Global Optimization Using Local Search Approach for Course Scheduling Problem

Course scheduling problem is a combinatorial optimization problem which is defined over a finite discrete problem whose candidate solution structure is expressed as a finite sequence of course events scheduled in available time and space resources. This problem is considered as non-deterministic polynomial complete problem which is hard to solve. Many solution methods have been studied in the past for solving the course scheduling problem, namely from the most traditional approach such as graph coloring technique; the local search family such as hill-climbing search, taboo search, and simulated annealing technique; and various population-based metaheuristic methods such as evolutionary algorithm, genetic algorithm, and swarm optimization. This article will discuss these various probabilistic optimization methods in order to gain the global optimal solution. Furthermore, inclusion of a local search in the population-based algorithm to improve the global solution will be explained rigorously

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

Solving Graph Coloring Problems Using Discrete Artificial Bee Colony

筑波大学 (University of Tsukuba)201

Voice Over Sensor Networks

Wireless sensor networks have traditionally focused on low duty-cycle applications where sensor data are reported periodically in the order of seconds or even longer. This is due to typically slow changes in physical variables, the need to keep node costs low and the goal of extending battery lifetime. However, there is a growing need to support real-time streaming of audio and/or low-rate video even in wireless sensor networks for use in emergency situations and shortterm intruder detection. In this paper, we describe a real-time voice stream-capability in wireless sensor networks and summarize our deployment experiences of voice streaming across a large sensor network of FireFly nodes in an operational coal mine. FireFly is composed of several integrated layers including specialized low-cost hardware, a sensor network operating system, a real-time link layer and network scheduling. We are able to provide efficient support for applications with timing constraints by tightly coupling the network and task scheduling with hardware-based global time synchronization. We use this platform to support 2-way audio streaming concurrently with sensing tasks. For interactive voice, we investigate TDMA-based slot scheduling with balanced bi-directional latency while meeting audio timeliness requirements. Finally, we describe our experimental deployment of 42 nodes in a coal mine, and present measurements of the end-to-end throughput, jitter, packet loss and voice quality