Algorithm For Sensor Networks Research Articles

Anti-delay Kalman filter fusion algorithm for inter-vehicle sensor network with finite-step convergence

Intelligent vehicle applications in autonomous driving and obstacle avoidance commonly require the precise relative state of vehicles. Accordingly, this study focuses on the coordinate fusion of vehicle state problem experienced by an inter-vehicle sensor network with time-varying transmission delays. Using the ingeniously designed low-complexity integration with a consensus strategy and buffer technology, an anti-delay distributed Kalman filter (DKF) with finite-step convergence is proposed. By introducing an appropriate weight matrix to assess local estimates, the optimal fusion state result is available as a linear minimum variance. In addition, to accommodate practical engineering in intelligent vehicles, the communication weight coefficient and directed topology with unidirectional transmission are considered. From a theoretical perspective, the proof of error covariances’ upper bounds with different communication topologies with delays are presented. Furthermore, the maximum allowable delays of inter-vehicle sensor network are derived backwards. Simulations verify that while considering the various non-ideal factors presented above, the proposed DFK algorithm produces more accurate and robust fusion estimation state results than those of the existing algorithms, making it more valuable in practical applications. Moreover, a mobile car trajectory tracking experiment is conducted, which further verifies the feasibility of the proposed algorithm.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

Damage Identification Method Based on Ultrasonic Guided Wave Sensor Network and Path Optimization Bayesian Fusion Algorithm

Damage Identification Method Based on Ultrasonic Guided Wave Sensor Network and Path Optimization Bayesian Fusion Algorithm

SECURE and energy-efficient routing protocol based on micro-segmentation and batch authentication

An effective technique in designing routing algorithms for Wireless Sensor Networks (WSNs) is named clustering nodes, which augments the network lifespan. More tasks are performed by the Cluster Heads (CHs) of the clustered WSNs, thereby consuming more energy. Hence, a secure energy-efficient Routing Protocol (RP) centered on micro-segmentation and batch authentication is proposed here. The proposed method begins by initializing the WSN nodes. After that, a random key is engendered for every node centered on their Node ID. The nodes undergo a Micro-segmentation process; in addition, they are securely grouped utilizing the Supremum Distance based K-Prototype (SD-KP) algorithm. Now, a fog layer with a number of fog nodes is initialized; also, every single micro-segmented group is assigned with an optimal fog node. Then, by using the Quasi Deterministic Sequence-Black Widow Optimization (QDS-BWO) algorithm, the optimal fog nodes are selected. By using the Root Squared- Diffie Hellman (R2-DH) technique, a key agreement is created for each group centered on their Node IDs. By using the Bitwise Cyclic Shift –BLAKE 512 (BCS-BLAKE-512) algorithm, the created keys are converted into hashcode. Now, for the purpose of performing batch authentication, the hashcode of each group is sent to the trusted authority. Subsequent to verification, data sensing occurs. Lastly, by using the Elliptic Curve Cryptography (ECC) algorithm, the data is securely encrypted and it is transmitted via the routes selected utilizing the Learned Gradient Weight Initialization-based Geography and Energy Aware Routing (LGWI-GEAR) algorithm. The proposed framework's efficacy is proved by the experimental outcomes.

Multi-objective optimized multi-path and multi-hop routing based on hybrid optimization algorithm in wireless sensor networks

Multi-objective optimized multi-path and multi-hop routing based on hybrid optimization algorithm in wireless sensor networks

Mathematical Modeling and Analysis of Energy Aware Probabilistic Distribution Based Cluster Head Selection Algorithm for Wireless Sensor Networks

From a mathematical perspective, Wireless Sensor Networks (WSNs) are increasingly recognized for their utility across multiple sectors such as environmental oversight, healthcare monitoring, and process automation within industries. A paramount obstacle within WSNs is the management of energy efficiency, given that the sensor nodes are typically powered by batteries with finite energy reserves. The criticality of developing efficient routing protocols cannot be overstated, as they are instrumental in extending the operational lifespan of the network and guaranteeing dependable data communication. This study is centered around the mathematical modelling and performance evaluation of an innovative routing methodology that integrates machine learning algorithms to augment energy efficiency within WSNs. The novel routing strategy dynamically adjusts its operations in response to the immediate environmental and network states, aiming to minimize energy expenditure and elevate the network's overall efficiency. Through comprehensive numerical simulations, this research scrutinizes the efficacy of the machine learning-enhanced routing protocol against conventional routing methodologies, accentuating its advantages in energy savings and reliability in data transmission. The simulation framework encompasses a variety of network configurations, traffic distributions, and environmental contexts, employing metrics such as energy utilization, network longevity, packet delivery ratio, and latency to offer an in-depth examination of the machine learning-based routing approach's performance. Findings from the simulations affirm the algorithm's enhanced energy efficiency, which contributes to prolonged operation of sensor nodes and steadfast data communication across dynamically changing network landscapes. The implications of this study highlight the transformative potential of machine learning in redefining routing protocol design and optimization within energy-restricted WSNs. By elevating both energy efficiency and network functionality, this research marks a significant stride towards realizing sustainable and dependable WSNs, paving the way for their broader application in essential services.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Research on gaussian mixture model and its distributed data mining algorithm in wireless sensor networks

Optimization of the routing represents an important challenge when considering the rapid development of Wireless Sensor Networks (WSN), which involve efficient energy methods. Applying the effectiveness of a Deep Neural Network (DNN) and a Gaussian Mixture Model (GMM), the present article proposes an innovative method for attaining Energy-Efficient Routing (EER) in WSN. When it comes to dealing with dynamic network issues, conventional routing protocols generally conflict, resulting in unsustainable Energy consumption (EC). By applying algorithms based on data mining to adapt routing selections in an effective procedure, the GMM + DNN methodology that has been developed is able to successfully address this problem. The GMM is a fundamental Feature Extraction (FE) method for accurately representing the features of statistical analysis associated with network parameters like signal frequency, the amount of traffic, and channel states. By learning from previous data collection, the DNN, which relies on these FE, provides improved routing selections, resulting in more efficient use of energy. Since routing paths are constantly optimized to ensure dynamic adaptation, where less energy is used, networks last longer and perform more efficiently. Network simulations highlight the GMM + DNN method’s effectiveness and depict how it outperforms conventional routing methods while preserving network connectivity and data throughput. The GMM + DNN’s adaptability to multiple network topologies and traffic patterns and its durability make it an efficient EER technique in the diverse WSN context. The GMM + DNN achieves an EC of 0.561 J, outperforming the existing state-of-the-art techniques.

Secure communication using an adaptable multilevel RGB image encryption algorithm for wireless sensor networks

Internet use and other communications technologies like wireless networks, cloud computing, and the Internet of Things have skyrocketed in recent decades. This research seeks to provide a structured color picture encryption technique for wireless sensor networks (WSNs). The approach uses a 1-D exponential modified chaotic map (1-D EMC), 2-D discrete wavelet transform, and a 1-D modified version of the logistic map (1-D MVL) to reduce data and improve transmission, especially for images. The XOR operator and random permutation of image matrix rows and columns strengthen the technique against numerous attacks. Statistical analyses are performed on the proposed encryption technique, and the values of these statistical tests presented in the tables are satisfactory. A fair comparison with other competitive existing algorithms shows the effectiveness of the proposed encryption algorithm and can be opted for practical use.

An energy efficient routing algorithm in wireless underground sensor networks

An energy efficient routing algorithm in wireless underground sensor networks

An Improved Multi-hop LEACH Protocol Based on Chaotic Genetic Algorithm for Wireless Sensor Networks

An Improved Multi-hop LEACH Protocol Based on Chaotic Genetic Algorithm for Wireless Sensor Networks

Retracted: A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

Retracted: A Weighted Cluster Head Selection Algorithm for Energy Efficient Wireless Sensor Networks

Retracted: Optimization Analysis of Power Coal-Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Retracted: Optimization Analysis of Power Coal-Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Cross-Layer Energy Efficient (CLEE) Routing Algorithm for Mobile Ad-Hoc Networks

Ad-hoc routing algorithms in Wireless Sensor Networks (WSN) rely on nodes’ position awareness by regularly updating routing data of neighbouring nodes. Meanwhile, the transmission energy usage is not optimised as a result of this repeated updates and routing table deployment. Therefore, it is very critical to consider techniques of optimising or conserving energy in the design of wireless sensor networks (WSNs) in order to prolong the lifetime of the individual nodes. One of these techniques is the Cross-layer design which, is considered as an efficient method for addressing this challenge with WSNs. In this paper, we propose a Cross-Layer Energy Efficient (CLEE) routing algorithm to establish an optimal route from the source node to the destination node by selecting candidate nodes from neighbouring nodes based on the distance between these nodes and the rate of energy consumption by a possible candidate node. Then to select a designated node from the candidate nodes, the algorithm further computes the Signal Strength Quality (SQS) and the Link Lifetime (LL) as well as the Throughput (TH) rate of selected nodes. The proposed algorithm was simulated with a network size of 600 X 600 on Network Simulator 3 (NS3) in order to analyse its performance. An evaluation of the performance of the proposed CLEE protocol with existing similar protocols such as Ad-hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) reveals that CLEE outperformes AODV and DSR by conserving about 44% of available energy.

Energy-efficient image compression algorithm for wireless sensor networks in structural health monitoring applications

In Wireless Sensor Networks (WSN), transmitting an uncompressed image consumes more energy than a compressed image, and it is, therefore, the prime requirement to establish energy-aware compression methods to extend the life of the sensor node, and ultimately the network as a whole. This work suggests an image compression algorithm with a low degree of complexity for WSNs in structural health monitoring applications. This algorithm represents a pruning approach to a Discrete Cosine Transform approximation transform in which the transformation matrix is modified to reduce the series of steps and the compression ratio achieved is better compared to the actual image which makes it easy for the data storage. Because of the reduction in the number of data bits, it enhances the lifetime of the network by reducing the number of node failures caused by resource scarcity. The implementation is tested by capturing real-time images of concrete walls in buildings using Raspberry Pi3B + WSN gateway fitted with camera modules. The scheme is also investigated in terms of a variety of parameters like Peak Signal to Noise Ratio, Mean Square Error, Structural SIMilarity Index and Compression Ratio. This technique achieves the best arbitration of energy consumption and image quality.

CBU-SMAC: an energy-efficient CLUSTER-BASED UNIFIED SMAC algorithm for wireless sensor networks

CBU-SMAC: an energy-efficient CLUSTER-BASED UNIFIED SMAC algorithm for wireless sensor networks

A self-adaptive attraction and repulsion-based naked mole-rat algorithm for energy-efficient mobile wireless sensor networks

Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor (λ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\lambda )$$\\end{document} is made self-adaptive using simulated annealing (sa) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs.

GWO-SMSLO: Grey wolf optimization based clustering with secured modified Sea Lion optimization routing algorithm in wireless sensor networks

GWO-SMSLO: Grey wolf optimization based clustering with secured modified Sea Lion optimization routing algorithm in wireless sensor networks

A whale swarm based energy efficient routing algorithm for wireless sensor networks

A whale swarm based energy efficient routing algorithm for wireless sensor networks

An Improved MPSO Coverage Enhancement Algorithm for Directional Video Sensor Networks

An Improved MPSO Coverage Enhancement Algorithm for Directional Video Sensor Networks