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The Internet of Things (IoT) is a rapidly emerging application space, poised to become the largest electronics market for the semiconductor industry. IoT devices are focused on sensing and actuating of our physical environment and have a nearly unlimited breadth of uses. In this paper, we explore the IoT application space and then identify two common challenges that exist across this space: ultra-low power operation and system design using modular, composable components. We survey recent low power techniques and discuss a low power bus that enables modular design. Finally, we conclude with three example ultra-low power, millimeter-scale IoT systems. IoT Application Space and ChallengesIoT devices are unique in that they are focused on physical interfaces, allowing them to sense and actuate the world around us. This is in contrast to previous computing platforms, such as desktop and handheld devices, which have primarily focused on human interfaces. These human interfaces require relatively large input and output devices and require the devices to be co-located with their users. In contrast, IoT devices receive input through sensors that are often highly miniaturized through MEMS technology and send out information through wired or wireless interfaces to mobile and cloud computers. This enables the placement of IoT devices in myrad new locations and applications where computing was previously absent. As a result, IoT presents the semiconductor industry with a market opportunity that may exceed that of all previous computing classes.The diversity of our physical environment results in a similarly diverse IoT application space that ranges from tiny implanted heart-rhythm monitors, to large, extremely long lifetime HVAC sensors, to sensors for oil reservoir diagnosis [1]. To categorize this wide application space we recognize four ontological properties that are critical in determining IoT device requirements: sensing modality, form factor (size), energy source / sensor lifetime, and connectivity. Table 1 lists example values for these properties. For instance, physical form factor can range from tiny millimeter size for implantable sensors to 10s of centimeters for infrastructure monitoring. Sensing modalities can include pressure, chemical, strain, and temperature, etc. In addition, the sensing modality can be characterized by measurement frequency and possible triggers , such as a timer, a detected event in the monitored value, or an external event such as a radio message.Similarly, communication connectivity is characterized by communication interval, distance, data rate/size, etc. Clearly the listed items are not exhaustive. Each property can take a very large number of possible values resulting in a highly varied application space. Some examples include 1) tiny sensors that measure temperature every 10 minutes using a secondary battery and harvesting with infrequent communication for studying Pika's in the Rocky mountains [2]; 2) continuous monitoring of brain activity in seizure patients with a contin...

Abstract. Extensive forest fires occurred during late July 2014 across the forested region of Siberia, Russia. Smoke plumes emitted from Siberian forest fires underwent long-range transport over Mongolia and northeast China to the Korean Peninsula, which is located ∼ 3000 km south of the Siberian forest. A notably high aerosol optical depth of ∼ 4 was observed at a wavelength of 500 nm near the source of the Siberian forest fires. Smoke plumes reached 3-5 km in height near the source and fell below 2 km over the Korean Peninsula. Elevated concentrations of levoglucosan were observed (119.7 ± 6.0 ng m −3 ), which were ∼ 4.5 times higher than those observed during non-event periods in July 2014. During the middle of July 2014, a haze episode occurred that was primarily caused by the long-range transport of emission plumes originating from urban and industrial complexes in East China. Sharp increases in SO 2− 4 concentrations (23.1 ± 2.1 µg m −3 ) were observed during this episode. The haze caused by the long-range transport of Siberian forest fire emissions was clearly identified by relatively high organic carbon (OC) / elemental carbon (EC) ratios (7.18 ± 0.2) and OC / SO 2− 4 ratios (1.31 ± 0.07) compared with those of the Chinese haze episode (OC / EC ratio: 2.4 ± 0.4; OC / SO 2− 4 ratio: 0.21 ± 0.05). Remote measurement techniques and chemical analyses of the haze plumes clearly show that the haze episode that occurred during late July 2014 was caused mainly by the long-range transport of smoke plumes emitted from Siberian forest fires.