Energy Efficient Routing Research Articles

Fog-Based Energy Efficient Routing Protocol for Smart Building Evacuations

Fog-Based Energy Efficient Routing Protocol for Smart Building Evacuations

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. 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). 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.

ENERGY EFFICIENT ROUTING PROTOCOL FOR SECURITY ANALYSIS SCHEME USING HOMOMORPHIC ENCRYPTION

Wireless Sensor Networks (WSNs) have gained widespread interest as a result of developments in IT technology and the electronics industry. This ground-breaking sensing technology consists of multiple sensor nodes or motes that are placed in an atmosphere to detect constantly changing physical phenomena. These compact sensor nodes gather and interpret data via radio waves. The tiny size of these sensors is advantageous since they may be readily incorporated into any system or system. This capability has prompted the adoption of WSNs, particularly for form monitoring and tracking; most notably, monitoring apps. However, this small scale of sensor nodes limits the capacity of resources. Usually, the WSNs are installed in environments of unsafe or difficult human interference. Critical decisions in emergency applications can require sensible knowledge. It is necessary to check the network security. To extend the network security using Homomorphic Encryption effectively, the available resources must be expanded to a full view. This refers to the development of energy-efficient routing protocol strategies to ensure low energy consumption of common routing protocols and improve the availability and security of WSNs. Homomorphic encryption is effective in improving the security of wireless sensor networks.

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

Bio-Inspired Based Optimization of Machine Learning Techniques for Energy Efficient Routing for UWSN

Bio-Inspired Based Optimization of Machine Learning Techniques for Energy Efficient Routing for UWSN

Fuzzy based Energy Efficient Rider Remora Routing protocol for secured communication in WSN network

Due to the resource constraint nature of WSN, ensuring secured communication in WSN is a challenging problem. Moreover, enhancing the network lifetime is one of the major issues faced by the existing studies. So, in order to secure the communication between WSNs and achieve improved network lifetime, a novel trust enabled routing protocol is proposed in this study. Initially, the clusters are constructed using Direct, Indirect, and Total Trust evaluations, which helps to identify the faulty nodes. After, an Improved Fuzzy-based Balanced Cost Cluster Head Selection (IFBECS) method is used to choose the cluster head (CH). Finally, to determine the best path from source to destination, a hybrid bionic energy-efficient routing model known as an Energy Efficient Rider Remora Routing (EERRR) protocol is introduced. To improve the network lifetime and throughput, the parameters like remaining energy of the CH, sensor node space, CH, etc., are considered by the utilized protocol. The proposed mechanism is implemented in NS-2 programming tool. The simulation results show that the proposed routing protocol has attained improved PDR of 97.92 % at the time period of 50 ms, reduced energy consumption of 3.336 at the time period of 100 ms, higher throughput of 86262.7 at the time period of 250 ms, and enhanced network lifetime of 1028.08 rounds in 200 nodes. Therefore, by attaining better results as compared with other existing protocols, it is clearly revealed that the proposed routing protocol is highly suitable for secured energy efficient WSN communication.

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem.

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

QoS based Adaptive Multi-Constrained Energy Efficient Routing for Vehicular Ad hoc Networks

QoS based Adaptive Multi-Constrained Energy Efficient Routing for Vehicular Ad hoc Networks

IFKC and Optimized DeTrac Deep Convolutional Neural Network Based Location Prediction in the Mobile IOT

Wireless Sensor Network (WSN) is a noteworthy technological advancement in the Internet of Things (IoT). Due to its severe resource constraints, this network necessitates the development of energy-efficient routing strategies. In the context of the Internet of Things, a cluster-based routing problems frequently struggle with unequal power usage. Therefore, this paper proposes an Improved Fractional Rough Fuzzy K-means Clustering and Optimized DeTraC Deep Convolutional Neural Network based Location Prediction in Mobile Internet of Things (IFKC-ODTN-LP-MIoT) system to address unequal power consumption in cluster-based IoT routing. By proposing Improved Fractional Rough Fuzzy K-means (IF-RFKM) for cluster head selection and an optimized DeTraC Deep Convolutional Neural Network (DT-DCNN) the system aims to enhance energy efficiency for location prediction. Then Adolescent Identity Search Algorithm (AISA) optimizes DT-DCNN parameters for improved accuracy. The experimental results demonstrate significant improvements in Computational efficiency, network lifetime and delay compared to the existing approaches.

