A Trusted Real-Time Scheduling Model for Wireless Sensor Networks

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods.

[1]  Sajal K. Das,et al.  Privacy preservation in wireless sensor networks: A state-of-the-art survey , 2009, Ad Hoc Networks.

[2]  Wenbo He,et al.  KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[3]  Albert Mo Kim Cheng,et al.  Assigning real-time tasks to heterogeneous processors by applying ant colony optimization , 2011, J. Parallel Distributed Comput..

[4]  Ishfaq Ahmad,et al.  A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids , 2009, IEEE Transactions on Parallel and Distributed Systems.

[5]  Krzysztof Rzadca,et al.  Heterogeneous multiprocessor scheduling with differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Shuvra S. Bhattacharyya,et al.  Efficient techniques for clustering and scheduling onto embedded multiprocessors , 2006, IEEE Transactions on Parallel and Distributed Systems.

[7]  Yong Zhu,et al.  An Approach for Energy Aware Multipath Service Composition Based on Workflow: An Approach for Energy Aware Multipath Service Composition Based on Workflow , 2012 .

[8]  Albert Mo Kim Cheng,et al.  Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms , 2015, Sensors.

[9]  P. Ranganathan.  Simulating Complex Enterprise Workloads using Utilization Traces , 2007 .

[10]  Fatos Xhafa,et al.  Genetic Algorithms for Energy-Aware Scheduling in Computational Grids , 2011, 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[11]  Weizhe Zhang,et al.  Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm , 2014 .

[12]  Yang Xiao,et al.  PRDA: polynomial regression-based privacy-preserving data aggregation for wireless sensor networks , 2015, Wirel. Commun. Mob. Comput..

[13]  Lizy Kurian John,et al.  Run-time modeling and estimation of operating system power consumption , 2003, SIGMETRICS '03.

[14]  Ishfaq Ahmad,et al.  Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors , 1996, IEEE Trans. Parallel Distributed Syst..

[15]  Gregor von Laszewski,et al.  Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[16]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[17]  Albert Y. Zomaya,et al.  Minimizing Energy Consumption for Precedence-Constrained Applications Using Dynamic Voltage Scaling , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.