Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted-living and residential monitoring that uses a two-way flow of data and analysis between the front and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living.AlarmNet integrates environmental, physiological, and activity sensors in a scalable, heterogeneous architecture.The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian Activity Rhythm (CAR) analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.

Abstract-In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.Index Terms-Dead reckoning, pedestrian navigation system, wireless sensor network.

Abstract-In wireless sensor networks (WSNs), sensors' locations play a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number of location discovery (localization) schemes have been proposed. Most of these schemes share a common feature: they use some special nodes, called beacon nodes, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration). Other sensors discover their locations based on the reference information provided by these beacon nodes.Most of the beacon-based localization schemes assume a benign environment, where all beacon nodes are supposed to provide correct reference information. However, when the sensor networks are deployed in a hostile environment, where beacon nodes can be compromised, such an assumption does not hold anymore.In this paper, we propose a general scheme to detect localization anomalies that are caused by adversaries. Our scheme is independent from the localization schemes. We formulate the problem as an anomaly intrusion detection problem, and we propose a number of ways to detect localization anomalies. We have conducted simulations to evaluate the performance of our scheme, including the false positive rates, the detection rates, and the resilience to node compromises.

Figure 1. Examples created by Text-to-Viz. (a)-(d) are generated from the statement: "More than 20% of smartphone users are social network users." (e) and (f) are generated from the statement: "40 percent of USA freshwater is for agriculture." (g) and (h) are generated from the statement: "3 in 5 Chinese people live in rural areas." (i) and (j) are generated from the statement: "65% of coffee is consumed at breakfast." (k)-(m) are generated from the statement: "Among all students, 49% like football, 32% like basketball, and 21% like baseball." (n) and (o) are generated from the statement: "Humans made 51.5% of online traffic, while good bots made 19.5% and bad bots made 29%."Abstract-Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is still not an easy task, requiring much time and design expertise. Therefore, these tools are generally not attractive to casual users, who are either unwilling to take time to learn the tools or lacking in proper design expertise to create a professional infographic. In this paper, we explore an alternative approach: to automatically generate infographics from natural language statements. We first conducted a preliminary study to explore the design space of infographics. Based on the preliminary study, we built a proof-of-concept system that automatically converts statements about simple proportionrelated statistics to a set of infographics with pre-designed styles. Finally, we demonstrated the usability and usefulness of the system through sample results, exhibits, and expert reviews.

In wireless sensor networks (WSNs), sensor location plays a critical role in many applications. Having a GPS receiver on every sensor node is costly. In the past, a number of location discovery schemes have been proposed. Most of these schemes share a common feature: they use some special nodes, called beacon nodes, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration). Other sensors discover their locations based on the information provided by these beacon nodes. In this paper, we show that efficient location discovery can be achieved in sensor networks without using beacons. We propose a beacon-less location discovery scheme. based on the following observations: in practice, it is quite common that sensors are deployed in groups, i.e., sensors are put into n groups, and sensors in the same group are deployed together at the same deployment point (the deployment point is different from the sensors' final resident location). Sensors from the same group can land in different locations, and those locations usually follow a probability distribution that can be known a priori. With this prior deployment knowledge, we show that sensors can discover their locations by observing the group memberships of its neighbors. We model the location discovery problem as a statistical estimation problem, and we use the Maximum Likelihood Estimation method to estimate the location. We have conducted experiments to evaluate our scheme.

Testing the genuineness of a manufactured chip is an important step in an IC product life cycle. This becomes more prominent with the outsourcing of the manufacturing process, since the manufacturer may tamper the internal circuit behavior using Trojan circuits in the original design. Traditional testing methods cannot detect these stealthy Trojans because the triggering scenario, which activates it, is unknown. Recently, approaches based on side-channel analysis have shown promising results in detecting Trojans. In this paper, we propose a novel test generation technique that aims at magnifying the disparity between side-channel signal waveforms of tampered and genuine circuits to indicate the possibility of internal tampering. Experimental results indicate that our approach could magnify the likelihood of Trojans 4 to 20 times more than existing side-channel analysis based approaches.

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