Events Calendar

PhD Defense - Chengjie Zhang

Design of Cost-Efficient Multi-Sensor Collaboration in Wireless Sensor Networks

committee

Chair:
John Heidemann

Member:
Ramesh Govindan
Bhaskar Krishnamachari


Recent years have witnessed an increasing demand for monitoring tasks in industrial applications. Although effective in some applications, sensornet uptake has been slow in this emerging area, as shown by slow sensornet deployment in industry. Industries prefer well-tested but often expensive or complex sensing solutions because they provide known levels of accuracy, but this choice makes many problems uneconomic. The sensornet research community has made significant progress towards real-world applications with some pilot deployments. While sensornet research is promising, current prototypes are often not cost-effective or still in the early stages. Industry remains slow to adopt these approaches, because they are not seen to address concerns about accuracy for industry-relevant problems.

In this thesis, we propose Multi-Sensor Collaboration to achieve low-cost yet accurate event detection and enable sensornet usage in cost-sensitive industrial applications. We find that four advantages of collaboration improve cost-effectiveness. First, collaborative sensing can improve accuracy by suppressing false alarms with redundant or heterogeneous sensors. Second, collaborative sensing reduces capital cost by enabling low-cost sensors to be accurate enough to reach actionable results. Third, collaboration can reduce deployment cost, because it enables non-invasive sensing to provide sufficient accuracy with much less expensive installation. Finally, auto-tuning is important to reduce deployment costs, and collaborative sensing can assist auto-tuning by allowing sensors in different modalities to tune each other with their unique information.

The thesis of this proposed dissertation is that Multi-sensor collaboration enables sensor networks to accomplish real-world event detection tasks that are impractical for single-sensor systems. To frame the application domain, we categorize sensing applications by their two orthogonal properties— collaboration scheme and sensing modality. Collaboration scheme, namely the relationship between sensors, is usually in two distinctive forms—competitive and complementary. Competitive collaboration means redundantly suppressing less accurate sensors’ results; complementary collaboration combines all relevant sensors’ partial results to form the final result. Modality means the type of the raw input of a sensor. To explore the application domain, we study both collaborations with either modality choice in three example applications. First, we evaluate signature matching in a vehicle classification context to study single-modal competitive collaboration. Second, we present a design of steam-choke blockage-detection system to study single-modal complementary collaboration. Finally, we implement and evaluate a detection system for oil-retrieval-line blockage to extend our complementary type study to multi-modality. We prove sensor collaboration can achieve cost-efficiency in the forgoing three specific applications. These applications are useful and allow deployment of sensing where not possible before. Further, the commonality between these applications and a large group of relevant applications strongly suggest that collaborative sensing help a larger application domain.

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Lizsl De Leon