IMPROVING NETWORK LIFETIME BY MINIMIZING ENERGY HOLE PROBLEM IN WSN FOR THE APPLICATION OF IoT

The world today is at the Internet of Things (IoT) inflection point with more number of products adding to its intelligence system through a wide range of connectivity. Wireless sensor Networks (WSN) have been very useful in IoT application for gathering and processing of data to the end user. However, limited battery power and network lifetime are few of the major challenges in the designing process of any sensor network. One of those  is the Energy Hole Problem (EHP) that arises when the nodes nearer to the sink or base station die out early due to excess load as compared to other nodes that are far away. This breaks the connection of the network from the sink which results in shortening the lifetime of the network. In this paper, a trade-off is maintained between network lifetime and power requirement by implementing a sleep-awake mechanism.With the help of MATLAB simulations, it is found that after applying the mechanism, the network lifetime was extended to almost 300 and 700 rounds for TEEN and LEACH protocol respectively. The results will be beneficial for the design process in WSN for IoT application

Energy-Aware Clustering in the Internet of Things by Using the Genetic Algorithm

Internet of things (IoT) uses a lot of key technologies to collect different types of data around the world to make an intelligent and integrated whole. This concept can be as simple as a connection between a smartphone and a smart TV, or can be complex communications between the urban infrastructure and traffic monitoring systems. One of the most challenging issues in the IoT environment is how to make it scalable and energy-efficient with regard to its growing dimensions. Object clustering is a mechanism that increases scalability and provides energy efficiency by minimizing communication energy consumption. Since IoT is a large scale dynamic environment, clustering of its objects is a NP-Complete problem. This paper formulates energy-aware clustering of things as an optimization problem targeting an optimum point in which, the total consumed energy and communication cost are minimal. Then. it employs the Genetic Algorithm (GA) to solve this optimization problem by extracting the optimal number of clusters as well as the members of each cluster. In this paper, a multi objective GA for clustering that has not premature convergence problem is used. In addition, for fast GA execution multiple implementation, considerations has been measured. Moreover, the consumed energy for received and sent data, node to node and node to BS distance have been considered as effective parameters in energy consumption formulation. Numerical simulation results show the efficiency of this method in terms of the consumed energy, network lifetime, the number of dead nodes and load balancing

Conformance Checking for Manufacturing Processes using Control-flow Perspective and Time Perspective

Department of Management EngineeringRecently, the amount of manufacturing data being collected has been increasing dramatically due to growing interests of convergence of manufacturing and IT. As such, it is possible to analyze the recorded manufacturing data for various purposes. One of the most important goals of manufacturing data analysis is to understand the current situation of manufacturing processes based on comparing actual and plan data. In order to execute such analysis, conformance checking, which is to check for deviations between models and logs, can be applied. However, existing conformance checking research mostly focuses on the control-flow perspective. Thus, it is hard to apply existing conformance checking methods in the manufacturing industry since other factors such as resources, machines, groups, deadlines, and processing time needs to be determined and considered as well. Therefore, this paper proposes a comprehensive conformance checking method using the control-flow perspective and time perspective and validates the proposed method by applying actual data extracted from a manufacturing company in Korea.ope