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.
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 wireless sensor 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 an environmentally-powered wireless sensor network, where nodes are powered solely by harvesting energy from ambient sources, node operation highly depends on the energy availability and harvesting rate. For duty-cycling schemes to support existing wireless sensor network applications, they need to adapt the nodes' sleep-wake schedules according to energy harvesting and consumption rates. In this paper, we propose a harvesting-aware duty-cycling scheme, perform an empirical study of this scheme over a solar harvesting powered wireless sensor network in a source-relay-sink configuration, and provide some experimental results to illustrate its throughput performance under various light conditions.
In this demonstration, we implement and deploy a solarpowered 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.
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