The primary goal of breadcrumb trail sensor networks is to transmit in real-time users' physiological parameters that measure life-critical functions to an incident commander through reliable multihop communication. In applications using breadcrumb solutions, there are often many users working together, and this creates a well-known body shadowing effect (BSE). In this article, we first measure the characteristics of body shadowing for 2.4GHz sensor nodes. Our empirical results show that the body shadowing effect leads to severe packet loss and consequently very poor real-time performance. Then we develop a novel Intentional Forwarding solution. This solution accurately detects the shadowing mode and enables selected neighbors to forward data packets. Experimental results from a fully implemented testbed demonstrate that Intentional Forwarding is able to improve the end-to-end average packet delivery ratio (PDR) from 58% to 93% and worst-case PDR from 45% to 85%, and is able to meet soft real-time requirements even under severe body shadowing problems.

Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. In this paper, we describe the design, implementation, and evaluation of an automatic and robust multi-user breadcrumb system for indoor first response applications. Our solution includes a breadcrumb dispenser with a link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time. Moreover, we propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain lengths while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results from our study show that compared to the state of the art solution , our breadcrumb system achieves 200 percent link redundancy with only 23 percent additional deployed nodes. Our deployed breadcrumb chain can achieve 90 percent PRR when one node fails in the chain. In addition, by applying the UF coordination algorithm, the system can maintain connectivity for up to 87 percent longer distances than baseline greedy coordination approach while maintaining 96 percent packet delivery ratio.

The primary goal of breadcrumb trail sensor networks is to transmit in real-time users' physiological parameters that measure life critical functions to an incident commander through reliable multihop communication. In applications using breadcrumb solutions, there are often many users working together, and this creates a well-known body shadowing effect (BSE). In this paper, we first measure the characteristics of body shadowing for 2.4 GHz sensor nodes. Our empirical results show that the body shadowing effect leads to severe packet loss, and consequently very poor real-time performance. Then we develop a novel Intentional Forwarding solution. This solution accurately detects the shadowing mode and enables selected neighbors to forward data packets. Experimental results from a fully implemented testbed demonstrate that Intentional Forwarding is able to improve the end-to-end average packet delivery ratio (PDR) from 58% to 93% and worst-case PDR from 45% to 85%, and meet soft real-time requirements even under severe body shadowing problems.

Previous localization solutions in wireless sensor networks mainly focus on using various techniques to estimate node positions. In this paper, we argue that quantifying the uncertainty of these estimates is equally important in practice. By using the quantitative uncertainty of measurements and estimates, we can derive more accurate estimates by better fusing the measurements, provide confidence information for confidence-based applications, and know how to select the best anchor nodes so as to minimize the total mean square errors of the whole network. This paper quantifies the estimation uncertainty as an error covariance matrix, and presents an efficient incremental centralized algorithm---INOVA and a decentralized algorithm---OSE-COV for calculating the error covariance matrix. Furthermore, we present how to use the error covariance matrix to infer the confidence region of each node's estimate, and provide an optimal strategy for the anchor selection problem. Extensive simulation results show that INOVA significantly improves the computation efficiency when the network changes dynamically; the confidence region inference is accurate when the measurement number to node number ratio is more than 2; and the optimal anchor selection strategy reduces the total mean square error by four times as much as the variation-based algorithm in best case.

Breadcrumb systems (BCS) aid first responders by communicating their physiological parameters to remotely located base stations. However, state-of-the-art research only focuses on deploying breadcrumb systems on the assumption of uncoordinated users, which is inefficient. In this paper, we present the first design, implementation, and evaluation of reliable multiuser breadcrumb systems (MUBCS) which exploits efficient and automatic coordination among system users to achieve better utilization of limited breadcrumbs. We propose UF, a distributed cooperative deployment algorithm, to achieve longer breadcrumb chain length while maintaining fairness and high system reliability via selecting appropriate benefit and cost functions. UF also requires no prior assumptions about users' mobility models, making the design practical for real applications. We deployed and evaluated our system in real buildings with several different first responder mobility patterns. Experimental results indicate that this approach can maintain connectivity for up to 87% longer distances than baseline greedy coordination approach while maintaining 96% packet delivery ratio.

As wireless devices and sensors are increasingly deployed on people, researchers have begun to focus on wireless body-area networks. Applications of wireless body sensor networks include healthcare, entertainment, and personal assistance, in which sensors collect physiological and activity data from people and their environments. In these body sensor networks, quality of service is needed to provide reliable data communication over prioritized data streams. This article proposes BodyQoS, the first running QoS system demonstrated on an emulated body sensor network. BodyQoS adopts an asymmetric architecture, in which most processing is done on a resource-rich aggregator, minimizing the load on resource-limited sensor nodes. A virtual MAC is developed in BodyQoS to make it radio-agnostic, allowing a BodyQoS to schedule wireless resources without knowing the implementation details of the underlying MAC protocols. Another unique property of BodyQoS is its ability to provide adaptive resource scheduling. When the effective bandwidth of the channel degrades due to RF interference or body fading effect, BodyQoS adaptively schedules remaining bandwidth to meet QoS requirements. We have implemented BodyQoS in NesC on top of TinyOS, and evaluated its performance on MicaZ devices. Our system performance study shows that BodyQoS delivers significantly improved performance over conventional solutions in combating channel impairment.

Continuous and reliable operation of WSNs is notoriously difficult to guarantee due to hardware degradation and environmental changes. In this paper, we propose and demonstrate a methodology for run-time assurance (RTA), in which we validate at run time that a WSN will function correctly, despite any changes to the operating conditions since it was originally designed and deployed. We use program analysis and compiler techniques to facilitate automated testing of a WSN at run time. As a proof of concept, we implemented a framework for designing and automatically testing WSN applications. We evaluate our implementation on a network of 21 TelosB nodes, and compare performance with an existing network health monitoring solution. Our results indicate that in addition to providing the application-level verification function, RTA misses 75% fewer system failures, produces 70% fewer maintenance dispatches, and incurs 33% less messaging overhead than network health monitoring.

Breadcrumb systems (BCS) have been proposed to aid firefighters inside buildings by communicating their physiological parameters to base stations outside the buildings. In this paper, we describe the design, implementation and evaluation of an automatic and robust breadcrumb system for firefighter applications. Our solution includes a breadcrumb dispenser with an optimized link estimator that is used to decide when to deploy breadcrumbs to maintain reliable wireless connectivity. The solution includes accounting for realities of buildings and dispensing such as the height difference between where the dispenser is worn and the floor where the dispensed nodes are found. We also include adaptive power management to maintain link quality over time.

The current rapid development and deployment of wireless sensor networks (WSNs) and their application in mission critical systems are exacerbating the need for high confidence WSNs. Achieving high confidence WSNs will require new assurance technologies. Most current solutions deal with faults and reliability and not with application level semantics and associated assurances. We propose the use of a novel WSN design and assurance mechanism, run time assurance (RTA), to guarantee that important application-level requirements are met in mission critical applications.

We demonstrate MetroNet, which is an application that illustrates collaborative data sharing on the Wide Wide Web. MetroNet has two parts. First, our sensors gather data about pedestrian foot traffic in front of and into stores, which is made available online to shopkeepers. Second, this sensor data can be made public, at the discretion of the shopkeeper, for use by city planners, other shopkeepers, or residents of the city. MetroNet is an application through which we study fundamental problems of sharing data on the Web, such as search, data fusion, and privacy.