Called Cluster Head Research Articles

Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs

With the explosion of big data, the implementation of distributed machine learning mechanisms in wireless sensor networks (WSNs) is becoming required for reducing the number of data traveling throughout the network and for identifying anomalies promptly and reliably. In WSNs, the above need has to be considered along with the limited energy and processing resources available at the nodes. In this paper, we tackle the resulting complex problem by designing a multi-criteria protocol CINE that stands for “Clustered distributed learnIng exploiting Node centrality and residual Energy” for distributed learning in WSNs. More specifically, considering the energy and processing capabilities of nodes, we design a scheme that assumes that nodes are partitioned in clusters and selects a central node in each cluster, called cluster head (CH), that executes the training of the machine learning (ML) model for all the other nodes in the cluster, called cluster members (CMs). In fact, CMs are responsible for executing the inference only. Since the CH role requires the consumption of more resources, the proposed scheme rotates the CH role among all nodes in the cluster. The protocol has been simulated and tested using real environmental data sets.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).

Selection of Cluster Head in Wireless Sensor Network using Convolution Neural Network Algorithm

Network lifetime enhancement is one of the primary goals for all the Wireless Sensor Network (WSN) applications. Clustering process helps the WSNs to improve its lifetime by forming a group with nearby sensor nodes. A primary node called cluster head is elected from the group for managing the data transmission from the other clustering groups to the basic sensor nodes. Also, the cluster heads are placed to transfer the signal collected from its sensor nodes to the base stations. Identifying an optimum node for making it as a cluster head is a challengeable process for many WSN applications. The proposed work is employed with a neural network algorithm for electing a cluster head by analyzing the residual energy, base station distance and neighbor sensor counts. The experimental work carried out in the paper indicates a betterment on survival node time and energy consumption.

An Efficient Intrusion Detection Approach for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are vulnerable to various kinds of security attacks that can compromise many nodes and therefore the performance of the network may be degraded. Failures to prevent intrusions could also decrease the credibility of security services, e.g., data confidentiality, integrity, and availability. Traditional Intrusion Detection Systems (IDS) suffer from many issues in performance and increased overhead which are considered the main challenge in WSNs. The common architecture of WSN is that nodes are organized into a set of clusters; each one contains a set of nodes with a specialized node called Cluster Head (CH) node which is responsible for managing activities through the cluster and communicating with other nodes and a Base Station (BS). The CH plays a critical role in the case attacking WSN in the two cases; signature-based and anomaly-based. This paper proposes an efficient hybrid IDS to analyze and secure WSN in multiple phases because it combines the best features of two different approaches to achieve better performance. In the proposed approach, BS evaluates and updates attacks information for the entire network which is the main advantage where BS doesn’t suffer from any limitations in sensor nodes. Moreover, BS selects CH among other nodes based on power capabilities and computational resources. The efficiency and adaptability of the proposed method have been tested by simulation experiments deployed on JiST/Swans simulation. The experimental results show that the proposed system is efficient with acceptable performance in comparison with other hybrid IDSs. The experimental evaluation also expresses that the proposed technique reduces the communication costs on the cluster head (CH) which improves the lifetime of the entire WSN.

An energy consumption minimization approach in wireless sensor networks

There is no doubt that the most challenging aspect in the wireless sensor networks (WSN) is the lifetime, due to limitations in their energy. WSN depends on a specific group of sensor nodes to gather the data from other nodes and forward it to the base station (BS). These nodes are called cluster heads. Having reliable cluster head’s (CH) means longer life to the network. In this paper, a versatile calculation has been acquainted and analyzed for selecting the CH that maintains the least vitality utilization in the network with appropriate life time during every correspondence round. The altered methodology depends on the improved calendar of the time division multiple access (TDMA) plans. This methodology is created to decide the next CH based on lifetime, expended vitality, number of CH’s, and the frequent contact to the BS. A comparative analysis is introduced, the proposed algorithm assistant cluster heads (ACHS) shows energizing outcomes in vitality utilization in WSNs just as expanding the general system dependability with reasonable viability and productivity in terms of lifetime. The ACHS strategy shows a decrease in the WSN vitality utilization up to about 25% and shows an expansion in the system life time by 30% than the upgraded timetable of time TDMA plan approach.

