| Title | (Invited Paper) Powering the IoT: Storage-Less and Converter-Less Energy Harvesting |
| Author | *Hyung Gyu Lee (Daegu University, Republic of Korea), Naehyuck Chang (KAIST, Republic of Korea) |
| Page | pp. 124 - 129 |
| Keyword | Internet of Things, Energy harvesting, Storageless, Converterless |
| Abstract | Wide spread of Internet of Things (IoTs) still have huddles in cost and maintenance. Energy harvesting is a promising option to mitigate battery replacement, but the current energy harvesting methods still rely on batteries or equivalent and power converters for the maximum power point tracking (MPPT). Unfortunately, batteries are subject to wear and tear, which is a primary factor to prevent from being maintenance free. Power converters are expensive, heavy and lossy as well. In this paper, we introduce a novel energy harvesting and management technique to power the IoT, which does not require any long-term energy storages nor voltage converters unlike traditional energy harvesting systems. Extensive simulations and measurements from our prototype demonstrate that the proposed method harvests 8% more energy and extends the operation time of the device 60% more during a day. This paper also demonstrates a UV (ultraviolet) level meter for skin protect, named SmartPatch, using the proposed energy harvesting method. The proposed method is not limited to photovoltaic energy harvesting but applicable to most energy harvesting IoT power supplies that require impedance tracking. |
| Title | (Invited Paper) Distributed Computing in IoT: System-on-a-Chip for Smart Cameras as an Example |
| Author | *Shao-Yi Chien, Wei-Kai Chan, Yu-Hsiang Tseng (National Taiwan University, Taiwan), Chia-Han Lee (Academia Sinica, Taiwan), V. Srinivasa Somayazulu, Yen-Kuang Chen (Intel Corporation, U.S.A.) |
| Page | pp. 130 - 135 |
| Keyword | IoT, video sensors, smart camera, distributed computing |
| Abstract | There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover the context information and knowledge behind signals. To support such large-scale data size and computation tasks, it is not feasible to employ centralized solutions on cloud servers. Thanks for the advances of silicon technology, the cost of computation become lower, and it is possible to distribute computation on every node in IoT. In this paper, we take video sensing network as an example to show the idea of distributed computing in IoT. Existing related works are reviewed and the architecture of a system-on-a-chip solution for distributed smart cameras is proposed with coarse-grained reconfigurable image stream processing architecture. It can accelerate various computer vision algorithms for distributed smart cameras in IoT. |
| Title | (Invited Paper) Data Sensing and Analysis: Challenges for Wearables |
| Author | James Williamson, Qi Liu, Fenglong Lu, Wyatt Mohrman, Kun Li (University of Colorado Boulder, U.S.A.), Robert P. Dick (University of Michigan, U.S.A.), *Li Shang (University of Colorado Boulder, U.S.A.) |
| Page | pp. 136 - 141 |
| Keyword | Wearable technology, Low-power design, Quantified self |
| Abstract | Wearables are a leading category in the Internet of Things. Compared with mainstream mobile phones, wearables target one order of magnitude form factor reduction, and offer the potential of providing ubiquitous, personalized services to end users. Aggressive reduction in size imposes serious limits on battery capacity. Wearables are equipped with a range of sensors, such as accelerometers and gyroscopes. Most economical sensors were developed for mobile phones, with energy consumptions more appropriate for phones than for ultra-compact wearables. This article describes the energy challenges for wearable sensing technologies, with a primary focus on the most widely used wearable sensors: MEMS-based inertial measurement units. Using sports and fitness wearables as the pilot application, we analyze the energy characteristics of MEMS IMU data sensing, analysis, and wireless communication. We then discuss the technologies needed to solve the power and energy consumptions challenges for wearables. |