Cluster Head Selection Protocol Research Articles

Optimized scheme for cluster head selection and EAMPR routing protocol design in WSN

Wireless sensor networks (WSN) is a popularly emerging technology with several opportunities to sustain in various field that require multipurpose sensor nodes, less energy and non-expensive system. But in the WSN, the radio transmission needs high amount of energy and this creates the critical problem. Hence consumption of energy has to be decreased to extend the network durability. Even though there are so many techniques existing for clustering approach of WSN, they have limitations like increased energy consumption, less delivery rate of data, redundancy and unbalanced network load. Hence, these problems are solved by introducing the energy efficient deep learning techniques for clustering and finding the optimal route. Initially the initialization process of system model is performed with the implementation of energy model. In WSN, energy consumption should be reduced to enhance the QoS and balance the network traffic. Hence clustering method is used to group up the sensor nodes and the optimal cluster head is selected with the proposed technique of hybrid cuckoo search and particle swarm optimization (CSO-PSO). As the CH is chosen, the optimal path of routing data should be found in addition with the procedure of optimization and it is done through the proposed model of Optimization based routing protocol that incorporates the Energy Aware Multi Point Routing (EAMPR) protocol along with the Improved Tuna Search Optimization (ITSO) algorithm. Finally, by the use of ITSO-EAMPR technique the energy consumption will get reduced with the decrease in relative mobility and high stability of nodes would be achieved. The simulations are proceeded and the outcomes are validated. The result obtained is compared with the traditional methods to show the effectiveness of proposed technique. As per the results obtained the proposed ITSO-EAMPR attains maximized PDR and Throughput, higher energy efficiency with extension in lifetime of WSN along with decrease in BER, end-to-end latency as compared to the existing techniques.

Efficient Load Balancing in WSN Using Quasi – oppositional Based Jaya Optimization with Cluster Head Selection

Researchers have been paying close attention to the wireless sensor (WSN) networks area because of its variety of applications, including industrial management, human detection, and health care management. In Wireless Sensor (WSN) Network, consumption of efficient energy is a challenging problem. Many clustering techniques were used for balancing the load of WSN network. In clustering, the cluster head (CH) is selected as a relay node with greater power which is compared with the nodes of non-CH. In the existing system, it uses LBC-COFL algorithm to reduce the energy consumption problem. To overcome this problem, the proposed system uses Quasi oppositional based Jaya load balancing strategy with cluster head (QOJ-LCH) selection protocol to boost the lifespan of network and energy consumption. The QOJ-LCH method improves the relay nodes life and shares the load on relay nodes equitably across the network to enhance the lifespan. It also reduces the load-balancing problems in Wireless Sensor networks. It uses two routing methods single-hop and multiple-hop. The proposed QOJ-LCH with cluster head selection method enhances the network’s lifespan, total amount of power utilization and the active sensor devices present in the Single-hop routing,it worked with 1600 rounds in network and 300 sensor nodes, for Multiple-hop routing, it worked with 1800 rounds in network and 350 sensor nodes. It achieves better performance, scalability and reliability.

FBCHS: Fuzzy Based Cluster Head Selection Protocol to Enhance Network Lifetime of WSN

With enormous evolution in Microelectronics, Wireless Sensor Networks (WSNs) have played a vital role in every aspect of daily life. Technological advancement has led to new ways of thinking and of developing infrastructure for sensing, monitoring, and computational tasks. The sensor network constitutes multiple sensor nodes for monitoring, tracking, and surveillance of remote objects in the network area. Battery replacement and recharging are almost impossible; therefore, the aim is to develop an efficient routing protocol for the sensor network. The Fuzzy Based Cluster Head Selection (FBCHS) protocol is proposed, which partitions the network into several regions based on node energy levels. The proposed protocol uses an artificial intelligence technique to select the Cluster Head (CH) based on maximum node Residual Energy (RE) and minimum distance. The transmission of data to the Base Station (BS) is accomplished via static clustering and the hybrid routing technique. The simulation results of the FBCHS protocol are com- pared to the SEP protocol and show improvement in the stability period and improved overall performance of the network.

EECHEP: Energy efficient cluster head election protocol for extending lifetime of heterogeneous sensor networks

EECHEP: Energy efficient cluster head election protocol for extending lifetime of heterogeneous sensor networks

A Brief Survey on Cluster based Energy Efficient Routing Protocols in IoT based Wireless Sensor Networks

The wireless sensor network (WSN) consists of a large number of randomly distributed nodes capable of detecting environmental data, converting it into a suitable format, and transmitting it to the base station. The most essential issue in WSNs is energy consumption, which is mostly dependent on the energy-efficient clustering and data transfer phases. We compared a variety of algorithms for clustering that balance the number of clusters. The cluster head selection protocol is arbitrary and incorporates energy-conscious considerations. In this survey, we compared different types of energy-efficient clustering-based protocols to determine which one is effective for lowering energy consumption, latency and extending the lifetime of wireless sensor networks (WSN) under various scenarios.

Improved spider monkey optimization‐based multi‐objective software‐defined networking routing with block chain technology for Internet of Things security

AbstractInternet of Things (IoT) can be connected with remote smart devices to offer global connectivity, which consists of handling, sensing, and transmission of real‐time data gathered from different devices with human intervention. The conventional security schemes are also faced with limitations like collecting and monitoring data through the IoT network to provide defense against cyber‐attacks for delivering the optimal security. To address the current challenges, this proposed model plans for integrating the energy‐efficient software‐defined networking (SDN) and block chain‐based IoT networks. The proposed model adopts a new meta‐heuristic‐based clustering protocol for optimal communication. The architecture uses the private and public block chains for offering the peer to peer communication to eliminate the proof‐of‐work (POW) issue. The contribution of a deep learning algorithm called optimized deep neural network (ODNN) is employed for detecting the malicious IoT device based on the energy characteristics of each node. Once the malicious nodes are detected, the optimal cluster head selection and optimal routing is accomplished by the adoption of neighborhood‐based spider monkey optimization (N‐SMO). The experimentation results show that the proposed model ensures the efficient cluster head selection and optimal routing protocol on the cluster structure with the traditional meta‐heuristic‐based algorithms.

A QoS Aware Cluster Head Selection and Hybrid Cryptography Routing Protocol for Enhancing Efficiency and Security of VANETs

Nowadays, VANET (vehicular ad hoc network) is one of the key aspects of developing advanced intelligent transportation systems. Due to its huge mobility and rapid topology alteration, the network exposes to link failure that affects the firmness of the network and causes delay and congestion. Additionally, the dynamic change in the network routing affects the network’s security, makes it vulnerable to various attacks, and causes data loss. In order to overcome these drawbacks an efficient and highly secured routing protocol is needed. Subsequently, in this research, a new routing protocol is proposed that has the combination of quality of service (QoS)-aware cluster head (CH) selection and hybrid cryptography, which is named QoS+. The QoS+ protocol is mainly divided into QoS-based CH selection and hybrid cryptography modules. The CH selection module performs based on QoS parameters attempt to provide reliable and stable clusters and improve the firmness and connectivity during the communication process of the network. The hybrid cryptography module contains advanced encryption standard (AES) and elliptic curve cryptosystems (ECC) algorithms. It attempts to improve the security and privacy of the network. The QoS+ protocol is evaluated by a developed VANET simulator using NS2 software. The simulator consists of a network model, a load model, and an attack model. Various speed and transmission ranges and gray hole and wormhole attacks are used in the simulator. The outcome calculated from the performance analysis shows that the proposed QoS+ protocol has 7% to 24% higher message success rate, 500 to 800 higher packets normalized routing load, 350 to 550 Kbps higher throughput, 5% to 17% higher efficiency and 50ms to 12ms lower end to end delay when compared with the earlier works of ECHS and KMSUNET. The proposed QoS+ protocol also achieves superior performance in terms of CH efficiency, cluster member efficiency, and average cluster number with various speeds and transmission ranges.

An Intelligent Vice Cluster Head Election Protocol in WSN

Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.

A Novel Cluster Head Selection and Data Aggregation Protocol for Heterogeneous Wireless Sensor Network

A Novel Cluster Head Selection and Data Aggregation Protocol for Heterogeneous Wireless Sensor Network

Revisiting Routing Protocols to Design Energy Aware Wireless Body Area Network

Wireless body area networks (WBANs) a special type of wireless sensor networks (WSNs) in which sensor nodes to actualize continuous wearable wellbeing observing of patients are able to provide improved healthcare services in a distributed infrastructure less environments. However, the mobile node, due to less battery power, can easily suffer from the problem of energy level when control packets are transfer among nodes—a problem that can occurs by the fact that some sensor nodes may select wrong cluster head with inappropriate path and waste the resources. Although many energy efficient methods have been designed for the traditional sensor networks, there has been limited focus on incorporating WBANs into energy efficient schemes. Therefore, in order to incorporate above issue we revisit the already designed traditional energy efficient methods with cluster head selection protocols and optimal path transformation. Therefore, we encourage researchers to insert WBANs with existing methods to improve performance. However, some work has been done in WBANs that uses energy efficient methods to manage the routing issue, this research domain requires further research attention. Therefore, we discuss the current research work and purpose many future directions of research.

An Energy-Aware Cluster Head Selection Protocol based on Multi-Objective Dolphin Swarm Optimization in Wireless Sensor Network

The Wireless Sensor Network (WSN) is spread widely in many applications due to its portable, cost-effective, and user-friendly approach. This needs to adopt routing schemes to forward the sensed information to the sink nodes or base station through minimum-cost links. The frequently utilized data accumulation approach is clustering which organizes the nodes into clutches to minimize the consumption of energy. Likewise, in clustering protocols, the additional load will be bear by the Cluster Head (CH) for organizing various deeds inside the cluster. As a result, appropriate CH selection and their power balance using efficient clustering protocol are significant features for the long-term operation of the Wireless Sensor Network (WSN). In this paper, An Energy-Aware Cluster Head Selection Protocol based on Multi-Objective Dolphin Swarm Optimization (EACHP-MODSO) is proposed to enrich the lifetime of Wireless Sensor Network (WSN). This proposed method considers six criteria to select effective Cluster Head like residual energy, inter cluster distance, intra-cluster distance, mobility, node density, and delay. The results of the proposed system show better enhancement of network lifetime compared to existing protocols.

EADCR: Energy Aware Distance Based Cluster Head Selection and Routing Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are used for data collection from the surrounding environment using the enormous amount of sensor nodes. Energy saving is the most fundamental challenge of WSNs which primarily depends on the Cluster Head (CH) selection and packet routing strategy. In this paper, we are proposing an Energy Aware Distance-based Cluster Head selection and Routing (EADCR) protocol to extend the lifetime of WSN using the FCM technique, residual energy of the nodes, their relative Euclidean distances from the Base Station (BS) and cluster centroid. Since the nodes consume a considerable amount of energy during the clustering phase, thus to avoid this, here, we are introducing a new clustering approach where the CH selection is now based on the newly proposed fitness function. We are also providing a new strategy for packet routing using the shortest path technique for routing between node and its destination which reduces the energy consumption of the CHs by employing the multi-hop communication. We also save the energy of the nodes using their Euclidean distances among them, from their CH and from BS. The simulation results exhibit that the EADCR enhances the network lifetime as compared to the other similar algorithms, e.g., FCM, REHR, UCRA–GSO and CCA–GWO under different scenarios. It also saves the residual energy of the network and enhances the network coverage.

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

SummaryOne of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.

