energy consumption

List of Articles energy consumption

Open Access Article
- Abstract Page
- Full-Text
1 - Next Hop Selection to Configuring the Route in LEAP Protocol Based on Fuzzy Logic in WSNs
Vahid Sattari-Naeini F. Movahhedi
20.1001.1.16823745.1396.15.4.13.8

Since in wireless sensor networks, selection of next hop is critical in attack avoidance and lowering the power consumption, a method based on fuzzy logic is proposed in this paper considering status and report transmission of the nodes. In this method, the next hop is More

Since in wireless sensor networks, selection of next hop is critical in attack avoidance and lowering the power consumption, a method based on fuzzy logic is proposed in this paper considering status and report transmission of the nodes. In this method, the next hop is selected considering four factors, based on fuzzy logic system. These factors, indicating four optimized parameters; i.e., degree of node proximity to the shortest path, degree of node proximity to the sink, residual energy ratio of each node, and the number of false filtered messages. This method leads to an increase in energy level as well as maintaining security level in comparison with LEAP protocol. Meanwhile, it is possible to identify safe paths. Comparing with other related methods, it is shown that this method leads to significant reduction in energy consumption level and consequently the life-time of the network is increased. Meanwhile with selecting the appropriate next hop, packet drops are reduced as well. Manuscript profile
Open Access Article
- Abstract Page
- Full-Text
2 - Priority-Based Task Scheduling Using Fuzzy System in Mobile Edge Computing
Entesar Hosseini Mohsen Nickray SH. GH.
20.1001.1.16823745.1401.20.4.1.1

Mobile edge computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are i More

Mobile edge computing (MEC) are new issues to improve latency, capacity and available resources in Mobile cloud computing (MCC). Mobile resources, including battery and CPU, have limited capacity. So enabling computation-intensive and latency-critical applications are important issue in MEC. In this paper, we use a standard three-level system model of mobile devices, edge and cloud, and propose two offloading and scheduling algorithms. A decision-making algorithm for offloading tasks is based on the greedy Knapsack offloading algorithm (GKOA) on the mobile device side, which selects tasks with high power consumption for offloading and it saves energy consumption of the device. On the MEC side, we also present a dynamic scheduling algorithm with fuzzy-based priority task scheduling (FPTS) for prioritizing and scheduling tasks based on two criteria. Numerical results show that our proposed work compared to other methods and reduces the waiting time, latency and system overhead. Also, provides the balance of the system with the least number of resources. And the proposed system reduces battery consumption in the smart device by up to 90%. The results show that more than 92% of tasks are executed successfully in the edge environment. Manuscript profile
Open Access Article
- Abstract Page
- Full-Text
3 - Provide an Energy-aware Markov Based Model for Dynamic Placement of Virtual Machines in Cloud Data Centers
mehdi rajabzadeh Abolfazl Toroghi Haghighat Amir Masoud Rahmani
20.1001.1.16823745.1401.20.1.4.8

The use of energy-conscious solutions is one of the important research topics in the field of cloud computing. By effectively using virtual machine placement and aggregation algorithms, cloud suppliers will be able to reduce energy consumption. In this paper, a new mode More

The use of energy-conscious solutions is one of the important research topics in the field of cloud computing. By effectively using virtual machine placement and aggregation algorithms, cloud suppliers will be able to reduce energy consumption. In this paper, a new model is presented that seeks to achieve the desired results by improving the algorithms and providing appropriate methods. Periodic monitoring of resource status, proper analysis of the data obtained, and prediction of the critical state of the servers using the proposed Markov model have reduced the number of unnecessary migrations as much as possible. The combination of genetic algorithm and simulated annealing in the replacement section along with the definition of the adsorbent Markov chain has resulted in better and faster performance of the proposed algorithm. Simulations performed in different scenarios in CloudSim show that compared to the best algorithm compared, at low, medium and high load, energy consumption has decreased significantly. Violations of service level agreements also fell by an average of 17 percent. Manuscript profile

Current Issue

Published Issues

Menu

Browse

List of Articles energy consumption