Clustered Wireless Sensor Networks Research Articles

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment

In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance. Although clustering is a discrete problem, the computational intelligence techniques are more suitable for load balancing and minimizing energy consumption with different operating constraints. The literature reveals that the swarm intelligence-inspired computational approaches give excellent results among population-based meta-heuristic approaches because of their more remarkable exploration ability. Conversely, in this work, load-balanced clustering for sustainable WSNs is presented using improved gray wolf optimization (IGWO). In a smart city environment, the significant parameters of energy-efficient load-balanced clustering involve the network lifetime, dead cluster heads, dead gateways, dead sensor nodes, and energy consumption while ensuring information exchange and communication among the sensors and cluster heads. Therefore, based on the above parameters, the proposed IGWO is compared with the existing GWO and several other techniques. Moreover, the convergence characteristics of the proposed algorithm are demonstrated for an extensive network in a smart city environment, which consists of 500 sensors and 50 cluster heads deployed in an area of 500 × 500 m2, and it was found to be significantly improved.

A Novel Chaotic Shark Smell Optimization With LSTM for Spatio-Temporal Analytics in Clustered WSN

Wireless sensor networks (WSN) include massive deployment of sensor nodes to observe the physical environment. At the same time, the spatial and temporal correlations that come with the collaborative characteristics of the WSN pose considerable benefits in the design of effective communication protocols designed for the WSN environment. At the same time, energy efficiency is considered a vital challenge in WSN and can be solved by the use of clustering techniques. In this aspect, this study presents a new chaotic shark smell optimization (CSSO) with long short-term memory (LSTM), called the CSSO-LSTM technique for spatiotemporal analytics in clustered WSN. Primarily, a chaotic shark smell optimization (CSSO) based clustering technique is derived, which is based on the spatial correlation that exists among the sensor nodes. The CSSO algorithm derives an objective function involving different input parameters to select cluster heads (CHs) and construct clusters.

Data aggregation algorithm based on clustering for wireless sensor networks

Data aggregation algorithm based on clustering for wireless sensor networks

Interference and secrecy analysis based on randomly spacial model in clustered WSNs

Clustering and layering are widely employed in large-scale wireless sensor networks (WSNs) to alleviate data implosion, reduce transmission delay and improve energy efficiency. It is vital to conduct performance analysis in clustered WSNs for better defining and designing networks. Here, a general analytical framework based on a randomly spacial model is proposed to conduct inter-cluster interference analysis and physical-layer security analysis for clustered WSNs. It is assumed that two adjacent cluster heads exchange confidential massage with a passive eavesdropper and the non-head nodes in clusters could be selected as cooperative jamming (CJ) nodes to disturb the eavesdropper. All mentioned nodes are randomly located in their specific areas. Under the above settings, three scenarios, namely interference, eavesdropping and CJ are considered, and a stochastic geometry tool based on kinematic measure is employed for analysis. Comparing to existing stochastic geometry methods, the proposed analytical framework can handle arbitrarily shaped, disjoint and tiered networks. The results obtained from extensive simulations have verified the rationality of the framework and demonstrated the impact of different parameters on the performance metrics of interest. The comparison with PPP also shows the advantages of this proposed model.

A performance efficient joint clustering and routing approach for heterogeneous wireless sensor networks

AbstractWireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm.

Adaptive Parallel Seeker Optimization-based Route Planning for clustered WSN in smart cities

Smart city is a phenonmenon that integrates physical and social infrastructures with Information Technology to keep a city's cooperative intelligence under control. Smart cities primarily rely on Wireless Sensor Networks (WSN) to manage and maintain its service offerings. In literature, clustering and multihop routing techniques have been proposed, validated and implemented to reduce the consumption of energy in the network. With this motivation, the current study develops Adaptive Parallel Seeker Optimization-based Energy Aware Route Planning Technique (APSO-EARPT) for clustered WSN in smart cities. The presented APSO-EARPT technique concentrates on appropriate selection of Cluster Heads (CHs) and optimal routes in WSN. To accomplish this, APSO-EARPT model encompasses Weight-Based Clustering Scheme (WBCS) for effective selection of CHs. Then, routing process is performed with the help of APSO algorithm. The proposed APSO-EARPT technique computes a Fitness Function (FF) that comprises of three variables such as Residual Energy (RE), distance to Base Station (BS), and node degree. This fitness function helps in optimal selection of routes in WSN. In order to validate the supremacy of the proposed APSO-EARPT model in terms of network lifespan and energy efficiency, simulations were conducted and the results confirmed the excellent performance of the proposed model.

