Routing Algorithm For Wireless Sensor Networks Research Articles

Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused clustering Protocol for Wireless Sensor Networks

Nowadays, Wireless Sensor Networks (WSNs) plays several important application fields, specially monitoring events without any human interference. The sensor nodes (SNs) have lesser life time in wireless sensor network owing to its continual sensing with battery drains very rapidly. As a result of the heavy traffic, sensors nearby quickly expire and energy hole issues start to appear. To overcome these issues, an Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused Clustering Protocol for Wireless Sensor Networks (EE-SDRN-BHHO-CP-WSN) is proposed in this manuscript for sustaining the energy efficiency in the wireless sensor network. One of the main issues in wireless sensor network assisted applications is making use of the available energy. An optimum path selection is a key process of saving energy from sensor nodes to the sink. Spiking Deep Residual Network (SDRN) and Binary Horse Herd Optimization (BHHO) optimize energy efficiency in WSN. The cluster head (CH) is designated by effectual fitness function generated by multi-objectives. It benefits in low power consuming and decreases a count of idle sensor nodes. After CH selection, Binary horse herd optimization algorithm is considered for optimal route selection and data transmission by sink node. The EE-SDRN-BHHO-CP-WSN method is implemented in MATLAB and the efficiency of the proposed method is analyzed with different metrics, like network lifetime, number of alive nodes, number of dead nodes, throughput, energy consumption, packet delivery ratio (PDR). The proposed EE-SDRN-BHHO-CP-WSN method attains 32.35 %, 42.34 %, 49.27 % higher network lifetime, 26.34 %, 31.94 % and 39.35 % higher number of alive nodes and 46.26 %, 38.64 % and 27.83 % lower number of dead nodes compared with other existing models, such as Multi-objective CH based Energy-aware Optimized Routing Algorithm in WSN (MCH-EOR-WSN), Energy efficient CH selection utilizing improved Sparrow Search Algorithm in WSN (EECH-ISSA-WSN), and New Energy Aware CH Selection Algorithm for WSN (NEA-CHSA-WSN respectively).

Regional Link Routing Algorithm for Wireless Sensor Networks based on Improved Grey Wolf Optimization Algorithm

In order to improve the survival time, connectivity and the active number of nodes in the wireless sensor network, this paper proposes a regional chain wireless sensor network routing algorithm (RWA) based on improved grey Wolf optimization algorithm. The algorithm uses the method of dividing regions into chains, and in each region, chains are formed to select cluster heads before communication. The simulation results show that RWA has an improvement in avoiding too long chain, balancing energy consumption and prolonging the network life cycle.

Energy Saving Routing Algorithm for Wireless Sensor Networks Based on Minimum Spanning Hyper Tree

With the rapid development of wireless sensor networks (WSNs), designing energy-efficient routing protocols has become essential to prolong network lifetime. This paper proposes a minimum spanning tree-based energy-saving routing algorithm for WSNs. First, sensor nodes are clustered using the LEACH protocol and minimum spanning trees are constructed within clusters and between cluster heads. The spanning tree edge weights are optimized considering transmission energy, residual energy, and energy consumption rate. This avoids channel competition and improves transmission efficiency. An energy-saving routing model is then built whereby deep reinforcement learning (DRL) agents calculate paths optimizing the energy utilization rate. The DRL reward function integrates network performance metrics like energy consumption, delay, and packet loss. Experiments show the proposed approach leads to 10-15W lower average switch energy consumption compared to existing methods. The throughput is high since overloaded shortest paths are avoided. The average path length is close to shortest path algorithms while maintaining energy efficiency. In summary, the proposed minimum spanning tree-based routing algorithm successfully achieves energy-saving goals for WSNs while guaranteeing network performance. It provides an efficient and adaptive routing solution for resource-constrained WSNs.

