Energy Efficient Routing Protocol Research Articles

Fog-Based Energy Efficient Routing Protocol for Smart Building Evacuations

Fog-Based Energy Efficient Routing Protocol for Smart Building Evacuations

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.

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.

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.

Retraction Note: Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

Retraction Note: Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

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

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.

Threshold-driven K-means sector clustering algorithm for wireless sensor networks

The clustering algorithm is an effective method for developing energy efficiency routing protocol for wireless sensor networks (WSNs). In clustered WSNs, cluster heads must handle high traffic, thus consuming more energy. Therefore, forming balanced clusters and selecting optimal cluster heads are significant challenges. The paper proposes a sector clustering algorithm based on K-means called KMSC. KMSC improves efficiency and balances the cluster size by employing symmetric dividing sectors in conjunction with K-means. For the selection of cluster heads (CHs), KMSC uses the residual energy and distance to calculate the weight of the node, then selects the node with the highest weight as CH. A hybrid single-hop and multi-hop communication is utilized to reduce long-distance transmissions. Furthermore, the impact of the number of sectors, the threshold for clustering, and the network size on the performance of KMSC has been explored. The simulation results show that KMSC outperforms EECPK-means, K-means, TSC, LSC, and SEECP in terms of FND, HND, and LND.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed in this paper by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to find a predefined number of clusters with their initial cluster heads (CHs) and the centroid location of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from these DDBLACH nodes of clusters to a sink of the network. Here simulation results indicate the supremacy of our proposed scheme over other existing methods in various aspects, such as improving the presence of alive nodes and average network lifetime.

Energy-efficient routing protocol for WSN based on relay node selection and A-star algorithm

Due to miniaturization constraints and manufacturing costs, wireless sensor networks (WSN) sensor nodes are generally outfitted with restricted resources concerning computing capability, energy, data storage capacity, and data delivery rate. These constraints encourage a significant portion of the research issues in WSNs, particularly the energy constraint, which is a fundamental issue. This paper suggests an innovative routing protocol that optimizes energy use called RNS_A-star, which combines a routing algorithm using a relay node and an improved heuristic for the A-star algorithm. The RNS_A-star protocol first applies a data routing algorithm using a relay node, which allows either using a relay node or simply sending the intra-cluster data to the CH. Then, the A-star algorithm is used to construct a multi-hop routing route between the CHs and the base station (BS) in order to find the best path for reducing energy consumption during data transmission. Simulation results show that the suggested RNS_A-star protocol performs better than ECO-BAT, LEACH-IACA, and LEACH protocols regarding energy conservation and extending network lifetime.

Mobility and Temperature Sensitive Energy Efficient Routing (MTS-EER) protocol in a wireless body area network

Sensor technology advancements have provided the platform to implement wireless body area networks, thanks to the nanosized sensor units capable of sensing, aggregating, and forwarding physiological information. The collected information is routed to the desired destination unit for data analysis and decision-making in remote healthcare. However, improving energy utilization remains a brain-teasing problem for the research community, especially considering imbalanced energy consumption and postural movements. Mobility contributes to disconnectivity issues, high energy drainage, and retransmission delays. In addition, the sensor node’s thermal level also poses a challenge in maintaining safer and reliable data transmission. To overcome these issues, a novel Mobility and Temperature Sensitive Energy-Efficient Routing ¬(MTS-EER) algorithm has been proposed that includes a two-step process. In step 1, an Intelligent Path Estimation Function (IPEF) is designed considering the sensor’s mobility, temperature, and energy level. IPEF depends on crucial parameters i.e.: - residual energy, signal-to-noise ratio (SNR), distance, total energy, and most importantly temperature of the sensor unit. The sensor node with the least IPEF is selected as the Cluster Head (CH). In step 2, an optimized and sustainable energy conservation model (OSECM) is implemented based on the Adaptive Transmission Power (ATP) and the Power Management Module (PMM). The ATP conserves the energy via intelligently varying the transmission power and PMM manages the sleep pattern of sensor nodes to yield a high network lifetime and efficient energy utilization. The algorithm includes a clustering approach with dual sink nodes to conserve energy and improve reliability. Finally, the results are compared with the recent state-of-the-art research work. The proposed algorithm provides better results considering residual energy, throughput, network lifetime, and end-to-end delay.

Retraction Note: An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks

Retraction Note: An evolutionary multipath energy-efficient routing protocol (EMEER) for network lifetime enhancement in wireless sensor networks

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

A Comprehensive Review of Recent Developments in VANET for Traffic, Safety & Remote Monitoring Applications

Strategic integration of Wireless Sensor Networks (WSNs) and IoT (Internet of Things) into VANET infrastructure is crucial for ensuring vehicular safety, mobility management, and vehicular applications. The integration collects information on traffic and road conditions without relying on traditional internet connectivity. It also addresses applications such as early warnings in areas with limited coverage, safety and health emergency messages in highly congested zones, and air monitoring without depending on traditional TCP/IP internet connectivity. This article provides a comprehensive view of network technologies, data acquisition devices, clustering techniques, and energy-efficient routing protocols to optimize Vehicle-to-Everything (V2X) communications in VANETs. This study also addresses how to leverage the frequency channels of the 802.11p protocol and expands the possibilities for developing numerous applications dedicated to remote ambient, traffic and safety monitoring without compromising network performance. Moreover, the recent developments of clustering algorithms and energy-efficient schemes for these VANET applications are analyzed from a novel perspective.

Energy efficient routing protocol for enhancing the network lifetime in wireless sensor network

Wireless sensor networks (WSNs) confront significant challenges related to battery capacity, as sensor nodes operate on limited energy resources. To address this issue, low energy adaptive clustering hierarchy (LEACH) protocol is commonly employed for power management in WSNs. LEACH is commonly used for power management. Here, sensing region is divided into clusters and sectors, placing a gateway node at the center to minimize energy consumption during data transmission. It employs one-hop, two-hop, or three-hop pathways based on node proximity to the base station (BS) to optimize energy usage. Network performance is assessed using rounds, throughput, and energy usage. MATLAB simulations compare the proposed approach with dual layer LEACH (DL-LEACH) and LEACH, showing significant improvements in network lifetime. The proposed scheme outperforms LEACH by 515% and 347% for 20% and 50% node depletion, respectively. Compared to DL-LEACH, it extends network lifetime by 27% and 59% under similar scenarios. Sectoring, clustering, and multi-hop communication reduce energy consumption, enhancing network lifetime and addressing WSN challenges effectively.

Design of an optimized energy-efficient routing protocol for reliable wireless body area networks

Energy limitation is one of the essential parameters in the design of a Wireless body area networks (WBANs) as it is important to improve the lifetime of the network. WBAN routing is an effective approach for establishing energy efficiency sets and assign time slots for the network. Many algorithms that deal with interference model treats the whole WBAN as a minimum interference unit and increase their lifetime cycle. In this research, we report an effective low-energy adaptive clustering hierarchy (LEACH) routing protocol using MATLAB simulation and related C++ simulation codes to enhance the overall performance of the network by improving the energy efficiency and network lifetime cycles. Furthermore, the study sheds light up on the comparison of the protocol and proposes a modified protocol for WBAN. Based on the results obtained from conducting different configurations in the proposed design, the base station should be situated near the network to insure high network performance.

Depth based stable election routing protocol for heterogeneous internet of underwater things (IoUT) energy efficiency

Internet of underwater things (IoUT) is a technology enabling underwater sensor nodes to communicate with the IoT networks to support various applications. Efficient energy utilization and network lifetime are the two main critical issues related to the IoUT. Both performance factors are related to how efficiently the routing protocol is tailored especially for heterogeneous IoUT. In this context, efficient routing protocols that consider underwater nodes (UNs) energy heterogeneity can adjust the energy imbalance, especially in heterogeneous IoUT scenarios. In IoUT, both the UN's energies and their depths can greatly affect the energy efficiency and clustering-based routing protocol. This paper provided an energy and depth-based stable election routing protocol (SEP) to suit the requirements of underwater communications. The objective of the depth-based SEP protocol is to enhance energy-efficient routing while considering the varying energy levels of UNs in IoUT. The proposed protocol depends on the depth-based cluster head (CH) election approach by defining distinct functions tailored for UNs with different energy levels and depths, in addition to the distance between the selected CH and the surface base station. Experimental results indicate that depth-based SEP-IoUT protocol outperforms network lifetime more than conventional SEP and SEP-IoUT. It gives an average stability period of 19.6 % in addition to reducing energy consumption by 39 % and 15.7 % when compared to SEP and SEP-IoUT respectively.

A Novel Energy-Efficient Routing Protocol for Industrial WSN Using Hybrid Coot-Ls Algorithm with LSTM-Based Dom Prediction

In industrial Wireless Sensor Networks (WSNs), energy efficiency and reliable data transmission are critical challenges that need to be addressed to ensure sustainable and robust network operations. This paper proposes a novel energy-efficient routing protocol that integrates a Hybrid COOT-LS (Coot- Levy Search) algorithm with Long Short-Term Memory (LSTM)-based Dominant Object Motion (DOM) prediction. The routing protocol leverages the strengths of Hybrid Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to enhance routing efficiency and reduce energy consumption. The Hybrid PSO and ACO algorithms are employed to optimize routing paths by balancing exploration and exploitation, considering multiple factors such as energy levels, node distance, and reliability. The COOT-LS algorithm further refines these paths by incorporating a Levy flight mechanism to enhance the search process. Additionally, the LSTM-based DOM prediction provides accurate forecasts of network conditions, enabling dynamic adjustments to routing strategies in real time. Simulation results demonstrate that the proposed protocol significantly improves network lifetime, reduces energy consumption, and enhances data transmission reliability compared to traditional routing protocols. This approach provides a robust and scalable solution for industrial WSN applications, ensuring efficient and reliable network performance in dynamic and complex industrial environments.

Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree

In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system’s lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%.

Hybrid Boosted Chameleon and modified Honey Badger optimization algorithm-based energy efficient cluster routing protocol for cognitive radio sensor network

Single clustering protocols cannot meet the event-driven and time-triggered traffic requirements of Cognitive Radio Sensor Networks (CRSNs). The long wait between the completion of events and the process of clustering and searching for accessible routes results in increased time for information transmission. This paper proposed a Hybrid Boosted Chameleon and Modified Honey Badge optimization Algorithm-based Energy Efficient cluster routing protocol (HBCMHBOA) for handling the issues of traffic driven information transfer with energy efficiency in the CRSNs. This HBCMHBOA is proposed as one among few event-driven and time-triggered clustering protocol for the requirements of CRSNs. The integration of Boosted Chameleon and Modified Honey Badge optimization Algorithm is adopted for determining optimal number of clusters and constructs the structure of primitive clusters in an automated way to serve the time-triggered traffic in a periodic manner. It adopted priority-based schedule and its associated frame structure for guaranteeing reliable event-driven information delivery. It leveraged the merits of time-triggering for the construction of clustering architecture and confirmed than none of the cluster construction and selection of routes are facilitated after the emergent events. This characteristic helps in permitting only the nodes and their associated Cluster Heads (CHs) of CRSNs to discover emergent events. It facilitates the coverage of a fewer nodes, especially when sink is positioned in a corner to minimize the delay and node energy consumption. The simulation results of the proposed HBCMHBOA confirmed a reduction in total energy consumption and number of covered nodes on an average of 34.12 %, and 26.89 % than the prevailing studies.