Sangeeta Bhattacharya

A Spatiotemporal Query Service for Mobile Users in Sensor Networks

This paper presents MobiQuery, a spatiotemporal query service that allows mobile users to periodically gather information from their surrounding areas through a wireless sensor network. A key advantage of MobiQuery lies in its capability to meet stringent spatiotemporal performance constraints crucial to many applications. These constraints include query latency, data freshness and fidelity, and changing query areas due to user mobility. A novel just-in-time prefetching algorithm enables MobiQuery to maintain robust spatiotemporal guarantees even when nodes operate under extremely low duty cycles. Furthermore, it significantly reduces the storage cost and network contention caused by continuous queries from mobile users. We validate our approach through both theoretical analysis and simulation results under a range of realistic settings

Jog falls: a pervasive healthcare platform for diabetes management

This paper presents Jog Falls, an end to end system to manage diabetes that blends activity and energy expenditure monitoring, diet-logging, and analysis of health data for patients and physicians. It describes the architectural details, sensing modalities, user interface and the physicians backend portal. We show that the body wearable sensors accurately estimate the energy expenditure across a varied set of active and sedentary states through the fusion of heart rate and accelerometer data. The GUI ensures continuous engagement with the patient by showing the activity goals, current and past activity states and dietary records along with its nutritional values. The system also provides a comprehensive and unbiased view of the patients activity and food intake trends to the physician, hence increasing his/her effectiveness in coaching the patient. We conducted a user study using Jog Falls at Manipal University, a leading medical school in India. The study involved 15 participants, who used the system for 63 days. The results indicate a strong positive correlation between weight reduction and hours of use of the system.

Multi-Application Deployment in Shared Sensor Networks Based on Quality of Monitoring

Wireless sensor networks are evolving from dedicated application-specific platforms to integrated infrastructure shared by multiple applications. Shared sensor networks offer inherent advantages in terms of flexibility and cost since they allow dynamic resource sharing and allocation among multiple applications. Such shared systems face the critical need for allocation of nodes to contending applications to enhance the overall Quality of Monitoring (QoM) under resource constraints. To address this need, this paper presents Utility-based Multi-application Allocation and Deployment Environment (UMADE), an integrated application deployment system for shared sensor networks. In sharp contrast to traditional approaches that allocate applications based on cyber metrics (e.g., computing resource utilization), UMADE adopts a cyber-physical system approach that dynamically allocates nodes to applications based on their QoM of the physical phenomena. The key novelty of UMADE is that it is designed to deal with the inter-node QoM dependencies typical in cyber-physical applications. Furthermore, UMADE provides an integrated system solution that supports the end-to-end process of (1) QoM specification for applications, (2) QoM-aware application allocation, (3) application deployment over multi-hop wireless networks, and (4) adaptive reallocation of applications in response to network dynamics. UMADE has been implemented on TinyOS and Agilla virtual machine for Telos motes. The feasibility and efficacy of UMADE have been demonstrated on a 28-node wireless sensor network testbed in the context of building automation applications.

Near optimal multi-application allocation in shared sensor networks

Recent years have witnessed the emergence of shared sensor networks as integrated infrastructure for multiple applications. It is important to allocate multiple applications in a shared sensor network, in order to maximize the overall Quality of Monitoring (QoM) subject to resource constraints (e.g., in terms of memory and network bandwidth). The resulting constrained optimization problem is a difficult and open problem since it is discrete, nonlinear, and not in closed-form. This paper makes several important contributions towards optimal multi-application allocation in shared sensor networks. (1) We formulate the optimal application allocation problem for a common class of distributed sensing applications whose QoM can be modeled as variance reduction functions. (2) We prove key theoretical properties of the optimization problem, including the monotonicity and submodularity of the variance reduction functions and the multiple knapsack structure of constraints; (3) By exploiting these properties, we propose a local search algorithm, which is efficient and has a good approximation bound, for application allocation in shared sensor networks. Simulations based on both real-world datasets and randomly generated networks demonstrate that our algorithm is competitive against simulated annealing in term of QoM, with up to three orders of magnitude reduction in execution times, making it a practical solution towards multi-application allocation in shared sensor networks.

FAR: Face-aware routing for mobicast in large-scale sensor networks

This article presents FAR, a Face-Aware Routing protocol for mobicast---a spatiotemporal variant of multicast tailored for sensor networks with environmental mobility. FAR features face-routing and timed-forwarding for delivering a message to a mobile delivery zone. Both analytical and statistical results show that FAR achieves reliable spatial and just-in-time message delivery with only moderate communication and memory overhead. This article also presents a novel distributed algorithm for spatial neighborhood discovery for FAR bootstrapping. The spatiotemporal performance and reliability of FAR are demonstrated via network simulations.

Dynamic wake-up and topology maintenance protocols with spatiotemporal guarantees

Many mission-critical applications require spatiotemporal data services for mobile users or objects. Examples include distributed object tracking and fire monitoring by firefighters. To support such applications, wireless sensor networks must satisfy a set of stringent spatiotemporal constraints despite having low network duty cycles and scarce resources. We have developed two new wake-up and topology maintenance protocols, directional tree maintenance (DTM) and omnidirectional tree creation (OTC), to support spatiotemporal services in mobile environments. A key feature of our protocols is that they provide robust spatiotemporal performance while maintaining low overhead and energy consumption. Our simulations showed that both DTM and OTC can successfully deliver over 85% of query results to a mobile user within desired spatiotemporal constraints, even when the sleep schedule is as long as 15 s, the user changes direction every minute, and the location error is as high as 10 m. The benefits of our protocols have been validated through theoretical analysis and empirical results on a testbed of Mica2 motes.

Roadmap query for sensor network assisted navigation in dynamic environments

Mobile entity navigation in dynamic environments is an essential part of many mission critical applications like search and rescue and fire fighting. The dynamism of the environment necessitates the mobile entity to constantly maintain a high degree of awareness of the changing environment. This criteria makes it difficult to achieve good navigation performance by using just on-board sensors and existing navigation methods and motivates the use of wireless sensor networks (WSNs) to aid navigation. In this paper, we present a novel approach that integrates a roadmap based navigation algorithm with a novel WSN query protocol called Roadmap Query (RQ). RQ enables collection of frequent, up-to-date information about the surrounding environment, thus allowing the mobile entity to make good navigation decisions. Simulation results under realistic fire scenarios show that in highly dynamic environments RQ outperforms existing approaches in both navigation performance and communication cost. We also present a mobile agent based implementation of RQ along with preliminary experimental results, on Mica2 motes.

Reliable Data Collection from Mobile Users for Real-Time Clinical Monitoring

Real-time patient monitoring is critical to early detection of clinical patient deterioration in general hospital wards. A key challenge in such applications is to reliably deliver sensor data from mobile patients. We present an empirical analysis on the reliability of data collection from wireless pulse oximeters attached to users. We observe that most packet loss occur from mobile users to their first-hop relays. Based on this insight we developed the Dynamic Relay Association Protocol (DRAP), a simple and effective mechanism for dynamically discovering the right relays for wireless sensors attached to mobile users. DRAP enables highly reliable data collection from mobile users without requiring any change to complex routing protocols. We have implemented DRAP on the TinyOS platform and a prototype clinical monitoring system. Empirical evaluation showed DRAP delivered at least 96% of pulse oximetry data from multiple users, while maintaining a radio duty cycle below 2.8% and reducing the RAM footprint by 65% when compared to CTP. Our results demonstrates the feasibility and efficacy of wireless sensor network technology for real-time clinical monitoring. Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Reliable Data Collection from Mobile Users for Real-Time Clinical Monitoring Octav Chipara1, Christopher Brooks1, Sangeeta Bhattacharya1, Chenyang Lu1, Roger Chamberlain1, Gruia-Catalin Roman1, Thomas C. Bailey2 Department of Computer Science and Engineering, Washington University in St. Louis Washington University School of Medicine, Washington University in St. Louis

MobiQuery: a spatiotemporal data service for sensor networks

Spatiotemporal query is a new type of data service that allows a user to periodically gather information from a geographic area that moves with the user. A defining feature of this new service model is that each query result is subject to a set of spatiotemporal constraints in terms of response time, data freshness, and changing locations of data sources due to user mobility. We have developed MobiQuery to support spatiotemporal query services in a wireless sensor network environment. MobiQuery can work with in-network aggregation, energy conservation, and motion prediction techniques. A novel just-in-time prefetching mechanism is employed to provide spatiotemporal performance guarantees despite extremely low node duty cycles and unpredictable communication delays in sensor networks, while still achieving significant energy conservation. We also provide theoretical analysis and ns-2 simulation results which demonstrate the robustness and efficiency of MobiQuery under a range of realistic settings.

MLDS: A flexible location directory service for tiered sensor networks

Many location based services, such as a those used in healthcare facilities to track medical personnel and equipment, need to keep track of mobile entities across wide areas. These services can be realized in a cost-effective and efficient manner using tiered sensor networks comprised of multiple wireless sensor networks connected by an IP network. To simplify the realization of such services, we present MLDS, a Multi-resolution location directory service for tiered sensor networks. MLDS provides a rich set of spatial query services ranging from simple queries about entity location, to complex nearest-neighbor queries. Furthermore, MLDS supports multiple query granularities which allow an application to achieve the desired tradeoff between query accuracy and communication cost. We implemented MLDS on Agimone, a unified middleware for sensor and IP networks. We then deployed and evaluated the service on a tiered sensor network testbed consisting of tmotes and PCs. Our experimental results show that, when compared to a centralized approach, MLDS achieves significant savings in communication cost while still providing a high degree of accuracy, both within a single sensor network and across multiple sensor networks.