Energy Efficient Clustering Routing Protocol Research Articles

Enhanced centroid-based energy-efficient clustering routing protocol for serverless based wireless sensor networks

Serverless computing is a new concept as cloud computing, which dynamically manages the networks and is applied in Serverless Wireless Sensor Networks (SWSN) to help the networks. These networks are becoming famous for monitoring various physical and environmental factors. Serverless computing also facilitates the networks by offering an extensive range of applications. Different applications have been designed for monitoring purposes where the sensor nodes sense the data and transmit it to the base station through single or multi-hop routing. However, existing routing protocols cannot manage the sensor nodes’ energy issues because of the complex routing processes and depleted their power before their time. Because of these limitations, the nodes close to BS continuously rely on the network for data forwarding. As a result, these nodes cause energy consumption and lead to a useless state. This paper proposes a serverless architecture and designs an Enhanced Centroid-based Energy Efficient Clustering (ECEEC) protocol for SWSN networks. The proposed serverless architecture provides automated scalability, cost-effective services, and stateless execution. In addition, the proposed protocol offers the cluster head selection and its rotation to maximize the energy efficiency in the network. Furthermore, gateway nodes are chosen in every cluster to overcome the load on the cluster head. Simulation results indicated the excellent performance of the proposed protocol as compared to the existing routing protocols concerning network lifetime and energy consumption. The proposed protocol shows better reliability with nodes failing at 650 rounds compared to 600 rounds, especially with 5 % and 10 % Cluster Heads. The proposed protocol exhibits superior energy efficiency consumption of SNs under varying CH percentages, indicating the protocol’s consistent performance across different scenarios.

An Energy Efficient Clustering Routing Protocol Based on Arithmetic Progression for Underwater Acoustic Sensor Networks

An Energy Efficient Clustering Routing Protocol Based on Arithmetic Progression for Underwater Acoustic Sensor Networks

Energy Efficient Multi-Hop Multipath Sub Clustering Routing Protocol for Wireless Sensor Network

The aim of this paper is to design Energy Efficient Multi-Hop Multipath Sub Clustering Routing Protocol for Wireless Sensor Networks. This paper presented a routing protocol for heterogenous WSN for the selection of cluster head based on the highest remaining residual energy in a node by using the optimal probabilistic equation. The heterogenous technique contains the multi-hoping of inter clustering communication and connectivity of remaining sensor nodes by using sub clustering technique. The sensor nodes with high residual energy will become cluster head and sends data packets to the base station. After successive simulations of this routing protocol on to MATLAB 2022b software, it was consequently found out that it improves the number of alive nodes, number of data packets transmitted to the sink in a network and also improves the network lifetime by 75%-80% in comparison with other protocols. The simulation results were carried out by comparing the proposed method with O LEACH, EEE LEACH, Z-SEP, LEACH. The result shows that, our proposed method is more stable and energy efficient than the other routing protocols.

A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches

A survey of energy-efficient clustering routing protocols for wireless sensor networks based on metaheuristic approaches

IACRA: Lifetime Optimization by Invulnerability-Aware Clustering Routing Algorithm Using Game-Theoretic Approach for Wsns.

Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount of energy consumed for data transmission, how to prolong the network lifespan by designing various hierarchical routing protocols has attracted more and more attention. As a result, numerous achievements have emerged successively. However, these presented mechanisms can rarely guarantee the satisfactory quality of service (QoS), while lowering the energy cost level of WSNs. Meanwhile, invulnerability is undoubtedly an excellent quantitative index to assess QoS. Therefore, it is critical to develop a practical routing method to optimize network lifetime by considering both invulnerability and energy efficiency. Game theory is suitable for such a critical problem as it can be used in node or at network level to encourage the decision-making capabilities of WSNs. In this paper, a novel invulnerability-aware clustering routing algorithm (IACRA) using game-theoretic method is proposed to solve the predicament. The core features of the addressed game-theory-based routing protocol include integral invulnerability awareness, optimal cluster head selection in hierarchical routing, distance-aware cluster head discovery, and cluster rotation update mechanism for lifetime optimization. Particularly, the integral network invulnerability based on weighted fusion is constructed for further defining the profit model by combining the invulnerability indicators used to evaluate the local and whole network. Meanwhile, the optimal probability function of every node elected as CH in per cluster is established through the game between invulnerability and node energy consumption. In addition, the cluster update mechanism base on cluster rotation is proposed to avoid the rapid death of nodes with large energy consumption for maximizing network lifetime. The experimental results indicated a significant improvement in energy balance as well as in invulnerability compared with the other three kinds of well-known clustering routing protocols including GEEC (Game-theory-based energy efficient clustering routing protocol), HGTD (Hybrid, game-theory-based distributed clustering protocol), and EEGC (Efficient energy-aware and game-theory-based clustering protocol). Concretely, at the 400 communication rounds, the invulnerability of IACRA was higher than that of GEEC, HGTD, and EEGC by 77.56%, 29.45% and 15.90%, respectively, and the average residual energy of IACRA was 8.61%, 18.35% and 6.36% larger than that of GEEC, HGTD, and EEGC, respectively. Based on these results, the proposed protocol can be utilized to increase the capability of WSNs against deterioration of QoS and energy constraints.

Energy efficient clustering routing protocol using novel admission allotment scheme (AAS) based intra-cluster communication for Wireless Sensor Network

Energy efficient clustering routing protocol using novel admission allotment scheme (AAS) based intra-cluster communication for Wireless Sensor Network

A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks.

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network’s lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.

Design of an enhanced threshold sensitive distributed energy efficient clustering routing protocol for WSN-based IoT

ABSTRACT Internet of Things (IoT) is a smart technology that allows communication between humans and all things that contain sensors, actuators, embedded electronics, software, or storage via the internet. As a result, IoT should be seen as a part of the overall Internet of the future, which is likely to be very different from what we are using today. Despite the advantages of IoT, energy efficiency is still considered one of the main challenges in IoT. There are many algorithms that have been proposed to solve the energy efficiency problem. Unfortunately, most of them have some drawbacks. As a result, an Enhanced Threshold Sensitive Distributed Energy-Efficient Clustering routing protocol (ETSDEEC) for WSN-based IoT was proposed in this paper. Hard and soft thresholds are used in the proposed protocol to determine whether nodes are allowed to send their sensed data. From the simulation, we found that the proposed ETSDEEC protocol performs better than EDEEC and TDEEC protocols in terms of network lifetime, energy dissipationand throughput. It is also found that the proposed ETSDEEC can improve the lifetime by 59.8% in comparison to EDEEC and about 37.7% in comparison to TDEEC protocols.

Distributed energy-efficient clustering routing protocol for wireless sensor networks using affinity propagation and fuzzy logic

Organizing nodes into clusters and forwarding data to the base station (BS) in clustering routing protocols have been widely utilized to improve the energy efficiency, scalability and stability of wireless sensor networks. Making decisions on how many clusters are formed, which nodes are selected as cluster heads (CHs) and who become the relay nodes significantly impact the network performance. Therefore, a distributed clustering routing protocol combined affinity propagation (AP) with fuzzy logic called DAPFL is proposed in this paper, which considers not only energy efficiency but also energy balance to extend the network lifetime. In DAPFL, AP is firstly used to determine the number of clusters and select the best CHs simultaneously based on residual energy, distance between nodes. Then, the optimal next-hop CHs are chosen by using fuzzy logic system with residual energy, data length and distance to BS as descriptors. Simulations in different scenarios are carried out to verify the effectiveness of DAPFL, and the results show that DAPFL exhibits the promising performance in terms of network energy consumption, standard deviation of residual energy, network throughput and lifetime, compared with the up-to-date distributed clustering routing protocols EEFUC, EEFRP, LEACH-AP and APSA.

