An improved half life variable quantum time with mean time slice round robin CPU scheduling (IMHLVQTRR)

Round Robin (RR) CPU scheduling is a scheduling technique that allocate equal time slice known as quantum time (QT) to processes wanting to use the CPU. Processes are allocated the CPU in a circular manner in such a way that if QT is greater than or equal to a process’ burst time, the process will run to completion otherwise the process will be interrupted and return to the tail of the ready queue for next round of execution. The average waiting time and turnaround time in a classical RR is higher when compared with First Come First Serve and Shortest Job First CPU scheduling algorithms. The existing technique Half Life Variable Quantum Time Round Robin (HLVQTRR) further increases the average waiting time and average turnaround time for the system. Researchers proposed a dynamic QT in order to improve the classical RR which has a static QT. In dynamic RR, there are more than one QT used for allocating time slot to processes as opposed to classical RR where a fix QT is used for the allocation. This research work is a proposal that modified HLVQTRR to be ‘An Improved Half Life Variable Quantum Time with Mean Time Slice Round Robin CPU Scheduling (ImHLVQTRR)’. In the proposed technique, two quantum time (QT1 and QT2) is calculated. QT1 is the average of all the processes in the ready queue and it is constant while QT2 is the half of each process burst and it changes depending on which process is in execution. The proposed approach was developed and simulated using python programming language. Using python programing language, the proposed approach was developed and the system was simulated and compared against the classical RR and HLVQTRR. The result showed that the proposed technique (ImHLVQTRR) minimized average waiting time, average turnaround time and number of context switching by 1292.087, 1292.089 and 27.40 time units respectively against the existing technique (HLVQTRR) and the classical RR

Energy Consumption in Wireless Sensor Network

Energy is a limited resource in wireless sensor networks. In fact, the reduction of power consumption is crucial to increase the lifetime of low power sensor networks. Wireless sensor networks consist of small, autonomous devices with wireless networking capabilities. In order to further increase the applicability in real world applications, minimizing power consumption is one of the most critical issues. Therefore, accurate power model is required for the evaluation of wireless sensor networks. To estimate the lifetime of sensor node, the energy characteristics of sensor node are measured. Research in this area has been growing in the past few years given the wide range of applications that can benefit from such a technology. Based on the proposed model, the estimated lifetime of a battery powered sensor node can be increased significantly. Keywords—Sensor, Wireless Sensor Network, Energy Consumptio

A simulation to minimize traffic violation in Nigeria through the use of smart spike strip

Traffic light violations always have negative effects on lives and environment and to quantify these negative effects is complex. For traffic light violation to be mitigated or eliminated, gathering of information on traffic incidents such as nature of the road, congestion spots, and volume of traffic on each road cannot be overemphasized. The elimination of traffic light violation particularly in developing countries like Nigeria may not be a realistic goal, but controlling or managing it to reduce the intensity of violation may be achievable. The unbearable traffic congestion is the highest cause of traffic light violation, most especially within the rush hours of the morning when individuals go to work (between 7.00am – 8.00am) and coming back in the evenings (4.00 – 5.00pm and 6.00 – 7.00pm) at most cross roads in Kaduna metropolis. In this research work, an algorithm is developed to control the traffic light violation on one lane of a traffic junction by introducing the smart spike strip which is synchronized with the traffic light control system. The implementation of the algorithm to simulate the control of the traffic light violation on a traffic junction is achieved using Visual Studio 2012 IDE as a platform for the simulation. Screenshots to illustrate the different of the vehicles and lanes which are states static, ready and motion states shown