Existing works in photovoltaic (PV) power generation focus on accurately predicting the PV power output on a forecast horizon. As the solar power generation is heavily influenced by meteorological conditions such as solar radiation, the weather forecast is a critical input in the prediction performance. However, the weather forecast is traditionally considered to have coarse granularity, so many are compelled to use on-site meteorological sensors to complement it. However, the approach involving on-site sensors has several issues. First, it incurs the cost in the installation, operation, and management of the sensors. Second, the physical model of the sensor dynamics itself can be a source of forecast errors. Third, it requires an accumulation of sensory data that represent all seasonal variations, which takes time to collect. In this paper, we take an alternative approach to use a relatively large deep neural network (DNN) instead of the on-site sensors to cope with the coarse-grained weather forecast. With historical PV output power data from our grid-connected building with a rooftop PV power generation facility and the publicly available weather forecast history data, we demonstrate that we can train a six-layer feedforward DNN for the day-ahead forecast. It achieves the average mean absolute error (MAE) of 2.9%, comparable to that of the conventional model, but without involing the on-site sensors.

Bluetooth Low Energy (BLE) is a proper technology for studies on close human interactions. It has a wide deployment base, has peer discovery as an inherent feature, does not require infrastructure to operate, and sparingly uses energy that is good for extended monitoring. In this paper, we show that we can use the BLE peer discovery capability on smartphones to monitor massive and dynamic encounters, which would provide valuable insights into many epidemiological or sociological phenomena. However, being designed for more leisurely interactions, BLE needs some stretching in order to be used in large-scale operations. Specifically, the protocol design is not optimal to rapidly discover hundreds of devices in the communication range. Moreover, if the crowd is dynamic, discovery becomes even more time-pressed because encounters should be recorded before churn. In this paper, we propose the changes to BLE to discover hundreds of devices before co-presence expires, and to work continually over typical smartphone recharge intervals. We show that with the proposed changes and configurations, we can accelerate the speed of discovery for massive and dynamic crowds by more than an order of magnitude compared to the case that we naively follow the guidance of the current BLE standard.

The platooning application has a significantly higher safety messaging rate requirement that could threaten harmonious coexistence with nearby non-platoon vehicles. We explore an alternative delivery mode for platoon communication in order to address the issue. The proposed mode is called pseudo-broadcast, since it emulates broadcast using unicast. In pseudo-broadcast, the platoon member with the worst reception condition is designated as the destination of the unicast from the leader vehicle. Then other platoon members promiscuously receive the unicast packet. And vice versa, the messages from the platoon followers to the leader are pseudo-broadcast to the leader. The unicast transmission in pseudo-broadcast allows timely retransmission, which is not possible with broadcast. It not only improves the reliability of platoon communication, but also helps minimize the additional beaconing activity in meeting the required latency bound. Consequently, it lessens the abusive bandwidth impact on the non-platoon vehicles in the vicinity, fostering harmonious coexistence. To the best of our knowledge, this is the first work that looks in detail into the question of coexistence between platooning vehicles and others sharing the same channel, and also provides some first insights into its quantitative effects.

The high linear correlation between the smartphone magnetometer readings in close proximity can be exploited for physical human contact detection, which could be useful for such applications as infectious disease contact tracing or social behavior monitoring. Alternative approaches using other capabilities in smartphones have aspects that do not fit well with the human contact detection. Using Wi-Fi or cellular fingerprints have larger localization errors than close human contact distances. Bluetooth beacons could reveal the identity of the transmitter, threatening the privacy of the user. Also, using sensors such as GPS does not work for indoor contacts. However, the magnetometer correlation check works best in human contact distances that matter in infectious disease transmissions or social interactions. The omni-present geomagnetism makes it work both indoors and outdoors, and the measured magnetometer values do not easily reveal the identity and the location of the smartphone. One issue with the magnetometer-based contact detection, however, is the energy consumption. Since the contacts can take place anytime, the magnetometer sensing and recording should be running continuously. Therefore, how we address the energy requirement for the extended and continuous operation can decide the viability of the whole idea. However, then, we note that almost all existing magnetometer-based applications such as indoor location and navigation have used high sensing frequencies, ranging from 10 Hz to 200 Hz. At these frequencies, we measure that the time to complete battery drain in a typical smartphone is shortened by three to twelve hours. The heavy toll raises the question as to whether the magnetometer-based contact detection can avoid such high sensing rates while not losing the contact detection accuracy. In order to answer the question, we conduct a measurement-based study using independently produced magnetometer traces from three different countries. Specifically, we gradually remove high frequency components in the traces, while observing the correlation changes. As a result, we find that the human coexistence detection indeed tends to be no less, if not more, effective at the sampling frequency of 1 Hz or even less. This is because unlike the other applications that require centimeter-level precision, the human contacts detected anywhere within a couple of meters are valid for our purpose. With the typical smartphone battery capacity and at the 1 Hz sensing, the battery consumption is well below an hour, which is smaller by more than two hours compared with 10 Hz sampling and by almost eleven hours compared with 200 Hz sampling. With other tasks running simultaneously on smartphones, the energy saving aspect will only become more critical. Therefore, we conclude that sensing the ambient magnetic field at 1 Hz is sufficient for the human contact monitoring purpose. We expect that this finding will have a significant practicability implication in the smartphone magnetometer-based contact monitoring applications in general.