Application-specific clustering in wireless sensor networks using combined fuzzy firefly algorithm and random forest

Clustering in wireless sensor networks (WSNs) has proved to be one of the most efficient ways to hierarchically organize the network topology for the purposes of load-balancing and elongating the network lifetime. However, achieving optimal clustering in WSNs is an NP-hard problem, and consequently, heuristics and metaheuristics have been widely adopted. In this paper, a combined clustering technique based on a fuzzy-firefly algorithm (FFA) and random forest (RF), shortly named as: FFA-RF, is presented as an application-specific routing protocol for WSNs. Our FFA-RF protocol entails offline tuning and online routing phases: the offline phase consists of data collection using FFA, training and test of the RF, while the online phase is the actual application of the FFA-RF model to new network instances. In the offline phase, we construct an optimized fuzzy inference system based on FFA and apply it on different network topologies, to collect a comprehensive dataset. We then divide the resulting dataset into training and test sets to train and test the RF model. In the online phase, the trained RF model is used as an online clustering algorithm to estimate the fuzzy priority factor of the nodes for being cluster heads (CHs) in new network instances. To increase the generalizability of the RF for different configurations, node features as well as application features are used as inputs of the RF model. Simulation results for different network topologies demonstrate the superiority of the proposed FFA-RF protocol in prolonging the application-specific lifetime when compared against existing crisp heuristic, fuzzy heuristic, metaheuristic, and combined fuzzy-metaheuristic protocols.

Intelligent and Secure Clustering in Wireless Sensor Network (WSN)-Based Intelligent Transportation Systems

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and ‘security compromise’ are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and ‘number of packets sent’, which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

A sink based data gathering technique by using clustering for wireless sensor networks

New advance and integrated technologies have changed the traditional systems and convert these systems into more intelligent, feasible and cost effective systems. In all data communications domain, data gathering is one of the significant task performed by using any techniques, tools and devices. Wireless sensor networks (WSN) also gained popularity in various fields where the sensor node sensed the information by using sink or gateway nodes and further send to central units for decision making. With passage of time, these networks have faced complexities where most of the existing techniques have suffered with load balancing, complex processes, overhead and energy consumption issues. Firstly, this paper provides detail comparison of existing data gathering techniques adopted for WSN and then provides their performance analysis. After comparison, this paper proposes a novel data gathering techniques called a sink based data gathering techniques (ASDG) to collect the data from the sensor nodes and further send for decision making. Experimental results show that proposed techniques is better than existing techniques and provide more efficient data delivery ratio with more network lifetime. The results also indicated that when using the proposed technique, the no of dead sensor nodes are less as compared to the existing ones at different rounds.

Implementation of energy-efficent routing protocol within real time clustering wireless sensor networks

Wireless sensor networks (WSNs) are characterized by huge sensors that are having a limited amount of energy and specifying their behavior. Sensor networks combine distributed sensing, processing, and communication capabilities to create a powerful system. There are issues with the length of sensors connected to the network since features inside the sensors frequently require energy, which is likely to limit WSN performance. In creating WSN application architectures, extending network longevity, scalability, and load balancing are important factors. Using clustering techniques, the challenge of prolonging overall network lifetime and increasing the first dead node duration in the network may be tackled. Clustering is a valuable approach for breaking a network into parts known as clusters and giving solutions for energy consumption concerns including data collection, aggregating, and routing to sink nodes by cluster heads (CHs). In this paper, we suggest optimum clustering in multi-path and multi-hop protocols as a feasible option for reducing energy consumption and extending the lifetime of wireless sensor networks. In compared to the low energy adaptive clustering hierarchy (LEACH) clustering method, simulation results show that the new technique, optimal real time clustering (ORTC), is promising in terms of extending network lifetime.

Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance

Recent developments in low-power communication and signal processing technologies have led to the extensive implementation of wireless sensor networks (WSNs). In a WSN environment, cluster formation and cluster head (CH) selection consume significant energy. Typically, the CH is chosen probabilistically, without considering the real-time factors such as the remaining energy, number of clusters, distance, location, and number of functional nodes to boost network lifetime. Based on the real-time issues, different strategies must be incorporated to design a generic protocol suited for applications such as environment and health monitoring, animal tracking, and home automation. Elementary protocols such as LEACH and centralized-LEACH are well proven, but gradually limitations evolved due to increasing desire and need for proper modification over time. Since the selection of CHs has always been an important criterion for clustered networks, this paper overviews the modifications in the threshold value of CH selection in the network. With the evolution of bio-inspired algorithms, the CH selection has also been enhanced considering the behavior of the network. This paper includes a brief description of LEACH-based and bio-inspired protocols, their pros and cons, assumptions, and the criteria of CH selection. Finally, the performance factors such as longevity, scalability, and packet delivery ratio of various protocols are compared and discussed.

