Emerging in December 2019, the COVID-19 pandemic caused widespread health, economic, and social disruptions. Rapid global transmission overwhelmed healthcare systems, resulting in high infection rates, hospitalisations, and fatalities. To minimise the spread, governments implemented several non-pharmaceutical interventions like lockdowns and travel restrictions. While effective in controlling transmission, these measures also posed significant economic and societal challenges. Although the WHO declared COVID-19 no longer a global health emergency in May 2023, its impact persists, shaping public health strategies. The vast amount of data collected during the pandemic offers valuable insights into disease dynamics, transmission, and intervention effectiveness. Leveraging these insights can improve forecasting models, enhancing preparedness and response to future outbreaks while mitigating their social and economic impact. This paper presents a large-scale case study on COVID-19 forecasting in Cyprus, utilising a two-year dataset that integrates epidemiological data, vaccination records, policy measures, and weather conditions. We analyse infection trends, assess forecasting performance, and examine the influence of external factors on disease dynamics. The insights gained contribute to improved pandemic preparedness and response strategies.Clinical relevance—This study relies on anonymised, aggregated epidemiological data, including total infections, hospitalisations, ICU admissions, and deaths, rather than individual patient tracking. Our findings could potentially contribute to healthcare by improving forecasting models that help hospitals anticipate surges, allocate resources more efficiently, and prevent system overload. By analysing the effects of policy interventions and external factors such as weather conditions, this research may provide valuable insights for refining public health strategies. Beyond COVID-19, our approach could be used to enhance infectious disease forecasting, supporting proactive decision-making in future outbreaks.

Unmanned Aerial Vehicles (UAVs) have become essential in search and rescue operations, especially in disaster management scenarios. Their effective navigation and the integration of a plethora of sensors assist in efficient person detection, making them an essential technological tool to first responders. Multi-UAV systems extend these benefits by using coordinated strategies to cover large areas efficiently, reducing overall mission response time and enhancing its success. Despite these advantages, challenges remain in ensuring the safety, security, and dependability of (mutli-)UAV missions. Issues such as navigation risks, potential cyber threats, and hardware-/software-related reliability issues can impact the mission results. Additionally, UAVs are highly constrained devices with limited battery capacity, requiring the use of lightweight technologies. In this paper, we present part of the results of the SESAME project, an EU multi-partner project that aims to develop safe and secure multi-robot Systems. In particular, we present some of the developed SESAME Executable Digital Dependability Identities (EDDI) technologies based on Markov models, statistical distance measures, and other advanced approaches for enhancing safety, security and dependability of the UAV platform and underlying models. These EDDI technologies are seamlessly integrated using the ConSerts framework in a multi-UAV platform and tested using search and rescue scenarios. The results demonstrate significant improvements in multi-UAV safety, with an availability rate of 91% and a search and rescue algorithmic accuracy of 99.8%. Additionally, the system achieves precise detection of spoofing attacks, using collaborative localization as a mitigation technique to guide the UAV to a safe landing, even in the absence of GPS signals,

The crowd counting task plays a key role in ensuring public safety during large gatherings events. Most prominent works in this area, use large and computationally demanding deep learning model architectures, which require substantial computational power, limiting their usage in a real-world scenario under resource constraints. In this work we consider the trade-off between the model’s predicted accuracy and computational speed. We propose an improved version of HR-Net, which is substantially smaller and faster than the original, but preserves its localization and counting performance. Through targeted removal of unnecessary modules and branches, we demonstrate an increase in frames-per-second by 37.71% on an Nvidia Jetson Orin, and a reduction of GMACs and parameters by 77.41% and 73.07% respectively, while retaining competitive localization and counting performance, specifically for aerial imagery scenarios. Our modifications enable the algorithm to process in real-time higher resolution images, which is crucial when dealing with small objects. Furthermore, because most crowd counting datasets contain random images gathered from the web, and limited aerial images of crowds, we introduce a specialized dataset of high-resolution aerial imagery for sparse and dense crowds in various environments, that contains new drone-captured annotated image data.

Rapid urbanization trends have resulted in a significant increase in global urban population over the past few decades. This growth is projected to continue, further exacerbating traffic congestion in road networks. Urban air mobility presents a promising solution to alleviate this congestion, offering substantial environmental, economic, and societal benefits. However, to fully realize these benefits, autonomous flight and automation are crucial for safely managing the high density of aircraft expected in urban airspaces.To address this challenge, this work proposes an innovative model predictive control (MPC) framework for generating ondemand, safe 3D trajectories for fleets of unmanned aerial vehicles (UAVs) operating in dense urban environments. Specifically, it introduces a near real-time MPC method designed to enhance the airspace’s capacity to accommodate an increasing number of flights within a confined area, while consistently adhering to stringent safety standards. An extensive simulation study is conducted to demonstrate the effectiveness of the proposed framework in planning safe, on-demand trajectories in complex urban settings.

