Comparison and Analysis on AI Based Data Aggregation Techniques in Wireless Networks

In modern era WSN, data aggregation technique is the challenging area for researchers from long time. Numbers of researchers have proposed neural network (NN) and fuzzy logic based data aggregation methods in Wireless Environment. The main objective of this paper is to analyse the existing work on artificial intelligence (AI) based data aggregation techniques in WSNs. An attempt has been made to identify the strength and weakness of AI based techniques.In addition to this, a modified protocol is designed and developed.And its implementation also compared with other existing approaches ACO and PSO. Proposed approach is better in terms of network lifetime and throughput of the networks. In future an attempt can be made to overcome the existing challenges during data aggregation in WSN using different AI and Meta heuristic based techniques

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

Survey on Multi Agent Energy Efficient Clustering Algorithms in Wireless Sensor Networks

In the last few years, there are many applications for Wireless Sensor Networks (WSNs). One of the main drawbacks of these networks is the limited battery power of sensor nodes. There are many cases to reduce energy consumption in WSNs. One of them is clustering. Sensor nodes partitioned into the clusters so that one is chosen as Cluster Head (CH). Clustering and selection of the proper node as CH is very significant in reducing energy consumption and increasing network lifetime. In this paper, we have surveyed a multi agent clustering algorithms and compared on various parameters like cluster size, cluster count, clusters equality, parameters used in CHs selection, algorithm complexity, types of algorithm used in clustering, nodes location awareness, inter-cluster and intra-cluster topologies, nodes homogeneity and MAC layer communications

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

CAF: Cluster Algorithm and A-Star with Fuzzy Approach for Lifetime Enhancement in Wireless Sensor Networks

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria

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

Energy efficient clustering and routing optimization model for maximizing lifetime of wireless sensor network

Recently, the wide adoption of WSNs (Wireless-Sensor-Networks) is been seen for provision non-real time and real-time application services such as intelligent transportation and health care monitoring, intelligent transportation etc. Provisioning these services requires energy-efficient WSN. The clustering technique is an efficient mechanism that plays a main role in reducing the energy consumption of WSN. However, the existing model is designed considering reducing energy- consumption of the sensor-device for the homogenous network. However, it incurs energy-overhead (EO) between cluster-head (CH). Further, maximizing coverage time is not considered by the existing clustering approach considering heterogeneous networks affecting lifetime performance. In order to overcome these research challenges, this work presents an energy efficient clustering and routing optimization (EECRO) model adopting cross-layer design for heterogeneous networks. The EECRO uses channel gain information from the physical layer and TDMA based communication is adopted for communication among both intra-cluster and inter-cluster communication. Further, clustering and routing optimization are presented to bring a good trade-off among minimizing the energy of CH, enhancing coverage time and maximizing the lifetime of sensor-network (SN). The experiments are conducted to estimate the performance of EECRO over the existing model. The significant-performance is attained by EECRO over the existing model in terms of minimizing routing and communication overhead and maximizing the lifetime of WSNs

EFFICIENT ROUTING PROTOCOL ALGORITHM FOR WIRELESS SENSOR NETWORKS

Recently, different applications of wireless sensor networks (WSNs) in the industry fields using different data transfer protocols has been developed. As the energy of sensor nodes is limited, prolonging network lifetime in WSNs considered a significant occurrence. To develop network permanence, researchers had considered energy consuming in routing protocols of WSNs by using modified Low Energy Adaptive Clustering Hierarchy. This article presents a developed effective transfer protocols for autonomic WSNs. An efficient routing scheme for wireless sensor network regarded as significant components of electronic devices is proposed. An optimal election probability of a node to be cluster head has being presented. In addition, this article uses a Voronoi diagram, which decomposes the nodes into zone around each node. This diagram used in management architecture for WSNs

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