Hybrid grasshopper and differential evolution algorithm for prolonging network life expectancy in wireless sensor networks (WSNs)

SummaryIn wireless sensor networks (WSNs), restricted battery power, balanced energy consumption, and collaborative data processing are considered as key challenges that need to be handled with utmost care. Clustering in WSNs is an optimal methodology for conserving energy of sensor nodes and attaining efficient data processing that attributes toward the maximization of network lifetime. An efficient cluster head (CH) selection scheme is essential for achieving superior collaborative data processing in WSNs. Swarm intelligent metaheuristic algorithm‐based CH selection approaches are identified to be better for designing energy‐efficient schemes that select optimal CHs from nodes in a fair way. In this paper, Hybrid Grasshopper and Differential Evolution‐based Optimization Algorithm (HGDEOA) is proposed for targeting on the objective of attaining energy stability and prolonging network lifetime. This HGDEOA incorporates an adaptive strategy into differential evolution (DE) for improving the global searching capability during the process of optimization. It is proposed for improving the convergence efficiency and maintaining population diversity. It also possesses the capability of enhancing the degree of convergence speed and precision in calculation. This integration of GOA into DE prevents the premature convergence of the algorithm, since the deviation amid the scaling individuals induces the population to be more randomly distributed, targeting at the retention of population diversity. The simulation results confirm that the proposed HGDEOA sustains residual energy by 19.32%, improves throughput by 16.21%, prolongs network lifetime by 18.76%, and maintains stability by18.94% when compared to the benchmarked approaches used for investigation.

Retraction Note to: Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks

Retraction Note to: Multi-hop optimized routing algorithm and load balanced fuzzy clustering in wireless sensor networks

Application the Wireless Sensory Network Cluster in Digital Agriculture

The authors made a review of "Information technology of express-estimation of plant state in large territories in stressful environment." The essence of digital agriculture and its main components are briefly described. The main part of the article describes the cluster of Wireless Sensor Network. The main components of the cluster and the principle of the cluster are given. The work of the cluster is based on the "Information technology of express-estimation of plant state in large territories in stressful environment." This technology is based on the method of chlorophyll fluorescence induction. The introduction of new information technology into industrial digital agriculture will make it possible to determine in real time the condition of plants suffering from the influence of one or another stress factor and develop an appropriate managerial decision to compensate the influence of a certain factor. The main technical requirements for the wireless node of the cluster are the ability to work in the field conditions; easy location on the plant; low cost; lightweight up to 25 g, small size, etc. The WSN cluster is intended for use in the agricultural sector and for environmental monitoring. Using the data collected by the cluster and an express analysis of the state of plants is carried out, which allows making the necessary managerial decision on the use of fertilizers, fungicides, pesticides, herbicides and the need for irrigation. The authors took into account that the cultivation of corn for grain occupies a large sector in the agrarian sector of Ukraine and is an urgent task. The authors analyzed the industrial technology of growing of crop for grain and it was adapted for the information technology for measuring the CFI. The main points of the technological process for the use of the WSN cluster in industrial agriculture on the example of corn are determined, and on their basis, a scheme for measuring the CFI of plants by the WSN cluster was developed. A brief step-by-step methodology has been developed for using the WSN cluster in measuring the CFI of corn. The authors also presented an analysis of energy consumption in the WSN and proposed the ways to improve the energy efficiency of the WSN nodes. Keywords: sensors, wireless sensor network, express diagnostics of plant, smart agriculture.

Energy efficient cluster based routing for wireless sensor networks using moth levy adopted artificial electric field algorithm and customized grey wolf optimization algorithm

Clustering is an effective strategy for creating routing algorithms in Wireless Sensor Networks (WSNs), which increases the network's lifetime and scalability. In the clustered WSN, the Cluster Head (CH) plays a vital role in data transmission. So far, much research work has already existed in regards to cluster-based routing. Despite this, they have challenges with fault tolerance, unequal load balancing, and local optimal solutions. To address these problems, this research presents a novel method for cluster based routing that makes the routing progress more effective to maximize the network lifetime. This has been carried out under two phases: selecting the optimal cluster head via the new Moth Levy adopted Artificial Electric Field Algorithm (ML-AEFA), and the data transmission is carried out by the new Customized Grey Wolf Optimization (CGWO) algorithm. Here, the selection of the optimal CH is performed under the consideration of energy, node degree, distance among the sensor nodes, distance among the CH and Base Station (BS), and time of death node. Finally, the implemented method's performance is compared to that of existing schemes using various measures. In particular, the network life time of the proposed work for scenario 1(number of nodes = 100) is 35.77%, 35.77%, 35.04%, 34.43%, and 33.08% better than the existing GWO, MSA, AEFA, BOA + ACO, and improved ACO methods respectively.

