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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. 

Multi-sensor IoT devices can gather different types of data by executing different sensing activities or tasks. Therefore, IoT applications are also becoming more complex in order to process multiple data types and provide a targeted response to the monitored phenomena. However, IoT devices which are usually resource-constrained still face energy challenges since using each of these sensors has an energy cost. Therefore, energy-efficient solutions are needed to extend the device lifetime while balancing the sensing data requirements of the IoT application. Cooperative monitoring is one approach for managing energy and involves reducing the duplication of sensing tasks between neighboring IoT devices. Setting up cooperative monitoring is a scheduling problem and is challenging in a distributed environment with resource-constrained IoT devices. In this work, we present our Distributed Token and Tier-based task Scheduler (DTTS) for a multi-sensor IoT network. Our algorithm divides the monitoring period (5 min epochs) into a set of non-overlapping intervals called tiers and determines the start deadlines for the task at each IoT device. Then to minimize temporal sensing overlap, DTTS distributes task executions throughout the epoch and uses tokens to share minimal information between IoT devices. Tasks with earlier start deadlines are scheduled in earlier tiers while tasks with later start deadlines are scheduled in later tiers. Evaluating our algorithm against a simple round-robin scheduler shows that the DTTS algorithm always schedules tasks before their start deadline expires. 

Multi-sensor IoT devices enable the monitoring of different phenomena using a single device. Often deployed over large areas, these devices have to depend on batteries and renewable energy sources for power. Therefore, efficient energy management solutions that maximize device lifetime and information utility are important. We present SEMA, a smart energy management solution for IoT applications that uses a Model Predictive Control (MPC) approach to optimize IoT energy use and maximize information utility by dynamically determining task values to be used by the IoT device’s sensors. Our solution uses the current device battery state, predicted available solar energy over the short-term, and task energy and utility models to meet the device energy goals while providing sufficient monitoring data to the IoT applications. To avoid the need for executing the MPC optimization at a centralized sink (from which the task values are downloaded to the SEMA devices), we propose SEMA-Approximation (SEMA-A), which uses an efficient MPC Approximation that is simple enough to be run on the IoT device itself. SEMA-A decomposes the MPC optimization problem into two levels: an energy allocation problem across the time epochs, and task-dependent sensor scheduling problem, and finds efficient algorithms for solving both problems. Experimental results show that SEMA is able to adapt the task values based on the available energy, and that SEMA-A closely approximates SEMA in sensing performance. 

This paper considers the problem of thermal management in a typical shared indoor space that may be equipped with multiple heterogeneous heat sources and have different temperature require- ments in different sections (thermal zones) of the shared space. Utilizing an on-campus smart conference room as a testbed, we discuss the practical challenges involved in real-time data-driven model learning, when a simple first-order dynamical model is used to capture the dependencies between the heat controls and the air temperatures measured at sensor locations. The data-driven model is then utilized for predictive control of the thermal environment towards minimizing the error between the desired and attained temperatures, and the integrated solution is evaluated against a standard thermal control employed by the BMS.