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1. Using Mobile Device Batteries as Thermometers

   https://doi.org/10.1145/3471440.3471442  

   He, Liang; Lee, Youngmoon; Shin, Kang (June 2021, GetMobile: Mobile Computing and Communications)

   null (Ed.)

   Sensing the ambient temperature is the key to many applications, such as smart homes/buildings/ communities/cities [1, 2]. However, the ability to sense the ambient temperature pervasively is still deficient, causing various problems in people's daily lives, including but not limited to: 

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2. Programming support for sharing resources across heterogeneous mobile devices

   https://doi.org/10.1145/3197231.3197250  

   Song, Zheng; Chadha, Sanchit; Byalik, Antuan; Tilevich, Eli (January 2018, Proceedings of the 5th International Conference on Mobile Software Engineering and Systems)

   Modern mobile users commonly use multiple heterogeneous mobile devices, including smartphones, tablets, and wearables. Enabling these devices to seamlessly share their computational, network, and sensing resources has great potential benefit. Sharing resources across collocated mobile devices creates mobile device clouds (MDCs), commonly used to optimize application performance and to enable novel applications. However, enabling heterogeneous mobile devices to share their resources presents a number of difficulties, including the need to coordinate and steer the execution of devices with dissimilar network interfaces, application programming models, and system architectures. In this paper, we describe a solution that systematically empowers heterogeneous mobile devices to seamlessly, reliably, and efficiently share their resources. We present a programming model and runtime support for heterogeneous mobile device-to-device resource sharing. Our solution comprises a declarative domain-specific language for device-to-device cooperation, supported by a powerful runtime infrastructure. we evaluated our solution by conducting a controlled user study and running performance/energy efficiency benchmarks. The evaluation results indicate that our solution can become a practical tool for enhancing the capabilities of modern mobile applications by leveraging the resources of nearby mobile devices. 

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3. Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types

   https://doi.org/10.1109/sp.2018.00041  

   Han, Jun; Chung, Albert Jin; Sinha, Manal Kumar; Harishankar, Madhumitha; Pan, Shijia; Noh, Hae Young; Zhang, Pei; Tague, Patrick (May 2018, 39th IEEE Symposium on Security and Privacy (Oakland 2018))

   Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device’s microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio’s pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device. 

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4. MaRTiny—A Low-Cost Biometeorological Sensing Device With Embedded Computer Vision for Urban Climate Research

   https://doi.org/10.3389/fenvs.2022.866240  

   Kulkarni, Karthik K.; Schneider, Florian A.; Gowda, Tejaswi; Jayasuriya, Suren; Middel, Ariane (May 2022, Frontiers in Environmental Science)

   Extreme heat puts tremendous stress on human health and limits people’s ability to work, travel, and socialize outdoors. To mitigate heat in public spaces, thermal conditions must be assessed in the context of human exposure and space use. Mean Radiant Temperature (MRT) is an integrated radiation metric that quantifies the total heat load on the human body and is a driving parameter in many thermal comfort indices. Current sensor systems to measure MRT are expensive and bulky (6-directional setup) or slow and inaccurate (globe thermometers) and do not sense space use. This engineering systems paper introduces the hardware and software setup of a novel, low-cost thermal and visual sensing device (MaRTiny). The system collects meteorological data, concurrently counts the number of people in the shade and sun, and streams the results to an Amazon Web Services (AWS) server. MaRTiny integrates various micro-controllers to collect weather data relevant to human thermal exposure: air temperature, humidity, wind speed, globe temperature, and UV radiation. To detect people in the shade and Sun, we implemented state of the art object detection and shade detection models on an NVIDIA Jetson Nano. The system was tested in the field, showing that meteorological observations compared reasonably well to MaRTy observations (high-end human-biometeorological station) when both sensor systems were fully sun-exposed. To overcome potential sensing errors due to different exposure levels, we estimated MRT from MaRTiny weather observations using machine learning (SVM), which improved RMSE. This paper focuses on the development of the MaRTiny system and lays the foundation for fundamental research in urban climate science to investigate how people use public spaces under extreme heat to inform active shade management and urban design in cities. 

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5. Healable, Recyclable, and Multifunctional Soft Electronics Based on Biopolymer Hydrogel and Patterned Liquid Metal

   https://doi.org/10.1002/smll.202201643  

   Hao, Xing Peng; Zhang, Chuan Wei; Zhang, Xin Ning; Hou, Li Xin; Hu, Jian; Dickey, Michael D.; Zheng, Qiang; Wu, Zi Liang (May 2022, Small)

   Abstract Recent years have witnessed the rapid development of sustainable materials. Along this line, developing biodegradable or recyclable soft electronics is challenging yet important due to their versatile applications in biomedical devices, soft robots, and wearables. Although some degradable bulk hydrogels are directly used as the soft electronics, the sensing performances are usually limited due to the absence of distributed conducting circuits. Here, sustainable hydrogel‐based soft electronics (HSE) are reported that integrate sensing elements and patterned liquid metal (LM) in the gelatin–alginate hybrid hydrogel. The biopolymer hydrogel is transparent, robust, resilient, and recyclable. The HSE is multifunctional; it can sense strain, temperature, heart rate (electrocardiogram), and pH. The strain sensing is sufficiently sensitive to detect a human pulse. In addition, the device serves as a model system for iontophoretic drug delivery by using patterned LM as the soft conductor and electrode. Noncontact detection of nearby objects is also achieved based on electrostatic‐field‐induced voltage. The LM and biopolymer hydrogel are healable, recyclable, and degradable, favoring sustainable applications and reconstruction of the device with new functions. Such HSE with multiple functions and favorable attributes should open opportunities in next‐generation electronic skins and hydrogel machines. 

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