Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

DACC: Dynamic agile congestion control scheme for effective multiple traffic wireless sensor networks

Abstract: Recent years have witnessed a stunning rise in the deployment of wireless sensor motes for diverse applications due to its flexibility in sensing real-time monitoring for large scale remote applications. The many-to-one data transmission introduces network congestion/buffer overflow at the sensor gateways that cripples the overall WSN's performance in real time monitoring. In this paper, a new duty cycle based congestion aware algorithm known as Dynamic Agile Congestion Control (DACC) designed to overcome the limitations of FIFO based sensor motes at the gateways. The DACC uses two sub-algorithms one at the gateway that intelligently senses congestion during its initial stage and another at the sensor node that dynamically alters the duty cycle based on packet marking field. The DACC algorithm tested and verified under real-time wireless sensor test bed, results show that the DACC improves the stability, alerts sensor nodes, classify both preemptive and non-preemptive data and reduces congestion control front nodes to the sink node in wireless sensor network.

Study on Concrete Using Waste Materials by Partial Replacement of Aggregates to Reduce Global Warming Gases

Abstract: The climate change due to global warming is one of the greatest environmental issues we face now. The green house gases including CO2 are released during cement and steel manufacturing process. In order to reduce the amount of green house gases, an attempt has been made to reuse the waste materials along with concrete in construction industries. In recent years, attempts have been made to increase the utilization of fly ash, quarry dust, granite and construction and demolition debris to partially replace the use of fine aggregate and coarse aggregate in concrete are gathering momentum. This paper presents information on fly ash, granite and quarry dust based concrete, material and the mixture proportions, the manufacturing process, and the influence of various parameters on the properties of fresh and hardened concrete with plain concrete and partial replacement of fine aggregate by flyash and quarry dust and coarse aggregate by granite and C & D debris concrete. The column specimens were tested under axial compression to investigate the effects of waste materials. Since the materials used were locally available waste materials, a detailed characterization was planned. In this paper, an attempt was made to utilize the waste materials by effectively recycling and filling in steel tubular circular columns with recycled aggregate concrete instead of conventional concrete. An empirical equation for calculating the design load carrying capacity of the composite column was developed using the experimental results. The test results were compared with the international codes and new theoretical models were suggested for the design. In this paper, experimental and analytical investigations were carried out to study the strength and behaviour of CFST columns over the entire range of loading. The ultimate loads and behaviour of CFST were compared with those of the hollow steel tube columns. From these elaborative experimental and analytical investigations that were done, it is concluded that out of all the waste materials used, the contribution of C & D debris and quarry dust are significant. The remaining materials that include fly-ash and granite are reasonably contributed in the performance enhancement under axial loading conditions. Finally, it is concluded that materials recovered from various waste stream are suitable to be used as secondary aggregates in concrete. The advantage of using such waste materials provides generally a low cost construction than using virgin aggregates and the elimination of the need for waste disposal in landfills. Utilization of these waste materials in concrete leads to an effective solid waste management technique and will also be cost effective. The exploitation of available natural resources and raw materials required for the construction industries can also be reduced which in turn reduces the

A Hybrid Routing Protocol for Wireless Sensor Network

Abstract: Routing protocols in wireless ad-hoc networks are not well suited for Wireless Sensor Networks as they have huge number of nodes, densely deployed and limited power processing and storage. Energy consumption of sensor nodes and their lifetime has become the major issues of Wireless Sensor Networks. This paper aims at building up of hybrid routing protocol based on PROUD and LEACH in Wireless Sensor Networks. For randomly generated nodes with different energy levels, cluster heads are chosen. Every other common node chooses its own cluster heads based on the available cluster choices and thus forming clusters. Cluster heads get sensed data from the common nodes and they send their aggregated data to the Base station for monitoring. So, node to node communication happens through cluster heads. Only cluster heads spend their energy for long range data transmission and thus avoiding energy consumption of other nodes. This procedure of head selection and cluster formation distributes the energy consumption among the nodes thus maximizing the lifetime of Wireless Sensor Networks by extending the life of the nodes. The proposed approach has been justified with results.

An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

Abstract: Fuzzy c-means (FCM) clustering is one of the most widely used fuzzy clustering algorithms. However, the main disadvantage of this algorithm is its sensitivity to noise and outliers. Intuitionistic fuzzy set is a suitable tool to cope with imperfectly defined facts and data, as well as with imprecise knowledge. So far, there exists a little investigation on FCM algorithm for clustering intuitionistic fuzzy data. This paper focuses mainly on two aspects. Firstly, it proposes an intuitionistic fuzzy representation (IFR) scheme for numerical dataset and applies the modified FCM clustering for clustering intuitionistic fuzzy (IF) data and comparing results with that of crisp and fuzzy data. Secondly, in clustering of IF data, different IF similarity measures are studied and a comparative analysis is carried out on the results. The experiments are conducted for numerical datasets of UCI machine learning data repository.