Algorithms which take uncertainty into account make better systems for fleet management and lead to greater efficiency. These algorithms are faster and can manage real-time traffic and even compute the location and status of vehicles with miscellaneous requests from users. Parallel computing technologies enable us to implement fuzzy-based algorithms which route the traffic in a much more efficient mechanism. This also improves the overall system of user request management by using meta-heuristics. © Springer Nature Singapore Pte Ltd. 2019.

Mohanasundaram R

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

A Mummoorthy

Shubham Saraff

R Arun

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

Fleet Management and Vehicle Routing in Real Time Using Parallel Computing Algorithms

Advances in Intelligent Systems and Computing Soft Computing and Signal Processing

The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches.

Fulltext

The Scientific World Journal

Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

In most wireless sensor network applications a large amount of data has been continuously collected for future data query and analysis. Data storage management becomes an important challenge in wireless sensor networks. A popular application of sensor networks is event monitoring. In such applications, the observers may not be interested in the sensors or the raw data from the sensors, but more interested in the events. To address the issues of data storage for event monitoring, an optimal data storage scheme is proposed specifically for Fire detection event. In this work, mainly storage node position problem is considered. Hybrid particle swarm optimization is integrated with FCM clustering to attain the suitable positions for k storage nodes in WSN based on the energy cost of data transmission which in turn assists in the detection of the fire event. To reduce data access energy consumption, FCM clustering based on data storage (CBDS) algorithm has been proposed. CBDS has appropriately adapted to adjust the location of data storage through the process of data storage and query access cost calculations respectively. This paper is focused on monitoring the fire event detection in WSN based on the hybrid PSO with FCM clustering. © 2015, Springer Science+Business Media New York.

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Soft Computing and Signal Processing
Publisher	Data powered by TypesetSpringer Singapore
Open Access	No