminator Conditional Generative Adversarial Network Optimized with Hybrid Momentum Search Algorithm and Giza Pyramids Construction Algorithm for Cluster-based Routing in WSN Assisted IoT

Wireless sensor network (WSN) efficiently sends and receives the data on the internet of things (IoT) environment. As a large-scale WSN's nodes are powered by batteries, it is essential to create an energy-efficient system to decrease energy consumption and increase the network's lifespan. The existing methods not present effectual cluster head (CH) selection and trust node computation. Therefore, dual-discriminator conditional generative adversarial network optimized with a hybrid Momentum search algorithm and Giza Pyramids Construction algorithm for Cluster Based Routing in WSN Assisted IoT is proposed in this manuscript, for securing data transmission by identifying the optimum CH in the network (DDcGAN-MSA-GPCA-CBR-WSN-IoT). Initially, the proposed method is acting routing process via cluster head. Therefore, Dual-Discriminator conditional Generative Adversarial Network (DDcGAN) is considered to select the CH depending on multi-objective fitness function. The multi-objective fitness function, such as energy, delay, throughput, distance among the nodes, cluster density, capacity, collision, traffic rate, and cluster density. Based on fitness function, CH is selected. After cluster head selection, a malicious node depends on three parameters: trust, delay, and distance. These three parameters are optimized by hyb MSA-GPCA for ideal trust path selection. The proposed DDcGAN-MSA-GPCA-WSN-IoT technique is activated in PYTHON and network simulator (NS2) tool. Its effectiveness is analyzed under performance metrics, such as number of alive nodes, dead nodes, delay, energy consumption, packet delivery ratio, a lifetime of sensor nodes, and total residual energy. The simulation outcomes display that the proposed method attains lower delay, higher packet delivery ratio and high network lifetime when comparing to the existing models.

Wormhole attack detection and recovery for secure range free localization in large-scale wireless sensor networks

Wormhole attack detection and recovery for secure range free localization in large-scale wireless sensor networks

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

A UAV-based compressive algorithm for minimizing the energy consumption of large-scale WSNs

In large-scale environmental monitoring, massive sensor nodes regularly collect data and transmit them over long distances, which burdens the network and leads to the increased energy consumption. To deal with this problem, we develop an Unmanned Aerial Vehicle (UAV)-based compressive algorithm. In this algorithm, the network clustering technologies and Compressive Sensing are exploited to decrease the amount of data throughout the network. Monte Carlo method is used to group sensor nodes into clusters. The UAV is dispatched as a mobile agent to collect data from the cluster heads. Compared with the benchmark algorithms, the simulations demonstrate that the algorithm proposed in this paper can decrease the network’s energy consumption and obtain better environmental monitoring results.