Energy-Efficient Data Aggregation and Cluster-Based Routing in Wireless Sensor Networks Using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm

One of the major significant problems in the existing techniques in Wireless Sensor Networks (WSNs) is Energy Efficiency (EE) because sensor nodes are battery-powered devices. The energy-efficient data transmission and routing to the sink are critical challenges because WSNs have inherent resource limitations. On the other hand, the clustering process is a crucial strategy that can rapidly increase network lifetime. As a result, WSNs require an energy-efficient routing strategy with optimum route election. These issues are overcome by using Tasmanian Fully Recurrent Deep Learning Network with Pelican Variable Marine Predators Algorithm for Data Aggregation and Cluster-Based Routing in WSN (TFR-DLN-PMPOA-WSN) which is proposed to expand the network lifetime. Initially, Tasmanian Fully Recurrent Deep Learning Network (TFR-DLN) is proposed to elect the Optimal Cluster Head (OCH). After OCH selection, the three parameters, trust, connectivity, and QoS, are optimized for secure routing with the help of the Pelican Variable Marine Predators Optimization Algorithm (PMPOA). Finally, the proposed method finds the minimum distance among the nodes and selects the best routing to increase energy efficiency. The proposed approach will be activated in MATLAB. The efficacy of the TFR-DLN- PMPOA-WSN approach is assessed in terms of several performances. It achieves higher throughput, higher packet delivery ratio, higher detection rate, lower delay, lower energy utilization, and higher network lifespan than the existing methods.

A review of routing algorithms for underwater wireless sensor networks

With the rise of the upsurge of exploiting marine resources and the rapid development of the research of terrestrial wireless sensor networks, the research of underwater wireless sensor networks (UWSNs,) has become a new research hotspot. In UWSNs, routing protocol plays a very important role as one of the important components. This paper introduces several classical routing algorithms from the following categories: location, sector, reinforcement learning, and intelligent optimization algorithms. Finally, the paper also summarizes and prospects the research direction of underwater wireless sensor network routing algorithms.

MOCRAW: A Meta-heuristic Optimized Cluster head selection based Routing Algorithm for WSNs

In Wireless Sensor Networks (WSNs), sensors are deployed in a specific region to sense the environment’s physical parameters. After sensing, data is processed and sent to the base station through a given route. Sensing and transmitting nodes consume a lot of energy; hence nodes die quickly; therefore, hot spot problems occur. Henceforth, data transmission is done by a single route; thus, WSNs experience network overhead problems. Nowadays, the enhancement of the energy of WSNs remains a challenging issue. Alternatively, efficient processes such as routing or clustering may be improved. Dynamic cluster head selection can be considered an important decision approach for optimal path selection and saving energy. This paper proposes a Meta-heuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW) to minimize node’s energy consumption and fast data transmission. MOCRAW removes isolated nodes or hot-spot problems and provides loop-free routing with the help of the Dragonfly Algorithm (DA), wherein the decision is based on Local Search Optimization (LSO) and Global Search Optimization (GSO). This protocol exploits two sub-processes: the optimal Cluster Head Selection Algorithm (CHSA) and Route Search Algorithm (RSA). CHSA uses Energy Level Matrix (ELM). ELM is based on node density, residual energy, the distance between Cluster Head (CH) and Base Station (BS), and inter-cluster formation. The inter-cluster discovers the optimum path between source to destination in RSA by levy distribution. MOCRAW performance is compared with other clustering and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its peers and competitors in terms of energy efficiency.

An Adaptive QoS and Trust-Based Lightweight Secure Routing Algorithm for WSNs

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

Improved trustworthy, speed, and energy-efficient GPSR routing algorithm in large-scale WSN

Geographic routing networks broadcast their readiness to modify the routing protocol using the traditional methodology in wireless sensor network (WSN). These are effectively dealt with to preserve electricity. Researchers propose the Modified - Greedy Perimeter Stateless Routing (GPSR) system for efficient sensor network connection to determine the optimal path based on energy usage and avoids malicious activity. Depending on the limited memory nodes and battery power, rapid dynamic topology, and unpredictable equipment, the deployment of GPSR in sensor networks faces several challenges. The cost of communication across the network is close to ideal because of our Modified-GPSR method. The simulation results show that minimizing power and latency considerably increases the durability of the sensor node system. The study also focuses on estimating implementation costs and learning about design, implementation, and statistical practice.

