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Learning how to control congestion remains a challenge despite years of progress. Existing congestion control protocols have demonstrated efficacy within specific network conditions, inevitably behaving suboptimally or poorly in others. Machine learning solutions to congestion control have been proposed, though relying on extensive training and specific network configurations. In this paper, we loosen such dependencies by proposing Mutant, an online reinforcement learning algorithm for congestion control that adapts to the behavior of the best-performing schemes, outperforming them in most network conditions. Design challenges included determining the best protocols to learn from, given a network scenario, and creating a system able to evolve to accommodate future protocols with minimal changes. Our evaluation on real-world and emulated scenarios shows that Mutant achieves lower delays and higher throughput than prior learning-based schemes while maintaining fairness by exhibiting negligible harm to competing flows, making it robust across diverse and dynamic network conditions 

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our “Sensing Together” solution includes two algorithms: Distributed Task Adaptation (DTA) and Distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed “token passing” protocol. DTA adapts the sensing rates from their “raw” values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling. 

The rapid evolution of the Internet of Things (IoT) has underscored the importance of comprehensive educational strategies to impart IoT concepts and applications to a diverse audience. Given IoT’s pervasive impact, there is hence a pressing need for effective education in this area. Currently, there is a significant gap between existing educational strategies for IoT and the dynamic, engaging approaches needed to captivate a diverse audience, particularly young learners. The challenge lies in developing a methodology that not only educates but also motivates students e.g., from Grade 2 to Grade 12. To address this need, we developed an innovative, activity-based educational framework, integrating interactive and immersive learning methods, aimed at simplifying complex IoT concepts with smart agricultural application in mind for early learners. We outline this novel pedagogical approach, detailing how specific IoT components are taught through targeted activities. The paper should serve as a guide for educators to implement this framework and encourage readers to recognize the importance of adopting new teaching strategies for IoT. Through the imple- mentation of this framework, exemplified in a case study of a plant care game, we have observed an increased engagement and understanding of IoT concepts among our target students. These findings indicate the effectiveness of our approach in real-world educational settings.