Vehicle Patrol Scheduling (VPS) is vital for urban security, requiring broad coverage and rapid emergency responses. This paper addresses VPS through computational optimization to enhance patrol efficiency. We propose two methods: Adaptive Hill-Climbing-Based Patrol Scheduling (AHBPS2), which uses an iterative hill-climbing strategy with integrated emergency response, and Patrol Planning with Proximal Policy Optimization $({{\mathcal{P}}^4}{\mathcal{O}})$, which models VPS as a Markov Decision Process and employs Deep Reinforcement Learning to learn optimal strategies. Extensive simulations on real-world urban maps show that ${{\mathcal{P}}^4}{\mathcal{O}}$ achieves higher visit frequencies and superior emergency handling compared to AHBPS2, underscoring the potential of these techniques for developing adaptive, efficient urban patrol systems.

The adoption of electric vehicles (EVs) continues to grow, driven by rising fuel prices, environmental concerns, and advancements in battery technology. However, challenges such as limited charging infrastructure and complex route planning still hinder large-scale deployment. This paper addresses the problem of minimizing total travel costs by jointly optimizing route selection, travel time, and charging expenses. An MILP model is introduced for small-scale scenarios, and a scalable heuristic, Minimizing Travel Cost (MTC), is proposed for real-time decisions. MTC integrates real-time traffic and dynamic charging rates, ensuring adaptive, cost-efficient routing without breaching battery safety thresholds. The results show that MTC achieves near-optimal performance with significantly shorter computation time. This work offers a practical and robust solution for intelligent EV routing and charging optimization in real-world transportation systems.

Autonomous vehicles (AVs) are anticipated to play a pivotal role in intelligent transportation systems, particularly in the context of future smart cities. Common performance measures like throughput and latency are not sufficient for capturing the timing and freshness of data in applications like autonomous driving and accident prevention. Therefore, this paper addresses the challenge of minimizing the Age of Information (AoI) in AVassisted vehicular networks. First, the problem is mathematically formulated as linear programming to derive optimal solutions. Recognizing the computational complexity, a scalable heuristic method tailored for large networks is proposed. Additionally, for comparative analysis, the problem is modeled as a Markov decision process and solved using Q-learning, an algorithm of Reinforcement Learning (RL). The numerical results highlight the efficacy of the proposed heuristic method in minimizing the average AoI, considering both computational efficiency and its potential to complement RL algorithms in a hybrid approach.

Autonomous vehicles (AVs) are poised to become integral components of intelligent transportation systems, particularly within the framework of future smart cities. Traditional performance metrics such as throughput and latency fall short in adequately addressing the temporal relevance and freshness of data in critical applications such as autonomous driving and accident prevention. Consequently, this paper delves into the challenge of reducing the Age of Information (AoI) for disseminating data streams within AV-assisted vehicular networks. Given the dynamic nature of the environment, the problem is formulated as a Markov decision process and tackled using Q-learning and DDQN, both prominent reinforcement learning (RL) algorithms. Additionally, a heuristic approach is introduced to augment the performance of the RL algorithms, expediting environmental learning convergence. The numerical findings underscore the effectiveness of the proposed methodologies in minimizing the aggregate AoI across all data streams.

Vehicle-to-vehicle (V2V) communications retain immense potential in elevating network throughput for next-generation vehicular applications. This study investigates the problem of maximizing the total number of communications while ensuring fairness among V2V communication pairs (MVGCF). In the experiments conducted, each vehicle has a dedicated data stream to be shared among others in the same group. However, not all pairs can directly communicate due to communication range limitations. Hence, the current study focuses on relaying data packets in networks through multi-hop vehicles sharing resource blocks within a time frame while adhering to signal-to-interference-plus-noise ratio (SINR) and half-duplex constraints. To accomplish the research objectives mentioned above, two reinforcement learning (RL) algorithms, namely Q-Learning and Double Deep Q-Networks (DDQN), are proposed. However, to overcome the scalability and computational limitations of RL methods, we devise hybrid heuristic-based reinforcement learning methods, MVGCF_QLearning and MVGCF_DDQN. The numerical results demonstrate the hybrid approaches' effectiveness in terms of the number of successful communications and max-min fairness when compared to a random_agent, and the conventional RL methods for small and large networks.

