Energy Efficient Routing Scheme Research Articles

Sensing-Aware Machine Learning Framework for Extended Lifetime of IoT Sensors

Bluetooth Low Energy (BLE) beacon network is one of the essential infrastructures for many IoT and smart city applications that involve a plethora of sensing tasks. However, the BLE beacon network usually suffers from poor reliability and high maintenance costs due to the short-lived battery lifetime. Multiple works have attempted to extend the lifetime via energy harvesting hardware, adaptive advertising interval by user existence-aware operation, and energy-efficient routing schemes. However, few attempts were made to reduce the energy consumption related to sensing tasks. In light of this shortcoming, a sensing-aware framework is proposed to adjust the sensing task interval adaptively based on the predicted portion of changes of the sensor measurements. Furthermore, to estimate the impact of varying sensing task intervals on the amount of sensed information, a model that correlates energy and amount of information is proposed. The sensor portion of changes is predicted with a novel neural network, coined oracle-interpreter network, that significantly reduces the energy consumption while upkeeping a good prediction accuracy by leveraging two independent neural networks tailored for feature extraction and prediction tasks. The effectiveness of the proposed framework is verified by comprehensive simulations based on real-life data. The results demonstrate that the proposed framework can effectively reduce the energy consumption involved in sensing tasks up to 30%, machine learning tasks up to 60%, and finally, extend the lifetime up to 75%.

Delay-sensitive energy-efficient routing scheme for the Wireless Sensor Network with path-constrained mobile sink

The use of mobile sinks (MSs) can address several challenges for traditional wireless sensor networks (WSNs), such as energy holes, meanwhile it also introduces some new issues. Especially, for the path constraint MS, the fixed trajectory and uncontrollable moving speed results in significant delay and even buffer overflows. Existing research mainly focused on delay-tolerant applications which are unsuitable for delay-sensitive data or adopted the greedy policy to collect data which is inefficient in energy. To minimize the energy consumption and guarantee delay requirement simultaneously, we propose a delay-sensitive and energy-efficient routing scheme. Firstly, to obtain the MS position, we propose a K-nearest neighbor-based prediction model, which can reduce the energy and delay overhead of the location update. Based on the prediction result, an efficient cluster routing scheme is designed. To ensure both search speed and the optimality of the cluster head (CH), we improve the Reed-Deer algorithm (RDA) as IRDA, and use the IRDA to find out CHs. To remove the redundancy of the CH, a removal method is proposed. After constructing clusters, a method is used to determine the next-hop of the CH by using the predication position of the MS, the received timestamp and the delay requirement of the data. A fault tolerance mechanism is supplied to deal the predication error and other unexpected situations. By applying IRDA and the prediction model, the proposed routing scheme avoids the frequent construction of new routes, reduces the hops of the end-to-end path, minimizes network energy consumption, and ensures timely data transmission. The comparison results indicates that the proposed protocol can effectively improve the network performance while guarantee delay requirement. Especially in realistic scenario, the proposed algorithm can effectively reduce energy consumption and the end-to-end delay and improve the packet delivery ratio.

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.

Performance evaluation with scalability varying on energy consumption in S-DYMO, M-DYMO and FB-B-DYMO routing algorithm in MANETs

Mobile Ad Hoc Networks (MANETs) are dynamic and self-configuring networks characterized by node mobility and decentralized operations. Efficient routing protocols play a crucial role in ensuring reliable communication while optimizing resource usage, particularly energy consumption. This paper presents a comparative performance evaluation of three prominent routing algorithms, such as S-DYMO, M-DYMO, and FB-B-DYMO, focusing on their scalability and energy efficiency in MANET environments. This study utilizes simulation-based experiments to assess the performance metrics of S-DYMO, M-DYMO, and FB-B-DYMO. Key parameters evaluated include packet delivery ratio (PDR), end-to-end delay, routing overhead, and energy consumption under varying network sizes and mobility scenarios. Each algorithm's ability to scale with network size and maintain performance under dynamic conditions is analyzed to understand its suitability for real-world MANET deployments. The results indicate that S-DYMO exhibits robust scalability and efficient energy utilization in small to medium-sized networks, leveraging its proactive route discovery approach. Conversely, M-DYMO demonstrates competitive performance in larger networks due to its reactive route maintenance strategy, which balances energy efficiency with scalability challenges. FB-DYMO, integrating feedbackbased mechanisms, shows promising results in mitigating routing overhead and energy consumption but exhibits sensitivity to network dynamics. The findings provide insights into the trade-offs between scalability and energy consumption inherent in S-DYMO, M-DYMO, and FB-B-DYMO routing algorithms, offering guidance for selecting appropriate protocols based on specific MANET deployment scenarios. Future research directions include enhancing protocol adaptability to dynamic network conditions and optimizing energy-efficient routing strategies tailored for evolving MANET environments.

