In wireless networks, it is important to determine the outcome of packet transmissions for networking protocols. In this paper, we design a transmission outcome classifier for IEEE 802.15.4 wireless networks based on received signal strength indicator and link quality indicator values. Our classifier performs loss differentiation by analyzing statistical differences between weak signal and collision losses. We implement our proposed classifier using the CC2500 RF transceiver and evaluate it experimentally. The results show that our classifier can accurately detect packet transmissions as well as distinguish wireless losses due to weak signals and multiple access collisions, with a maximum error rate of 15%. We apply the classifier to probabilistic polling, which is a MAC protocol designed for energy harvesting wireless sensor networks, and show experimentally that it is able to achieve close to or even exceed the theoretical throughput due to packet capture effect.

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.

Motion and intrusion detection are often cited among various Wireless Sensor Network (WSN) applications. A typical configuration comprises clusters of wireless nodes equipped with motion sensors to detect human motion. Currently, the performance of WSN is subject to several constraints, mainly the phenomenon of radio irregularity and finite onboard computation/energy resources. In Radio Frequency (RF) propagation, radio irregularity rises to a higher level in the presence of human activity due to the absorption effect of the human body. In this paper, the feasibility of monitoring RF transmission for the purpose of intrusion detection is investigated. With empirical data obtained from the Crossbow TelosB platform in several different environments, the impact of human activity on the signal strength of RF signals in a WSN is evaluated. This paper offers a novel approach to intrusion detection by turning a constraint in WSN, namely radio irregularity, into an advantage for the purpose of intrusion detection. Unlike most related work, the ¿intruders¿ neither transmit nor receive any RF signals. By enabling existing wireless infrastructures to serve as intrusion detectors instead of deploying numerous costly sensors, this approach shows great promise for providing novel solutions.

Wireless sensor networks are expected to be deployed in harsh environments characterised by extremely poor and fluctuating channel conditions. With the commonly adopted single-sink architecture, such conditions are exemplified by contention near the sink as a result of multipath delivery. This may be reduced by deploying multiple sinks spatially- apart e.g., along the edges of the network such that multiple spatially diverse paths that diverge like a starburst from each node towards these sinks can be set-up. Such an architecture opens up new challenges to the data delivery scheme, which determines the performance of the network. Since the compactness of sensors with limited energy resources restrict the use of sophisticated mechanisms, we consider simple data delivery schemes suited for such a multi-sink architecture. We optimise a single-path data delivery scheme with simple ARQ for a spatially-invariant environment, and demonstrate that its optimality over a spatially-diverse multipath scheme extends to spatially-variant environments. We also verify our analysis with simulations obtained using the Qualnet simulator.