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ETSA: An Efficient Task Scheduling Algorithm in Wireless Sensor Networks
Abstract
To minimize the execution time (makespan) of a given task, an efficient task scheduling algorithm (ETSA) 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. ETSA builds from eliminating transmission collisions
and idle gaps between two successive data transmissions. Simulation results are presented to demonstrate the impacts of different
network parameters on the number of rounds, makespan and energy consumption.
Group target tracking is a challenge for sensor networks. It occurs where large numbers of closely spaced targets move together in different groups. In these applications, the sensor selection scheme plays a vital role in extending network lifetime while providing high tracking accuracy. Existing schemes cause an extreme imbalance between energy usages and tracking accuracy. They are capable of tracking only individual groups and without using prior knowledge about the groups. These problems make them impractical for group target tracking. With the aim of balancing the trade-off between lifetime and accuracy, we present a novel Multi-Sensor Group Tracking (MSGT) scheme. MSGT comprises the following steps to accomplish concurrent tracking of multiple groups: (1) Clustering to capture changes in the behavioural properties of groups, such as formation, merging, and splitting; (2) Sensor selection to activate the contributory sensors for the estimated group regions; and (3) Group tracking using the activated sensors. We develop a probabilistic decision-making strategy that triggers the clustering step adaptively with any detected change in group behavioural patterns. The sensor selection step coordinates periodic selection of leader and tracking sensor nodes in a distributed manner. We introduce cost metrics that include sensor′s energy parameters in the selection of active sensors that fully cover the group regions. The tracking step is a Bayesian modelling of the target groups which uses particle filtering algorithm to estimate the group locations. Simulation results show that our scheme achieves substantial improvements over existing approaches in terms of network lifetime and tracking accuracy.
In this paper, we propose a novel task scheduling algorithm (Divisible Task scheduling Algorithm for Wireless sensor networks (DTAW)) based on divisible load theory in heterogeneous wireless sensor networks to complete the tasks within the shortest possible time and reduce the sensors' energy-consuming. In DTAW, the tasks are distributed to the wireless sensor network by the (SINK) on the basis of the processing and communication capacity of each sensor. After receiving the subtasks, the intracluster sensors carry out its tasks simultaneously and send the results to cluster head sequentially. By removing communication interference between each sensor, reduced task completion time and improved network resource utilization are achieved. Each cluster head simultaneously finishes sending fused data to the SINK after fusing the data obtained from intracluster sensors. In this way, the overlap between the task performing and communication phase would be much better. Simulation results are presented to demonstrate the impacts of different network parameters on the makespan and energy consumption. The results show that the algorithm enables to reasonably distribute tasks to each sensor and then effectively reduces the time-consuming and energy-consuming. Copyright © 2012 John Wiley & Sons, Ltd.
In recent times the interest in wireless sensor mesh networks has grown considerably from personal, to local and metropolitan areas deployment. These networks consist of several mesh routers with minimal mobility and mesh clients that can be either mobile or stationary. The clients may also form a client mesh network among themselves and with routers. The various nodes over the network are interconnected via wireless links which might possibly employ multiple radio interfaces. One major attribute of such networks is the presence of redundant links which removes the single point failure that is present in the classical star or tree networks. Most researches in this field focus on the study of various routing protocols while we sought to introduce the application divisible load theory to find an optimum data aggregation strategy that optimize the networks performance with respect to response time and network delay. We define data aggregation as the process of data sensing and reporting back to the sink nodes, typically routers. The performance of wireless mesh network with 25 sensor nodes is examined by varying network bandwidth and sensing power of sensor nodes. Basic recursive equations for sensing and data reporting are developed for the case of homogeneous and heterogeneous mesh networks and the performance results of two representative data sensing and reporting strategies are presented.
Divisible load theory is a methodology involving the linear and continuous modeling of partitionable computation and communication loads for parallel processing. It adequately represents an important class of problems with applications in parallel and distributed system scheduling, various types of data processing, scientific and engineering computation, and sensor networks. Solutions are surprisingly tractable. Research in this area over the past decade is described.
One of the challenge in developing smart sensor networks is the minimization of network delay or at the very least be able to have upper and lower boundaries of network delay when sensor nodes respond to higher level applications. In this paper, we present a highly efficient task scheduling method based on linear programming that integrates both sensing and networking communication delay. The objective is to minimize the total response time and global power consumption of the network with respect to the total number of sensor nodes in the network. Simulation results based on closed-form solutions for the task scheduling problem are presented for two scenarios with homogeneous and six scenarios with heterogeneous sensor nodes using single level tree-network topology. Specifically, for the heterogeneous scenarios, responding sequence that results in global optimum total respond time has also been found.
Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.
An optimal load allocation approach is presented for measurement and data reporting in wireless sensor networks with a single level tree network topology. The measurement problem investigated involves a measurement space, part of which can be sampled by each sensor. We seek to optimally assign sensors part of the measurement space to minimize reporting time and energy usage. Three representative measurement and reporting strategies are studied. This work is novel as it considers, for the first time, the measurement capacity of processors and assumes negligible computation time which is radically different from the traditional divisible load scheduling research to date. Aerospace applications include satellite remote sensing and monitoring and sensor networks deployed and monitored from the air.
Optimal data scheduling strategies in a hierarchical wireless sensor network (WSN) are considered. Data aggregation in clusterheads is considered to find a closed form solution for the optimal amount of data that is to be reported by each sensor node using a newly introduced parameter, information utility. The optimal conditions for the feasible measurement instruction assignment time and for the minimum round time are derived and examined. Based on the optimal conditions, a performance evaluation is demonstrated via simulation study.
Due to uncertainties in target motion and limited sensing regions of sensors, single-sensor-based collaborative target tracking in wireless sensor networks (WSNs), as addressed in many previous approaches, suffers from low tracking accuracy and lack of reliability when a target cannot be detected by a scheduled sensor. Generally, actuating multiple sensors can achieve better tracking performance but with high energy consumption. Tracking accuracy, reliability, and energy consumed are affected by the sampling interval between two successive time steps. In this paper, an adaptive energy-efficient multisensor scheduling scheme is proposed for collaborative target tracking in WSNs. It calculates the optimal sampling interval to satisfy a specification on predicted tracking accuracy, selects the cluster of tasking sensors according to their joint detection probability, and designates one of the tasking sensors as the cluster head for estimation update and sensor scheduling according to a cluster head energy measure (CHEM) function. Simulation results show that, compared with existing single-sensor scheduling and multisensor scheduling with a uniform sampling interval, the proposed adaptive multisensor scheduling scheme can achieve superior energy efficiency and tracking reliability while satisfying the tracking accuracy requirement. It is also robust to the uncertainty of the process noise.
The task scheduling in the network demands as far as possible the shortest task completion time, the lowest energy consumption and the highest balanced use of energy under limited energy of nodes. Therefore, traditional multiprocessor Directed Acyclic Graph (DAG) scheduling algorithm can not be directly applied to sensor task scheduling. This paper proposes an Energy Balanced DAG Task Scheduling algorithm for Wireless sensor network (EBDAG_WSN). Its main idea is to get initial Chromosome by heuristic optimization algorithms. By redefinition of operations in the Genetic Algorithm (GA), the task scheduling in WSN is optimized synthetically. Through simulations based on randomly generated task graphs, experiment results show that combining heuristic optimization algorithm with bionic algorithm, the optimization technology has good real-time performance and high efficiency of energy.
In the divisible load distribution, the classic methods on linear arrays divide the computation and communication processes into multiple time intervals in a pipelined fashion. K.Li (1998) has proposed a set of improved algorithms for linear arrays that can be generalized to k -dimensional meshes. In this paper, we first propose the algorithm M (multi-installment) that employs the multi- installment technique to improve the best algorithm Q proposed by Li. Second, we propose the algorithm S (start-up cost), which includes the computation and communication start-up costs in the design. Although the asymptotic speed-ups of our algorithms M and S derived from the closed-form solutions are the same as algorithm Q, our algorithms approach the optimal speed-ups considerably faster than algorithm Q as the number of processors increases. Finally, we combine algorithms M and S and propose the algorithm MS. Although algorithm MS has the same the asymptotic performance as algorithms Q and S, it achieves a better speed-up when the load to be processed is very large and the number of processors is fixed or when the load to be processed is fixed and the number of processors is small.
Divisible load applications occur in many fields of science and engineering and can be easily parallelized in a master-worker fashion, but pose several scheduling challenges. While a number of approaches have been proposed that allocate load to workers in a single round, using multiple rounds improves overlap of computation with communication. Unfortunately, multiround algorithms are difficult to analyze and have thus received only limited attention. In this paper, we answer three open questions in the multiround divisible load scheduling area: 1) how to account for latencies, 2) how to account for heterogeneous platforms, and 3) how many rounds should be used. To answer 1), we derive the first closed-form optimal schedule for a homogeneous platform with both computation and communication latencies, for a given number of rounds. To answer 2) and 3), we present a novel algorithm, UMR. We evaluate UMR in a variety of realistic scenarios.
A Highly Efficient DAG Task Scheduling Algorithm for Wireless Sensor Networks
- Z Zeng
- A Liu
- D Li
- Z. Zeng