Unmanned aerial vehicles (UAVs) are increasingly relied upon in a variety of critical applications, including infrastructure inspection, search-and-rescue, and traffic monitoring. While modern UAVs are typically equipped with global positioning system (GPS), inertial measurement unit (IMU) modules, and often include safeguards against adverse environmental conditions, they remain susceptible to sensor malfunctions and signal disruptions. These challenges have led to the need for robust, GPS-free solutions capable of maintaining accurate trajectory prediction and state identification. This work proposes a real-time multi-task learning framework for UAVs that employs Transformer-based neural networks to perform simultaneous trajectory prediction and state identification. The proposed system enables GPS-free UAV operations with the employment of a feature-based visual odometry algorithm that is implemented and fused with telemetry data to achieve accurate localization. The proposed system is implemented (hardware and software modules) in a functional prototype and validated through extensive outdoor experiments using a custom-built dataset, demonstrating strong performance and improved prediction accuracy in GPS-denied environments. The results demonstrate the framework’s ability to enhance the autonomy and reliability of UAV systems in challenging operational scenarios.

In recent years, emergency response systems have been employed extensively in critical operations, particularly for disaster management scenarios. During these operations, robust and efficient communication is crucial to ensure that first responders can obtain critical information for coordination and adaptation to the dynamic conditions of an emergency. The employment of unmanned aerial vehicles (UAVs) in conjunction with Internet of Things (IoT) devices can provide support and enable the transmission and reception of relevant information in emergency situations. A number of different communication methods have been explored to address communication performance and robustness issues that arise during emergencies. This work tackles these issues by developing and evaluating the performance of an integrated LoRa (long-range communication)-based system for UAV-to-X communications. For evaluation purposes, a LoRa-based prototype system is designed and implemented to achieve robust, real-time, and efficient communication for both static and mobile nodes in indoor and outdoor environments. The prototype of the proposed communication architecture is subsequently tested in a real-world environment, demonstrating the feasibility and effectiveness of the proposed solution in terms of communication range, transmission latency and security for the applications investigated.

This work proposes a jointly optimized trajectory generation and camera control approach, enabling an autonomous agent, such as an unmanned aerial vehicle (UAV) operating in 3D environments, to plan and execute coverage trajectories that maximally cover the surface area of a 3D object of interest. Specifically, the UAV's kinematic and camera control inputs are jointly optimized over a rolling planning horizon to achieve complete 3D coverage of the object. The proposed controller incorporates ray-tracing into the planning process to simulate the propagation of light rays, thereby determining the visible parts of the object through the UAV's camera. This integration enables the generation of precise look-ahead coverage trajectories. The coverage planning problem is formulated as a rolling finite-horizon optimal control problem and solved using mixed-integer programming techniques. Extensive real-world and synthetic experiments validate the performance of the proposed approach.

Current research on robust trajectory planning for autonomous agents aims to mitigate uncertainties arising from disturbances and modeling errors while ensuring guaranteed safety. Existing methods primarily utilize stochastic optimal control techniques with chance constraints to maintain a minimum distance among agents with a guaranteed probability. However, these approaches face challenges, such as the use of simplifying assumptions that result in linear system models or Gaussian disturbances, which limit their practicality in complex realistic scenarios. To address these limitations, this work introduces a novel probabilistically robust distributed controller enabling autonomous agents to plan safe trajectories, even under non-Gaussian uncertainty and nonlinear systems. Leveraging exact uncertainty propagation techniques based on mixed-trigonometric-polynomial moment propagation, this method transforms non-Gaussian chance constraints into deterministic ones, seamlessly integrating them into a distributed model predictive control framework solvable with standard optimization tools. Simulation results demonstrate the effectiveness of this technique, highlighting its ability to consistently handle various types of uncertainty, ensuring robust and accurate path planning in complex scenarios.

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.

This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities. While current interception technologies focus mainly on the jamming/spoofing tasks, the challenges of effectively locating and tracking rogue drones have not received adequate attention. Solving this problem and integrating with recently proposed interception techniques will enable a holistic system that can reliably detect, track, and neutralize rogue drones. Specifically, this work considers a team of pursuer UAVs that can search, detect, and track multiple rogue drones over a sensitive facility. The joint search and track problem is addressed through a novel multiagent reinforcement learning scheme to optimize the agent mobility control actions that maximize the number of rogue drones detected and tracked. The performance of the proposed system is investigated under realistic settings through extensive simulation experiments with varying number of agents demonstrating both its performance and scalability.