Voice Detection and Deep Learning Algorithms Application in Remote English Translation Classroom Monitoring

With the continuous development of cellular networks, the traffic from voice services increases gradually. The wireless sensor network (WSN) is a distributed network consisting of a large number of peripheral nodes distributed in the surveillance area. The nodes in the network complete it in a self-organizing form, and the sink node collects the data from each sensor node. When sending data, the nodes near the receiver will quickly run out of energy and cannot perform further transmission tasks. The resulting “power supply emptiness” problem has a great impact on network performance. Therefore, the power consumption of the network must be considered when designing the WSN routing algorithm. In order to effectively improve students’ academic performance and monitor students’ teaching conditions, the classroom remote monitoring system places two cameras in the university’s English translation classroom and uses technology to merge the information to execute the entire process. By recording the course, we can save the teacher’s classroom content and the student’s classroom performance and upload the recorded video in real time. In addition, the classroom remote monitoring system is a multiclient system, divided into teacher and student terminals. The user can log in, watch the video, and perform other necessary operations.

Wireless sensor networks routing algorithm based on block clustering and springboard nodes

Wireless sensor networks routing algorithm based on block clustering and springboard nodes

Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs

Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the energy levels of the node battery by utilizing various energy harvesting techniques in order to extend the network lifetime. In this paper, a new energy-aware clustering algorithm (EHEARA) has been proposed. The proposed algorithm is based on a dynamic clustering function and adopts a solar energy harvesting scheme in order to improve network lifetime. Furthermore, the active-sleep mechanism was used to distribute node activity and balance communication among nodes within clusters and cluster heads with the base station. The proposed algorithm is simulated using matrix laboratory (MATLAB), and the results show that it outperforms the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC), and stable election protocol (SEP) algorithms in terms of network lifetime, energy consumption, and network throughput.

LBR‐GWO: Layered based routing approach using grey wolf optimization algorithm in wireless sensor networks

SummaryThe sensors deployed inside a wireless sensor network (WSN) have limited energy sources, significantly impacting the network throughput. This article's research objective is to increase network lifetime by developing an energy‐efficient layered‐based routing algorithm for WSNs using the grey wolf optimization (LBR‐GWO) algorithm. In this, the grey wolf's leadership hierarchy has followed, which improves the network's energy capability. The entire region of the deployed nodes divides into four layers. In these nodes, layer one is chosen as cluster heads. If more than two nodes are present in layer one, then the cluster head is selected based on the game theory model; otherwise, the decision is made based on the node's residual energy. While the existing algorithm has several complex control parameter points, the current algorithm has fewer complex parameters. Therefore, in comparison to other algorithms, this algorithm is easy to apply in cluster‐based sensor networks. Simulation findings prove the LBR‐GWO algorithm supremacy for balancing energy consumption across the nodes and improving the network's lifetime compared to the LEACH, HEED, and PSO protocols.

A Survey on Enhanced Energy Efficient Routing Protocol of WSN for Internet of Things

Wireless Sensor Networks (WSNs) are a resource-constrained network class recognized as a major energy consumer. Wireless sensor technologies are used in many commercialized industrial automation processes and other real-world applications. The WSN protocol is well-suited to harsh situations where deployment is difficult or impossible, such as the battlefield, a toxic chemical plant, the cloud, fog computing, and the Internet of Things, but not in a high-temperature network infrastructure environment. WSNs have introduced various Energy-Efficient Routing Protocols based on network (NW) organization and protocols in recent years. Various WSN routing options for energy efficiency are explored in this work. The WSN Energy Efficient Routing Protocol is compared to other routing systems. We also compare and investigate better WSN routing algorithms for cloud computing, fog computing, and the Internet of Things.

An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs.

A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm

Energy-efficiency and reliability are vital metrics of the robustness of Wireless Sensor Networks (WSNs). Various data reduction techniques are used to improve them, among them compressive sensing (CS) is a data reduction technique used to recover extensive data from fewer samples in case of sparse representation of sensor-readings. Unfortunately, energy-efficiency and accuracy are contradictory metrics, as increased accuracy requires a large number of measurements, and data transmissions. Therefore, in this paper, a CS-based algorithm is proposed for efficient data transfer through WSNs, which uses multiple objective genetic algorithms (MOGA) to optimize the number of measurements, transmission range, and the sensing matrix. The algorithm aims at striking the right balance between energy-efficiency and accuracy. It constructs a path in a multi hop manner based on the optimized values. Numerical simulations and experiments show that Pareto-front, which is the output of MOGA, helps the user to select the right combination of the number of measurements and the transmission range fitting the application at hand, and to strike a good balance between energy efficiency and accuracy. The results also demonstrate the existence of measurement matrices which lower mutual coherency improve the accuracy of CS.

