The use of digital television broadcasting standards has resulted in transmission of perpetually on wireless digital-TV signals over the air at wider bandwidths in ultrahigh-frequency bands for high-definition video and audio broadcasts to TV and smart phones. This paper presents a unique embedded wireless energy-harvesting prototype (E-WEHP) that exploits the unique makeup of ambient digital-TV signals, and scavenges wireless power from them at distance of over 6.3 km from the TV broadcast source. The harvested wireless power is successfully used to power and sustain a 16-bit embedded microcontroller for sensing and machine-to-machine applications without the use of batteries. The E-WEHP uses a miniaturized planar log-periodic antenna and RF-dc charge-pump circuit with maximum sensitivities of - 14.6 and -18.86 dBm and an embedded firmware-based power management scheme to power microcontroller peripherals from different types of ambient digital-TV signals.

Energy harvesting is a key technique that can be used to overcome the barriers that prevent the real world deployment of wireless sensor networks (WSNs). In particular, solar energy harvesting has been commonly used to overcome this barrier. However, it should be noted that WSNs operating on solar power suffer form energy shortage during nighttimes. Therefore, to solve this problem, we exploit the use of TV broadcasts airwaves as energy sources to power wireless sensor nodes. We measured the output of a rectenna continuously for 7 days; from the results of this measurement, we showed that Radio Frequency (RF) energy can always be harvested. We developed an RF energy harvesting WSN prototype to show the effectiveness of RF energy harvesting for the usage of a WSN. We also proposed a duty cycle determination method for our system, and verified the validity of this method by implementing our system. This RF energy harvesting method is effective in a long period measurement application that do not require high power consumption.

In this paper, we present a software control method that maximizes the sensing rate of wireless sensor networks (WSNs) that are solely powered by ambient RF power. Unlike all other energy harvesting WSN systems, RF-powered systems present new challenges for energy management. A WSN node repeatedly charges and discharges at short intervals, depending on the energy intake. Typically in energy harvesting systems, a capacitor is used for energy storage because of its efficient charge and discharge performance and infinite recharge cycles. When the charging time is too short, a node is more likely to experience an energy shortage. On the contrary, if it is too long, more energy is lost because of leakage in the capacitor. In this paper, we introduce an adaptive duty cycle control scheme optimized for RF energy harvesting. This method maximizes the sensing rate by taking into account the leakage problem, a factor that has never been previously studied in this context. Our control scheme improves the efficiency by aggregate evaluation of operation reliability and leakage reduction.

Understandings of human activity through wearable sensors will enable the next-generation human-oriented computing. However, most of researches on the activity recognition so far are based on small number of test subjects, and not well adapted for real world applications. To overcome the situation, we have started a project named "HASC Challenge" to collect a large scale human activity corpus. By the end of 2010, by the collaboration of 20 teams, more than 6700 accelerometer data with 540 subjects have been collected through our project. We also developed a tool named "HASC Tool" for management, evaluation and collection of the large number of activity sensor data.