Clustering Based Two Dimensional Motion of Sink Node in Wireless Sensor Networks

The wireless sensor network (WSN) is always known for its limited-energy issues and finding a good solution for energy minimization in WSNs is still a concern for researchers. Implementing mobility to the sink node is used widely for energy conservation or minimization in WSNs which reduces the distance between sink and communicating nodes. In this paper, with the intention to conserve energy from the sensor nodes, we designed a clustering based routing protocol implementing a mobile sink called ‘two dimensional motion of sink node (TDMS)’. In TDMS, each normal sensor node collects data and send it to their respective leader node called cluster head (CH). The sink moves in the two dimensional direction to collect final data from all CH nodes, particularly it moves in the direction to that CH which has the minimum remaining energy. The proposed protocol is validated through rigorous simulation using MATLAB and comparisons have been made with WSN’s existing static sink and mobile sink routing protocols over two different geographical square dimensions of the network. Here, we found that TDMS model gives the optimal result on energy dissipation per round and increased network lifetime.

R-MUCH: A Clustering Routing Algorithm Using Fuzzy Logic for WSNs

An adequate usage of energy on nodes with restricted energy source is one of the major challenges in WSNs. Since data transmission is the main task that shortens node’s lifetime, it is very necessary to balance the transmission of data among the network paths. Cluster-based architecture in WSNs is one of the keys to improve energy efficiency and extend network lifetime. It reduces the number of messages transmitted towards the sink or Base Station. This is due to restricting the communication with the sink to few nodes, called Cluster Head nodes (CHs). One of the major concerns is how to choose cluster heads and route data through energy-efficient paths towards destination. In this paper, we propose R-MUCH a clustering routing algorithm. It is a multi-hop version of MUCH algorithm (Multi-Criteria Cluster Head Delegation Based on Fuzzy Logic). CHs send data in a multi-hop fashion to the sink by choosing the path that has the lowest cost in terms of energy consumption. R-MUCH chooses for each CH its next-hop. It uses fuzzy logic and relies on three factors: the distance, the node’s remaining energy and the number of times the node has served as next hop. Simulation shows that network lifetime is increased over existing approaches in the state of the art.

Fuzzy modelling based energy aware clustering in wireless sensor networks using modified invasive weed optimization

Energy conservation in a highly resource restricted environment of Wireless Sensor Networks (WSNs) is a crucial task. This issue can be efficiently tackled by adopting the mechanism of clustering in sensor networks. The primary objective of any of the designed clustering protocols for WSNs is to obtain energy efficiency and to lengthen the network life duration. This achievement is made through the election of a single deserving node as a head node called cluster head. This paper suggested an evolutionary approach based clustering protocol named Modified-Invasive Weed Optimization Based Clustering Algorithm (M-IWOCA) for WSNs. M-IWOCA focuses on the selection of fittest node as a cluster head to enhance the network’s lifetime as well as to address the issue of energy conservation in sensor networks. A fuzzy inference model is designed to evaluate the fitness of each node in the network. Simulation results for M-IWOCA depict significant reduction in dead nodes per round and results in minimizing the energy utilization of the sensors. Moreover, M-IWOCA enhances the network stability period by 45% in comparison to Artificial Bee Colony (ABC) protocol and by 18% in comparison to Quantum Artificial Bee Colony (QABC) protocol.

Cluster Based Localization Scheme with Backup Node in Underwater Wireless Sensor Network

The localization is a process of finding out the exact location of sensor nodes. For underwater, it is very difficult task to locate sensor nodes as the underwater environment keeps on changing with respect to time and to locate the sensors is time consuming as well as wastage of energy. It is a difficult task to make the system more efficient and for long time. We proposed a cluster-based localization technique to improve energy efficiency and prolonged network lifetime in the underwater environment. Our algorithm for localization of sensor nodes in the underwater reduced cost as well as provides an efficient way to find sensors location. The proposed clustering protocol form a cluster within random time and a cluster head. Anchor node positioned into the cluster head for communicating with the outer world through sink node. Here we have enhanced the performance with the help of backup node. It will extend the lifetime of the network as well as speed up the accurate information. This novel idea behind the constraint of energy in UWSN. It is responsible to gather information of other nodes of cluster and transfer to the floating nodes. This cluster head and the nodes associated with cluster keeps on changing with respect to time. The proposed method decreases the energy consumption by deploying some nodes of lower energy while some nodes of higher energy called cluster head to transfer information to longer distance. The proposed method uses the concept of the backup node, cluster head, cluster nodes and floating nodes to transfer the information of nodes.

EEFCM‐DE: energy‐efficient clustering based on fuzzy C means and differential evolution algorithm in WSNs

Efficient energy utilisation is a fundamental challenge that needs to be dealt with while deploying a wireless sensor network (WSN). These networks consist of thousands of small-sized battery-operated devices called sensors. Sensors are resource-constrained devices, and hence have very limited energy available with them. By lessening the energy usage of these nodes, the life span of the whole network can be enhanced up to a great extent. Clustering of WSNs is a speedily flourishing research area. In clustered WSNs, the major concerns are choosing an appropriate number of clusters and then selecting a coordinator node called cluster head (CH) within each formed cluster. This study introduces a hybrid energy-efficient clustering based on fuzzy C means and differential evolution algorithm (EEFCM-DE) based on fuzzy clustering and fuzzy-based evolutionary technique. The idea is to use FCM for the cluster creation and then selecting the best node as a CH within each cluster formed, by using an evolutionary technique DE. For the CH selection, the fitness of each node is calculated through a designed fuzzy inference system. The simulation results validate the energy-efficient performance of EEFCM-DE in comparison with the other existing algorithms.

Energy‐efficient clustering algorithm for structured wireless sensor networks

Wireless communication is preferred in numerous sensing applications due to its convenience, cost-effectiveness, and flexibility. Modern sensors are versatile to sense the environmental factors and send them wirelessly. The information collection centres prefer to collect confined clustered information from a group of sensors rather than collecting them from individual sensors. Good connectivity, speedy communication, and effective data gathering can be ensured in the network when a good clustering algorithm is utilized. In this paper, a simple and effective clustering algorithm called energy efficient structured clustering algorithm (EESCA) is proposed for the environmental monitoring fields. Cluster heads (CHs) are elected based on average communication distance and lingering energy. Further, a new parameter called cluster head to normal ratio (CTNR) is introduced to rotate the cluster head role among the nodes. The performance evaluation is carried out in terms of first node die (FND), simulation time, scalability, load balancing, and a new parameter called complete useful data percentage (CUDP). Simulations are conducted for three different network scenarios. Results are compared with the renowned existing algorithms low energy adaptive clustering hierarchy (LEACH) and scalable energy efficient clustering hierarchy (SEECH) and it is proved that the proposed technique is beneficial for WSNs.

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

SummaryDue to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.

An Unequal Cluster-Radius Approach Based on Node Density in Clustering for Wireless Sensor Networks

Lifetime is a major challenge in wireless sensor networks which depends on the energy consumption of the nodes. Energy limitation in nodes leads to many researches to find out new solutions for this problem. Clustering is an efficient solution for this issue. In clustering, all nodes that are distributed in an application zone should be partitioned into some virtual sections that are called clusters. Each cluster must have one node for managing intra-cluster tasks that is called cluster-head (CH). CHs are used for transmitting collected data to the base-station (BS). The multi-hop is one way of transferring CHs data to the BS which increases the data traffic on CHs near the BS. The unequal clustering is one of the solutions for solving bottleneck CH problems. The difference between equal and unequal clustering is in the type of cluster-radius, which could be fixed or variable. In this paper, a distributed unequal cluster-radius approach is proposed based on node density for clustering in WSNs that has two main phases: CH-selection and Member-join. CH-selection phase is based on global and local search, and member-join phase is done according to an evaluation function. Experimental results show that the proposed method leads to an increase in the lifetime of WSNs in comparison with state-of-the-art methods.

Optimal Sensor Data Harvesting Using A Mobile Sink

We investigate the idea of using a fixed-wing Unmanned Aerial Vehicle (UAV) as a mobile sink in a Wireless Sensor Network (WSN) to increase the network lifetime. We formulate and solve an optimization problem to minimize the scanning delay incurred by the UAV subject to a motion energy constraint. We also formulate and solve another optimization problem to minimize the total energy consumption for motion of the UAV and communication between the UAV and the sensor nodes subject to a delay constraint. These two optimization problems are for the case when the UAV collects the sensor data by communicating with certain special nodes called Cluster Heads. For the alternative architecture, where the UAV directly communicates with each sensor node, we investigate the performance using NS-2 simulations and provide several insightful observations.

Firefly Algorithm Solution to Improving Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks

The improvement of the lifetime of heterogeneous wireless sensor networks is a challenge for many researches. One of the most important protocols to achieve this goal is to divide the network into clusters that run by a single node called cluster head and the others have attached. However, all nodes must form the cluster including the nearest nodes to the base station which should be excluded from the clustering process. Furthermore these nodes consume more energy since each member node communicates directly with their cluster head and not with the base station. To eliminate these notes from cluster process, we need to formulate a new energy total of the network which depends on the number of these nodes. In this paper we propose a new technic to optimize this energy which basing on the firefly algorithm. The developed approach allows the boundary of the excluded nodes efficiently. Computer simulation in MATLAB proves the superiority of this method concerning the increase of the lifetime and the number of the received packet messages compared to the others protocols.

Distributed fuzzy logic based energy‐aware and coverage preserving unequal clustering algorithm for wireless sensor networks

SummaryIn an energy‐constrained wireless sensor networks (WSNs), clustering is found to be an effective strategy to minimize the energy depletion of sensor nodes. In clustered WSNs, network is partitioned into set of clusters, each having a coordinator called cluster head (CH), which collects data from its cluster members and forwards it to the base station (BS) via other CHs. Clustered WSNs often suffer from the hot spot problem where CHs closer to the BS die much early because of high energy consumption contributed by the data forwarding load. Such death of nodes results coverage holes in the network very early. In most applications of WSNs, coverage preservation of the target area is a primary measure of quality of service. Considering the energy limitation of sensors, most of the clustering algorithms designed for WSNs focus on energy efficiency while ignoring the coverage requirement. In this paper, we propose a distributed clustering algorithm that uses fuzzy logic to establish a trade‐off between the energy efficiency and coverage requirement. This algorithm considers both energy and coverage parameters during cluster formation to maximize the coverage preservation of target area. Further, to deal with hot spot problem, it forms unequal sized clusters such that more CHs are available closer to BS to share the high data forwarding load. The performance of the proposed clustering algorithm is compared with some of the well‐known existing algorithms under different network scenarios. The simulation results validate the superiority of our algorithm in network lifetime, coverage preservation, and energy efficiency.

Fountain Coded Cooperative Communications for LTE-A Connected Heterogeneous M2M Network

Machine-to-machine communication over long-term evolution advanced (LTE-A) network has emerged as a new communication paradigm to support a variety of applications of Internet of Things. One of the most effective techniques to accommodate a large volume of machine type communication (MTC) devices in LTE-A is clustering where devices (nodes) are grouped into number of clusters and forward their traffics to the base station (e.g., LTE eNodeB) through some special nodes called cluster heads (CHs). In many applications, the CHs change location with time that causes variation in distances between neighboring CHs. When these distances increase, the performance of data transmission may degrade. To address this issue, we propose to employ intermediate non-CH nodes as relays between neighboring CHs. Our solution covers many aspects from relay selection to cooperative formation to meet the user’s QoS requirements. As the number of total relay plays a significant role in cooperative communications, we first design a rateless-coded-incremental-relay selection algorithm based on greedy techniques to guarantee the required data rate with a minimum cost. After that, we develop both source-feedback and non-source-feedback-based fountain coded cooperative communication protocols to facilitate the data transmission between two neighbor CHs. Numerical results are presented to demonstrate the performance of these protocols with different relay selection methods under Rayleigh fading channel. It shows that the proposed source-feedback-based protocol outperforms its non-source-feedbackprotocol counterpart in terms of a variety of metrics.

Design and Simulation of a Blacklisting Technique for Detection of Hello flood Attack on LEACH Protocol

Wireless sensor networks consist of number of small, low power nodes with limited computational capabilities. In such networks, data is collected from various low power nodes by nodes with higher energy than others, called cluster heads. These cluster heads then send this data to a major node called sink. However, this aggregation is prone to many attacks. This paper intends to present a study of the algorithms, protocols and techniques for secure data aggregation in sensor networks. The Hello flood attack is explained and a novel algorithm for defense against Hello flood attack is proposed and simulated using Matlab.

Forecast Weighted Clustering in MANET

Quality of Service (QoS) has become an indispensable concern in cluster based routing in MANET (Mobile Ad Hoc Network). Various distributed algorithms like Weighted Clustering Algorithm (WCA), Lowest Identifier Algorithm (LIA), Highest Degree Algorithm (HAD) etc. have been proposed to select special node called cluster head in clusterbased routing in MANET. In this paper we have presented another new mechanism to select cluster head called Forecast Weighted Clustering Algorithm (FWCA). It has been observed that FWCA performs better than WCA.

A Clustering Protocol for Maximum Coverage in Large-Scale Wireless Sensor Networks

The optimum use of coverage in wireless sensor networks (WSNs) is very important. The hierarchical routing protocol LEACH (Low Energy Adaptive Clustering Hierarchy) is referred to as the basic algorithm of distributed clustering protocols. LEACH allows clusters formation. Each cluster has a leader called Cluster Head (CH). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node join a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus several sensor nodes cannot reach any CH. As a result, the remaining part of the controlled field will not be covered; some sensor nodes will be outside the network. To solve this problem, the authors propose O-LEACH (Orphan Low Energy Adaptive Clustering Hierarchy), a routing protocol that takes into account the orphan nodes. O-LEACH presents two scenarios, a gateway and sub cluster that allow the joining of orphan nodes.