Cluster Based Routing Protocol Research Articles

Developed cluster-based load-balanced protocol for wireless sensor networks based on energy-efficient clustering

One of the most pressing issues in wireless sensor networks (WSNs) is energy efficiency. Sensor nodes (SNs) are used by WSNs to gather and send data. The techniques of cluster-based hierarchical routing significantly considered for lowering WSN’s energy consumption. Because SNs are battery-powered, face significant energy constraints, and face problems in an energy-efficient protocol designing. Clustering algorithms drastically reduce each SNs energy consumption. A low-energy adaptive clustering hierarchy (LEACH) considered promising for application-specifically protocol architecture for WSNs. To extend the network's lifetime, the SNs must save energy as much as feasible. The proposed developed cluster-based load-balanced protocol (DCLP) considers for the number of ideal cluster heads (CHs) and prevents nodes nearer base stations (BSs) from joining the cluster realization for accomplishing sufficient performances regarding the reduction of sensor consumed energy. The analysis and comparison in MATLAB to LEACH, a well-known cluster-based protocol, and its modified variant distributed energy efficient clustering (DEEC). The simulation results demonstrate that network performance, energy usage, and network longevity have all improved significantly. It also demonstrates that employing cluster-based routing protocols may successfully reduce sensor network energy consumption while increasing the quantity of network data transfer, hence achieving the goal of extending network lifetime.

RANDOM WAYPOINT MOBILITY ROUTING ALGORITHM FOR MOBILE SENSOR NETWORKS

Mobile Sensor Network an emerging trend in recent network platform. Random waypoint mobility routing proposed on virtual infrastructure based mobile sensor network. Routing is highly challenging task in mobile sensor network, in which the random waypoint model used to ascertain through put and energy depletion. Sensor network classified into multiple regions. Scheduled time slot is assigned for data transmission in between source to sink. Routing table is periodically updated with its neighborhood information. In order to discovers sink node random waypoint mobility routing algorithm checks either clockwise or anti clockwise in the region to find an optimal route for reliable data transmission. In comparison to other cutting-edge cluster-based routing protocols, the effectiveness of the suggested technique assessed using the essential network characteristics. In addition proposed algorithm reduces normalized routing overhead and reselection of region head by varying the node density in the network.

Self-Organized Ad Hoc Mobile (SOAM) Underwater Sensor Networks

The need of underwater wireless sensor networks (UWSNs) having mobile sensor nodes has been there for a long time in form of underwater warfare or explorations by autonomous underwater vehicles (AUVs) or remote unmanned vehicles (ROVs). There are very few protocols for ad hoc mobile UWSNs (AMUWSNs). Designing a protocol for AMUWSN is quite challenging because of continuous random movement of the sensor nodes. In addition to random movement, the challenges to design a routing protocol for AMUWSN are more demanding than terrestrial ad hoc networks due to acoustic communications, which has large propagation delay in water. In this article, we present a self-organized ad hoc mobile (SOAM) routing protocol for AMUWSN. The sensor nodes may need to communicate with each other to the gateway (GW). The protocol, which we also refer to as SOAM, is a reactive, self-configuring, and self-organizing cluster-based routing protocol that uses received signal strength (RSS) for distance estimation. A beacon (BCN) packet will be sent by the GW, which will traverse through all the cluster heads (CHs) to form forwarding paths between the GW and the CHs. The ordinary sensor nodes (OSNs) will select the CHs every time they intend to forward a packet based on the BCN and they will receive from CHs. The formation of the forwarding path between the GW and the CHs and the selection CHs by OSN is explained in <xref ref-type="sec" rid="sec4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Section IV</xref> .

Enhanced clustering based routing protocol in vehicular ad‐hoc networks

AbstractA vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicating with roadside units in an infrastructure mode. Secure clustering is required for the communication between nodes in the whole network. The fundamental problem with the VANET is the instability of the network that occurs due to vehicles' mobile nature, which decreases the network's efficiency. This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The Sugeno model fuzzy inference system is used for assessing the cluster head (CH) that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters. Our enhanced routing protocol shows that the proper channel model with an efficient routing protocol enhances the link throughput of the VANET for fixed network size. Our results show an efficient selection method of CH through the fuzzy system and a 10% increase in network lifetime. Furthermore, performance evaluation also demonstrates the impact of network sizes and routing protocols on packet delivery ratio and packet loss, average end‐to‐end delay, and overhead transmission.

