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1. Estimating outdoor temperature from CPU temperature for IoT applications in agriculture

   https://doi.org/10.1145/3277593.3277607  

   Krintz, Chandra; Wolski, Rich; Golubovic, Nevena; Bakir, Fatih (January 2018, International Conference on the Internet of Things)

   In the paper, we investigate using CPU temperature from small, low cost, single-board computers to predict out- door temperature in IoT-based precision agricultural settings. Temperature is a key metric in these settings that is used to in- form and actuate farm operations such as irrigation schedul- ing, frost damage mitigation, and greenhouse management. Using cheap single-board computers as temperature sensors can drive down the cost of sensing in these applications and make it possible to monitor a large number of micro-climates concurrently. We have developed a system in which devices communicate their CPU measurements to an on-farm edge cloud. The edge cloud uses a combination of calibration, smoothing (noise removal), and linear regression to make pre- dictions of the outdoor temperature at each device. We eval- uate the accuracy of this approach for different temperature sensors, devices, and locations, as well as different training and calibration durations. 

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2. SecT: A Lightweight Secure Thing-Centered IoT Communication System

   https://doi.org/10.1109/MASS.2018.00018  

   Gao, Chao; Ling, Zhen; Chen, Biao; Fu, Xinwen; Zhao, Wei (October 2018, 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS))

   In this paper, we propose a secure lightweight and thing-centered IoT communication system based on MQTT, SecT, in which a device/thing authenticates users. Compared with a server-centered IoT system in which a cloud server authenticates users, a thing-centered system preserves user privacy since the cloud server is primarily a relay between things and users and does not store or see user data in plaintext. The contributions of this work are three-fold. First, we explicitly identify critical functionalities in bootstrapping a thing and design secure pairing and binding strategies. Second, we design a strategy of end-to-end encrypted communication between users and things for the sake of user privacy and even the server cannot see the communication content in plaintext. Third, we design a strong authentication system that can defeat known device scanning attack, brute force attack and device spoofing attack against IoT. We implemented a prototype of SecT on a $10 Raspberry Pi Zero W and performed extensive experiments to validate its performance. The experiment results show that SecT is both cost-effective and practical. Although we design SecT for the smart home application, it can be easily extended to other IoT application domains. 

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3. A privacy‐preserving mobile location‐based advertising system for small businesses

   https://doi.org/10.1002/eng2.12416  

   Ahmed, Ahmed Abdelmoamen (May 2021, Engineering Reports)

   Abstract Small‐to‐medium businesses are always seeking affordable ways to advertise their products and services securely. With the emergence of mobile technology, it is possible than ever to implement innovative Location‐Based Advertising (LBS) systems using smartphones that preserve the privacy of mobile users. In this paper, we present a prototype implementation of such systems by developing a distributed privacy‐preserving system, which has parts executing on smartphones as a mobile app, as well as a web‐based application hosted on the cloud. The mobile app leverages Google Maps libraries to enhance the user experience in using the app. Mobile users can use the app to commute to their daily destinations while viewing relevant ads such as job openings in their neighborhood, discounts on favorite meals, etc. We developed a client‐server privacy architecture that anonymizes the mobile user trajectories using a bounded perturbation strategy. A multi‐modal sensing approach is proposed for modeling the context switching of the developed LBS system, which we represent as a Finite State Machine model. The multi‐modal sensing approach can reduce the power consumed by mobile devices by automatically detecting sensing mode changes to avoid unnecessary sensing. The developed LBS system is organized into two parts: the business side and the user side. First, the business side allows business owners to create new ads by providing the ad details, Geo‐location, photos, and any other instructions. Second, the user side allows mobile users to navigate through the map to see ads while walking, driving, bicycling, or quietly sitting in their offices. Experimental results are presented to demonstrate the scalability and performance of the mobile side. Our experimental evaluation demonstrates that the mobile app incurs low processing overhead and consequently has a small energy footprint. 

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4. IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes

   https://doi.org/10.1109/INFOCOM48880.2022.9796908  

   Wan, Yinxin; Xu, Kuai; Wang, Feng; Xue, Guoliang (May 2022, IEEE INFOCOM2022)

   Recent advances in cyber-physical systems, artificial intelligence, and cloud computing have driven the wide deployments of Internet-of-things (IoT) in smart homes. As IoT devices often directly interact with the users and environments, this paper studies if and how we could explore the collective insights from multiple heterogeneous IoT devices to infer user activities for home safety monitoring and assisted living. Specifically, we develop a new system, namely IoTMosaic, to first profile diverse user activities with distinct IoT device event sequences, which are extracted from smart home network traffic based on their TCP/IP data packet signatures. Given the challenges of missing and out-of-order IoT device events due to device malfunctions or varying network and system latencies, IoTMosaic further develops simple yet effective approximate matching algorithms to identify user activities from real-world IoT network traffic. Our experimental results on thousands of user activities in the smart home environment over two months show that our proposed algorithms can infer different user activities from IoT network traffic in smart homes with the overall accuracy, precision, and recall of 0.99, 0.99, and 1.00, respectively. 

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5. SolicitudeSavvy: An IoT-based Edge Intelligent Framework for Monitoring Anxiety in Real-time

   https://doi.org/10.1109/ISQED51717.2021.9424302  

   Sundaravadivel, Prabha; Wilmoth, Parker; Fitzgerald, Ashton (April 2021, 2021 22nd International Symposium on Quality Electronic Design (ISQED))

   null (Ed.)

   Anxiety disorders affect more than 18 percent of the population and is the most common mental illnesses in the US. There is a great demand to address this emerging epidemic with tools to differentiate and diagnose such disorders, and to create awareness especially in places like NorthEast Texas which is home to 1.5 million people with 58 percent of them living in rural areas. The goal of the proposed device is to diagnose as many anxiety disorders as possible, in real-time using the diagnosing wearable framework, SolicitudeSavvy, which uses technology such as the Internet of Things (IoT), a network of interconnected devices, to accomplish such a task. The proposed IoT-based device has two components: a custom-built wearable necklace that contains sensors to collect data about the user as they go about their day and a low-cost portable system that monitors Electrooculography (EoG) signals using a camera. The partial necklace attaches to the shirt and opens halfway around the wearer's neck and the EoG can be attached to any eyewear. The device monitors the user throughout the day, and even as they go to bed at night. This information is accumulated in the IoT cloud and analyzed to see exactly what type of disorder(s) the patient may suffer from. The authorized personnel i.e. doctor or therapist can use this pattern to find a treatment that best suits them and is most likely to resolve their affliction. 

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