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

EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks

Improving the network lifetime is still a vital challenge because most wireless sensor networks (WSNs) run in an unreached environment, and offer almost impossible human access and tracking. Clustering is one of the most effective methods for ensuring that the relevant device process takes place to improve network scalability, decrease energy consumption and maintain an extended network lifetime. Many research have been developed on the numerous effective clustering algorithms to address this problem.  Such algorithms almost dominate on the cluster head (CH) selection and cluster formation; using the intelligent type1 fuzzy-logic (T1-FL) scheme. In this paper, we suggest an interval type2 FL (IT2-FL) methodology that assumes uncertain levels of a decision to be more efficient than the T1-FL model. It is the so-called energy-efficient interval type2 fuzzy (EEIT2-F) low energy adaptive clustering hierarchical (LEACH) protocol. The IT2-FL system depends on three inputs of the residual energy of each node, the node distance from the base station (sink node), and the centrality of each node. Accordingly, the simulation results show that the suggested clustering protocol outperforms the other existing proposals in terms of energy consumption and network lifetime

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

FTDA: Performance Enhancement of WSN using Fuzzy based Traffic Data Analysis

In the present time in Wireless Sensor Network plays an essential role in the monitoring of different physical phenomena. Monitoring of city traffic data analysis is very important in different metro cities due to rapid increase in population.  This research work proposes a model for traffic data analysis using wireless sensor network  incorporated with fuzzy technique. The proposed model is tested for performance parameters such as node dead rate , data packed received. The proposed model improved the efficiency compared to existing techniques of the WSN network for traffic data collection and analysis

Optimization of routing-based clustering approaches in wireless sensor network: Review and open research issues

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In today’s sensor network research, numerous technologies are used for the enhancement of earlier studies that focused on cost-effectiveness in addition to time-saving and novel approaches. This survey presents complete details about those earlier models and their research gaps. In general, clustering is focused on managing the energy factors in wireless sensor networks (WSNs). In this study, we primarily concentrated on multihop routing in a clustering environment. Our study was classified according to cluster-related parameters and properties and is subdivided into three approach categories: (1) parameter-based, (2) optimization-based, and (3) methodology-based. In the entire category, several techniques were identified, and the concept, parameters, advantages, and disadvantages are elaborated. Based on this attempt, we provide useful information to the audience to be used while they investigate their research ideas and to develop a novel model in order to overcome the drawbacks that are present in the WSN-based clustering models

Research Challenges of Improved Cluster Chain Power-Efficient Routing Using Natural Computing Methods for Wireless Sensor Network

Wireless Sensor Networks (WSNs) primarily operate on batteries, making energy conservation crucial, especially in routing processes. Efficient routing in WSNs is challenging due to the network's distinct attributes. Among various routing protocols, CCPAR is noteworthy as it utilizes a chain between cluster heads to relay data to the base station. This research delves into nature-inspired techniques for energy-efficient routing in WSNs. It introduces the Moth-Dolphin Optimization Algorithm, capitalizing on the communication between moths to enhance routing performance. This innovative method combines the navigational skills of moths and the communicative attributes of dolphins. When benchmarked against other optimization methods, the Moth-Dolphin algorithm offers favorable results. The study then applies this algorithm to improve CCPAR routing, aiming for reduced energy consumption. The modified routing's effectiveness is evaluated against other top-tier algorithms, considering factors like energy consumption, throughput, network longevity, and delay

A Hybrid Modified Ant Colony Optimization - Particle Swarm Optimization Algorithm for Optimal Node Positioning and Routing in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed in hostile locations for environmental monitoring. Sensor placement and energy management are the two main factors that should be focused due to certain limitations in WSNs. The nodes in a sensor network might not stay charged when energy draining takes place; therefore, increasing the operational lifespan of the network is the primary purpose of energy management. Recently, major research interest in WSN has been focused with the essential aspect of localization. Several types of research have also taken place on the challenges of node localization of wireless sensor networks with the inclusion of range-free and range-based localization algorithms. In this work, the optimal positions of Sensor Nodes (SNs) are determined by proposing a novel Hybrid M-ACO – PSO (HMAP) algorithm. In the HMAP method, the improved PSO utilizes learning strategies for estimating the relay nodes\u27 optimal positions. The M-ACO assures the data conveyance. A route discovers when it relates to the ideal route irrespective of the possibility of a system that includes the nodes with various transmission ranges, and the network lifetime improves. The proposed strategy is executed based on the energy, throughput, delivery ratio, overhead, and delay of the information packets

Optimize Energy Consumption of Wireless Sensor Networks by using modified Ant Colony Optimization ACO

Routing represents a pivotal concern in the context of Wireless Sensor Networks (WSN) owing to its divergence from traditional network routing paradigms. The inherent dynamism of the WSN environment, coupled with the scarcity of available resources, engenders considerable challenges for industry and academia alike in devising efficient routing strategies. Addressing these challenges, a viable recourse lies in applying heuristic search methodologies to ascertain the most optimal path in WSNs. Ant Colony Optimization (ACO) is a well-established heuristic algorithm that has demonstrated notable advancements in routing contexts. This paper introduces a modify routing protocols based on Ant colony optimization. In these protocols, we incorporate the inverse of the distance between nodes and their neighbours in the probability equations of ACO along with considering pheromone levels and residual energy. These formulation modifications facilitate the selection of the most suitable candidate for the subsequent hop, effectively minimizing the average energy consumption across all nodes in each iteration. Furthermore, in this protocol, we iteratively fine-tune ACO's parameter values based on the outcomes of several experimental trials. The experimental analysis is conducted through a diverse set of network topologies, and the results are subjected to comparison against well-established ACO algorithm and routing protocols. The efficacy of the proposed protocol is assessed based on various performance metrics, encompassing throughput, energy consumption, network lifetime, energy consumption, the extent of data transferred over the network, and the length of paths traversed by packets. These metrics collectively provide a comprehensive evaluation of the performance attainments of the routing protocols