Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency

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

Komparasi Algoritma Klasifikasi Berbasis Particle Swarm Optimization Pada Analisis Sentimen Ekspedisi Barang

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.Kebutuhan masyarakat akan ekspedisi barang saat ini mulai meningkat dengan adanya marketplace. Opini pengguna tentang pelayanan ekspedisi barang saat ini dilakukan masyarakat melalui banyak hal salah satunya media sosial Twitter. Dengan sentimen analisis, kecenderungan sebuah opini akan mampu terlihat apakah mempunyai kecenderungan positif atau negatif. Metode yang dapat diterapkan pada analisis sentimen yaitu Algoritma classifier Naive Bayes dan Support Vector Machine (SVM). Penelitian ini akan melakukan penerapan kedua algoritma tersebut yang dioptimasi menggunakan algoritma PSO pada analisis sentimen. Pengujian dilakukan dengan melakukan setting parameter pada PSO di masing-masing algoritma classifier. Hasil dari pengujian yang dilakukan mampu menghasilkan peningkatan akurasi sebesar 15.11% pada penerapan PSO di algoritma Naive Bayes. Peningkatan akurasi pada algoritma SVM berbasis PSO senilai 1.74% pada kernel sigmoid

Efficient MAC Adaptive Protocol on Wireless Sensor Network

Wireless Sensor Networks (WSNs) have attracted a lot of attention from the research community and industry in recent years. WSNs maintenance associated with battery replacement can increase system operating costs, especially for wireless sensor networks located in hard-to-reach and dangerous places. In this study, an adaptive Medium Access Control (MAC) is proposed that can regulate the period of data acquisition and transmission. In contrast to conventional MAC, the applied adaptive MAC regulates the data transmission period based on the estimated energy use in the previous cycle. This study focuses on comparing energy efficiency between conventional and adaptive MAC. Energy usage information is retrieved directly on the sensor node. In star topology, the proposed MAC can increase the lifetime of the sensor network up to 6.67% in a star topology. In the hierarchical topology, the proposed MAC can increase network energy efficiency up to 9.17%. The resulting increase in network throughput is 17.73% for the Star network and 33.81% for the Hierarchy network. The star topology without implementing adaptive MAC has the lowest throughput of 0.188 kb/s. The highest throughput is achieved by a hierarchical topology that applies MAC with a throughput of 2.157 kb/s

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

EODC: An Energy Optimized Dynamic Clustering Protocol for Wireless Sensor Network using PSO approach

Wireless Sensor Network comprises of a number of small wireless nodes whose role is to sense, gather, process and communicate. One of the primary concerns of the network is to optimize the energy consumption and extend the network lifespan. Sensor nodes can be clustered to increase the network lifespan. This is done by selecting the cluster head for every cluster and by performing data fusion on the cluster head. The proposed system is using an energy efficient hierarchical routing protocol named Energy Optimized Dynamic Clustering (EODC) for clustering large ad-hoc WSN and route the data towards the sink. The sink receives the data collected from the set of cluster heads after every round. The cluster head was selected using Particle Swarm Optimization (PSO) approach and the cluster members are allocated based on Manhattan distance. The metrics used to find the fitness function are location, link quality, energy of active node and energy of inactive node. The system employs shortest path approach to communicate between the cluster heads till it reaches the base station. By this, we have increased the energy efficiency and lifetime of the network. The analysis and outcomes show that the EODC was found to outperform the existing protocol which compares with this algorithm

Efficient MAC Adaptive Protocol on Wireless Sensor Network

Wireless Sensor Networks (WSNs) have attracted a lot of attention from the research community and industry in recent years. WSNs maintenance associated with battery replacement can increase system operating costs, especially for wireless sensor networks located in hard-to-reach and dangerous places. In this study, an adaptive Medium Access Control (MAC) is proposed that can regulate the period of data acquisition and transmission. In contrast to conventional MAC, the applied adaptive MAC regulates the data transmission period based on the estimated energy use in the previous cycle. This study focuses on comparing energy efficiency between conventional and adaptive MAC. Energy usage information is retrieved directly on the sensor node. In star topology, the proposed MAC can increase the lifetime of the sensor network up to 6.67% in a star topology. In the hierarchical topology, the proposed MAC can increase network energy efficiency up to 9.17%. The resulting increase in network throughput is 17.73% for the Star network and 33.81% for the Hierarchy network. The star topology without implementing adaptive MAC has the lowest throughput of 0.188 kb/s. The highest throughput is achieved by a hierarchical topology that applies MAC with a throughput of 2.157 kb/s

Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm

Maximizing network lifetime is a major objective for designing and deploying a wireless sensor network. Clustering sensor nodes is an effective topology control approach helping achieve this goal. In this paper, we present a new method to prolong the network lifetime based on the improved particle swarm optimization algorithm, which is an optimization method designed to select target nodes. The protocol takes into account both energy efficiency and transmission distance, and relay nodes are used to alleviate the excessive power consumption of the cluster heads. The proposed protocol results in better distributed sensors and a well-balanced clustering system enhancing the network's lifetime. We compare the proposed protocol with comparative protocols by varying a number of parameters, e.g., the number of nodes, the network area size, and the position of the base station. Simulation results show that the proposed protocol performs well against other comparative protocols in various scenarios