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

FAR: Face-Aware Routing for Mobicast in Large-Scale Sensor Networks

This paper 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 and just-in-time mes-sage delivery with only moderate communication and memory overhead. This paper also presents a novel distributed algorithm for spatial neighborhood discovery for FAR boot-strapping. The spatiotemporal performance and reliability of FAR are demonstrated via ns-2 simulations

Submodular utility optimization in sensor networks for capacity constraints

With the fast development of wireless sensor network (WSN) technologies, WSNs have widely shifted from a specialized platform for a single application to an integrated infrastructure supporting multiple applications. It is hence a critical problem to allocate multiple applications to multiple sensors in order to maximize user utility subject to various resource constraints. The resulting constrained optimization problem is difficult since it is discrete, nonlinear, and not in closed-form. In this report, we develop an efficient optimization algorithm with rigorous approximation bounds for submodular monotonic optimization with multiple knapsack constraints. Based on a variance reduction formulation, we prove several important theoretical properties, including the monotonicity and submodularity of functions and the multiple knapsack structure of constraints. Then, by exploiting these properties, we propose a local search algorithm with fractional relaxation of constraints and prove the approximation bound that is better than any known results. Experimentally, we verify the theoretical properties on a large dataset from the Intel Berkeley Lab. Comparison against other constrained search algorithms show that our algorithm is superior in both solution time and quality, making it a practical choice for WSN design

MLDS: A Flexible Location Directory Service for Tiered Sensor Networks

Many emergent distributed sensing applications need to keep track of mobile entities across multiple sensor networks connected via an IP network. To simplify the realization of such applications, 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 testbed consisting of tmote nodes and base stations. 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

An observational study to compare adenosine deaminase level among diabetes patients

Background: Obesity is a complex disorder that involves some degree of over-consumption1 coupled with a metabolic derangement. As ADA has been putatively associated with inflammation, and adipose tissue inflammation is the hallmark of insulin resistance in obese T2DM patients. This study attempted to compare serum ADA in T2DM subjects.Methods: This observational cross-sectional study was conducted in the Department of Biochemistry at MGM Medical College and MY Hospital. All the patients and controls were clinically examined, and routine biochemical tests were analyzed for all subjects. ANOVA has been applied to assess the variance between groups.Results: Mean age of controls was 56.91 years where as mean age in the group of a subject with Obesity with diabetes was 40.91 years and with Obesity without diabetes was 48.10 years. ADA level was more among diabetes patients having obesity than diabetes patients having no obesity and controls.Conclusions: This is very much evident through this study that ADA may be treated as prognostic predictor of diabetes either linked to obesity or not, though more studies are warranted in same direction to make this finding conclusive and acceptable biochemical evidence

MobiQuery: A Spatiotemporal Query Service for Mobile Users in Sensor Networks

This paper presents MobiQuery, a spatiotemporal query service that allows mobile users to periodically collect sensor data from the physical environment through wireless sensor networks. A salient feature of \MQ is that it can meet stringent spatiotemporal performance constraints, including query latency, data freshness, and changing areas of interest due to user mobility. We present three just-in-time prefetching protocols that enable MobiQuery to achieve desired spatiotemporal performance despite low node duty cycles, while significantly reducing communication overhead. We validate our approach through both theoretical analysis and extensive simulations under realistic settings including varying user movement patterns and location errors