Rusty Shepard

Data collection with battery and buffer consideration in a large scale sensor network

In a pure sensor network thousands of sensors are distributed over a large area. Sensor data is relayed from sensor node to node until it reaches the edge node, where it is then routed by wire to the host. In this paper we study the effect of data collection on the battery and buffer of a sensor node, and on the overall sensor network. We determine the relationship among battery life, sampling frequency, buffer size, etc. We claim that using identical sensors throughout a large scale sensor network may not achieve the best result. We suggest some guidelines for deploying a large scale sensor network. We then propose routing algorithms that are robust, truly distributed, and efficient in utilizing the sensor network.

Real-time tasks with data output

A real-time task usually generates data. The study on real-time tasks either ignores this fact like most of the research do, or models data as execution time like when handling multimedia data. On the other hand, the network research community usually pays little attention to the data source other than assuming certain characteristics. There are many real world applications where data is not generated at a constant rate during task execution, and the average data rate is not the same among tasks. In this paper we study a new task model with data output as an explicit parameter. We analyze the data output of such task set. We shall derive the data source parameters assumed by network studies. Different scheduling policy results in different data output curve, we look at this in detail with simulations. Our work extends the real-time research to cover a new set of real world applications; it also complements the network study on data transmission.