Maximizing vehicle-to-vehicle (V2V) communications have become crucial for ever-increasing demands for more complicated vehicular applications of smart cities. In this study, the problem of establishing communications between all pairs of vehicles in a group by considering relaying data packets is investigated. The objective is to maximize the total number of communications for groups of vehicles while maintaining fairness among all V2V communication pairs (MCFGV). Reusing resource blocks under the signal-to-interference-plus-noise ratio (SINR) constraint is allowed. We first mathematically formulate the MCFGV problem to find optimal solutions. Then, due to NP-hardness of the problem, we propose a scalable method to solve it for large networks. Finally, through numerical results, the proposed method is compared with the optimum solutions for small networks, and its performance on larger instances is compared to a baseline heuristic.

Internet of Things (IoT) applications, such as augmented/virtual reality, tactile Internet, immersive gaming, etc., are currently experiencing an unprecedented growth in their demand. IoT devices are constrained by limited computation and power features and might experience excessive computational latency to support resource-intensive tasks. Multiaccess edge computing (MEC) appears to be a promising solution in this regard to expedite the computations of resource-intensive tasks by offloading them to the edge of the network. This article considers a scenario where a base station (BS) serves traffic streams from multiple IoT devices. The packets from each stream arrive at the BS (following a stochastic process) and then forwarded to their respective destinations after they are processed by the MEC node. The scheduling decisions are aimed to keep the information fresh at the destination. The information freshness is captured by Age of Information (AoI) metric. We aim to minimize the expected sum AoI for the MEC-assisted IoT network and provide mathematically traceable expressions for the AoI. First, an optimization problem is formulated to find the optimal scheduling policy in order to minimize the expected sum AoI. The optimization problem is an integer linear programming (LP) problem, which is generally difficult to solve. Hence, we provide a simpler formulation of the problem and derive a more traceable expression for the expected sum AoI. With this approach, the joint impact of stochastic arrivals, scheduling policy, and unreliable channel conditions on the AoI is assessed. We also propose low-complexity algorithms to obtain results for larger networks. Finally, through extensive simulations, we demonstrate the effectiveness of our proposed methods as compared to other existing strategies in terms of achievable AoI.

Fully automated vehicles deployed with high computational/perceptive capabilities will soon become a reality. Such capabilities enable the cooperation among vehicles and the realization of interacting autonomous driving systems. Edge computing has emerged to provide a plethora of computational services to reduce network latency. Applications at the edge that apply analytics on the sensory data are therefore indispensable for self-driving vehicles. We consider in this paper a network that interconnects vehicles to an edge server at a roadside unit. Each vehicle extracts multiple information by sampling multiple processes and sends them to the corresponding edge application. To make timely decisions, “fresh” information needs to be offloaded, processed, and delivered back to vehicles; in this context, we adopt a new metric called Age of Information (AoI) that has been lately used to measure the freshness of information. We seek to jointly schedule vehicles’ transmission of information and schedule information processing at the edge to minimize the AoI of all processes. We mathematically formulate the problem and prove its NP-Hardness. To overcome this hardness, we propose a logic-based Benders decomposition to divide the problem into a master and several subproblems. Then, we present an exact polynomial-time solution for the subproblems, a scalable heuristic for the master, and devise a valid yet efficient Benders cut. We implement the system simulation on the well-known traffic simulator SUMO and compare the decomposition with CPLEX branch-and-cut; Although the problem is highly intricate, our method finds a near-optimal solution (maximum deviation is 7% from optimal solution) with a speedup that reaches 95%. We study the system performance by varying different system parameters.

Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes.

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.

The success in transitioning towards smart cities relies on the availability of information and communication technologies that meet the demands of this transformation. The terrestrial infrastructure presents itself as a preeminent component in this change. Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) are expected to become an integral component of future smart cities that provide seamless coverage for vehicles on highways with poor cellular infrastructure. Motivated by the above, in this paper, we introduce UAVs cell-free network for providing coverage to vehicles entering a highway that is not covered by other infrastructure. However, UAVs have limited energy resources and cannot serve the entire highway all the time. Furthermore, the deployed UAVs have insufficient knowledge about the environment (e.g., the vehicles' instantaneous location). Therefore, it is challenging to control a swarm of UAVs to achieve efficient communication coverage. To address these challenges, we formulate the trajectories decisions making as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning (DRL) to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the vehicular coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. Finally, simulations results are provided to verify our findings and demonstrate the effectiveness of the proposed design and show that during the mission time, the deployed UAVs adapt their velocities in order to cover the vehicles.

