Data Mule Scheduling: Controlled Mobility for Data Collection

Members: Ryo Sugihara, Rajesh Gupta

Project summary:

In this project, we study the effective use of controlled mobility for data collection applications in wireless sensor networks. Collecting data from spatially distributed sensors is one of the most generic forms of sensor network applications. Multihop forwarding approach has been broadly used for this purpose, but it can be inefficient in terms of energy consumption especially when the nodes are deployed sparsely. On the other hand, using a mobile device (often called "data mule") to traverse the sensor field and collect data from each sensor is an alternative approach.

Data mule approach can reduce energy consumption at each node significantly by eliminating the need for forwarding other sensors' data. However, the data delivery latency will be larger than in multihop forwarding, since it is mainly governed by the movement of data mule, which is slower than the speed of packets transmitted over wireless channels. Minimizing the delivery latency is critical for data mule approach to be useful in practice.

We are interested in optimizing the motion of data mule. For that purpose, we have devised a novel formulation of the problem, which we call the Data Mule Scheduling (DMS) problem, so that the mobility and communication capabilities are precisely captured. In the DMS problem, the question is "how to control a data mule so that it collects data from all the nodes in the shortest amount of time?''

We can decompose the DMS problem into subproblems as shown in the figure below:

Each of the subproblems is summarized as follows: We have analyzed the computational complexity of the DMS problem in various different assumptions on mobility and designed some online/offline algorithms and heuristics. We have also studied the combination of data mule and multihop forwarding approaches to achieve the energy-latency trade-off.

Publications:

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