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1. Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy

   Liri, Elizabeth ; Ramakrishnan, K K ; Kar, Koushik ; Esposito, Flavio ( September 2024 , IEEE)

   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. 

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2. Optimizing Timely Coverage in Communication Constrained Collaborative Sensing Systems

   Rahal, Jean Abou ; de Veciana, Gustavo ; Shimizu, Takayuki ; Lu, Hongsheng ( June 2020 , Proceedings of the International Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks)

   We consider a collection of distributed sensor nodes periodically exchanging information to achieve real-time situa- tional awareness in a communication constrained setting, e.g., collaborative sensing amongst vehicles to enable safety-critical decisions. Nodes may be both consumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, whilst producers provide updates on what they are currently able to see. Accordingly, we introduce and explore optimizing trade-offs between the coverage and the space-time average of the “age” of the information available to consumers. We consider two settings that capture the fundamental character of the problem. The first addresses selecting a subset of producers which optimizes a weighted sum of the coverage and the average age given that producers provide updates at a fixed rate. The second addresses the minimization of the weighted average age achieved by a fixed subset of producers with possibly overlapping coverage by optimizing their update rates. The former is shown to be submodular and thus amenable to greedy optimization while the latter has a non-convex/non-concave cost function which is amenable to effective optimization using tools such as the Frank- Wolfe algorithm. Numerical results exhibit the benefits of context dependent optimization information exchanges among obstructed sensing nodes in a communication constrained environment. 

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3. Optimizing Networked Situational Awareness

   Jean Abou Rahal, Gustavo de ( June 2019 , IEEE WiOPT RAWNET Workshop)

   This paper proposes a framework to explore the op- timization of applications where a distributed set of nodes/sensors, e.g., automated vehicles, collaboratively exchange information over a network to achieve real-time situational-awareness. To that end we propose a reasonable proxy for the usefulness of possibly delayed sensor updates and their sensitivity to the network re- sources devoted to such exchanges. This enables us to study the joint optimization of (1) the application-level update rates, i.e., how often and when sensors update other nodes, and (2), the transmission resources allocated to, and resulting delays associated with, exchanging updates. We first consider a network scenario where nodes share a single resource, e.g., an ad hoc wireless setting where a cluster of nodes, e.g., platoon of vehicles, share information by broadcasting on a single collision domain. In this setting we provide an explicit solution characterizing the interplay between network congestion and situational awareness amongst heterogeneous nodes. We then extend this to a setting where such clusters can also exchange information via a base station. In this setting we characterize the optimal solution and develop a natural distributed algorithm based on exchanging congestion prices associated with sensor nodes’ update rates and associated network transmission rates. Preliminary numerical evaluation provides initial insights on the trade-offs associated with optimizing situational awareness and the proposed algorithm’s convergence. 

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4. Line Coverage with Multiple Robots

   https://doi.org/10.1109/ICRA40945.2020.9197292  

   Agarwal, Saurav ; Akella, Srinivas ( June 2020 , IEEE International Conference on Robotics and Automation)

   The line coverage problem is the coverage of linear environment features (e.g., road networks, power lines), modeled as 1D segments, by one or more robots while respecting resource constraints (e.g., battery capacity, flight time) for each of the robots. The robots incur direction dependent costs and resource demands as they traverse the edges. We treat the line coverage problem as an optimization problem, with the total cost of the tours as the objective, by formulating it as a mixed integer linear program (MILP). The line coverage problem is NP-hard and hence we develop a heuristic algorithm, Merge- Embed-Merge (MEM). We compare it against the optimal MILP approach and a baseline heuristic algorithm, Extended Path Scanning. We show the MEM algorithm is fast and suitable for real-time applications. To tackle large-scale problems, our approach performs graph simplification and graph partitioning, followed by robot tour generation for each of the partitioned subgraphs. We demonstrate our approach on a large graph with 4,658 edges and 4,504 vertices that represents an urban region of about 16 sq. km. We compare the performance of the algorithms on several small road networks and experimentally demonstrate the approach using UAVs on the UNC Charlotte campus road network. 

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5. Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition

   Liu, Xuan ; Fu, Jie ( July 2019 , Annual American Control Conference)

   In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in the form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under co-safe temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying constraints specified in the fragment of co-safe temporal logic. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications. 

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