ABSTRACT

The proliferation of increasingly capable and affordable sensing devices that pervade every corner of the world has given rise to distributed sensing systems that have fundamentally changed people's ways of interacting with the physical world. Despite their tremendous benefits, distributed sensing systems pose great new research challenges, of which one important facet stems from the conflicts between the Quality of Information (QoI) provided by the sensor nodes and the consumption of system and network resources. On one hand, individual sensors are not reliable, due to various reasons such as incomplete observations, background noise, and poor sensor quality. To address this problem, a possible solution is to integrate information from multiple sensors that observe the same events, as this will likely cancel out the errors of individual sensors. On the other hand, distributed sensing systems usually have limited resources (e.g., bandwidth, energy, storage, etc). Therefore, it is usually prohibitive to collect data from a large number of sensors due to the potential excessive resource consumption. Targeting on this challenge, this project seeks to develop a resource-efficient information integration framework that can intelligently integrate information from distributed sensors so that the highest quality of information can be achieved, under the constraint of system resources. Successful completion of the proposed research will benefit a wide spectrum of applications that rely on distributed sensing systems for the collection, transmission and analysis of sensory data.

This project aims to make several contributions in this area of research. First, it will develop a novel information integration algorithm that can jointly estimate the QoI of each sensor and integrate the information provided by the sensors. This algorithm puts more weights on the sensors with high QoIs, and thus can achieve improved accuracy than the straightforward integration methods such as averaging and voting that treat all the sensors equally. Second, to address the challenge brought by the constrained system resources, this project will propose a set of QoI-aware resource allocation mechanisms for the data collection on different types of distributed sensing systems. For physical sensing systems that are usually wireless systems deployed at remote, harsh or even hostile locations, an optimization framework will be developed to maximally utilize the network bandwidth as well as renewable energy in order to achieve the optimal aggregate quality of delivered information. For crowd sensing systems where data collections are carried out by a human population, a novel incentive mechanism will be designed to compensate participants' resource consumption and potential privacy breach, based on not only the efforts a user has spent but also the QoI the user can provide.

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