An Energy Efficient Secure routing Scheme using LEACH protocol in WSN for IoT networks

One of the most recent and exciting new applications can be found in the realm of the IoT, which makes it possible to connect various objects or devices over the internet. Many options exist to mitigate the negative effects of communication protocols on WSNs' power consumption. LEACH protocol is a cluster-based one, that uses clusters to divide the network. LEACH uses the random numbers produced by each sensor node to choose a group of nodes to serve as cluster heads (CHs). An enhanced version of the LEACH procedure for stable & energy-efficient CH selection is the subject of the proposed work. The proposed work stabilizes the random number generation by including Rank-based CH selection using Node coverage, Energy consumption rate, and the characteristics of the base station's distance. The node coverage parameter guarantees that the chosen CHs have more neighbors in their coverage circle, it, in turn, lowers the energy required for member nodes to go to the CH. The quantity of energy used rate ensures that the selected CHs have higher energy. According to the findings of the simulations, it is clear that the suggested strategy significantly improves the Energy efficiency performance of WSNs, network longevity, and communication quality.

A social relationship-based energy efficient routing scheme for Opportunistic Internet of Things

Opportunistic Internet of Things (OppIoT) combines Opportunistic Networks and the Internet of Things to provide opportunistic communication between IoT devices and human-created communities in order to improve network connectivity, reliability, and longevity. This is significant because it introduces a new approach to connect and communicate smart devices in situations where there is no end-to-end connectivity. Due to the non-stable existence of connectivity between nodes, building a scalable, flexible, interoperable, and energy efficient routing protocol is a difficult issue. This study presents a new routing protocol for the Opportunistic Internet of Things called Social relationship based Energy Efficient Routing (SEER), which bases routing decisions on the forwarding feasibility degree, residual energy, and buffer capacity of nodes. SEER surpasses EBR and EDR in terms of delivery probability, hop count, and overhead ratio, according to simulation results utilizing the Opportunistic Network Environment (The ONE) simulator.

A Systematic Review Paper on Energy-Efficient Routing Protocols in Internet of Things

The Internet of Things (IoT) has witnessed remarkable growth, leading to the emergence of numerous applications and services. However, energy efficiency remains a critical concern in IoT networks due to the resource-constrained nature of many IoT devices. Energy-efficient routing protocols play a pivotal role in mitigating this challenge, optimizing energy consumption, and extending the operational lifetime of IoT deployments. This survey paper aims to provide a holistic understanding of the state-of-the-art approaches, their characteristics, strengths, limitations, and performance metrics. The survey encompasses a wide range of energy-efficient routing protocols proposed in the literature, including traditional routing protocols adapted for IoT scenarios and protocols specifically tailored for energy optimization. We discuss the impact of different network characteristics on the performance of energy-efficient routing protocols, such as network size, node density, mobility, and heterogeneity. Through this systematic survey, we aim to provide researchers, engineers, and practitioners with a comprehensive overview of energy-efficient routing protocols for IoT networks. The survey findings can serve as a valuable reference for protocol selection, performance evaluation, and the development of novel energy-efficient routing strategies in future IoT deployments.

MLRS-RL: An Energy-Efficient Multilevel Routing Strategy Based on Reinforcement Learning in Multimodal UWSNs

In recent years, Multimodal Underwater Wireless Sensor Networks (M-UWSNs) have attracted widespread concern in academia. Due to the complex underwater communication environment and the increasing marine applications, it is a crucial issue for M-UWSNs to design an energy efficient routing strategy that can satisfy multiple transmission latency requirements of different marine applications. Reinforcement learning (RL) approaches with distributed dynamic optimization ability provide a prospective way to solve the aforementioned problem. Therefore, we propose an improved RL framework and then design an energy efficient multi-level routing strategy (MLRS-RL) based on this framework for multiple transmission latency requirements. In MLRS-RL, a method of model knowledge collection based on the time backoff principle is proposed to preliminarily learn the network environment information before network operates. The convergence speed of the RL framework can be accelerated by utilizing the model knowledge. Then, underwater nodes use the improved RL model to calculate the transmission rewards that data packets with different transmission latency requirements are sent to different candidate relay nodes. Finally, a cooperative transmission strategy using multiple relay nodes is designed to further improve the reliability of data transmission. We verify the effectiveness of the MLRS-RL strategy in terms of packet delivery ratio, transmission latency, energy efficiency, network lifetime, and delivery quantity.

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.

Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks

A group of small sensors can participate in the wireless network infrastructure and make appropriate transmission and communication sensor networks. There are numerous uses for drones, including military, medical, agricultural, and atmospheric monitoring. The power sources available to nodes in WSNs are restricted. Furthermore, because of this, a diverse method of energy availability is required, primarily for communication over a vast distance, for which Multi-Hop (MH) systems are used. Obtaining the optimum routing path between nodes is still a significant problem, even when multi-hop systems reduce the cost of energy needed by every node along the way. As a result, the number of transmissions must be kept to a minimum to provide effective routing and extend the system's lifetime. To solve the energy problem in WSN, Taylor based Gravitational Search Algorithm (TBGSA) is proposed, which combines the Taylor series with a Gravitational search algorithm to discover the best hops for multi-hop routing. Initially, the sensor nodes are categorised as groups or clusters and the maximum capable node can access the cluster head the next action is switching between multiple nodes via a multi-hop manner. Initially, the best (CH) Cluster Head is chosen using the Artificial Bee Colony (ABC) algorithm, and then the data is transmitted utilizing multi-hop routing. The comparison result shows out the extension of networks longevity of the proposed method with the existing EBMRS, MOGA, and DMEERP methods. The network lifetime of the proposed method increased by 13.2%, 21.9% and 29.2% better than DMEERP, MOGA, and EBMRS respectively.

An energy efficient routing scheme in internet of things enabled WSN: neuro-fuzzy approach

An energy efficient routing scheme in internet of things enabled WSN: neuro-fuzzy approach

Energy-efficient routing based on a genetic algorithm for satellite laser communication.

Low earth orbit satellite laser communication has become an important part of communications due to its large capacity and low latency. The lifetime of the satellite mainly depends on the recharge and discharge cycles of the battery. The low earth orbit satellites frequently recharge under sunlight and discharge in the shadow, which leads satellites to age quickly. This paper studies the energy-efficient routing problem for satellite laser communication and builds the satellite ageing model. Based on the model, we propose an energy-efficient routing scheme based on the genetic algorithm. Compared with shortest path routing, the proposed method improves the satellite lifetime by about 300%, and the performances of the network are only slightly degraded, the blocking ratio increases by only 1.2%, and the service delay increases by 1.3 ms.

Pizzza: A Joint Sector Shape and Minimum Spanning Tree-Based Clustering Scheme for Energy Efficient Routing in Wireless Sensor Networks

Pizzza: A Joint Sector Shape and Minimum Spanning Tree-Based Clustering Scheme for Energy Efficient Routing in Wireless Sensor Networks

CSCRT Protocol with Energy Efficient Secured CH Clustering for Smart Dust Network Using Quantum Key Distribution

Energy efficient protocols for Mobile wireless smart dust networks are getting prominence in the present scenario. This paper describes a novel scheme for intrusion detection framework for secured clustered mobile smart dust networks which better suits the periodical data gathering. The essential objective of a battery equipped mobile wireless smart dust nodes is to upgrade the transmission energy. The proposed protocol has the favourable position that the communication and computational overheads get reduced and gives improved energy efficient routing protocol as far as energy efficient and intrusion detection system. The intrusion and interruption are fundamentally same as that it is difficult to recognize. The design becomes more complex, any security protection algorithms utilize more energy. This can be achieved by using IDS based clustering tactic, Specific Encryption algorithm with Quantum Key Distribution and Chance Succession Comparison Ratio Test protocol. Particular encryption for data based communication is carried out to low utilization of energy and also to discover attacks, when it occurs in mobile nodes. Data transmission rate is a factor for energy utilization. Simulation is done in NS2, results show that better energy efficient routing scheme for mobile wireless smart dust network.

Enhanced energy efficient routing scheme based ant colony optimization

Mobile Ad hoc Networks (MANETs) are independent of central administration or any infrastructure, hence it is flexible in nature. Though, the network is self organizing, the mobile nodes have some resource constraints. There is always a requirement forefficient routing protocol to manage the energy consumption and reduce the energy wastages in MANETWith that in mind, this article proposes Ant Colony Optimization for Enhanced Energy Efficient Routing (EEER-ACO). Furthermore, the network design balances the Transition Probability Standard (TPS)offset Coefficient to maximize navigation processing effectiveness and decrease path finding packets. Furthermore, the Surviving route lifespan is calculated based upon that node’s position and speed rate. Through incorporating the Residual Node Lifetime (RNL) and the Residual Link Lifetime (RLL), the Residual Node Lifetime (RNL), the ACO based pheromone has been designed. Further, the algorithm involves in choosing an optimal quality route for assuring continuous and efficient data packet transmission over the defined MANET. The investigation took into account the energy consumption of nodes as well as associated motility. Furthermore, the results indicate that the EEER-ACO algorithm improves network durability by reducing end-to-end delay, data packet loss, and path discovery rate. In comparison to previous algorithms, the proposed study has demonstrated that it achieves a 35 percent better performance than traditional protocols.

SecAODV: A Secure Healthcare Routing Scheme Based on Hybrid Cryptography in Wireless Body Sensor Networks.

