Energy Level Of Nodes Research Articles

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN.

Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation of WSN protocols that reduce energy depletion in the network is still an open scientific problem. In this paper, we propose a new clustering protocol that combines DEC (deterministic energy-efficient clustering) protocol with K-means clustering, called DEC-KM (deterministic energy-efficient clustering protocol with K-means). DEC is a very energy-efficient clustering protocol that outperforms its predecessors, such as LEACH and SEP. K-means ensures more effective clustering and shorter data transmission distances within the network. The shorter distances improve the network's lifetime and stability and reduce power consumption. Additional heuristic rules in DEC-KM ensure improved cluster head selection, taking into account node energy level and position and minimising the risk of premature cluster head exhaustion. The simulation results for the DEC-KM protocol using MATLAB show that cluster heads have shorter distances to nodes in cluster areas than for the original DEC protocol. The proposed protocol ensures reduced energy consumption, outperforms the standard DEC, and extends the stability period and lifetime of the network.

Optimizing energy efficiency and routing in wireless sensor networks through genetic algorithm-based cluster head selection in a grid-based topology

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design.

Network Stability Based Multicriteria Weighted MPRs Selection Algorithm for Mobile Ad Hoc Networks

Manets are self-governing networks using several mobile nodes, which communicate together through wireless media, without the necessity of prior infrastructure. Therefore, due to highly dynamic and unconfident environments, Manets are weak to guarantee link lifetime, security, reliability and stability. Since, mobility, energy and security are the main factors for improved performance in the network. Paths and relay devices with enhanced stability and security are considered as critical subjects. The study suggests an improved process based on a Multicriteria purpose consistent with numerous systems of measurement. These systems of measurement are energy level, trust measure and mobility of mobile nodes. The concept is to select improved Multipoint relays (MPRs) able to transmit data efficiently, and increase lifetime and reachability. The proposed improvement supports the routing and the selection of paths. The selection of the greatest effective relay nodes can enhance the classification performance. The scheme is based on the OLSR protocol and performance results are defined using the network simulator (NS3) with different movement models. The results showed significant improvements in MCW_OLSR. The proposed version enhances network proficiency for different mobile environments.

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Genetic algorithms for optimizing the layout of wireless charging networks

This study explores the improvement of wireless charging network configurations for electric cars (EVs) using genetic algorithms, with the goal of increasing charging efficiency and network performance. The network optimization process takes into account the starting characteristics of include their geographical coordinates, power capacity, and beginning energy levels. Examination of the distance matrix exposes diverse distances between nodes, which impact energy consumption and charging efficiency. The energy consumption estimates between pairs of nodes illustrate the charging needs across the network, revealing that nodes that are farther away have greater energy consumption. The use of genetic algorithms yields a wide range of layouts that are assessed based on their fitness ratings, indicating the excellence of configurations in terms of coverage and connection. Percentage change study demonstrates the modifications in power capacity and node energy levels after optimization, showing prospective improvements in charging capabilities and efficiency. The correlation between node location and energy use is apparent, as nodes in closer proximity demonstrate decreased energy utilization. The convergence of fitness scores demonstrates the algorithm's effectiveness in achieving solutions that are very close to ideal, resulting in significant improvements in charging coverage and energy efficiency. The study highlights the effectiveness of genetic algorithms in improving wireless charging networks, providing valuable information on spatial optimization tactics, energy use patterns, and the resulting improvements in network performance. These results have implications for creating wireless charging infrastructures that are more efficient and long-lasting, in order to satisfy the changing needs of electric car charging networks.

Fuzzy Heuristics for Detecting and Preventing Black Hole Attack

Mobile Ad-hoc Networks (MANET) is a set of computing nodes with there is no fixed infrastructure support. Every node in the network communicates with one another through wireless links. However, in MANET, the dynamic topology of the nodes is the vital demanding duty to produce security to the network and the black hole attacks get identified and prevented. In this paper, a novel fuzzy inference system is designed for black hole attack detection depending on the node authentication, trust value, Certificate Authority (CA), energy level, and message integrity. Before initiating the route discovery process in MANET, the proposed work mainly concentrates on node authentication. The simulation gets carried out using the Network Simulator (NS2), wherein the fuzzy inference system designed shows better performance by providing a certificate to only the trusted nodes. This helps the malicious nodes detection and prevents the black hole attack. The improvement in Packet Delivery Ratio (PDR) enhances throughput and the end to end delay gets reduced through better performance results. This proves that the system is more reliable and recovered to be used in military applications

Impact of Renewable Energy Resources on the Performance of DTN Networks in the Context of Hierarchical Routing Tree Topology (HRTT)