The Vehicle-as-a-Resource is an emerging concept that allows the exploitation of the vehicles' computational resources for the purpose of executing tasks offloaded by passengers, vehicles, or even an Internet-of-Things devices. This article revolves around a scenario where a roadside unit located at the edge of a hierarchical multitier edge computing subnetwork resorts to the utilization of idle vehicles computational resources through a fog-enabled substructure yielding a cost-effective computational task offloading solution. In this context, scheduling the offload of these tasks to the appropriate vehicles is a challenging problem that is subject to the interaction of major role-playing parameters. Among these parameters are the variability of vehicles availability and their computational power, the individual tasks' weighted priorities and their deadlines, the tasks required computational power as well as the required data to upload/download. This article proposes an infrastructure-assisted task scheduling scheme where the roadside unit receives computational tasks from different sources and schedules these tasks over a computationally capable vehicle residing within the roadside unit's range. The aim is to maximize the weighted number of admitted tasks while considering the constraints mentioned above. Compared to other works, this article broaches a more realistic scenario by considering a more accurate computational task and system model. Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. After proving the NP-hardness of the scheduling problem, we formulated the problem as a mixed-integer linear program. A Dantzig-Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solved optimally with a polynomial-time dynamic programming approach. Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of our approach compared to branch and bound and greedy algorithms.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets. Among the emerging markets, Internet of Things (IoT) use cases are standing out with the proliferation of a wide range of sensors that can be configured to continuously monitor and transmit data for intelligent processing and decision making. Devices in such scenarios are normally extremely energy-constrained and often exist in large numbers and can be located in hard-to-reach areas; the fact that necessitates the design and implementation of effective energy-aware data collection mechanisms. To this end, we propose the utilization of unmanned aerial vehicles (UAVs) to collect data in dense wireless sensor networks using projection-based compressive data gathering (CDG) as a novel solution methodology. CDG is utilized to aggregate data en-route from a large set of sensor nodes to selected projection nodes acting as cluster heads (CHs) in order to reduce the number of needed transmissions leading to notable energy savings and extended network lifetime. The UAV transfers the gathered data from the CHs to a remote sink node, e.g., a 5G cellular base station, which avoids the need for long range transmissions or multihop communications among the sensors. Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected CH nodes based on projection-based CDG with minimized UAV trajectory distance. We formulate a joint optimization problem and divide it into four complementary subproblems to generate close-to-optimal results with lower complexity. Moreover, we propose a set of effective algorithms to generate solutions for relatively large-scale network scenarios. We demonstrate the superiority of the proposed approach and the designed algorithms via detailed performance results with analysis, comparisons, and insights.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets spanning a wide range of use cases. Among these, massive IoT is standing out with the proliferation of sensors and wearable devices that continuously monitor and transmit data for further processing. This paper proposes a novel data collection technique using Unmanned Aerial Vehicles (UAVs) in dense wireless sensor networks (WSNs) using projection-based Compressive Data Gathering (CDG) as a solution methodology. CDG is utilized to aggregate data en route from sets of sensor nodes to a set of projection nodes (heads) in order to notably reduce the number of transmissions leading to energy savings and extended WSN lifetime. The UAVs forward the gathered data from heads to a remote sink to enhance efficiency by avoiding long range transmissions from heads to the sink or multi-hop communications among sensors to the sink. We formulate a joint optimization problem that captures clustering, heads selection, routing trees construction, and UAV trajectory planning. In order to overcome the complexity of the joint optimization problem, we decompose the problem into separate parts and propose a heuristic to solve each subproblem for large-scale network scenarios.

Given the inability of Mobile Cloud Computing (MCC) to guarantee the requirements of the delay-sensitive applications, Mobile Edge Computing (MEC) has been proposed to drastically reduce that latency. But since edge servers suffer from limited capabilities that offset the latency benefits in periods of high load, a hierarchical edge cloud architecture has been studied as a way to mitigate that problem. However, such model incurs different computational costs that depend on the cloudlet layer. In this paper, we jointly minimize the mobile devices' energy consumption and computational cost in a multilayered MEC, by optimizing their transmission power and the assigned server computation while respecting their latency threshold. We mathematically formulate the mixed integer non-convex program and propose an efficient algorithm based on Successive Convex Approximation (SCA) method to solve and obtain a high-quality solution. Through numerical results, we analyze different scenarios, and show the efficiency of our algorithm in providing an approximate solution that efficiently decreases the total energy consumption and computational cost.

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each for a group of sensor nodes, to collect measurements at a single sink node. The goal is to construct such trees which gather measurements in the most energy efficient manner and minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus appropriate link scheduling is required to overcome interference. We refer to this problem as Forwarding Tree Construction and Scheduling (FTCS). Each tree may be constructed independently and then its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Further, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of best forwarding trees which, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of our work is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances where no known exact solutions can be found.

Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in large scale sensor networks; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the aggregation and forwarding load across the network. With CDG, multiple forwarding trees are constructed, each for aggregating a coded or compressed measurement, and these measurements are collected at the sink for recovering the uncoded transmissions from the sensors. This paper studies the problem of constructing forwarding trees for collecting and aggregating sensed data in the network under the realistic physical interference model. The problem of gathering tree construction and link scheduling is addressed jointly, through a mathematical formulation, and its complexity is underlined. Our objective is to collect data at the sink with both minimal latency and fewer transmissions. We show the joint problem is NP-hard and owing to its complexity, we present a decentralized method for solving the tree construction and the link scheduling subproblems. Our link scheduling subproblem relies on defining an interference neighbourhood for each link and co-ordinating transmissions among network links to control the interference. We prove the correctness of our algorithmic method and analyse its performance. Numerical results are presented to compare the performance of the decentralized solution with the joint model as well as prior work from the literature.

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each to collect measurements from a group of sensor nodes at a single sink node. The goal is to construct such trees that gather measurements in the most energy efficient manner and with minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus, appropriate link scheduling is required to manage interference. We refer to this problem as forwarding tree construction and scheduling (FTCS). Each tree may be constructed independently, and then, its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Furthermore, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of the best forwarding trees that, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of this paper is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances, where no known exact solutions can be found.

We investigate the joint application of compressive sensing and network coding to the problem of energy efficient data gathering in wireless sensor networks. We consider the problem of optimally constructing forwarding trees to carry compressed data to projection nodes; each compressed data refers to a weighted aggregation of measurements from sensors collected at one projection node. Projection nodes then forward their received compressed data to the sink, which subsequently recovers the original measurements. This aggregation technique based on compressive sensing is shown to reduce significantly the number of transmissions. We observe that the presence of multiple forwarding trees gives rise to many-to-many communication patterns which in turn can be exploited to perform network coding on the compressed data being forwarded on these trees. Such technique will further reduce the number of transmissions required to gather the measurements, and consequently result in a better network-wide energy efficiency. This paper addresses the problem of network coding aware construction of forwarding/aggregation trees and we present a mathematical model to optimally construct such trees. We also develop a decentralized method for solving the problem and we show that our method is both very scalable and accurate. We also show that when both network coding and compressive data gathering are considered jointly, modest gains may be attained.

Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in large scale sensor networks; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the aggregation and forwarding load across the network. With CDG, multiple forwarding trees are constructed, each for aggregating a coded measurement, and these measurements are collected at the sink for recovering the uncoded measurements from the sensors. This paper studies the problem of constructing forwarding trees for collecting and aggregating sensed data in the network under the physical interference model. The problem of aggregation tree construction and link scheduling is addressed jointly, through a mathematical formulation, and its complexity is underlined. Our objective is to collect data at the sink with minimal delays and fewer transmissions. Owing to the complexity of the joint problem, we present a decentralized method for solving the tree construction and the link scheduling sub-problems. Our link scheduling sub-problem relies on defining an interference neighbourhood for each link and coordinating transmissions among network links to control the interference. Numerical results are presented to compare the performance of the decentralized solution with the joint model as well as prior work from the literature.

This article investigates the joint application of compressive sensing (CS) and network coding (NC) to the problem of energy-efficient data gathering in wireless sensor networks. We consider the problem of optimally constructing forwarding trees to carry compressed data to projection nodes. Each compressed dataset refers to a weighted aggregation (or sum) of sensed measurements from network sensors collected at one projection node. Projection nodes then forward their received compressed data to the sink, which subsequently recovers the original measurements. This aggregation technique, based on CS, is shown to reduce significantly the number of transmissions in the network. We observe that the presence of multiple forwarding trees gives rise to many-to-many communication patterns in sensor networks that, in turn, can be exploited to perform NC on the compressed data being forwarded on these trees. Such a technique will further reduce the number of transmissions required to gather the measurements, resulting in a better network-wide energy efficiency. This article addresses the problem of NC--aware construction of forwarding/aggregation trees. We present a mathematical model to optimally construct such forwarding trees, which encourage NC operations on the compressed data. Owing to its complexity, we further develop algorithmic methods (both centralized and distributed) for solving the problem and analyze their complexities. We show that our algorithmic methods are scalable and accurate, with worst-case optimality gap not exceeding 3.96% in the studied scenarios. We also show that, when bothNC and compressive data gathering are considered jointly, performance gains (reduction in number of transmissions) of up to 30% may be attained. Finally, we show that the proposed methods distribute the workload of data gathering throughout the network nodes uniformly, resulting in extended network life times.