Research On Some Key Technology Of Task Scheduling In Wireless Sensor Networks

Collaboration among sensors emerges as a promising solution to achieve highprocessing power in resource-restricted wireless sensor networks (WSNs).Usually in aWSN, the resource usage is highly realated to the execution of tasks, which consumea certain amount of computing resource and communication bandwidth. Since theresources in a specific network are limited, they must be efficiently used to smooththe execution of tasks. Therefore, how to assign a task to its own most appropriatesensor node and simultaneously balance the network load in the context of theuncertain and dynamic network environments becomes an important and urgent issuein WSNsAlthough task scheduling algorithms in traditional network enviroment have beenwell studied in the past, their application to WSNs remains largely unexplored. Due tothe limitations of WSNs, such as dynamic network topology, limited energy, limitedsensor node resources and unreliable sensing data, existing algorithms cannot bedirectly implemented in WSNs and task scheduling problem in WSNs is very urgentand pivotal. Therefore, this thesis endeavors to do an integrated study in some aspectsof task scheduling in WSNs and attempts to improve some key techniques of taskscheduling. It mainly includes the following four aspects:First, in order to prolong the lifetime, reduce the energy consumption and balancethe network load, a task allocation algorithm based on the discrete particle swarmoptimization (PSO), called PSO-DA, is proposed in this thesis. Inspired by theprinciple of dynamic alliance, we build a dynamic alliance model for task allocationin WSNs. In PSO-DA, we design a function taking into account the overall executiontime of tasks, the energy consumption and the network balance. In addition, amutation operator is incorporated into PSO-DA to maintain the population diversityand improve the global searching ability. Experimental results show that the proposedalgorithm achieves a good balance of local solutions and global exploration,effectively reduces the computation time of network and the network energyconsumption, and balances the whole network load.Second, dynamic topology characteristic of WSNs requires a more optimal andefficient topology control mechanism, in which topology can be self-adjusting andself-configuration according to the status of sensor nodes, to ensure that it does notaffect the data transmission and the overall tasks for the damage, failure and mobile ofsome sensor nodes. Therefore, following an analysis of the major disadvantages, suchas higher connectivity redundancy, lower structural robustness etc, in the traditionaltopology control schemes, this thesis presents a novel discrete particle swarmoptimization (NDPSO) based on the local minimum spanning tree (MST). Due to the demand for the optimization of the network lifetime, we transform the topologycontrol problem into a multi-criteria degree-constrained minimum spanning tree(mcd-MST) problem and design a phenotype sharing function of the objective spaceto obtain a better approximation of true Pareto front. The global convergence of thealgorithm is proved using the theorem of Markov chain. Then a topology controlscheme based on NDPSO is put forward. Experimental results indicate that theobtained topology has low overall power consumption, is roust, controls theinter-node communication interference, and prolongs effectively the lifetime of theWSN.Third, the energy and resources constraints of sensor nodes in the WSN requirereducing the power consumption of sensor nodes as little as possible in real-timeexchange of task scheduling. Data aggregation can reduce the number oftransmissions of sensor nodes and energy consumption effectively and it also canexploit sensor node’s processing capabilities as much as possible. Therefore, based onBack-Progagation Neural Network (BPNN) and PSO, we propose an energy-efficientmulti-source temporal data aggregation model for WSNs, termed MSTDA. It consistsof two phases. In the first phase, we present a feature selection algorithm based onPSO to simplify the historical data source. In the second one, we introduce aBPNN-based data prediction algorithm with PSO (PSO-BPNN). Consequrently, thefirst phase reduces the number of input nodes for BPNN and the second one, one ofdata aggregation methods, effectively reduces the energy consumption what WSNsneed in real-time exchange of task scheduling. In addition, MSTDA is able to carry ondata prediction by aggregation multivariable data.Finally, in order to adapt task management to real-time applications of WSNs, wepropose a self-adaptive mechanism taking into consideration the network status ofWSNs in the context of uncertain, dynamic environments. Inspired by the multi-agentsystem (MSA) theory, we design a multi-agent model for WSNs. In this model, wegive an adaptive MAS-based task scheduling strategy, which self adaptively adjuststhe status of unfinished tasks on the fault nodes in order to minimize the cost of thenetwork recovery.