Delay-Tolerant Networks (DTN) are mobile networks specifically designed to operate in challenging wireless environments where connectivity is not guaranteed, and various issues such as frequent disconnections, long delivery delays, low delivery rates, and high error rates are common. One of the major problems of DTN networks is limited energy resources which impose strict constraints on the operational lifespan of DTN nodes. To solve these problems, we propose the adoption of a long-term strategy for energy management, based on the exploitation of renewable energies and the improvement of the HRTT topological architecture through the direct integration of renewable energy sources within DTN nodes. Indeed, DTN nodes powered by renewable energy resources, such as solar energy, have the potential to recharge themselves, extending their operational lifespan, increasing their storage and transmission capabilities, and reducing dependence on non-renewable energy resources. In this article, we assess the impact of limited energy resources and renewable energy resources like solar panels on the energy performance of DTN networks in the context of the Hierarchical Routing Tree Topology (HRTT), taking into account the average remaining energy as a performance metric. The evaluation of energy performance and the implementation of the HRTT topology are carried out using the ONE (Opportunistic Network Environment) simulator. According to the simulation results, the average remaining energy of nodes using renewable resources is significantly higher compared to the initial energy levels of nodes for all DTN protocols adopted by the nodes in the context of the HRTT topology. However, the average remaining energy of nodes using limited resources is lower compared to their initial energy levels for all DTN protocols adopted by the nodes in the context of the HRTT topology.

EAKRR : An energy-efficient key management in WSN with ECC-based cryptography

Environmental monitoring, industrial automation, and other applications require Wireless Sensor Networks (WSN). WSN must efficiently manage cryptographic keys for safe communication and energy efficiency. This study introduces “Energy-Aware Key Revocation and Rekeying” (EAKRR) to address energy efficiency and security in WSN using Elliptic Curve Cryptography (ECC). Energy-efficient key management is needed in WSN which have sensor nodes with limited resources and energy. EAKRR is designed to adapt to sensor node energy levels to make crucial key revocation and rekeying decisions while maintaining strong and resilient security. Energy profiling is used by EAKRR to assess sensor node energy consumption during cryptographic and operational operations. Energy conservation is achieved by localized key revocation when a node’s energy drops below a threshold. EAKRR also optimizes security and energy efficiency with ECC-based adaptive key length modifications. EAKRR is compared to LEACH and AES to determine its efficacy. The results show that EAKRR is highly energy efficient, extending the network’s lifespan and maintaining security. Cryptographic operations are careful and wise because they can adjust to energy levels in real time. The balance between security and energy usage shows that ECC-based encryption works. Energy-efficient key management in WSN is crucial. EAKRR solves WSN problems by saving energy, extending network life, and securing the network.

Hybrid packet routing algorithm based on ant colony system and tabu search in wireless sensor network

<span>Packet routing in wireless sensor network is one of the most crucial aspects as it controls the way packets move through sensor nodes with various capabilities to reach to the destination node. Inefficient routing process may lead to higher energy consumption, higher failure rate, and lower throughput. Metaheuristic algorithms have been some of the common approaches to solve these problems due to their adaptability with dynamic environment. This paper proposed a hybrid metaheuristics routing algorithm that hybridizes ant colony system and tabu search which focuses on exploitation and exploration mechanism while reducing the local optima. The proposed algorithm uses ant colony system technique to discover the best path for packet transmission by considering the energy level of each sensor node. Additionally, tabu search technique is applied to overcome the local optima problem by temporarily suspending the bad nodes and initiate backward movement with the aim to prevent the search agent from getting trapped in a blind alley. The proposed hybrid routing algorithm was evaluated against single and hybrid routing algorithms in terms of throughput, energy consumption, and energy efficiency. Experimental results showed that the proposed algorithm outperformed the other routing algorithms in terms of throughput, energy consumption, and energy efficiency.</span>

Irregularity Behaviour Detection - Ad-hoc On-Demand Distance Vector Routing Protocol (IBD - AODV): A Novel Method for Determining Unusual Behaviour in Mobile Ad-hoc Networks (MANET)

All the communication in the mobile ad-hoc network (MANET) will depend on the intermediate neighbour nodes or router nodes. Routing protocol is very important in MANET, because all the communication will be done in the MANET depending on the neighbour or intermediate node. If the intermediate node is a malicious node, all the data will be lost or changed by the intermediate or malicious node. Ad-hoc On-demand Distance Vector routing protocol is one of the moderate routing protocol in MANET. The Ad-hoc On-demand Distance Vector Routing protocol does not have any security mechanism. This work is going to find the Irregularity Behaviour Detection (IBD) over the Ad-hoc On-Demand Distance Vector Routing (AODV) protocol. IBD is finding a trusted node by using the trust value (TV) of the node. This TV includes network performance, node energy level, and node position value. IBD-AODV is implemented and tested in the OmNetpp 6.0 simulator.

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.

Providing an effective key management scheme to increase transaction security of homogeneous mobile wireless sensor networks

Network security is one of the key concerns with wireless sensor networks. Because sensor nodes employ radio channel frequencies, which are riskier than traditional networks, the attacker may be able to force the node to compromise, disrupt data integrity, eavesdrop, or insert false data into the network, wasting network resources. Creating dependable sensor networks that offer the highest level of security while using the fewest resources is thus one of the problems. The designers of wireless sensor networks can take into account providing an efficient key management system that can enhance any efficiency features such as communication overhead, calculation rate, memory demand, and energy consumption rate. The energy level of nodes is determined in this article using a novel approach based on fuzzy systems and simple to implement in both hardware and software. In this study, the memory needed to carry out the plan was decreased, and the search performance was raised by integrating elliptic curve cryptography with an AVL search tree and a LEACH model. Also, the frequency range of radio channels in this study is 2.4 GHz. Based on the theoretical analysis as well as the outcomes of the experiments, the suggested key management strategy for wireless sensor networks improves security while also reducing computational overhead by 23%, energy consumption by 14%, and memory consumption parameters by 14% . 18% of people have used the network. Additionally, it was demonstrated that the suggested approach is scalable and extendable. Because of this, the suggested technique has a wide range of applications in massive wireless sensor networks.

An Energy-Efficient Learning Automata and Cluster-Based Routing Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs), which may be used for a broad variety of applications, have recently emerged as a prominent data collection paradigm. The fundamental concerns in wireless sensor networks are the efficient use of energy and the reliable delivery of data, both of which are largely determined by the rate at which packets are dropped. When developing an energy-efficient routing protocol, one of the most important steps is selecting a node to act as a successor node in a routing path. The application of learning automata theory to guide the routing decisions made by the sensors in a WSN has recently been the subject of research in the field of WSNs, where it has been shown to have several advantages. In this paper, a learning automata-based PSO relay selection scheme for energyefficient relay selection and reliable data delivery is proposed. The network is clustered using the LEACH protocol. The random number in the traditional LEACH protocol will be stabilized with the sensor node energy level for CH stability. Every sensor node in the network estimates the best possible routes to the sink node using the PSO algorithm. Instead of retransmissions, here we introduce learning automata for successor node selection during packet loss. The proposed learning automata calculate the next node’s selection probability in a routing path using multi-objective parameters like communication cost, residual energy, distance from BS, buffer size, and previous selection probability. Performance evaluation clearly showed that the proposed approach decreases energy usage, transmission delays, and data transfers while extending network lifetime. According to the experimental results, the proposed scheme can improve energy efficiency by 21.68%, delay by 31%, PDR by 87%, routing overhead by 0.5%, and throughput by 18.76% as compared to existing techniques like O-LEACH (Optimized Low Energy Adaptive Clustering Hierarchy Protocol) and EEPC (enhanced energy proficient clustering).

Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

A Wireless Sensor Network (WSN) is a system of sensor nodes that communicate with one another and with their surroundings. Wireless sensor nodes are now used as the foundation for the higher-level industrial internet of things applications. Any type of real-time Internet of Things (IoT) application, as long as the data is collected utilizing the device's sensors. WSN are severely limited in terms of energy. Routing is one of the most challenging components of WSNs. Unbalanced energy utilization during data packet routing operations is one of the most serious difficulties with WSNs. This issue must be considered because the energy levels of sensor nodes are limited. We used WSNs multipath routing to save energy, improve scalability, and balance load. Many investigations are being undertaken to increase sensor node energy efficiency and network lifetime. More attention must be devoted to the clustering and routing components of communication in order to conserve energy. Clustering, also known as node clustering is a network organizing approach which helps with energy efficiency, workload distribution, data collection, and a connected hierarchy. The network is made up of numerous sensor nodes that must connect with one another in order for data to reach its goal, and when messages are not sent to intermediary nodes, problems arise. With multipath routing, numerous paths between the source and destination can be created, improving the possibility of data reaching the target and providing additional copies via alternate paths. The proposed routing solution uses less energy and extends the life of the network while compared with well-known approaches i.e., LEACH, ECPF, CHEF, UCR, DFLC, ACAWT, and Gupta. According to the simulation results and overall analysis, the proposed technique is much better than all the previous approaches under different number of nodes and simulation time in terms of end-to-end time reduced by 17.09% to 68.13%, energy consumption reduced by 13.23% to 29.12%, packet loss rate declined by 16.33% to 82.38% and drop rate of alive sensor nodes are much better shows high network resilience.

IoT adaptive threshold energy management algorithm based on energy harvesting

The Internet of Things (IoT) provides a promising solution to resolve pipeline conditions and oil and gas leakage detection problems. How to realize complex data monitoring and data uploading in energy-constrained sensor nodes is an urgent problem for oil and gas IoT. Energy harvesting technology and energy-efficient management have been introduced to the oil and gas IoT to improve energy utilization efficiency and good network performance. In this paper, we propose an energy management algorithm for oil and gas IoT. The scheme consists of: (1) the nodes mode switching strategy which allows sensor nodes to perform specific tasks based on the current energy level and (2) cluster head selection algorithm which considers energy harvesting and energy consumption ratios to achieve a balance between network performance and lifetime. Moreover, for the timely upload of essential data, we propose an adaptive threshold strategy that considers the energy level and importance level of sensor nodes to prioritize the transmission of important data under energy-limited conditions. The simulation results show that the proposed scheme can simultaneously prolong the network lifetime and improve energy utilization efficiency and is superior to existing schemes.

Modified squirrel search algorithm based data aggregation framework for improved network lifetime in wireless sensor network

WSNs (Wireless Sensor Networks) have various applications that include environments and traffic monitors, surveillance of battlefields, innovative agriculture because of their communication effectiveness. Individual sensor nodes in WSNs have restrictions in energy consumptions, packet deliveries, network longevities, and latencies. In previous work, an energy efficient data aggregation is achieved by developing an EADSLPBNC-DA framework for sensor network. Data aggregation performance is enhanced with minimum time through classifying the sensor nodes. But it has issue with data aggregation time, energy level of nodes will be changes periodically which is not concentrated in the previous work. The goal of this study is to address aforementioned difficulties and hence MSSA (Modified Squirrel Search Algorithm) which can improve data aggregations is proposed. Data aggregations based on CHs (cluster heads) improves energy efficiencies and network longevities. Aggregations of CHs related data is critical for minimizing sensor node energy consumptions in WSNs and their selections enhance WSNs lifespan. The MSSA-DA generates best fitness values using objective function which selects optimal sensor nodes as CHs to aggregate data optimally. The sensor nodes are chosen depending on their proximities, energies, and bandwidths. The suggested MSSA-DA system was simulated for assessing network lives, energy consumptions, accuracies, and data aggregation times. Experimental results suggested MSSA-DA model enhances data aggregation accuracies and network lives while consuming lesser energies and times.

FBCHS: Fuzzy Based Cluster Head Selection Protocol to Enhance Network Lifetime of WSN

With enormous evolution in Microelectronics, Wireless Sensor Networks (WSNs) have played a vital role in every aspect of daily life. Technological advancement has led to new ways of thinking and of developing infrastructure for sensing, monitoring, and computational tasks. The sensor network constitutes multiple sensor nodes for monitoring, tracking, and surveillance of remote objects in the network area. Battery replacement and recharging are almost impossible; therefore, the aim is to develop an efficient routing protocol for the sensor network. The Fuzzy Based Cluster Head Selection (FBCHS) protocol is proposed, which partitions the network into several regions based on node energy levels. The proposed protocol uses an artificial intelligence technique to select the Cluster Head (CH) based on maximum node Residual Energy (RE) and minimum distance. The transmission of data to the Base Station (BS) is accomplished via static clustering and the hybrid routing technique. The simulation results of the FBCHS protocol are com- pared to the SEP protocol and show improvement in the stability period and improved overall performance of the network.

Analysis and handling of biased data provided by wireless sensor networks with lack of energy

Wireless sensor networks consist of several battery-powered microelectronic devices whose purpose is to monitor the environment in which they are deployed. When a device's battery is almost completely discharged, it can stop working or start producing distorted or biased data, making monitoring unfeasible. Thus, many works seek to control the energy level of the sensor node to avoid the described behavior. However, battery-powered wireless networks inevitably run out of energy at some point in time. In this way, the objective of this study is to propose, implement, and compare methods that treat the data which are biased due to the lack of energy of the network devices. This method consists of joining estimating mechanisms like Kalman filter and GM (1, 1) with another technique to measure the reliability of the network devices based on the available energy in the nodes' batteries.

Analysis of power optimization and enhanced routing protocols for wireless sensor networks

Computers are connected through a network. Wireless sensor nodes are used in sensor networks to keep tabs on their surroundings. The building components of a network are sensor nodes. Processors, transceiver and memory are all onboard as well as a power supply. A WSN is a network of devices. Lifespan, data collection, and security are WSN parameters. Wireless sensors' low cost and quick deployment make them ideal for real-time applications. WSN's key problem is long-term energy supply. Energy resource, energy harvesting, topology, and effective routing may extend network lifespan. WSN energy is improved using an efficient manner. The recommended technique relies heavily on grouping and selecting the best possible path. The energy levels of the sensor nodes decide the cluster leader. Chikoo search algorithm selects the best path. This algorithm saves more energy than others. Optimum search ideas identify optimal sink location. Based on the network's energy dissipation and distribution, this technique finds the ideal location for all sinks to improve the network's lifespan and move with intelligent sink placement.