Mequanint Moges

Wireless sensor networks: scheduling for measurement and data reporting

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.

An Efficient Task Scheduling Method for Improved Network Delay in Distributed Sensor Networks

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.

Divisible Load Scheduling and Markov Chain Models

In this paper, the equivalence between various divisible load-scheduling policies and continuous time Markov chains is demonstrated. This provides a basic unification of both data parallel divisible load scheduling and Markov chain models for the first time in 16 years of research. Such equivalence is demonstrated for divisible scheduling on linear daisy chains and single and two level tree networks.

Grid scheduling divisible loads from two sources

To date closed form solutions for optimal finish time and job allocation are largely obtained only for network topologies with a single load originating (root) processor. However in large-scale data intensive problems with geographically distributed resources, load is generated from multiple sources. This paper introduces a new divisible load scheduling strategy for single level tree networks with two load originating processors. Solutions for an optimal allocation of fractions of load to nodes in single level tree networks are obtained via linear programming. A unique scheduling strategy that allows one to obtain closed form solutions for the optimal finish time and load allocation for each processor in the network is also presented. The tradeoff between linear programming and closed form solutions in terms of underlying assumptions is examined. Finally, a performance evaluation of a two source homogeneous single level tree network with concurrent communication strategy is presented.

Performance Analysis of Data Aggregation in Wireless Sensor Mesh Networks

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.

Integrated Scheduling Algorithm for Personalized Disease Management Applications

The rapid escalation of American health care costs compels a new approach to chronic disease. Personalizing chronic disease management can be improved by using biosensors and advanced communication technology. Recent advances in miniature wireless sensors supported by ubiquitous computing have fostered a growth of interest in wellness and illness management based on distributed sensor networks. A variety of factors need to be considered for effective integrated scheduling scheme that can assign sensing, computation, and communication task to different sensor nodes to assist them set priority of the task at hand. In this paper, we present a framework for integrated scheduling algorithm for sensing, computation, and communication tasks within a distributed intelligent sensor network. Preliminary simulation results are presented for linear daisy chain network with different number of sensor nodes that consider various sensing and communication speeds

STDAS: Sensing task and data aggregation scheduling for astronaut health monitoring using wireless mesh networks

Astronaut health monitoring (AHM) during long durations of space missions will play a significant role in mission success. Designing networked healthcare systems for aerospace exploration that will enable continual surveillance and timely notification of astronaut health information to terrestrial healthcare providers at minimal deployment and operation cost is an extremely challenging problem. However, such capabilities will enhance the opportunities for remote medical assistance during space missions. In this paper, we extend our task and data aggregation scheduling from single-hop and multi-hop network to mesh network. The algorithm aims to optimize the network performance with respect to response time and network delay. The upper and lower bounds are derived to provide certain guarantee on data delivery time. The performance of a 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.