A review of soft computing based cluster-heads selection algorithms in wireless sensor network

In the recent past wireless sensor network (WSN) has gained the immense popularity in various fields. The selection of appropriate and optimal cluster-heads (CHs), the energy consumption, bandwidth, selection of optimal route are some of the key problem areas in WSN. In order to improve the network life span of WSN and to provide the solution to the above mentioned issues various soft computing algorithms have been successfully applied and literature review revels that these soft computing approaches contribute best towards in finding out the optimal and appropriate CHs in various routing algorithms in WSN. Fuzzy logic (FL), Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Firefly algorithms, and Neural network are various soft computing approaches which have been proven best and appropriate for the selection of CHs in WSNs. In this article various soft computing based CH selection approaches for WSN have been discussed. The algorithms have also been compared on the basis of various parameters which will help the researcher to find out the best and appropriate soft computing based CH selction algorithm as per their needs and application for further research in this dynamically growing field. After review of various soft computing algorithms it is found that the application of these soft computing based algorithms have made these routing algorithms more adaptive, highly energy efficient with improvement in stability period and data transmission.

Optimized Traffic And Enhanced Lifetime Routing Algorithm In WSN


 
 
 
 Abstract:
 Wireless Sensor Networks (WSNs) have gained significant traction as a viable solution for a diverse range of applications that necessitate the monitoring and regulation of distant environments. Wireless sensor networks (WSNs) are frequently constrained by the energy resources of the individual sensor nodes, resulting in network failures and reduced operational lifetimes. In response to this challenge, we present a proposed optimised traffic and lifetime routing algorithm for Wireless Sensor Networks (WSNs). The algorithm has been specifically designed to minimise network traffic and ensure equitable energy consumption across all sensor nodes. The algorithm utilises a fusion of clustering and network coding methodologies to leverage spatial diversity and redundancy within the network. The performance of the proposed algorithm is assessed through simulation experiments and is juxtaposed with various contemporary routing algorithms. The findings indicate that the algorithm we employed exhibits superior performance compared to other algorithms with respect to network longevity, network flow, and energy usage. The algorithm under consideration exhibits potential for utilisation in diverse Wireless Sensor Network (WSN) applications that necessitate dependable and energy-efficient data transmission.
 
 
 

Energy Efficient Routing Algorithm Based on Harmony Search

Under the premise of limited node energy, an efficient and energy-saving wireless sensor network routing algorithm was proposed to prolong the lifetime of overall network. By analysing the operation pattern of wireless sensor network, the energy model and transmission model was introduced, and BTPHS algorithm based on the above two was proposed. BTPHS algorithm divided the network lifetime into 3 periods. According to the characteristics of the energy distribution and energy consumption in different periods of the network, two new node selection strategies were proposed and a new objective function as a reference standard was provided. The adjustment process of the HS algorithm is abandoned, which makes less parameters in proposed routing algorithm. The experiment results indicate that BTPHS algorithm can prolong the lifetime of overall WSN network effectively.

Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks

Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

Quantum-Inspired Ant-Based Energy Balanced Routing in Wireless Sensor Networks

Background: Limited energy capacity of battery operated Wireless Sensor Networks (WSNs) is the prime impediment in the ubiquity of WSNs as the network lifetime depends on the available energy at the nodes. Prolonging the network lifetime is the principal issue in WSNs and the challenge lies in devising a strategy for judicious use of available energy resources. Routing has been one of the most commonly used strategies for minimizing and balancing the energy consumption of nodes in a WSN. Methods: Routing in large networks has been proved to be NP-Hard and therefore meta heuristic techniques have been applied for handling this problem. Quantum-inspired algorithms are relatively new meta heuristic techniques which have been shown performing better than their traditional counter- parts. Therefore, Quantum inspired ant Based Energy balanced Routing (QBER) algorithm has been proposed in this paper for addressing the problem of energy balanced routing in WSNs. Results: Simulation results confirm that the proposed QBER algorithm performs comparatively better than other quantum inspired routing algorithms for WSNs. Conclusion: