KCG College of Technology

About: KCG College of Technology is a based out in . It is known for research contribution in the topics: Computer science & The Internet. The organization has 427 authors who have published 381 publications receiving 2193 citations.

Performance analysis of fuzzy co-operative adaptive cruise controller in vehicular ad hoc networks

Abstract: Mobility models, which determine the movement pattern of nodes communicating wirelessly, play a vital role in the simulation-based evaluation of Vehicular Ad Hoc Networks. Realistic mobility models enable us to assess the network performance more accurately, and it is required to design a realistic mobility model that mimics the real vehicle scenario. This paper made an attempt to bring out a suitable mobility model for vehicular networks, which improves the network performance in accordance with string stability, by incorporating the concept of Fuzzy Cooperative Adaptive Cruise Control mechanism among a platoon of vehicles. The experimental study and analysis find that fuzziness improves the performance of the network in terms of packet delay ratio, delay and throughput.

A Novel Oppositional Chaotic Flower Pollination Optimization Algorithm for Automatic Tuning of Hadoop Configuration Parameters.

Abstract: At present, due to the introduction of the big data era, numerous numbers of data are generated consistently. Many applications utilize big data platforms, namely Spark, Hadoop, Amazon web services, and so on, since these platforms use several parameters for tuning that further enhance the operating performances. It requires a long duration of time to tune the parameters because of the complex relationship and large quantity of parameters. As a result, the building of such parameters and performance optimization at a particular duration of time becomes a challenging task. Several auto-tuning approaches are developed to achieve an optimal design. However, these approaches increase the computation time and minimize the efficiency of the cluster. It is necessary to tune the parameters automatically with low computational and processing time as well as to improve the performance of the system. In this proposed approach, a novel automatic parameter tuning system named as Opt. Tuner is proposed to select the Hadoop configuration parameters with less computational time. The optimization of the proposed approach is achieved by the Flower Pollination Algorithm. Here, a chaotic mapping along with Opposition-Based Learning is introduced for population initialization to form a novel Oppositional Chaotic Flower Pollination Algorithm. The main motive of this initialization phase involves in generating better individuals and to guide the search agent more quickly. In this novel approach, 15 configuration parameters are considered for auto-tuning. Finally, the performance of the proposed approach utilizes the wordcount and sort application to investigate the exhibition and proficiency of diverse databases.

Master Cluster Head and Vice Cluster Head Algorithm for Wireless Sensor Networks Using PSO

Abstract: One of the most significant strategy measures of wireless sensor networks (WSNs) is energy efficiency Grouping affords an effective way for encompassing the lifetime of the network We offer a double cluster-heading clustering algorithm using particle swarm optimization (PSO-DH) The algorithm computes two cluster skulls The determination of the dominant cluster and the immorality cluster-head needs consider the state information, including position and energy reservation about nodes and their neighbors Because every node contains a list of information about his neighbors and location using connected dominating set The dominant cluster head (DCH) receives and masses data to analyst directly The algorithm poises the energy consumption, so it can encompass the network life time effectively