In this paper, we address the issue of human motion tracking in a smart space using a wireless sensor network with a small number of ultrasonic sensors. Ultrasonic sensing is preferable in situations where video monitoring is prohibited due to privacy concerns or is ruled out due to its higher cost and energy consumption. Unlike other common tracking techniques, the schemes proposed in this paper do not require the tracked person to wear a tag. In order to conserve energy, a single ultrasonic sensor that provides maximum information gain is selected. We use the Extended Kalman Filter (EKF) which provides robust state estimates from noisy signals as well as an uncertainty measure in the form of the state covariance, and propose the use of a process model which copes better with missed detections compared to the commonly used constant velocity process model. We propose two sensor selection schemes: (i) Current Node Sensor Selection (CNSS), and (ii) Distributed Neighbourhood node Sensor Election (DNSE), and evaluate their performance in terms of tracking accuracy, target detection ratio and sensor network lifetime.

In this demonstration, we implement and deploy a solar-powered wireless sensor network in an outdoor carpark to provide parking guidance to motorists. Combining energy harvesting, multi-hop opportunistic routing and adaptive duty-cycling technologies, the system provides low-cost, real-time, sustainable and eco-friendly operation. We make use of solar energy harvesting sources to power our wireless vehicle detection sensor nodes. Upon detecting the presence of a car, this information is transmitted via a multi-hop wireless network to a base station, before being forwarded to the central server for information dissemination to motorists. We will display, in real-time, the variance in the duty cycle and energy level according to the time of day throughout the conference period. For similar demos we typically provide both a live video stream and real-time sensor data from the carpark, displayed on desktop web browsers and mobile devices, so that visitors can see parking lot occupancy data in real-time and compare it with a live video feed, but due to the time difference between Singapore and Seattle, we will be showing a video recording instead.

Energy harvesting wireless sensor networks (EH-WSNs) are gaining importance in smart homes, environmental monitoring, health care and transportation systems, since they enable much longer operation time as energy can be replenished through energy harvesting. This is unlike sensor nodes that use non-rechargeable batteries which need to be replaced once energy is depleted. However, the sporadic availability of ambient energy makes the design of networking protocols and predicting network performance very challenging. In this paper, we perform an empirical energy characterization of a time-slotted solar energy harvesting node with different system and environmental parameters. We use six different statistical models (uniform distribution, geometric distribution, transformed geometric distribution, Poisson distribution, transformed Poisson distribution and a Markovian model) to fit the empirical datasets. Our results show that there is no single statistical model that can fit all the datasets, thus justifying the need to use empirical data to validate the theoretical analysis of time-slotted MAC protocols for EH-WSNs.