Chaotic Search-and-Rescue-Optimization-Based Multi-Hop Data Transmission Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have applications in several fields, such as disaster management, underwater navigation, and environment monitoring. Since the nodes in UWSNs are restricted to inbuilt batteries, the effective utilization of available energy becomes essential. Clustering and routing approaches can be employed as energy-efficient solutions for UWSNs. However, the cluster-based routing techniques developed for conventional wireless networks cannot be employed for a UWSN because of the low bandwidth, spread stay, underwater current, and error probability. To resolve these issues, this article introduces a novel chaotic search-and-rescue-optimization-based multi-hop data transmission (CSRO-MHDT) protocol for UWSNs. When using the CSRO-MHDT technique, cluster headers (CHs) are selected and clusters are prearranged, rendering a range of features, including remaining energy, intracluster distance, and intercluster detachment. Additionally, the chaotic search and rescue optimization (CSRO) algorithm is discussed, which is created by incorporating chaotic notions into the classic search and rescue optimization (SRO) algorithm. In addition, the CSRO-MHDT approach calculates a fitness function that takes residual energy, distance, and node degree into account, among other factors. A distinctive aspect of the paper is demonstrated by the development of the CSRO algorithm for route optimization, which was developed in-house. To validate the success of the CSRO-MHDT method, a sequence of tests were carried out, and the results showed the CSRO-MHDT method to have a packet delivery ratio (PDR) of 88%, whereas the energy-efficient clustering routing protocol (EECRP), the fuzzy C-means and moth–flame optimization (FCMMFO), the fuzzy scheme and particle swarm optimization (FBCPSO), the energy-efficient grid routing based on 3D cubes (EGRC), and the low-energy adaptive clustering hierarchy based on expected residual energy (LEACH-ERE) methods have reached lesser PDRs of 83%, 81%, 78%, 77%, and 75%, respectively, for 1000 rounds. The CSRO-MHDT technique resulted in higher values of number of packets received (NPR) under all rounds. For instance, with 50 rounds, the CSRO-MHDT technique attained a higher NPR of 3792%.

Fault-Tolerant Backup Clustering Algorithm for Smart Connected Underwater Sensor Networks

This paper addresses poor cluster formation and frequent Cluster Head (CH) failure issues of underwater sensor networks by proposing an energy-efficient hierarchical topology-aware clustering routing (EEHTAC) protocol. In this paper, fault-tolerant backup clustering (FTBC) algorithms and multi-parameter cluster formation (MPCF) model were developed for the EEHTAC operation. The MPCF model tackles the issue of poor cluster formation performance by integrating multiple parameters to achieve effective clustering process. The FTBC algorithms tackle the issue of frequent CH failures to avoid interruption in data transmission. Performance of the MPCF model was evaluated using normal, high-fault, and high routing overhead network scenarios. Performance metrics employed for this analysis are temporal topology variation ratio (TTVR), CH load distribution (CLD), and cluster stability (STB). Obtained results show that operating with a CH retention period of 90s achieves better CH duty cycling per round and improves the MPCF process with values of 25.69%, 55.56%, and 60% for TTVR, CLD, and STB respectively. Performance of the FTBC-based EEHTAC was evaluated relative to Energy-balanced Unequal Layering Clustering (EULC) protocol. Performance indicators adopted for this evaluation are routing overhead (Ω), end to end delay (Δ), CH failures recovered (CFR), CH failures detected (CFD), received packets (θ), and energy consumption (Σ). With reference to the best obtained values, EEHTAC demonstrated performance improvement of58.40%, 29.94%,81.33%, 28.02%, 86.65%, and 54.35% over EULC variants in terms of Ω, Δ,CFR, CFD, θ, and Σ respectively. Obtained results displayed that the MPCF model is efficient for cluster formation performance and the FTBC-based EEHTACprotocolcan perform effectively well against an existing CBR protocol.

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on Yellow Saddle Goatfish Algorithm

The usage of wireless sensor devices in many applications, such as in the Internet of Things and monitoring in dangerous geographical spaces, has increased in recent years. However, sensor nodes have limited power, and battery replacement is not viable in most cases. Thus, energy savings in Wireless Sensor Networks (WSNs) is the primary concern in the design of efficient communication protocols. Therefore, a novel energy-efficient clustering routing protocol for WSNs based on Yellow Saddle Goatfish Algorithm (YSGA) is proposed. The protocol is intended to intensify the network lifetime by reducing energy consumption. The network considers a base station and a set of cluster heads in its cluster structure. The number of cluster heads and the selection of optimal cluster heads is determined by the YSGA algorithm, while sensor nodes are assigned to its nearest cluster head. The cluster structure of the network is reconfigured by YSGA to ensure an optimal distribution of cluster heads and reduce the transmission distance. Experiments show competitive results and demonstrate that the proposed routing protocol minimizes the energy consumption, improves the lifetime, and prolongs the stability period of the network in comparison with the stated of the art clustering routing protocols.

Energy efficient dynamic clustering routing protocol in underwater wireless sensor networks

Energy efficient dynamic clustering routing protocol in underwater wireless sensor networks

Energy efficient dynamic clustering routing protocol in underwater wireless sensor networks

In underwater wireless sensor networks (UWSN) energy control is a vital issue. Few UWSN applications include both dynamic and static sensor nodes within the same networks. The mobility of dynamic nodes can lead to performance related issues as these nodes include small memory, battery power, short range communication, and low speed calculation ability. Hence, an energy efficient routing protocol is important to direct the incoming packet. Dynamic cluster technology utilising cluster based network arrangement is mainly learned in routing protocol. In this paper, proposed a unique routing algorithm called as energy efficient and mobility-based dynamic cluster head selection routing protocol (EE-MDCHSRP) is suggested for UWSN for successful dynamic cluster head choices. The dynamic cluster-head nodes are determined considering the factors including node density, remaining energy and least mobility factor. Simulation test and data analysis results prove that the overall performance of proposed or projected routing method is 96.98% more effective when compared to existing routing protocols of the UWSN, and it was simulated in Linux-based Network Simulator 2 (NS2) simulation software environment. This research work helps us to reduce power consumption, response time and avoid overload, improve the throughput and network lifetime.

Centralized Energy-Efficient Clustering Routing Protocol for Mobile Nodes in Wireless Sensor Networks

In wireless sensor networks with mobile nodes, it is important to enhance energy efficiency. For these networks, several routing protocols have been presented to reduce packet loss and increase energy efficiency. However, since these protocols cannot create appropriate clusters, they are not extremely energy efficient. In this letter, a centralized energy-efficient clustering routing protocol for mobile nodes (CEECR) is developed to minimize energy dissipation and maximize packet delivery ratio. In our protocol, a centralized cluster formation algorithm is presented to produce optimal clusters by utilizing node mobility and the node energy property. Furthermore, a detached node selects its optimal cluster head according to the node distance property, node mobility, and the node energy property. The performance of CEECR is then compared to clustering-based protocols, such as LEACH, LEACH-C, LEACH-Mobile, CBR, MBC, and LEACH-MF. Simulation results show that CEECR reduces average energy dissipation and improves packet delivery ratio over its comparatives.

An Energy-Efficient Clustering Routing Protocol Based on a High-QoS Node Deployment with an Inter-Cluster Routing Mechanism in WSNs.

Currently, wireless sensor network (WSN) protocols are mainly used to achieve low power consumption of the network, but there are few studies on the quality of services (QoS) of these networks. Coverage can be used as a measure of the WSN’s QoS, which can further reflect the quality of data information. Additionally, the coverage requirements of regional monitoring target points are different in real applications. On this basis, this paper proposes an energy-efficient clustering routing protocol based on a high-QoS node deployment with an inter-cluster routing mechanism (EECRP-HQSND-ICRM) in WSNs. First, this paper proposes formula definitions for information integrity, validity, and redundancy from the coverage rate and introduces a node deployment strategy based on twofold coverage. Then, in order to satisfy the uniformity of the distribution of cluster heads (CHs), the monitoring area is divided into four small areas centered on the base station (BS), and the CHs are selected in the respective cells. Finally, combined with the practical application of the WSN, this paper optimizes the Dijkstra algorithm, including: (1) nonessential paths neglecting considerations, and (2) a simultaneous introduction of end-to-end weights and path weights, achieving the selection of optimal information transmission paths between the CHs. The simulation results show that, compared with the general node deployment strategies, the deployment strategy of the proposed protocol has higher information integrity and validity, as well as lower redundancy. Meanwhile, compared with some classic protocols, this protocol can greatly reduce and balance network energy consumption and extend the network lifetime.

Energy efficient clustering routing protocol for wireless sensor networks

Energy efficient clustering routing protocol for wireless sensor networks

An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization.

To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4–0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.

An Energy-Efficient Clustering Routing Protocol for WSN based on MRHC

An Energy-Efficient Clustering Routing Protocol for WSN based on MRHC