Distributed Energy-Efficient Clustering and Routing for Wearable IoT Enabled Wireless Body Area Networks

Recently, the wearable internet of things (WIoT) has brought a new dimension of connectivity to wireless body area networks (WBANs). For instance, the WIoT provides valuable functions, such as collecting, analyzing, and transmitting data for real-time health monitoring of medical services. However, designing a clustering and routing protocol is challenging because WIoT attributes are subject to high interference in dense deployment, social mobility, and limited energy. In a WIoT-enabled WBAN, cooperative control is the fundamental function of clustering and routing. Cluster-based routing protocols are preferred for WBAN because of their scalability. However, one-hop neighbor-based clustering approaches do not ensure connectivity and reachability owing to node mobility. The existing two-hop-based clustering protocols are mostly centralized-based approaches, which are unsuitable for dynamic WBANs. In this paper, we propose a distributed energy-efficient two-hop-based clustering and routing protocol (DECR) targeting WIoT-enabled WBAN. In DECR, in the cluster formation phase, each node obtains the information of its neighbor nodes within the two-hop range. We utilize the modified grey-wolf optimization algorithm for the cluster head (CH) selection and routing optimization. Node connectivity and residual energy were jointly considered when determining the CH in each cluster. We also developed an analytical model to determine the optimal number of clusters by considering intra- and inter-cluster transmission distances to reduce the overall transmission distance and number of transmissions. Finally, we proposed a routing algorithm to ensure energy-efficient packet delivery from CH to sink. Our simulation outcomes revealed that the proposed DECR significantly outperforms the existing clustering and routing protocols in various performance metrics.

Countermeasure SDN-based IoT threats using blockchain multicontroller

The Internet of Things (IoT) is making significant progress in various fields; software-defined networks with multiple controllers have become popular because they make it easier to manage large networks. But they are open to several attacks, which makes controller topologies inconsistent. To solve this problem, we suggest a multi-controller blockchain for software-defined networking (SDN) network. This security architecture combines blockchain and multi-controller SDN and divides the network into several domains. We put forth a blockchain-based solution. This paper proposed a model blockchain-enabled SDN multi-controller architecture for IoT networks that uses a clustering algorithm and a new routing protocol that is both secure and energy-efficient. Experimental results indicate that the cluster-based routing protocol has a greater capacity, a shorter response time, and a lower overall power requirement than other protocols. It has been shown that our proposed architecture outperforms the classic blockchain.

In search of cluster-based routing protocol for WSN using consensus algorithm

In search of cluster-based routing protocol for WSN using consensus algorithm

Efficient Hybrid Fault-Management Clustering Algorithm (HFMCA) in WBANs Based on Weighted Bipartite Graph

Wireless Body Area networks (WBANs) have been adopted extensively in academic and industrial research and development in recent years. WBANs promise to provide a suitable environment for remote patient monitoring and enable caregivers to intervene in case of abnormal health conditions. However, WBANs sensors are resource-constrained and face challenges, including, for instance, sensor energy harvesting, efficient routing, parent node selection in case of two hubs links, and data security. The stringent healthcare requirements burden managing WBAN resources and sensor functionality. In this research work, we tackle the issue of total energy consumption, communication overhead, and the lifespan of WBAN. We study the effect of routing and parent node selection on the network lifespan. The study considers a cluster-based routing protocol to establish a WBAN. This approach divides the network into clusters, where each cluster consists of a cluster head called a Relay Node (RN) and a set of functional sensors. Each RN collects data from its sensor nodes and sends it to a base station. However, the relay nodes are susceptible to failures at any time due to the challenging deployment environments and resource constraints of the nodes. In addition, the network sensors are dynamic in positions due to user body movements. Therefore, improving the robustness, fault management, and reliability at different postures becomes necessary. We propose a mobility-aware, energy-efficient Hybrid Fault-Management Clustering Algorithm (HFMCA) adopting the Weighted Bipartite Graph (WBG) to handle the vulnerable RNs prone to failure. The WBG helps find the optimum path between the vulnerable RN and its members to the sink node while maintaining minimum energy consumption and transmission costs. Finally, we conducted extensive experiments, and the simulation results illustrate the proposed algorithm’s efficiency in WBANs regarding energy consumption, network lifespan, and communication overhead.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network’s longevity and optimizes the routing. As a result of the study’s findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.

Efficient and optimized communication in biomedical sensor networks based on bioinspired particle swarm optimization for medical applications.

Healthcare, sports, the military, location monitoring and wireless body networks are emerging as technology of major relevance. As a result of the widespread usage of biomedical sensor networks in medical applications, it is essential that data packets containing vital signs be reliably and effectively supplied to the medical center. Because of its mobility, real-time monitoring, cheap cost, and real-time feedback, it may be used in a broad variety of applications. Effective data transport and a limited energy supply are the main challenges in WBAN. Uses genetic heuristics to enhance routing algorithm efficiency. Low-energy adaptive clustering hierarchy (LEACH) and distributed energy efficiency clustering (DEC) are two kinds of clustering algorithms (DEEC). A clustering-based routing protocol may be optimized using this study's optimization approach so that the network's lifetime can be extended.. The cluster heads (CHs) in sensor nodes are picked with the least amount of overhead grading possible. The cluster is being balanced. Passive clustering based on Bioinspired Particle Swarm Optimization (BPSO) should be used for clustering purposes. Routing messages efficiently means sending them quickly and efficiently without using a lot of bandwidth. Using constraints such as distance and residual power, the optimal path for the cluster may be determined with the help of iterative and heuristic chicken swarm optimization (IHCSO) for short. An evaluation of the packet distribution allocation, capacity, and average end-to-end latency illustrates the practicability of the proposed system in research concerning its efficiency. According to the findings of the research, following the technique that was proposed leads to much better outcomes.

An Enhanced Localization Approach for Energy Conservation in Wireless Sensor Network with Q Deep Learning Algorithm

Wireless Sensor Networks (WSN) have distributed a collection of tiny sensor nodes deployed randomly in the given symmetry environment to sense natural phenomena. The sensed data are disseminated symmetrically to the control station using multi-hop communication. In WSN, the energy conservation during node coverage plays a major role in detecting node failure and providing efficient and symmetrical data transmission to the nodes of WSN. Using the cluster method and efficient localization techniques, the nodes are grouped and the precise location of the nodes is identified to establish the connection with the nearby nodes in the case of node failure. The location accuracy is achieved using the localization estimation of the anchor nodes and the nearest hop node distance estimation using the received signal strength measurement. The node optimization can be performed efficiently by the accurate estimation of the localization of the node. To optimize the node coverage and provide energy efficient and symmetrical localization among the nodes, in this paper, a cluster-based routing protocol and a novel bio-inspired algorithm, namely, Modified Bat for Node Optimization (MB−NO), to localize and optimize the unknown nodes along with the reinforcement-based Q learning algorithm is proposed with the motive of increasing the accuracy estimation between anchor nodes and the other neighbor nodes, with the objective function to optimize and improve the nodes’ coverage among the network’s nodes in order to increase the nodes’ localization accuracy. The distance metrics between the anchor nodes and other neighbor nodes have an estimated symmetry with three node positions, namely C-shape, S-shape and H-shape, using the Q learning algorithm. The proposed algorithm is implemented using the NS3 simulator. The simulation results show that the accuracy and precision of the proposed algorithm are achieved at 98% in the node coverage optimization with reduced Mean Localization Error (MLE) and computational process time compared with other bio-inspired algorithms, such as Artificial Bee Colony optimization and Genetic Algorithms.

Distance and energy aware extended LEACH using secondary cluster head for wireless sensor networks

Sensor node's energy consumption is a crucial consideration for designing a wireless sensor network (WSN) as the node is battery powered with limited energy that significantly affects the network lifetime. An efficient design of routing protocol can play an important role to ensure optimum use of node's energy to increase the scalability and lifetime of the network. A clustering-based routing protocol like low energy adaptive clustering hierarchical (LEACH) has attracted much attention in recent years as the network lifetime is effectively increased due to the formation of clusters using sensor nodes. However, the protocol shows the limited performance when the cluster head (CH) selection uses the node having low energy level and far distance from the base station (BS). Here, the selection of secondary cluster head (2CH) is useful, where the member node having the highest residual energy can act as a relay node for data aggregation and ensure the optimum utilization of available energy. In this article, two LEACH protocols namely E-LEACH and DE-LEACH are characterized and further extended by adding the secondary cluster heads, which eventually ensure the optimum choice of CH for sustaining more energy in the network. The proposed Extended E-LEACH and Extended DE-LEACH protocols are analyzed using the network lifetime and total energy consumption and the results show an improved system performance compared to existing 2CH based routing protocols like FLEACH and FLEACH-E. The Extended DE-LEACH shows the best results among all the protocols. In particular, total energy consumption for Extended DE-LEACH is improved by a maximum of 5%, 11% and 21% as compared to Extended E-LEACH, FLEACH-E and FLEACH respectively.

AI-TASFIS: An Approach to Secure Vehicle-to-Vehicle Communication

ABSTRACT VANET provides communication between vehicles. VANET nodes are highly dynamic. Therefore, it is essential to increase the stability of the communication between nodes. The cluster head and cluster node provide stable communication between vehicles. Vehicle communications are being challenged by factors such as security, confidential communication, and severe delay. This work proposes an Artificial Intelligence (AI)-based Sugeno fuzzy inference system to overcome these issues. The proposed secure trust-based cluster techniques are less complex, with lesser communication delays, lower overhead, and more efficient in accurately locating trusted nodes for communication. Vehicular Ad hoc Networks (VANET) should send data between vehicles and use traffic safety indicators using an Enhanced Cluster-based routing protocol. AI-TASFIS is Artificial Intelligence-based Trust Authentication Sugeno fuzzy inference system that uses ANFIS-based Sugeno Fuzzy inference systems to calculate the node weights for choosing trusted cluster head and cluster member that reduces malicious attacks like Black Hole Attacks, Wormhole attacks, and Timing Attacks while transferring data packets. Simulation results show that the proposed Artificial Intelligence (AI)-based Sugeno fuzzy inference system provides network security, reduces end-to-end delay, and increases packet delivery ratio and throughput.

Improved Performance on Wireless Sensors Network Using Multi-Channel Clustering Hierarchy

Wireless sensor network is a network consisting of many sensor nodes that function to scan certain phenomena around it. WSN has quite a large problem in the form of delay and data loss which results in low WSN performance. This study aims to improve WSN performance by developing a cluster-based routing protocol. The cluster formation is carried out in several stages. The first is the formation of the cluster head which is the channel reference to be used by node members by means of probability calculations. The second determines the closest node using the Euclidean approach when looking for the closest member of the node to the cluster head. The third is determination of the node members by means of single linkage grouping by looking for proximity to CH. The performance of the proposed MCCH method is then tested and evaluated using QoS parameters. The results of this research evaluation use QoS parameters for testing the MCCH method, channel 1 throughput 508.165, channel 2 throughput 255.5661, channel 3 throughput 479.8289, channel 4 throughput 646.5618.

An improved high performance clustering based routing protocol for wireless sensor networks in IoT

An improved high performance clustering based routing protocol for wireless sensor networks in IoT