An energy-efficient routing algorithm for dual-energy harvesting-assisted wireless sensor networks based on whale optimization strategy

An energy-efficient routing algorithm for dual-energy harvesting-assisted wireless sensor networks based on whale optimization strategy

Enhanced Energy efficient routing protocol for OnDemand distance vector routing to improve communication in border area Military communication

Enhanced Energy efficient routing protocol for OnDemand distance vector routing to improve communication in border area Military communication

WAOA: A hybrid whale-ant optimization algorithm for energy-efficient routing in wireless sensor networks

Wireless Sensor Networks (WSNs) are vital for collecting data from remote environments. Nevertheless, the limited energy resources of sensor nodes render energy-efficient routing a critical concern for the successful operation of WSNs. To address these concerns, clustering, and routing are essential tasks in WSNs; clustering aims to organize sensor nodes into groups or clusters to minimize energy usage and prolong the network's lifespan. On the other hand, routing involves determining the optimum paths for transmitting data from the source nodes to the destination nodes. Nonetheless, it has been established that the current energy-efficient routing problem is an NP-hard, requiring a trade-off between energy and overall network performance. In this paper, we proposed a Hybrid Whale-Ant Optimization Algorithm (WAOA) for energy-efficient routing in WSNs. The proposed WAOA utilizes the Whale Optimization Algorithm (WOA) to find the suitable cluster head in the predefined search space, while the Ant Colony Optimization (ACO) searches the optimal route from the source cluster sensors to the cluster head within its predefined space. Linear programming construction is employed to formulate optimization problems for cluster head selection and search for the optimal route. The performance analysis demonstrates that the proposed WAOA performs better than MOORP, MMABC, and AZEBR by 5.78 %,16.11 %, and 18.52 %, respectively, in terms of network lifetime.

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

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

Green Communication in IoT for Enabling Next-Generation Wireless Systems

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput.

Dodecagonal Parameter-Based Energy-Efficient Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are composed of several sensor nodes (SN) that are dispersed geographically and interact with one another over wireless media in order to track and log physical data from their environment. At the moment, WSNs frequently use routing and clustering strategies to extend the life of their networks. This paper proposes a DODECAGONAL parameter-based Energy-Efficient Routing in WSN (DOPE-WSN) for improvement of network lifetime and saving the energy consumption. Initially the cluster is selected using Agglomerative clustering. In the second phase, Pelican optimization (PELO) based Cluster head selection (CHs) meant for considering Congestion, Node Degree, Node Density, Network area, Network Coverage, Number of clusters, Number of nodes, Communication cost, Distance, Residual Energy, Distance to neighbor, Node Centrality. Moreover, the Sooty Tern Optimization (STO) model are utilized for the determination of the best routing path for the cluster heads. Taking into account node degree, residual energy, and distance, the STO maximizes network performance. The suggested approach has undergone thorough testing for ensuring network durability and energy efficiency. The proposed model achieved a maximum 97% Packet Delivery Ratio (PDR) with 900 nodes in comparison with 91%, 89%, 83%, and 82% for CRPSH, HQCA, EACRLEACH, and BWO-IACO algorithms respectively.

Rapid conversion of biomass to hierarchical porous carbons via one-step microwave carbonization/activation for long cycle-stable supercapacitor and zinc-ion capacitor

Biomass derived carbons as electrode materials for supercapacitors have drawn great attention owing to their low cost, high specific areas and abundant surface functional groups, but most of their preparation processes are time-consuming and power-wasting. In this work, we report a rapid and energy-saving strategy for preparing hierarchical porous carbons derived from loofah sponges for supercapacitors via one-step microwave carbonization/activation. Utilizing KOH as the microwave absorption medium and activator, the microwave carbonization/activation process can be completed within 6–10 min. The resulting loofah sponges derived porous carbons demonstrate a high specific capacitance of 294 F g−1 at 0.5 A g−1 and an outstanding retention rate of 74.8 % at 20 A g−1 in three-electrode setup for supercapacitor. The fabricated zinc-ion capacitor based on the as-prepared carbon materials displays a comparable energy density of 62.3 Wh∙kg−1 at the power density of 160 W kg−1. Notably, a remarkable long-term cyclic stability by retaining of 93.6 % of its initial capacitance is acquired after 30,000 cycles. Our research not only fabricates sustainable electrode materials for supercapacitors and zinc-ion capacitors but also provides a fast and energy efficient route for producing biomass derived carbon materials with high capacitive performances for commercial applications.

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network

Energy Efficient and Throughput Oriented Route Optimization Models in the Internet of Vehicles: A Survey

The Internet of Vehicles (IoV) has emerged as a promising paradigm that integrates vehicles, communication technologies, and the Internet to foster intelligent transportation systems. To fully exploit the potential of IoV, efficient route optimization models are crucial to optimize energy consumption while ensuring high throughput. This survey paper aims to provide a comprehensive analysis of existing research on energy-efficient and throughput-oriented route optimization models in IoV. The primary objective of this survey is to categorize and evaluate various route optimization techniques that focus on enhancing energy efficiency and throughput in the IoV context. We present a systematic review of literature, encompassing academic papers, conference proceedings, and technical reports up to the time of this research. By examining the state-of-the-art approaches, we identify the underlying principles, strengths, and limitations of each method. The survey first delves into the foundational concepts of IoV and their significance in modern transportation systems. We elucidate the challenges faced in IoV route optimization concerning energy consumption, data transformation, bandwidth contention, and network congestion. In the main body of the survey, we classify existing route optimization models based on their energy efficiency and throughput-oriented objectives. The energy-efficient category encompasses methodologies that aim to minimize a node’s energy consumption and extend its battery life, considering factors such as the routing process involved (massage broadcasting and rebroadcasting), link state, and traffic flow patterns. On the other hand, the throughput-oriented category focuses on maximizing information transformation and ensuring low latency during operational time. Furthermore, we identify open challenges and research gaps in the field of energy-efficient and throughput-oriented route optimization for IoV. These gaps pave the way for future research directions, which can lead to the development of more robust, scalable, and adaptive routing solutions. By evaluating the strengths and weaknesses of various approaches, we aim to inspire researchers, engineers, and entrepreneurs to develop innovative solutions that enhance energy efficiency, maximize throughput, and foster the realization of a smarter and more sustainable transportation ecosystem.

Advancements in IoT Routing and Energy Efficiency: A Comprehensive Review of Algorithms and Technologies

The rapid expansion of the Internet of Things (IoT) has led to a significant increase in connected devices, resulting in growing challenges related to efficient data transmission, energy consumption, and network scalability. Routing protocols and energy-efficient algorithms play a pivotal role in addressing these challenges, enabling IoT systems to optimize performance while minimizing power usage. This review provides a comprehensive analysis of recent advancements in IoT routing techniques and energy-efficient solutions, focusing on multi-objective optimization algorithms such as fractional gravitational search, grey wolf optimization, and hybrid salp swarm-differential evolution algorithms. Additionally, we explore context-aware routing, cluster-based methods, and the integration of machine learning to enhance decision-making in dynamic IoT environments. The review also highlights the critical role of energy-efficient routing in IoT applications, such as smart agriculture, healthcare, and smart cities, emphasizing its importance in prolonging network lifetime and reducing overall operational costs. The potential of blockchain technology and AI-driven algorithms to enhance security and energy efficiency in IoT networks is discussed, alongside future directions in green routing solutions. By addressing the complexities of IoT routing and energy management, this review aims to provide insights into the latest research trends and guide future innovations in IoT systems.