A New Variant of Game Theory Based Decision Making (GTDM) Algorithm Routing Protocols to Improve Energy Efficiency on Vehicular Delay Tolerant Network (VDTN)

These days, the application of Delay Tolerant Networks (DTN) have been expanded into various scenarios of communications field. Vehicular Ad hoc Networks (VANETs) as a communication scenario which treat its subject to disruption and disconnection with frequent partitioning and high latency. Therefore, Vehicular Delay Tolerant Network (VDTN) is introduced as a new research paradigm due to several characteristics match according to specific prerequisites. DTNs is proposed in Vehicular Network because its mechanisms which is using store-carry-forward, can be implemented to deliver the packets, without end-to-end connection, to the destination. One of challenging research of DTN in routing protocol is to meet prerequisites of many applications, especially in vehicular network (VDTN).  This paper presents a new variant of Game Theory based on Decision Making (GTDM) that can deliver packet to static node due to improve the energy efficiency of DTNs in city environments. Hence, its destination node (Receiver Node) needs to go to the static node to take their packet under Working Day Movement (WDM), because relay node will be passing by the static node with continuously move to its track to deliver packet. In this paper author will analyze the new variant of GTDM (NVGTDM) which can be more useful than original GTDM for application in city environment with using transportation movement. We conclude that modification of GTDM routing algorithm (NVGTDM) improves energy efficiency as much as 10.38% than the original GTDM. Hence, it can be ensured to compare either to Epidemic or PRoPHET routing algorithm with 55.44% and 68.75% in rates of energy efficiency respectively

Minimizing energy consumption in scheduling of dependent tasks using genetic algorithm in computational grid

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model