In recent decades, the use of sensors has dramatically grown to monitor human body activities and maintain the health status. In this application, routing and secure data transmission are very important to prevent the unauthorized access by attackers to health data. In this article, we propose a secure routing scheme called SecAODV for heterogeneous wireless body sensor networks. SecAODV has three phases: bootstrapping, routing between cluster head nodes, and communication security. In the bootstrapping phase, the base station loads system parameters and encryption functions in the memory of sensor nodes. In the routing phase, each cluster head node calculates its degree based on several parameters, including, distance, residual energy, link quality, and the number of hops, to decide for rebroadcasting the route request (RREQ) message. In the communication security phase, a symmetric cryptography method is used to protect intra-cluster communications. Also, an asymmetric cryptography method is used to secure communication links between cluster head nodes. The proposed secure routing scheme is simulated in the network simulator version 2 (NS2) simulator. The simulation results are compared with the secure multi tier energy-efficient routing scheme (SMEER) and the centralized low-energy adaptive clustering hierarchy (LEACH-C). The results show that SecAODV improves end-to-end delay, throughput, energy consumption, packet delivery rate (PDR), and packet loss rate (PLR).

A novel energy efficient routing scheme for wireless sensor networks

In the current rapid developing and smart world, the wireless sensor networks domain is among the most emergent fields with plenty of applications such as healthcare, wildlife, environmental monitoring, defense, and landslide detection. Distribution of cluster heads along with the selection of cluster heads is essentially vital for optimizing various network performance parameters. In this paper, a novel energy efficient routing scheme based on probabilistic sector wise clustering has been proposed, which results in energy saving in conjunction with superior network lifetime for wireless sensor networks. In the proposed routing technique, the sensing field is divided into multiple sectors and clusters in five different types. Nodes in the sector can communicate directly with the base station in one-hop or through the gateway node in two-hops. Nodes in the cluster communicate to the base station through the cluster head in three-hops. The simulation results of all five types are compared in terms of their life span. Life span of the network is analyzed by considering the round number in which the first node dies, the round number till which 80% of the nodes are alive, 50% of nodes are alive and 20% of the nodes are alive.

DTAR: A Dynamic Threshold Adaptive Ranking-Based Energy-Efficient Routing Algorithm for WSNs

Owing to uncertainties associated with energy and maintenance of large Wireless Sensor Networks (WSN) during transmission, energy-efficient routing strategies are gaining popularity. A Dynamic Threshold Adaptive Routing Algorithm (DTAR) is proposed for determining the most appropriate node to become a Cluster Head (CH) using adaptive participation criteria. For determining the next Forwarder Node (FN), an adaptive ranking scheme depends on distance ([Formula: see text]) and Residual Energy ([Formula: see text]). However, additional parameters such as Delivery Ratio (DR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) should be considered to achieve the most optimal approach to achieve energy efficiency. The proposed DTAR algorithm is validated on variable clustered networks in order to investigate the effect of opportunistic routing with increasing network size and energy resources. The proposed algorithm shows a substantial decrease in energy consumption during transmission. Energy Consumption (EC), Packet Delivery Ratio (PDR), End-to-End delay ([Formula: see text] delay), and Message Success Rate (MSR) are used to illustrate the effectiveness of the proposed algorithm for energy efficiency.

An Efficient Energy Management Routing and Scalable Topology in Wireless Sensor Network Using Virtual Backbone

The wireless sensor network (WSN) approach is one of the fastest growing approaches in the world of communications and engineering. The primary objective of a WSN is to discover the important information about the environment, depending on the nature of the applications under which it is implemented, and to communicate this information to a single base station (BS) so that appropriate measures can be taken. These sensor nodes communicate via a variety of protocols. The difficulty with the traditional system is that while collecting the observed data, each node transmits its felt information directly to a base station, which quickly exhausts its power. This study suggests a Backbone Energy-Efficient Sleeping (BEES) management strategy with two appealing features: (i) the capacity of backbone is scalable by basic parameters, and (ii) the backbone nodes were distributed equally, implying that the backbone on its own is energy efficient during routine activities. Reliable connections are expected to obtain QoS and routing protocols of such backbone nodes in wireless multihop systems. As a result, present localized routing in virtualized backbone schedule cannot ensure energy-efficient paths. An energy-efficient routing scheme for Virtual Back Bone Nodes (VBS) increases life of node and switches off its radio while in sleep state to spend less power. BEES’ performance is evaluated by comparing it to two different topology management techniques. The results show that BEES performs better algorithms. It ensures optimal routing with minimal node power consumption but also implements the essential communication range for backbone networks.

Energy efficient cluster-based routing scheme using type-2 fuzzy logic in underwater wireless sensor networks

Energy efficient cluster-based routing scheme using type-2 fuzzy logic in underwater wireless sensor networks