The need to find parking contributes to road congestion and leads to unnecessary fuel consumption. Of all emerging parking systems, Internet-of-Things (IoT)-based systems have demonstrated the feasibility of real-time delivery of parking availability using magnetic sensors. However, existing magnetic-based methods are prone to false positives caused by electromagnetic fields emitted from surrounding electric facilities. In this study, we conducted a 3-month data collection in a parking area. We identified the need to introduce learning and collaboration into the design of our detection algorithm which recognizes learned patterns associated with car arrivals or departures, and to filter out unreliable events based on spatial and temporal features.

To facilitate the collection of patient biosignals, designing extensible sensing devices in which sensor management is simplified is essential. This paper presents BioScope, an extensible sensing system that facilitates collecting data used in nursing assessments. We conducted experiments to demonstrate the potential of the system. The results obtained in this study can be applied in improving the design, thus enabling BioScope to facilitate data collection in numerous potential applications.

We present TUIC, a technology that enables tangible interaction on capacitive multi-touch devices, such as iPad, iPhone, and 3M's multi-touch displays, without requiring any hardware modifications. TUIC simulates finger touches on capacitive displays using passive materials and active modulation circuits embedded inside tangible objects, and can be used with multi-touch gestures simultaneously. TUIC consists of three approaches to sense and track objects: spatial, frequency, and hybrid (spatial plus frequency). The spatial approach, also known as 2D markers, uses geometric, multi-point touch patterns to encode object IDs. Spatial tags are straightforward to construct and are easily tracked when moved, but require sufficient spacing between the multiple touch points. The frequency approach uses modulation circuits to generate high-frequency touches to encode object IDs in the time domain. It requires fewer touch points and allows smaller tags to be built. The hybrid approach combines both spatial and frequency tags to construct small tags that can be reliably tracked when moved and rotated. We show three applications demonstrating the above approaches on iPads and 3M's multi-touch displays.

Saving DTNs' battery power to prolong the system life time has been an important research topic which scientists try to optimize. Most researches fall into two categories - Time Division Multiple Access based ones, and Low Power Listening ones by turning the listener's radio on/off intermittenly with long sending preambles. In this paper we propose solving the problem from a whole new angle - by using the motes' physical behavior, which is detectable by relatively energy-cheap accelerometer, as the judgment for turning the radio on/off. This scheme can be used in combination with any energy efficient DTN MAC protocols. We evaluate the protocol by running simulations with real hikers' traces and the result is shown in the evaluation section.

In this work, we present an experimental study, in which we deploy a 14-node 802.15.4-based sensor network in our department building. Using the k-nearest-neighbor (KNN) method to the estimation of location, we implement an RSSI-signature-based localization system on top of the network.

There has been a significant amount of work analyzing the Internet AS (Autonomous System)-level topology which gives rise to a number of topology models. Although each of these models is being refined to fit better a particular set of graph properties over time, there is a more fundamental need in knowing the topology invariants. To address this need, we examine how the Internet AS-level topology evolves, using a variety of graph metrics,-a super set of what has been used in recent works. We discover that the AS-level topology shows a clear converging trend only in the normalized Laplacian spectrum (nls). From the theory of nls, we discover further that the converging trend indicates a stabilizing ratio of leaf to core ASes (or customer to provider ASes) on the Internet.

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is three-fold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by as much as 37.01%. (3) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 49.76 % reduction in power consumption