Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS

Forest fires are a significant threat to the ecological system’s stability. Several attempts have been made to detect forest fires using a variety of approaches, including optical fire sensors, and satellite-based technologies, all of which have been unsuccessful. In today’s world, research on flying ad hoc networks (FANETs) is a thriving field and can be used successfully. This paper describes a unique clustering approach that identifies the presence of a fire zone in a forest and transfers all sensed data to a base station as soon as feasible via wireless communication. The fire department takes the required steps to prevent the spread of the fire. It is proposed in this study that an efficient clustering approach be used to deal with routing and energy challenges to extend the lifetime of an unmanned aerial vehicle (UAV) in case of forest fires. Due to the restricted energy and high mobility, this directly impacts the flying duration and routing of FANET nodes. As a result, it is vital to enhance the lifetime of wireless sensor networks (WSNs) to maintain high system availability. Our proposed algorithm EE-SS regulates the energy usage of nodes while taking into account the features of a disaster region and other factors. For firefighting, sensor nodes are placed throughout the forest zone to collect essential data points for identifying forest fires and dividing them into distinct clusters. All of the sensor nodes in the cluster communicate their packets to the base station continually through the cluster head. When FANET nodes communicate with one another, their transmission range is constantly adjusted to meet their operating requirements. This paper examines the existing clustering techniques for forest fire detection approaches restricted to wireless sensor networks and their limitations. Our newly designed algorithm chooses the most optimum cluster heads (CHs) based on their fitness, reducing the routing overhead and increasing the system’s efficiency. Our proposed method results from simulations are compared with the existing approaches such as LEACH, LEACH-C, PSO-HAS, and SEED. The evaluation is carried out concerning overall energy usage, residual energy, the count of live nodes, the network lifetime, and the time it takes to build a cluster compared to other approaches. As a result, our proposed EE-SS algorithm outperforms all the considered state-of-art algorithms.publishedVersio

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

A Novel Routing Protocol For Wireless Sensor Networks With Improved Energy Efficient LEACH

Wireless Sensor Networks (Wsns) Have Been Widely Considered As One Of The Most Important Technologies For The Twenty-First Century. A Typical Wireless Sensor Network(WSN) Used For Environmental Condition Monitoring, Security Surveillance Of Battle-Fields, Wildlife Habitat Monitoring, Etc. Cluster-Based Hierarchical Routing Protocols Play An Essential Role In Decreasing The Energy Consumption Of Wireless Sensor Networks (Wsns). A Low-Energy Adaptive Clustering Hierarchy (LEACH) Has Been Proposed As An Application-Specific Protocol Architecture For Wsns. However, Without Considering The Distribution Of The Cluster Heads (Chs) In The Rotation Basis, The LEACH Protocol Will Increase The Energy Consumption Of The Network. To Improve The Energy Efficiency Of The WSN, We Propose A Novel Modified Routing Protocol In This Paper. The Newly Proposed Improved Energy-Efficient LEACH (IEE-LEACH) Protocol Considers The Residual Node Energy And The Average Energy Of The Networks. To Achieve Satisfactory Performance In Terms Of Reducing The Sensor Energy Consumption, The Proposed IEE-LEACH Accounts For The Numbers Of The Optimal Chs And Prohibits The Nodes That Are Closer To The Base Station (BS) To Join In The Cluster Formation. Furthermore, The Proposed IEE-LEACH Uses A New Threshold For Electing Chs Among The Sensor Nodes, And Employs Single Hop, Multi-Hop, And Hybrid Communications To Further Improve The Energy Efficiency Of The Networks. The Simulation Results Demonstrate That, Compared With Some Existing Routing Protocols, The Proposed Protocol Substantially Reduces The Energy Consumption Of Wsns

Enhanced Network Connectivity And Node Lifetime Techniques In Wireless Sensor Networks

Low-cost sensors that may be integrated to form Wireless Sensor Networks (WSNs) have received increased research attention for their potential application in environmental monitoring, healthcare, and defence, among others. Connectivity is essential in WSNs. It represents the ability of a member node to communicate with other nodes within a network either through direct transmission or multi-hop relays. Low connectivity is a result of the network being broken up into different and disconnected groups of nodes. Whether they are dispersed from the air or are installed manually, nodes may still have a sparsely dense distribution. The limited radio range of the nodes can also cause the network to partition into disjoint groups, a process that interrupts or completely prevents communication. This dissertation proposes an Intelligent Multi-Hop Cluster Transmission of Information (IMHCTI) protocol which integrates a Mathematical Stochastic Cross-Layer (MSCL) model with the proposed MHCTI protocol to improve network connectivity and enhance network lifetime. The MHCTI protocol is based on network clustering coherent-cooperative transmission fields from closely spaced wireless nodes, to improve the data transmission distance range and to address the problem of poor network connectivity

Lifetime centric load balancing mechanism in wireless sensor network based IoT environment

Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model