A fault tolerant optimal relay node selection algorithm for Wireless Sensor Networks using modified PSO

Wireless Sensor Networks (WSNs) have energy-constraints that restricts to achieve prolonged network lifetime. To optimize energy consumption of sensor nodes, clustering is one of the efficient techniques for minimization of energy conservation in WSNs. This technique sends the collected data towards the SINK based on cluster head (CH) nodes that leads to the saving of energy. WSNs have been faced a crucial issue of fault tolerance and the overall data communication is collapsed due to the failure of cluster head. Various fault-tolerance clustering methods are available for WSNs, but they are not selected the backup nodes properly. The backup nodes’ closeness or location to the other remaining nodes is not considered in these methods. They may increase network overhead with the backup nodes accessibility. A fault-tolerance cluster-based routing method is presented in this paper that aims on providing fault tolerance for relay selection in addition to the data aggregation method for clustered WSNs. The proposed method utilizes backup mechanism & the Particle Swarm Optimization (PSO) to achieve this. Based on the distance from sink, residual energy, and link delay parameters, the CHs are chosen and the network is categorized into the clusters. The Backup CHs are selected by estimating the centrality among the nodes. As a part of intra-cluster communication for reducing the aggregation overhead among CHs, the Aggregator (AG) nodes are deployed in every cluster. So that they act as the bridge between the member nodes and CHs. These AG nodes aggregates the information from member nodes and deliver it to the CHs. The PSO with modified fitness function is used to identify the best relays between AG and member nodes. The proposed mechanism is compared with existing techniques such as EM-LEACH AI-Sodairi and Ouni (2018), QEBSR Rathee et al. (2019), QOS-IHC Singh and Singh (2019), and ML-SEEP Robinson et al. (2019). The simulation results proved that the proposed mechanism reduces overhead by 55% and improve the energy consumption & throughput by 40% & 60% respectively.

An energy‐aware and lightweight routing algorithm using clustering and mobile sinks in wireless sensor networks

AbstractA wireless sensor network (WSN) consists of a number of small sensors which are powered by low‐cost irreplaceable batteries. Therefore, one of the main issues in WSN design is to prolong the battery life of the individual sensors. Clustering and utilization of mobile sinks in collaboration with base station (BS) are two common solutions to address energy consumption challenge. Based on these solutions, a new algorithm in name “Energy‐aware Routing algorithm using Clustering and Mobile Sinks (ERCMS)” is proposed in this article. The distinguishing features of ERCMS are as follows: (1) a geometric pattern is applied to divide entire network area into some parts and make cluster in each part. This pattern adjusts the cluster size based on the distance from the sink. Thus, hotspot problem is expected to be reduced, (2) computational load is distributed over sensors, (3) length of messages are controlled and each message is defined in the format with minimum required information, (4) simple mathematics approaches those impose lightweight computations are adopted, and (5) accuracy of mobile sink dispatching procedure is improved in critical conditions of the network. The simulation results show that ERCMS outperforms DFCR, MDFCR, and TEO‐MCRP in terms of consumed energy, delivery ratio, delay, routing overhead, and throughput.

ESCVAD: An Energy-Saving Routing Protocol Based on Voronoi Adaptive Clustering for Wireless Sensor Networks

An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

IMPERIALIST COMPETITIVE ALGORITHM FOR INCREASING THE LIFETIME OF WIRELESS SENSOR NETWORK

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper presents an energy-efficient cluster head selection algorithm for the clustering of heterogeneous WSN, inspired by Imperialist Competitive Algorithm (ICA). In order to reduce the network energy consumption and subsequently increases the sensor network lifetime, the clustering problem is transformed into an optimization problem and the specific cost function is used to select the cluster heads in a way that the energy utilization of the network is optimized. Extensive simulation works are done based on MATLAB to test the algorithm in various network scenarios, with different network sizes and number of nodes. Simulation results have shown that the proposed algorithm is able to extend the network lifetime compared to its comparative by up to 154 percent in terms of first node death. Furthermore, choosing the optimum set of cluster heads at every round has proved that our proposed algorithm not only could reduce the network energy consumption, but also improves the total data delivery at the base station up to 59 percent compared to the well-known algorithm.

Retraction Note to: Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection

Retraction Note to: Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection