scholarly journals A Non-cooperative Game Algorithm for Task Scheduling in Wireless Sensor Networks

2011 ◽  
Vol 6 (4) ◽  
pp. 592 ◽  
Author(s):  
Liang Dai ◽  
Yilin Chang ◽  
Zhong Shen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Task Scheduling ◽  
Cooperative Game ◽  
Completion Time ◽  
Wireless Sensor ◽  
Task Completion ◽  
Network Resource ◽  
Task Completion Time ◽  
Processing Capability

Scheduling tasks in wireless sensor networks is one of the most challenging problems. Sensing tasks should be allocated and processed among sensors in minimum times, so that users can draw prompt and effective conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. But sensors may refuse to take pains to carry out the tasks due to the limited energy. To solve the potentially selfish problem of the sensors, a non-cooperative game algorithm (NGTSA) for task scheduling in wireless sensor networks is proposed. In the proposed algorithm, according to the divisible load theory, the tasks are distributed reasonably to every node from SINK based on the processing capability and communication capability. By removing the performance degradation caused by communications interference and idle, the reduced task completion time and the improved network resource utilization are achieved. Strategyproof mechanism which provide incentives to the sensors to obey the prescribed algorithms, and to truthfully report their parameters, leading to an effient task scheduling and execution. A utility function related with the total task completion time and tasks allocating scheme is designed. The Nash equilibrium of the game algorithm is proved. The simulation results show that with the mechanism in the algorithm, selfish nodes can be forced to report their true processing capability and endeavor to participate in the measurement, thereby the total time for accomplishing the task is minimized and the energy-consuming of the nodes is balanced.

scholarly journals An Optimal Task Scheduling Algorithm in Wireless Sensor Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 101 ◽  
Author(s):  
Liang Dai ◽  
Yilin Chang ◽  
Zhong Shen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimal Number ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Resource ◽  
Task Scheduling Algorithm ◽  
Two Phases

Sensing tasks should be allocated and processed among sensor nodes in minimum times so that users can draw useful conclusions through analyzing sensed data. Furthermore, finishing sensing task faster will benefit energy saving, which is critical in system design of wireless sensor networks. To minimize the execution time (makespan) of a given task, an optimal task scheduling algorithm (OTSA-WSN) in a clustered wireless sensor network is proposed based on divisible load theory. The algorithm consists of two phases: intra-cluster task scheduling and inter-cluster task scheduling. Intra-cluster task scheduling deals with allocating different fractions of sensing tasks among sensor nodes in each cluster; inter-cluster task scheduling involves the assignment of sensing tasks among all clusters in multiple rounds to improve overlap of communication with computation. OTSA-WSN builds from eliminating transmission collisions and idle gaps between two successive data transmissions. By removing performance degradation caused by communication interference and idle, the reduced finish time and improved network resource utilization can be achieved. With the proposed algorithm, the optimal number of rounds and the most reasonable load allocation ratio on each node could be derived. Finally, simulation results are presented to demonstrate the impacts of different network parameters such as the number of clusters, computation/communication latency, and measurement/communication speed, on the number of rounds, makespan and energy consumption.

DMTA: A Task Scheduling Algorithm in Multi-Sinks Wireless Sensor Networks Based on Divisible Load Theory

2010 ◽  
Vol 143-144 ◽  
pp. 143-147
Author(s):  
Liang Dai ◽  
Zhong Shen ◽  
Yi Lin Chang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Wireless Sensor ◽  
Task Completion ◽  
Divisible Load Theory ◽  
Divisible Load ◽  
Load Theory ◽  
Task Scheduling Algorithm

Multi-Sinks wireless sensor networks, a current research focus, has better stability and effectiveness compared to the traditional single-SINK structure. To solve the problem how to complete the tasks within the possibly shortest time, a task scheduling algorithm(DMTA) based on divisible load theory in multi-Sinks wireless sensor networks is proposed. In DMTA, the tasks are distributed to wireless sensor network based on the processing and communication capacity of each sensor by multiple Sinks respectively. By removing communications interference between each sensor, reduced task completion time and improved network resource utilization achieved. Simulation results show that DMTA reasonably distributes tasks to each node in wireless sensor networks, and effectively reduces the time-consuming of task completion.

scholarly journals Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fanghai Gong
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Mobile Cloud Computing ◽  
Task Completion ◽  
Workflow Scheduling ◽  
Mobile Cloud ◽  
Task Completion Time ◽  
Computing Machine

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

scholarly journals EOTS: An Energy-consumption Optimization-oriented Task Scheduling Algorithm for Wireless Sensor Networks

2016 ◽  
Vol 12 (07) ◽  
pp. 59
Author(s):  
Zeyu Sun ◽  
Yuanbo Li ◽  
Chuanfeng Li ◽  
Yalin Nie
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Task Scheduling ◽  
Energy Cost ◽  
Iterative Refinement ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Utilization Rate ◽  
Energy Consumption Optimization

<p><span style="font-family: Times New Roman;"><strong>The mismatch of task scheduling results in rapid network energy consumption during data transmission in wireless sensor networks. To address this issue, the paper proposed an </strong><strong>E</strong><strong>nergy-consumption </strong><strong>O</strong><strong>ptimization-oriented </strong><strong>T</strong><strong>ask </strong><strong>S</strong><strong>cheduling </strong><strong>A</strong><strong>lgorithm (EOTS algorithm) which formally described the overall power dissipation in the network system. On this basis, a network model was built up such that both the idle energy consumption in sensor nodes and energy consumption during the execution of tasks were taken into account, with which the whole task was effectively decomposed into sub-task sequences. They underwent simulated annealing and iterative refinement, with the intention of improving sensor nodes’ utilization rate, reducing local idle energy cost, as well as cutting down the overall energy consumption accordingly. The experiment result shows that under the environment of multi-task operation, from the perspective of energy cost optimization, the proposed scheduling strategy recorded an increase of 21.24% compared with the FIFO algorithm, and an increase of 16.77% in comparison to the EMRSA algorithm; while in light of network lifetimes, the EOTS algorithm surpassed the ECTA algorithm by a gain of 19.21%. Therefore, the effectiveness of the proposed EOTS algorithm is verified.</strong></span></p>

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