Performance of Quasi Oppositional Butterfly Optimization Algorithm for Cluster Head Selection in WSNs

Abstract Wireless sensor networks (WSNs) technology is attracting the most of researchers and industry due to its application in wide contexts that include the military, surveillance, monitoring of habitat, health care, public safety and environmental monitoring. The consumption of energy is a crucial challenge faced in the WSN research. Clustering is an efficient technique for limiting the energy consumption of WSNs. But considering the hierarchical and cluster-based WSNs, the cluster heads utilize a high-level of energy because of data aggregation from various sensor nodes and transmit the same to the sink node. So, proper cluster head (CH) selection is required for sensor nodes’ energy conservation to prolong the network’s lifespan of the WSNs. In this paper, a CH selection protocol is proposed that is based on the quasi oppositional butterfly optimization algorithm (QOBOA). The proposed method is compared with original butterfly optimization along with certain existing algorithms with respect to the networks’ lifetime and efficiency of energy. Simulation results show that the QOBOA based CH selection scheme outperforms the existing ones.

EDB‐CHS‐BOF: energy and distance‐based cluster head selection with balanced objective function protocol

In this research, two novel cluster head selection protocols are proposed, namely, Energy and Distance Based Cluster Head Selection (EDB-CHS) and EDB-CHS with Balanced Objective Function (EDB-CHS-BOF). They mainly aim at balancing energy consumption amongst all sensor nodes, prohibiting their precocious death, and extending the network lifetime. More specifically, in EDB-CHS protocol, a new cluster shape is proposed and consequently a tight closed-form expression for the optimal number of cluster heads (CHs) in the network is derived. Additionally, an efficacious CHS algorithm is presented, which is an expression of threshold probability that takes into account the residual energy of sensor nodes, how far they are located from the Base Station (BS), as well as the node's optimal probability of being a CH. On the other hand, the EDB-CHS-BOF protocol is proposed mainly to address the matter of having long-distance communications as a result of obtaining adjacent CHs. In EDB-CHS-BOF protocol, a new threshold probability for each sensor node to be a CH in any round is proposed. Not only to this extent, but rather a balanced objective function is proposed to ensure a well-proportioned distribution of CHs over the network. Simulation results demonstrate the superiority of the proposed protocols over other directly related works in terms of network lifetime and total data delivery.

A coverage and obstacle-aware clustering protocol for wireless sensor networks in 3D terrain

In Wireless Sensor Networks (WSNs), clustering techniques are often used to optimize energy consumption and increase Packet Delivery Rate (PDR). To date, most of the proposed clustering protocols assume that there is a Line of Sight (LOS) between all the sensors. In fact, most of the available WSN simulators assume the use of optimistic path loss models that neglect the effect of obstacles on the PDR. However, in real situations such as in 3D terrains, obstacles can interfere this LOS. Moreover, while clustering, it is also important to maintain the coverage of a given Region of Interest (ROI). Therefore, finding an integrated solution for both clustering and coverage problems in an irregular 3D field becomes a pressing concern.In this paper, we first adopt an obstacle-aware path loss model to reflect the effect of obstacles on the communication between any pair of sensors. To that end, the Castalia simulator is adapted to use this proposed path loss model. Then, we introduce a Coverage and Obstacle-Aware Cluster Head Selection (COACHS) protocol to solve the cluster heads selection problem while maintaining a good coverage of a WSN deployed in an irregular 3D field. Simulation results demonstrate that the effect of obstacles on the PDR cannot be neglected. Moreover, comparative evaluation results show that COACHS outperforms other competent protocols in terms of PDR while simultaneously maintaining an acceptable energy consumption and a good coverage of the ROI.

An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications

The more energy consumption is the major issue for wireless sensor network. In wireless sensor network (WSN), sensor nodes are fixed in various routing algorithms. In the same networks, mobile sensor and fixed sensor nodes are combine and its used in few applications. The function is corruption while mobility was achieved, since these nodes have minimum battery power, lower range of communication and a lessor amount of memory. To overcome this issue, Improved Energy Efficient Cluster Head Selection protocol (IEECHS-WSN) is proposed, in these technique is used to transfer the received information by using energy efficient routing protocol. In the CH election method, two cluster heads are selected in a separated cluster and its work in various functions, this can be prolong the network lifetime and decrease the energy consumption of IoT applications. Proposed technique is described on clustering of dual CHs in the method of data fusion for data entropy. This information entropy is used for fusion and classification, the result of fusion and classification are accurate and efficient for data transmission. Our proposed IEECHS protocol has better throughput, lifetime of network and energy consumption compared than the existing technique.