The wide adoption and use of unmanned aerial vehicles (UAVs) has created not only opportunities but also threats to the security of sensitive areas. Thus, effective and efficient counter-drone systems are required to protect these areas. This work tackles this issue by developing cooperative multi-agent jamming techniques using reinforcement learning (RL) to counter the operation of one or multiple rogue drones flying over a sensitive area. The aim of the proposed RL approach is to optimize the joint mobility and power control actions of the pursuer UAVs in order to maximize the received jamming power at the rogue drones aiming at disrupting communication links and sensing circuitry, while at the same time keeping the interference to surrounding pursuer agents below a predefined threshold. The effectiveness of the proposed approach in terms of scalability, learning speed, and agents' final joint performance is demonstrated through extensive simulation experiments for various agent and target configurations.

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform’s user interface the location traces and associated images collected during a real field exercise.

Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets.

This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target maneuvers in 3D space, avoiding collisions with obstacles and other 3D structures in its way, according to a stochastic dynamical model. Based on this, a track-and-jam control approach is proposed which allows a team of distributed aerial agents to decide their control actions online, over a finite planning horizon, to achieve uninterrupted radio-jamming and tracking of the malicious target, in the presence of jamming interference constraints. The proposed approach is formulated as a distributed model predictive control (MPC) problem and is solved using mixed integer quadratic programming (MIQP). Extensive evaluation of the system's performance validates the applicability of the proposed approach in challenging scenarios with uncertain target dynamics, noisy measurements, and in the presence of obstacles.

In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D). It is assumed that the agents can enter and exit the mission space at any point in time, and as a result the number of agents that actively participate in the mission varies over time. The proposed distributed search-planning framework takes into account the agent dynamical and sensing model, and the dynamically varying number of agents, and utilizes model predictive control (MPC) to generate cooperative search trajectories over a finite rolling planning horizon. This enables the agents to adapt their decisions on-line while considering the plans of their peers, maximizing their search planning performance, and reducing the duplication of work.

With the rise of the number of unauthorized operations of Unmanned Aerial Vehicles (UAVs), versatile counter-drone systems are becoming a necessity. In this work, a counter-drone system is developed, where a pursuer drone employs algorithms for detecting and tracking a rogue drone, in conjunction with wireless interception capabilities to jam the rogue drone, while also jointly achieving self positioning for the pursuer drone. In the proposed system, a software-defined-radio (SDR) is used for switching between jamming transmissions and spectrum sweeping functionalities to achieve the desired GPS disruption and self-localization, respectively. Extensive field experiments demonstrate the effectiveness of the proposed solution in a real-world environment under various parameter settings.

Identifying flight patterns in unmanned aerial vehicle (UAV) operations is a critical function, especially when UAVs are used in safety-critical missions. UAVs are used in various applications and their increased penetration in the market over the recent past has motivated investigations of their safety and security. For instance, due to their highly-non-linear dynamics it is inherently quite difficult to monitor and quickly and reliably identify a change in their behaviour or a fault in their sensors. Clearly, not being able to detect changes in the operations could cause a failure or event the complete loss of a UAV.To avoid such events, in this work we propose a mechanism for detecting the operational flight modes of a flying UAV based on onboard sensor data. We study and analyze two complementary approaches to identify these flight modes and evaluated their performance on real data gathered from various flights performed by a multirotor UAV. We concentrated our study on developing models that are both fast and accurate so that they could be executed at run-time and produce reliable results.

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs’ gas consumption. Contrary to most of the works in the literature, the CAVs’ location uncertainty is accounted for and CAVs controls are re-optimized periodically, allowing for the optimization of system performance while creating safe trajectories. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT), which reaches a better trade-off among performance and computational and communication complexity. In a realistic simulation scenario, AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.

In this work, we propose a joint route guidance and demand management strategy for multi-region networks with macroscopic traffic dynamics. Route guidance is used to identify the optimal transfer flows between neighbouring regions so that the trip completion rate across all regions is maximized. Demand management is utilized to control the traffic flows entering the network by forcing a portion of the traffic flows to wait at their origin. Towards this direction, we develop a Model Predictive Control (MPC) framework that aims to minimize the total time spent by all vehicles in the network (including the waiting time at the origin) by jointly optimizing the demand flows allowed in the network and the transfer flows between regions. To solve the resulting nonconvex and nonlinear optimization problem, by relaxing the nonconvex constraints, we develop a novel Linear Programming formulation that provides tight lower bounds on the optimal solution, as well as a feasible solution through the proposed MPC framework. Furthermore, (in another formulation) we restrict each region to only operate in the free-flow regime of the macroscopic fundamental diagram, which enables the transformation of the problem to a linear MPC formulation which can be solved in real-time using standard solvers and which provides a feasible solution to the original MPC problem. Extensive simulation results demonstrate that the linear MPC schemes execute in real-time and yield near-optimal results even under heavy traffic scenarios.

