scholarly journals Optimal energy-aware epidemic routing in DTNs

2012 ◽  
Author(s):  
MHR. Khouzani ◽  
Soheil Eshghi ◽  
Saswati Sarkar ◽  
Ness B. Shroff ◽  
Santosh S. Venkatesh
Keyword(s):  
Energy Aware ◽  
Optimal Energy ◽  
Epidemic Routing

scholarly journals Optimal Energy-Aware Epidemic Routing in DTNs

2015 ◽  
Vol 60 (6) ◽  
pp. 1554-1569 ◽  
Author(s):  
Soheil Eshghi ◽  
M. H. R. Khouzani ◽  
Saswati Sarkar ◽  
Ness B. Shroff ◽  
Santosh S. Venkatesh
Keyword(s):  
Energy Aware ◽  
Optimal Energy ◽  
Epidemic Routing

Energy Aware Grid Scheduling for Dependent Task Using Genetic Algorithm

2016 ◽  
Vol 7 (2) ◽  
pp. 18-36
Author(s):  
Shiv Prakash ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Job Scheduling ◽  
Computing System ◽  
Computational Grid ◽  
Task Execution ◽  
Energy Aware ◽  
Optimal Energy ◽  
Proposed Model ◽  
Dynamic Voltage

Consumption of energy in the large computing system is an important issue not only because energy sources are depleting fast but also due to the deteriorating environmental conditions. A computational grid is a large heterogeneous distributed computing platform which consumes enormous energy in the task execution. Energy-aware job scheduling, in the computational grid, is an important issue that has been addressed in this work. If the tasks are properly scheduled, keeping the optimal energy concern, it is possible to save the energy consumed by the system in the task execution. The prime objective, in this work, is to schedule the dependent tasks of a job, on the grid nodes with optimal energy consumption. Energy consumption is estimated with the help of Dynamic Voltage Frequency Scaling (DVFS). Makespan, while optimizing the energy consumption, is also taken care of in the proposed model. GA is applied for the purpose and therefore the model is named as Energy Aware Genetic Algorithm (EAGA). Performance evaluation of the proposed model is done using GridSim simulator. A comparative study with other existing models viz. min-min and max-min proves the efficacy of the proposed model.

scholarly journals BiPAD: Binomial Point Process Based Energy-Aware Data Dissemination in Opportunistic D2D Networks

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2073
Author(s):  
Seho Han ◽  
Kisong Lee ◽  
Hyun-Ho Choi ◽  
Howon Lee
Keyword(s):  
Point Process ◽  
Routing Protocol ◽  
Data Dissemination ◽  
Optimal Number ◽  
Delivery Ratio ◽  
Message Delivery ◽  
Energy Aware ◽  
Epidemic Routing ◽  
Efficient Data ◽  
Binomial Point Process

In opportunistic device-to-device (D2D) networks, the epidemic routing protocol can be used to optimize the message delivery ratio. However, it has the disadvantage that it causes excessive coverage overlaps and wastes energy in message transmissions because devices are more likely to receive duplicates from neighbors. We therefore propose an efficient data dissemination algorithm that can reduce undesired transmission overlap with little performance degradation in the message delivery ratio. The proposed algorithm allows devices further away than the k-th furthest distance from the source device to forward a message to their neighbors. These relay devices are determined by analysis based on a binomial point process (BPP). Using a set of intensive simulations, we present the resulting network performances with respect to the total number of received messages, the forwarding efficiency and the actual number of relays. In particular, we find the optimal number of relays to achieve almost the same message delivery ratio as the epidemic routing protocol for a given network deployment. Furthermore, the proposed algorithm can achieve almost the same message delivery ratio as the epidemic routing protocol while improving the forwarding efficiency by over 103% when k≥10.

Optimal energy-aware control policies for FIFO servers

2016 ◽  
Vol 103 ◽  
pp. 41-59 ◽  
Author(s):  
Misikir Eyob Gebrehiwot ◽  
Samuli Aalto ◽  
Pasi Lassila
Keyword(s):  
Energy Aware ◽  
Control Policies ◽  
Optimal Energy

scholarly journals Optimal Energy Aware Clustering in Sensor Networks

Sensors ◽  
2002 ◽  
Vol 2 (7) ◽  
pp. 258-269 ◽  
Author(s):  
Soheil Ghiasi ◽  
Ankur Srivastava ◽  
Xiaojian Yang ◽  
Majid Sarrafzadeh
Keyword(s):  
Sensor Networks ◽  
Energy Aware ◽  
Optimal Energy
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