A smart power saving protocol for IoT with wireless energy harvesting technique

Internet of things (IoT) is the wireless network of physical devices that are capable of sensing the environment. In order to prolong the network lifetime of the IoT nodes called sensor nodes, an effective energy consumption protocols to adapt the wireless sensor network. This paper explores in depth the energy harvesting techniques, and proposes a cluster algorithm with energy harvesting technique to improve energy and throughput of the network. The proposed algorithm implements the creation of clusters with cluster-head selection protocol and we have reduced the number of iteration required for electing the cluster head. HEED algorithm has been enhanced with an energy harvesting algorithm using wireless charging technique. Proposed algorithm is to select the appropriate channel in the multi-channel system. Simulation results shows that the proposed algorithm has better adaptability to ubiquitous environments than existing clustering algorithm in prolonging the network lifetime.

Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things

With ample technological developments and flourishing demand for digital assistance in daily life and work environments that goes along with it, technologies are required to derive this application domain to the subsequent level. Internet of Things (IoT) is considered as one of vision for such technologies. This paper intends to develop self adaptive whale optimization algorithm (SAWOA) for the accomplishment of energy-aware cluster head selection and clustering protocols under wireless sensor network (WSN)—based IoT. Along with the parameters like energy, distance, and delay of sensor nodes in WSN, this simulation considers both load and temperature of IloT devices. After modeling the simulation, it carries out a valuable performance analysis in terms of network efficiency, normalized energy and load and temperature of the selected cluster head. The performance analysis compares the effectiveness of proposed SAWOA over traditional artificial bee colony (ABC), genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), adaptive GSA (AGSA) and WOA-based cluster head selection models. The outcome from simulation model proves the successful performance of SAWOA in cluster head selection so that the lifetime of network is prolonged.

Low Energy Fuzzy based Unequal Clustering Multihop Architecture for Wireless Sensor Networks

This paper presents a Low Energy Fuzzy based Unequal Clustering Multihop Architecture (LEFUCMA) for wireless sensor networks consisting of several nodes that send sensed data to a Master Station (MS). LEFUCMA encompasses neighbor finding, cluster head selection, clustering and routing protocols. The neighbor finding protocol organizes the network into a sectored-layers structure. The cluster head selection uses fuzzy logic with residual energy, number of neighboring nodes, packet reception rate and distance of node from MS as fuzzy descriptors for cluster head selection. For even distribution of traffic, LEFUCMA uses fuzzy logic with node density and distance of area from MS as fuzzy descriptors to decide number of cluster heads in a given area. To avoid hot spots problem, LEFUCMA uses an unequal clustering mechanism with clusters closer to MS having smaller sizes than those farther from MS. Finally, inter-cluster routing protocol decides the next hop cluster head considering its residual energy, distance from MS and from current cluster head which represents energy required for communication, number of cluster members which represents intra-cluster traffic and number of descendant nodes which represents inter-cluster traffic. A comparative analysis of LEFUCMA; Unequal Hybrid, Energy Efficient and Distributed Clustering (Ever et al. in Proceedings of international conference on sensor networks, pp 185–193, 2012; Energy Aware Distributed Unequal Clustering (Yu et al. in Hindawi Int J Distrib Sens Netw, Article ID 202145:1–8, 2011); Constructing Optimal Clustering Architecture (Li et al. in J Comput Commun 36(3):256–268, 2013; and Energy Aware Unequal Clustering using Fuzzy logic (EAUCF) (Bagci and Yazici in J Appl Soft Comput 13(4):1741–1749, 2013) shows that LEFUCMA is 32–42% more energy efficient compared to EAUCF. Throughput of LEFUCMA is 46% more and network lifetime is 60–75% more compared to EAUCF.