Routing In Wireless Sensor Networks Research Articles

MeFi: Mean Field Reinforcement Learning for Cooperative Routing in Wireless Sensor Network

MeFi: Mean Field Reinforcement Learning for Cooperative Routing in Wireless Sensor Network

A Distributed Leach-AHP Routing for Wireless Sensor Networks

A Distributed Leach-AHP Routing for Wireless Sensor Networks

Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks.

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim's algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network.

A review of different techniques used for routing in wireless sensor networks

A review of different techniques used for routing in wireless sensor networks

The effectiveness of jaya optimization for energy aware cluster based routing in wireless sensor networks

The Internet of Things (IoT) has significantly impacted human life, enhancing quality of life and transforming various commercial sectors. The sensor nodes in the IoT are interconnected to facilitate the passage of data to the sink node over the network. Due to the constraints of battery power, energy in the nodes is preserved through the utilization of clustering techniques. Choosing a Cluster Head (CH) is crucial for prolonging the network's lifespan and increasing its throughput during the clustering process. Numerous optimization techniques have been developed to select the best Cluster Head (CH) to enhance energy efficiency in network nodes. Therefore, using incorrect CH selection methods leads to longer convergence times and faster depletion of sensor batteries. This research proposes a method that incorporates a CH selection strategy using the Jaya optimization method. The proposed methodology is evaluated against existing algorithms in terms of network longevity and energy efficiency. The simulation results indicate that the Jaya optimization algorithm-based CH selection scheme (Jaya-EEC) is much more effective in terms of network longevity compared to LEACH, LEACH-E, and PSO-C. Specifically, Jaya-EEC outperforms LEACH by 72%, LEACH-E by 64%, and PSO-C by 60%.

Multi-Tree-Based Avoidance Multicast Routing for Wireless Sensor Networks

Multi-Tree-Based Avoidance Multicast Routing for Wireless Sensor Networks

Deep learning-based decision-making system for cooperative routing in wireless multimedia sensor network

Deep learning-based decision-making system for cooperative routing in wireless multimedia sensor network

Survey on Energy-Efficient and Energy-Balanced WSN Routing Protocols

Wireless sensor network (WSN) is a network comprised of tiny sensor nodes. These sensors are characterized by limited memory, low processing capacity, limited power, and transmission bandwidth. The limited energy constraints inherent in WSNs have triggered an extensive research effort to develop Energy-efficient (EE) and Energy-balanced (EB) routing protocols to prolong the lifetime of the network. This study aims to investigate the overall research productivity and trends of the state-of-the-art research in energy-efficient and energy-balanced routing protocol designed for WSNs. The classification of energy-efficient and energy-balanced routing protocols is also a vital aim of this study. As a result, we conducted a selected relevant study with strict adherence to the pre-defined systematic mapping methodology to ensure unbiased inclusion of the relevant studies. This research provides great insight into the focus of the research community, tools being used, and taxonomies of research conducted in EE and EB WSN protocols for the past decade. In the end, this article also provides a comprehensive discussion of the challenges and future directions.

A Comprehensive Review of Geographic Routing Protocols in Wireless Sensor Network

To analyses the impact of high mobility, dynamic topologies, scalability and routing due to the more dynamic changes in network. To enhance mobile Ad-hoc network (MANET) self-organization capabilities by geographical routing algorithm during mobility. In this paper, a survey has been carried out on geographic routing protocols, such as hybrid routing, Greedy Routing, face-2 Algorithm, Perimeter Routing, quasi random deployment (QRD) techniques and time of arrival (TOA). An optimized multipath routing in wireless sensor network (WSN), energy utilization, detection of anonymous routing, node mobility prediction, data packet distribution strategies in WSN is analyzed. Geographic routing offers previous data packet information such as physical locations, packet elimination dependencies, storage capacity of topology, Associate costs and also identifies the dynamic behavior of nodes with respect to packets frequencies.

Improved Reinforcement Learning for Reliable Routing in Medical Wireless Sensor Networks

Improved Reinforcement Learning for Reliable Routing in Medical Wireless Sensor Networks

Transmission-Efficient Grid-Based Synchronized Model for Routing in Wireless Sensor Networks Using Bayesian Compressive Sensing

Transmission-Efficient Grid-Based Synchronized Model for Routing in Wireless Sensor Networks Using Bayesian Compressive Sensing

Genetic Algorithm-Based Secure Routing Protocol for Wireless Sensor Networks

In a wireless sensor network (WSN), security threats are prevalent as a result of the network's distributed nature and resource limitations. This paper proposes a secure routing protocol for WSNs based on Genetic Algorithms (GA). The protocol utilizes GA to optimize secure route selection while considering network lifetime and energy efficiency. To showcase the efficiency of the suggested protocol, we present a case study, provide a detailed algorithmic representation, and evaluate its performance through extensive simulations. The results showcase the protocol's ability to enhance security and improve network performance, including increased packet delivery ratio, reduced energy consumption, and robustness against attacks. The proposed GA-based secure routing protocol offers a promising approach to address the unique security challenges of WSNs, contributing to the development of secure and efficient communication frameworks for various applications. Additionally, we highlight the significance of incorporating genetic algorithms into secure routing protocols as a means to optimize route selection in WSNs. By leveraging the evolutionary nature of genetic algorithms, our proposed protocol adapts to dynamic network conditions and effectively balances security requirements with energy efficiency and network lifetime considerations. The case study, algorithmic representation, and comprehensive simulation results validate the protocol's ability to enhance security while maintaining robust network performance. This study contributes to the advancement of secure routing in WSNs, offering a viable solution to mitigate security threats and ensure reliable communication in resource-constrained environments.

Query Based Location Aware Energy Efficient Secure Multicast Routing for Wireless Sensor Networks Using Fuzzy Logic

Query Based Location Aware Energy Efficient Secure Multicast Routing for Wireless Sensor Networks Using Fuzzy Logic

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.

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

Node utilization index-based data routing and aggregation protocol for energy-efficient wireless sensor networks

Combined Osprey-Chimp Optimization for Cluster Based Routing in Wireless Sensor Networks: Improved DeepMaxout for Node Energy Prediction

The significant advances in Wireless Sensor Networks (WSNs) facilitate many latest applications, such as intelligent battlefield, home automation, traffic control, and more. WSNs comprise small autonomously organized sensor nodes that are powered by batteries. The processes of collecting information and data storage, processing, and transmission deplete the energy of these small devices. Energy efficiency is still a major issue to address in WSN routing. Clustering is the best method that has been developed to reduce node energy consumption. However, current clustering methods are unable to effectively distribute the energy requirements of the nodes without considering energy characteristics, number of nodes, and flexibility. This study proposed a new cluster-based routing model for WSNs and emphasized the need for an improved clustering process with new optimization techniques. In particular, the improved DeepMaxout model was adopted to predict the energy of the nodes. Cluster Head (CH) selection is performed considering the nodes' energy as a prime factor. After choosing the CH, the CIOO algorithm incorporates new link quality and trust evaluations while determining the routing process. Finally, a comparison of energy utilization factors was performed between the suggested and traditional approaches.

Optimal Cluster Head Selection in Wireless Sensor Network via Multi-constraint Basis using Hybrid Optimization Algorithm: NMJSOA

Due to its general use in various practical applications, number of innovations in Wireless Sensor Networks (WSN) is receiving a lot of consideration from researchers. It shows significant technological development with excessive capacity since it gives useful information to users in a particular field through real-time monitoring. Due to its characteristics, such as infrastructure-less adoption and resource limitations, wireless sensor networks bring several problems that could impair the system's operation. Cluster based routing in WSN is the major concern in this field that could conflicts with the effectiveness of energy, suitable Cluster Head (CH) selection, protected data transport as well as network lifetime augmentation, demand major consideration, etc. However, it remains challenging to make optimal clustering process of WSN that makes the routing process more effective. Thereby, this work proposes a new Namib Merged Jelly Search Optimization Algorithm (NMJSOA) approach for the optimal CH selection in WSN by the different constraints. Initially, the sensor nodes are grouped together to form the clusters by the k-means clustering techniques. Subsequently, allocating the CH for each cluster by computing the weight function for each cluster depends on the conditions such as energy, delay, distance and security. According to the NMJSOA method, the Namib Beetle Optimization (NBO) searching position and the Jellyfish swam old position are added together to get the best optimum position. Finally, the performance of the suggested model is investigated over the conventional methods in terms of different performance measures.

Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

SummaryA fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods.

Hybrid Multi-Objective-Derived Horse Herd and Dragonfly Algorithm-Based Energy-Efficient Secured Routing in WSN

Energy efficiency and security have become prominent aspects of Wireless Sensor Networks (WSNs) for transmitting data. The major challenge is to increase the “Quality of Service” (QoS) since it is restricted by fewer constrained resources. Although the existing routing protocols acquire efficient transmission, they are still subsisting with the challenges of improving security and energy conservation. It will be helpful in practical implications like the military, weather forecasting and healthcare industry. As the nodes are operated by the energy preservation of the battery, developing an energy-efficient protocol is challenging. Since WSN possesses massive numbers of sensor nodes, it also has to avoid long-term communication, and the clustering mechanism is a prerequisite. Considering the diverse objectives like delay, energy and distance, there are still remarkable challenges to develop the routing protocol. Moreover, security enhancement becomes another challenging issue over the network for data transmission through the sensor nodes. To overcome these issues, a novel energy-efficient routing model is designed in this paper by using a hybrid algorithm and the multi-objective derivatives. Initially, the clustering approach is employed to reduce the complexity and improve the security in an energy-efficient manner. Therefore, the Cluster Head (CH) is selected significantly using the novel Hybrid Horse Herd-Dragonfly Optimisation (HHHDO). The hybrid algorithm is developed by integrating Horse Herd Optimisation (HHO) and Dragonfly Algorithm (DA). Here, the optimal CH selection is achieved by deriving the multi-objective function regarding “distance, delay, energy, load and trust of nodes”. Trust is measured with the Neural Network (NN)-based HHHDO (NN-HHHDO) model. Depending on the trust evaluation and energy efficiency, the proposed model obtains better communication between the nodes. In the end, the experimentation of the recommended model is made using various measures and it’s ensured that the suggested technique enhances trust among the nodes and energy conservation in comparison to the existing algorithms.

Energy‐efficient cluster‐based artificial intelligence routing for wireless sensor networks

SummaryArtificial intelligence (AI)‐based wireless sensor network technology is the future of advancement for real‐time applications. With AI wireless sensor network technology, it is possible to collect data from any environment, analyze in real time, and use it to optimize processes and operations. AI wireless sensor network technology provides an unprecedented level of accuracy as well as the ability to detect even the slightest changes in a given environment. The AI‐based approach uses clustering‐based techniques with self‐organizing map (SOM) for energy conservation in resource‐constrained networks. By clustering the network, it becomes more energy efficient, as data can be shared among members of a cluster without needing to be transmitted across multiple nodes. The proposed AI cluster‐based routing approach outperforms in terms of energy consumption and computational challenges of the network. The results obtained demonstrate the proposed approach to achieve lower energy consumption than the existing algorithms while providing the same level of performance in terms of throughput and latency, as well as a comparison with traditional justification techniques.