Energy Efficient Routing Algorithm Research Articles

Enhanced CH selection and energy efficient routing algorithm for WSN

Enhanced CH selection and energy efficient routing algorithm for WSN

Evolution of Energy Efficient Wireless Sensor Network: A Survey on Energy and Clustering Issues

In different fields of human endeavor, wireless sensor networks (WSNs) are being developed to collect, aggregate, fuse, and send data collected by a wide range of sensors in real-time. When developing a wireless network's data transmission routing system, energy consumption is among the most essential components. Designing an energy-efficient (WSN) routing framework is challenging. Several energyefficient routing algorithms exist for effectively transferring data packets between cluster members (CM) and cluster heads (CH). Some suggest the change of state of the nodes that made up the network from active to sleep in the event where the nodes are not sensing of sending data as a method of energy optimisation while others reduce the distance between the nodes because the distance between nodes is directly proportional to the amount of energy use during data transmission. Whatever the method may be, achieving reduced energy consumption in a WSN environment is an enormous task due to the need for both long network life and network efficiency. This paper reviews some research and energy saving techniques that had been carried out on WSN over a period of time.

An Energy-Efficient Routing Protocol Based on Elephant Herding Optimization in MANET

Background: A mobile ad hoc network (MANET) is a collection of self-organizing mobile nodes creating an ad hoc network without fixed infrastructure. Routing is a major issue in mobile networks that may reduce network performance due to frequent network topology changes. Routing protocols are so important in dynamic multi-hop networks that many studies have focused on the routing problem in MANETs. Methods: In this research work, a proposed ad hoc on-demand distance vector (AODV) with an elephant herding optimization (EHO) called AODVEHO is used as an optimal path selection method that creates a set of best paths between destination and source. objective: The research's objectives include load balancing, minimizing latency, increasing network speed, and minimizing energy consumption. Results: To validate the performance of proposed AODVEHO, AntHocNet, AODV, and designation sequence distance vector (DSDV). Various performance measures are considered to validate our proposed research work, such as packet delivery ratio (PDR), end-to-end delay (E2ED), and average energy consumption (AEC). method: elephant herding optimization with MANET utilized to achieved a changes in network topology and circumstances, minimizing latency, increasing throughput, preserving energy, and achieving Quality of Service (QoS) standards. Conclusion: Experimental results are indicated that the AODVEHO achieved a higher routing overhead (73 %), PDR (89.87 %), low E2ED rate (95.22 %), AEC (75.60 %), and dead nodes (75%) when compared to other routing schemes. result: SI can assist in locating close to optimum routes by redefining the routing issue as an optimisation challenge. The present reserch work a novel routing approach is developed based on EHO for finding optimal path in MANET environment. Robustness of the developed ADOVEHO is compared with benchmark routing approaches such as AntHocNet[27], AODV[28] and DSDV[29] conclusion: Optimizing energy utilization in MANET is an important study topic that has recently expanded with a high concentration of advantages from swarm intelligence algorithms and their optimization capabilities. The proposed work introduces the AODVEHO energy-efficient routing algorithm, which is based on AODV and EHO. In studies, the NS-3 tool was used to create four routing protocols, including the newly designed AODVEHO in addition to the AODV protocol, AntHocNet, and DSRV. The recommended protocol outperformed the other deployed protocols, according to the results, when it comes to load balancing, as shown by its superior PDR.

Dynamic Co-Operative Energy-Efficient Routing Algorithm Based on Geographic Information Perception in Opportunistic Mobile Networks

Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05∼31.11%, and more messages were delivered by 14.82∼115.9%.

Energy efficient and intelligent routing algorithm using DAI and self organizing map hybrid algorithm for future optical wireless communication

Energy efficient and intelligent routing algorithm using DAI and self organizing map hybrid algorithm for future optical wireless communication

An energy efficient routing algorithm in wireless underground sensor networks

An energy efficient routing algorithm in wireless underground sensor networks

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

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

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

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

Machine learning based centralized dynamic clustering algorithm for energy efficient routing in vehicular ad hoc networks

AbstractVehicular ad hoc network (VANET) is a dynamic and self‐configure wireless network that connects vehicles to provide in‐vehicle infotainment services and comfort to both driver and passengers while on the move. Due to the highly dynamic environment, patchy connectivity, roadside barriers, and a scarcity of road side units (RSUs); existing routing protocols of VANET consume high energy during route construction. As a consequence, the VANET lifespan might be shortened. However, saving energy is a critical factor where VANET is projected to play a key role in the building of sustainable green infrastructure. Clustering is an imperative process where vehicle nodes join to make stable clusters based on common mobility features. In VANET, clustering‐based routing algorithms significantly improve network effectiveness and reduce infrastructure costs. This article proposes fuzzy c‐means (FM) machine learning‐based dynamic clustering algorithm (DCA) to identify trustworthy vehicles for energy‐efficient multi‐hop routing by considering different mobility parameters such as position, direction, speed, and remaining energy of each vehicle node. This article also proposes distance and power‐based variation of FM called fuzzy c‐means using distance and residual power (FMDP) to find stable cluster heads (CHs) for performing better data dissemination to the destination. The proposed DCA outperforms the existing k‐means based clustering algorithms as well as topology‐based DSR and AODV routing protocols in terms of packet delivery ratio, application throughput, end‐to‐end delay, network energy consumption, node energy consumption, and RSU energy consumption. Additionally, clustering methods FM and FMDP of suggested DCA improve end‐to‐end delay by 20%, application throughput by 5%, and reduce network energy consumption by 68% compared with the k‐means based clustering model.

Energy efficient clustering-based routing algorithm for internet of things

Routing process is one of the most critical processes in wireless sensor network (WSN). Due to WSN is mainly used in many applications in internet of things (IoT), routing algorithm can affect the performance of these applications. Thus, the usage of inefficient routing algorithm may lead to losing the data collected by sensors. Moreover, it will cause the sensors to waste energy. This paper proposes an energy efficient clustering-based routing algorithm that is based on tunicate swarm algorithm (TSA). TSAbased clustering algorithm selects the optimal cluster head by calculating the remaining energy, the distance to the base station (BS), the distance to each cluster member and balancing the load between the created clusters. TSAbased routing algorithm is used to create paths from cluster heads to the BS using relay nodes. The TSA-based routing algorithm creates the paths based on the path length, the count of relay nodes in the path, and the number of cluster members of each relay node. The result shows that the proposed algorithm is promising in respect of extending the lifetime of the network and conserving the energy.

Secure and Energy-Based STEERA Routing Protocol for Wireless Sensor Networks

In wireless sensor networks (WSN), the multifunctional low-power sensor nodes are linked to base stations and are highly responsible for sensing various information. However, maintaining the network’s lifespan is a major issue because of limited battery capacity and node failures. Thus, the proposed study introduced a new Scalable Trust-based Energy Efficient Routing Algorithm (STEERA) with Adaptive Grasshopper Algorithm (AGA) to enhance the network lifespan. At first, the trust values (Tv) are generated for each node to eliminate the malicious node with the help of penalty factor and volatilization. To make efficient routing process, the nodes are formed into cluster groups. A suitable cluster head is selected for each cluster to mitigate the energy consumption problem through the AGA approach by optimizing the parameters like Tv, distance and residual energy. Finally, the data packets are effectively transmitted through a multi-hop routing process by selecting an appropriate path and satisfying certain parameters through Fire Hawks Optimization (FHO). To simulate the proposed techniques, NS2 software is employed, and the performance is measured over various metrics like energy consumption, residual energy, the lifespan of a network, packet loss ratio and average end-to-end delay. The performance analysis shows that the proposed protocol design has obtained higher results than conventional routing protocols.

An Energy-Efficient Routing Algorithm for WSNs Using Fuzzy Logic.

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF.

Hybrid FFBAT optimized multi-hop routing in Internet of Nano-Things

Wireless Nano-Sensor Networks (WNSNs) are molecular-level networks consisting of nano-machines that have very limited energy capacity. Due to the high energy consumption of the nodes in WNSNs during the data transmission, energy-efficient routing algorithms may help to reduce overall energy consumption. On the other hand, metaheuristics are useful for solving problems such as routing, energy, security and connectivity for traditional sensor networks. Therefore, this study proposes a hybrid Firefly and BAT Optimization based energy-efficient multi-hop routing algorithm, namely nanoFFBAT, between nano-sensor nodes and randomly placed convenient nano-routers for WNSNs-supported Internet of Nano-Things (IoNT) applications. In this hybrid approach, Firefly optimization is used for selecting the most energy-efficient neighbor by a nano-sensor node based on the flashing behavior of fireflies on the way for forming multi-hop paths and the BAT algorithm is adapted for building energy-efficient routing path discovery in WNSNs and also finding the optimal solution in solution space. In addition, nanoFFBAT detects redundant nodes on the energy-efficient multi-hop routing paths and shortens these paths if there exists any neighbor node already has been selected. The results of nanoFFBAT are compared with the shortest path from a nano-node to the convenient nano-router, a genetic algorithm-based energy-efficient routing algorithm for sensor networks and a WNSN routing protocol in the literature. According to the experimental simulation results, nanoFFBAT saves 306.929 nJ on a path on average and prolongs network lifetime 16.298 times more on average compared to the mentioned algorithms.

Energy Efficient Cluster Head Selection and Routing Algorithm using Hybrid Firefly Glow-Worm Swarm Optimization in WSN

Energy Efficient Cluster Head Selection and Routing Algorithm using Hybrid Firefly Glow-Worm Swarm Optimization in WSN

A Review of Energy Efficient Routing Algorithms in Wireless Sensor Networks

A Review of Energy Efficient Routing Algorithms in Wireless Sensor Networks

Multi-Objective Cluster Head-Based Energy-Aware Routing Protocol using Hybrid Woodpecker and Flamingo Search Optimization Algorithm for Internet of Things Environment

Internet of Things (IoT) plays a vital role in the world of Internet and integrates computing devices. The digital machines provide unique identifiers. The capacity to send data through the network using a better path is one of the capabilities of digital machines. IoT generates huge data based on fast aggregation data and processes these data efficiently. In this paper, a multi-objective hybrid woodpecker and flamingo search optimization algorithm is proposed for finding optimum Cluster Head (CH)-based energy-aware routing protocol in IoTs environment (Hyb-WFSOA-CHS-IoT). Here, the proposed Hyb-WFSOA-CHS-IoT model executes routing method through CH. The woodpecker and flamingo search optimization algorithm is used as the intelligent CH selection that consumes same energy as sensors. Hybrid woodpecker and flamingo search optimization algorithm (Hyb-WFSOA) is examined by using fitness functions, like distance, delay, energy consumption, throughput. The proposed Hyb-WFSOA-CHS-IoT method is activated in MATLAB software. Then, the performance of the proposed system is examined with different metrics, like delay, packet delivery ratio, throughput, network lifetime, energy consumption. Therefore, the proposed method attains lower delay 28.38%, 32.34% and 47.45%, higher delivery ratio 19.34%, 23.12% and 18.96% and lower energy consumption 11.25%, 7.90% and 12.88% compared with existing methods, like multi-objective fractional gravitational search algorithm for energy-efficient routing on IoT (FGSA-CHS-IoT), multi-objective sunflower-based gray wolf optimization algorithm for multiple path routing on IoT network (SFG-CHS-IoT) and energy-aware routing in the IoT with improved grasshopper metaheuristic algorithm with chaos theory and fuzzy Logic (FLGOA-CHS-IoT).

Energy Efficient Routing Scheme for Performance Enhancement of MANET Through Dominating Set

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome.

Wireless Sensor Networks Based on Multi-Criteria Clustering and Optimal Bio-Inspired Algorithm for Energy-Efficient Routing

Wireless sensor networks (WSNs) are used for recording the information from the physical surroundings and transmitting the gathered records to a principal location via extensively disbursed sensor nodes. The proliferation of sensor devices and advances in size, deployment costs, and user-friendly interfaces have spawned numerous WSN applications. The WSN should use a routing protocol to send information to the sink over a low-cost link. One of the foremost vital problems is the restricted energy of the sensing element and, therefore, the high energy is consumed throughout the time. An energy-efficient routing may increase the lifetime by consuming less energy. Taking this into consideration, this paper provides a multi-criteria clustering and optimal bio-inspired routing algorithmic rule to reinforce network lifetime, to increase the operational time of WSN-based applications and make robust clusters. Clustering is a good methodology of information aggregation that increases the lifetime by group formation. Multi-criteria clustering is used to select the optimal cluster head (CH). After proper selection of the CH, moth flame and salp swarm optimization algorithms are combined to analyze the quality route for transmitting information from the CH to the sink and expand the steadiness of the network. The proposed method is analyzed and contrasted with previous techniques, with parameters such as energy consumption, throughput, end-to-end delay, latency, lifetime, and packet delivery rate. Consumption of energy is minimized by up to 18.6% and network life is increased up to 6% longer compared to other routing protocols.

A new on the fly energy-efficient opportunistic routing in wireless multi-hop networks

ABSTRACT Wireless channels have a broadcast nature based on which opportunistic routing protocols work. In opportunistic routing protocols, packets are forwarded by the intermediate nodes that hear their transmissions, which are called candidate forwarders. In most of these algorithms, the forwarder list is pre-determined. However, the energy-efficient selection of the forwarder list is a research topic that is not considered well. Energy Efficient OPportunistic Routing algorithm (EEOPR) is presented in this paper in which the forwarders are determined on the packets’ fly. EEOPR is a flexible method that performs the routing process locally, and the candidate forwarders are selected during the routing and for each packet. The process of the candidate nodes’ selection and their packet forwarding are managed by the Genetic algorithm according to the nodes’ remaining energy and their regions. Simulation results show that network performance is improved in EEOPR compared to ROMER and CORP-M in terms of throughput, the number of duplicate packets, and the network nodes’ residual energy.

An Energy Efficient Routing Algorithm using Chaotic Grey Wolf with Mobile Sink-based Path Optimization for Wireless Sensor Networks

An Energy Efficient Routing Algorithm using Chaotic Grey Wolf with Mobile Sink-based Path Optimization for Wireless Sensor Networks