In XR downlink transmission, energy-efficient power scheduling (EEPS) is essential for conserving power resource while delivering large data packets within hard-latency constraints. Traditional constrained reinforcement learning (CRL) algorithms show promise in EEPS but still struggle with non-convex stochastic constraints, non-stationary data traffic, and sparse delayed packet dropout feedback (rewards) in XR. To overcome these challenges, this paper models the EEPS in XR as a dynamic parameter-constrained Markov decision process (DP-CMDP) with a varying transition function linked to the non-stationary data traffic and solves it by a proposed context-aware constrained reinforcement learning (CACRL) algorithm, which consists of a context inference (CI) module and a CRL module. The CI module trains an encoder and multiple potential networks to characterize the current transition function and reshape the packet dropout rewards according to the context, transforming the original DP-CMDP into a general CMDP with immediate dense rewards. The CRL module employs a policy network to make EEPS decisions under this CMDP and optimizes the policy using a constrained stochastic successive convex approximation (CSSCA) method, which is better suited for non-convex stochastic constraints. Finally, theoretical analyses provide deep insights into the CADAC algorithm, while extensive simulations demonstrate that it outperforms advanced baselines in both power conservation and satisfying packet dropout constraints.

Scheduling university courses is extra challenging when classroom capacities are reduced because of social distancing requirements that are implemented in response to a pandemic such as COVID-19. In this work, we propose an expanded taxonomy of course delivery modes, present an integer program, and develop a course scheduling algorithm to enable all course sections -- even the largest -- to have a significant classroom learning component during a pandemic. Our approach is fair by ensuring that a certain fraction of the instruction in every course section occurs in the classroom. Unlike previous studies, we do not allow rotating attendance and instead require simultaneous attendance in which all students in a section meet in 1-5 rooms at the same time but less often than in a normal semester. These mass meetings, which create opportunities for in-person midterm exams and group activities, are scheduled at high precision across all days of the semester rather than a single, repeating week. A fast heuristic algorithm makes the schedule in an hour. Results: We consider the 1834 in-person course sections, 172 classrooms, and 96 days in the fall 2022 semester at [UniversityXYZ]. If average classroom capacity is reduced by 75% due to a pandemic, our approach still allows at least 25% of the instruction in every section, and more than 49% of all instruction across the entire campus, to be in the classroom. Our method also produces excellent results for regular classroom assignment. Managerial implications: An algorithm based on the principles of fairness and simultaneous attendance can significantly improve university course schedules during a pandemic and in normal times. High-precision schedules that prepare a campus for various pandemic possibilities can be created with minimal administrative effort and activated at a moment's notice before or during a semester if an outbreak occurs.

Battery-free sensor tags are devices that leverage backscatter techniques to communicate with standard IoT devices, thereby augmenting a network's sensing capabilities in a scalable way. For communicating, a sensor tag relies on an unmodulated carrier provided by a neighboring IoT device, with a schedule coordinating this provisioning across the network. Carrier scheduling--computing schedules to interrogate all sensor tags while minimizing energy, spectrum utilization, and latency--is an NP-Hard optimization problem. Recent work introduces learning-based schedulers that achieve resource savings over a carefully-crafted heuristic, generalizing to networks of up to 60 nodes. However, we find that their advantage diminishes in networks with hundreds of nodes, and degrades further in larger setups. This paper introduces RobustGANTT, a GNN-based scheduler that improves generalization (without re-training) to networks up to 1000 nodes (100x training topology sizes). RobustGANTT not only achieves better and more consistent generalization, but also computes schedules requiring up to 2x less resources than existing systems. Our scheduler exhibits average runtimes of hundreds of milliseconds, allowing it to react fast to changing network conditions. Our work not only improves resource utilization in large-scale backscatter networks, but also offers valuable insights in learning-based scheduling.

Harnessing the wide-spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in underserved communities, with maternal and infant health being a key area of such mHealth programs. Unfortunately, dwindling listenership is a major challenge, requiring targeted interventions using limited resources. This paper focuses on Kilkari, the world's largest mHealth program for maternal and child care - with over 3 million active subscribers at a time - launched by India's Ministry of Health and Family Welfare (MoHFW) and run by the non-profit ARRMAN. We present a system called CHAHAK that aims to reduce automated dropouts as well as boost engagement with the program through the strategic allocation of interventions to beneficiaries. Past work in a similar domain has focused on a much smaller scale mHealth program and used markovian restless multiarmed bandits to optimize a single limited intervention resource. However this paper demonstrates the challenges in adopting a markovian approach in Kilkari; therefore CHAHAK instead relies on non-markovian time-series restless bandits, and optimizes multiple interventions to improve listenership. We use real Kilkari data from the Odisha state in India to show CHAHAK's effectiveness in harnessing multiple interventions to boost listenership, benefiting marginalized communities. When deployed CHAHAK will assist the largest maternal mHealth program to date.

Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art schemes based on deterministic optimizations or single-objective reinforcement learning (RL) cannot reduce the backlog of task bits and simultaneously improve energy efficiency in highly dynamic network environments, where the design problem amounts to a sequential decision-making problem. In order to address the aforementioned problems, as well as the curses of dimensionality introduced by the growing number of terrestrial terrestrial users, this paper proposes a distributed multi-objective (MO) dynamic trajectory planning and offloading scheduling scheme, integrated with MORL and the kernel method. The design of n-step return is also applied to average fluctuations in the backlog. Numerical results reveal that the n-step return can benefit the proposed kernel-based approach, achieving significant improvement in the long-term average backlog performance, compared to the conventional 1-step return design. Due to such design and the kernel-based neural network, to which decision-making features can be continuously added, the kernel-based approach can outperform the approach based on fully-connected deep neural network, yielding improvement in energy consumption and the backlog performance, as well as a significant reduction in decision-making and online learning time.

Due to the limited driving range, inadequate charging facilities, and time-consuming recharging, the process of finding an optimal charging route for electric vehicles (EVs) differs from that of other vehicle types. The time and location of EV charging during a trip impact not only the individual EV's travel time but also the travel time of other EVs, due to the queuing that may arise at the charging station(s). This issue is at large seen as a significant constraint for uplifting EV sales in many countries. In this study, we present a novel Electric Vehicle Route Planning problem, which involves finding the fastest route with recharging for an EV routing request. We model the problem as a new graph problem and present that the problem is NP-hard. We propose a novel two-phase algorithm to traverse the graph to find the best possible charging route for each EV. We also introduce the notion of `influence factor' to propose heuristics to find the best possible route for an EV with the minimum travel time that avoids using charging stations and time to recharge at those stations which can lead to better travel time for other EVs. The results show that our method can decrease total travel time of the EVs by 50\% in comparison with the state-of-the-art on a real dataset, where the benefit of our approach is more significant as the number of EVs on the road increases.

Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural Networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views.

Swarms of autonomous interactive drones, with the support of recharging technology, can provide compelling sensing capabilities in Smart Cities, such as traffic monitoring and disaster response. Existing approaches, including distributed optimization and deep reinforcement learning (DRL), aim to coordinate drones to achieve cost-effective, high-quality navigation, sensing, and charging. However, they face grand challenges: short-term optimization is not effective in dynamic environments with unanticipated changes, while long-term learning lacks scalability, resilience, and flexibility. To bridge this gap, this paper introduces a new progressive approach that combines short-term plan generation and selection based on distributed optimization with a DRL-based long-term strategic scheduling of flying direction. Extensive experimentation with datasets generated from realistic urban mobility underscores an outstanding performance of the proposed solution compared to state-of-the-art. We also provide compelling new insights about the role of drones density in different sensing missions, the energy safety of drone operations and how to prioritize investments for key locations of charging infrastructure.

Efforts in the fight against Climate Change are increasingly oriented towards new energy efficiency strategies in Smart Grids (SGs). In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives. That action aspires to accelerate the spread of local renewable energy exploitation, whose costs could not be within everyone's reach. Since a REC is technically an SG, the strategies above can be applied, and specifically, practical Energy Management Systems (EMSs) are required. Therefore, in this work, an online Hierarchical EMS (HEMS) is synthesized for REC cost minimization to evaluate its superiority over a local self-consumption approach. EU technical indications (as inherited from Italy) are diligently followed, aiming for results that are as realistic as possible. Power flows between REC nodes, or Microgrids (MGs) are optimized by taking Energy Storage Systems (ESSs) and PV plant costs, energy purchase costs, and REC incentives. A hybrid Fuzzy Inference System - Genetic Algorithm (FIS-GA) model is implemented with the GA encoding the FIS parameters. Power generation and consumption, which are the overall system input, are predicted by a LSTM trained on historical data. The proposed hierarchical model achieves good precision in short computation times and outperforms the self-consumption approach, leading to about 20% savings compared to the latter. In addition, the Explainable AI (XAI), which characterizes the model through the FIS, makes results more reliable thanks to an excellent human interpretation level. To finish, the HEMS is parametrized so that it is straightforward to switch to another Country's technical legislation framework.

We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, weighted threshold potential functions, reduces to online convex optimization (OCO). This is precisely because functions in this class admit a concave relaxation; as a result, OCO policies, coupled with an appropriate rounding scheme, can be used to achieve sublinear regret in the combinatorial setting. We show that our reduction extends to many different versions of the online learning problem, including the dynamic regret, bandit, and optimistic-learning settings.