An Improved Energy Efficient Solution for Routing in IoT

The concept of the Internet of Things (IoT) is an innovation paradigm that suggests linking physical devices together to share data and work toward a common goal. Nodes in an IoT network are anticipated to communicate with one another and with third-party Internet services. However, not all network nodes have direct Internet access due to high deployment costs. As a result, additional network nodes should be using Internet-connected nodes as gateways to transmit data to online services. Due to its central role in establishing connections between nodes in a network and distributing data packets, routing protocols have emerged as a significant challenge in IoT use cases. The work of routing is complicated in mobile IoT scenarios due to the topology changes brought on by the mobility of nodes. To better handle the mobile nature of the devices, existing Iot routing solutions typically exhibit severe constraints and poor performance in such cases. This study proposes an improvement to the existing protocols; the Cluster Formation Protocol with Neuro Fuzzy Rules, in light of the fact that main routing techniques for low-power network are unable to achieve adequate performance in the shown IoT context. The energy efficiency of the routing process in WSN can be improved with the help of cluster based routing thanks to the proposed efficient routing algorithms. In addition, the efficiency of clustering &amp; cluster based routing protocols can be enhanced by employing intelligent approaches like fuzzy rules, temporal constraints, and categorization via deep learning. Therefore, the safe transfer of data packets from of the source point to the sink node necessitates careful consideration in the development of cluster dependent routing algorithms. The cluster dependent routing method presented is developed, evaluated with respect to a number of criteria, and shown to be effective.

TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks

On account of the highly dynamic topology of vehicular networks, network congestion and energy utilization are greatly increased, which directly affects the performance of VANETs. So, managing traffic and reducing energy consumption in the network becomes a challenging task in such huge mobility-based VANET networks. Thus, in this paper a new traffic and cluster-based network method is introduced, namely, Traffic-Aware Clustering based Routing Protocol (TACRP). The main aim of the approach is to improve traffic management in the network as well as to reduce energy consumption in it. In the constructed network, a Traffic Management Unit (TMU) is introduced to control the entire network traffic with the help of RSUs. Vehicles with similar speed and direction are grouped into a cluster to increase the network stability and help to reduce the energy consumption of the network. The clustering model provides principles associated with vehicles leaving the clusters, joining the clusters, cluster updates and inter-cluster communication, which makes the network more stable and reliable. For instance, in the proposed work the CH selection is based on centralization, weight, distance, and energy calculation. Such network settings facilitate successfully clustering of vehicles on the road. Simulation experimental analysis showed that the proposed TACRP routing protocol achieved better results in terms of energy efficiency, throughput, packet delivery ratio, and end to end delay of the network when compared with earlier methods, such as ECHS and NRHCS.

QoS enhanced energy efficient cluster based routing protocol realized using stochastic modeling to increase lifetime of green wireless sensor network

QoS enhanced energy efficient cluster based routing protocol realized using stochastic modeling to increase lifetime of green wireless sensor network

Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks

The clustering mechanism in wireless sensor networks (WSNs) is the ideal strategy for constructing an energy efficient protocol for achieving extended network lifetime, energy efficiency, and scalability. The design of energy efficient protocols in WSNs is essential, as the sensor nodes are battery powered, and may be completely drained, resulting in deterioration in network lifetime. Energy efficient protocols aim to maximize network lifetime while minimizing overall energy consumption to avoid interruptions to the wireless sensor nodes deployed for monitoring and recording physical information from the environment. Moreover, hybrid metaheuristic-optimization based clustering and routing protocols are widely used for attaining energy stability and network lifetime. In this study, the golden eagle optimization algorithm (GEOA) and improved grasshopper optimization algorithm (IGHOA) based on the energy efficient cluster-based routing protocol (GEIGOA) are proposed for sustaining energy stability and augmenting network lifetime longevity by overcoming challenges in the cluster head (CH) selection process. In particular, by optimizing the node centrality, node degree, distance to the base station, distance to the neighbors, and residual node energy, GEOA selects an optimal CH from the deployed group of sensor nodes in the network. Furthermore, IGHOA is utilized to determine a reliable and optimal route between the CH and base station (BS) by assessing node degree, residual energy, and distance parameters. Moreover, the proposed GEIGOA was confirmed to be sufficiently capable in improving the probability of preventing the worst nodes from being selected as CHs with different number of sensor nodes, which was predominantly enhanced by 12.64%, 15.82%, 18.96%, and 20.98% compared with the competitive CH selection schemes. In addition, the computational cost incurred by the proposed GEIGOA with different number of sensor nodes was also confirmed to be minimized by 14.98%, 17.21%, 19.76%, and 21.62% compared with the competitive CH selection schemes.

A novel routing protocol based on grey wolf optimization and Q learning for wireless body area network

Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result analysis shows its efficiency in terms of energy for WBAN.