In this work, the travel path of a set of drones is scheduled across a graph, where the nodes need to be visited multiple times at pre-defined points in time. The nodes can either be demand nodes requesting monitoring, or supply nodes that are used as take-off/landing locations for the drones and for battery replacement to cope with the limited flying range of the drones. This is an extension of the well-known multiple traveling salesman problem and the proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, the monitoring of remote locations to assist search and rescue missions, or the monitoring of critical infrastructure facilities for security and surveillance purposes. Aiming to find the optimal schedule, the problem is initially formulated as an Integer Linear Program (ILP). However, given that the problem is highly combinatorial, the optimal solution scales only for small-size problems. Thus, a greedy algorithm is also proposed that uses a one-step look-ahead heuristic search mechanism, as well as an algorithm that is based on ant colony optimization (ACO). In a detailed evaluation, it is observed that both algorithms achieve near-optimal performance for small settings, while also scaling to larger settings, with the ACO being more suitable for medium-size settings and the Greedy for larger ones. A field experiment is additionally performed to demonstrate the practical implementation of the proposed system under real-world conditions.

To date, a large body of the literature has looked into the problem of autonomous intersection crossings facilitated by Connected Autonomous Vehicles (CAVs). Nevertheless, existing approaches assume that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized intersection management, which considers vehicle location uncertainties for linear-Gaussian systems. Building upon the proposed framework, a family of$0-1$integer linear programming optimizations are presented that can set, sequentially or simultaneously, the acceleration profiles of all vehicles in the intersection. Extensive simulation results are presented, proving that the proposed framework represents a real-time near-optimal approach that maximizes intersection throughput with probabilistic collision avoidance guarantees.

Drones, including remotely piloted aircraft or unmanned aerial vehicles, have become extremely appealing over the recent years, with a multitude of applications and usages. However, they can potentially present major threats for security and public safety, especially when they fly across critical infrastructures and public spaces. This work investigates a novel counter-drone solution by proposing a multi-agent framework in which a team of pursuer drones cooperate in order to track and jam a rogue drone. Within the proposed framework, a joint mobility and power control solution is developed to optimize the respective decisions of each cooperating agent in order to best track and intercept the moving rogue drone. Both centralized and distributed variants of the joint optimization problem are developed and extensive simulations are conducted to evaluate the performance of the problem variants and to demonstrate the effectiveness of the proposed solution.

This work proposes a novel distributed control framework in which a team of pursuer agents equipped with a radio jamming device cooperate in order to track and radio-jam a rogue target in 3D space, with the ultimate purpose of disrupting its communication and navigation circuitry. The target evolves in 3D space according to a stochastic dynamical model and it can appear and disappear from the surveillance area at random times. The pursuer agents cooperate in order to estimate the probability of target existence and its spatial density from a set of noisy measurements in the presence of clutter. Additionally, the proposed control framework allows a team of pursuer agents to optimally choose their radio transmission levels and their mobility control actions in order to ensure uninterrupted radio jamming to the target, as well as to avoid the jamming interference among the team of pursuer agents. Extensive simulation analysis of the system’s performance validates the applicability of the proposed approach.

Connected and Autonomous Vehicles (CAVs) are expected to become a reality on roads in the near future bringing significant social, economic, and environmental benefits. Cooperation and coordination among CAVs will be enabled through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Each vehicle knows its current location and in many cases will have to communicate this information to its associated Roadside Unit (RSU). With the proliferation of CAVs, the RSU is expected to receive and process a large amount of feedback information from its assigned CAVs. Thus, an event-triggered communication scheme is proposed instead of conventional approaches where communication takes place in a periodic manner. Further, the effect of age of information on the resulting accuracy of the vehicle tracking error is taken into account by considering the message queue wait time at the RSU. A family of optimization problems is proposed, used to determine the optimal accuracy threshold for the event-triggered algorithm, so as to minimize the effect of the tracking error caused by the queue wait time.

Trajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology.

The global navigation satellite system (GNSS) is primarily employed for positioning by most modern navigation systems. However, the application requirements of fully autonomous vehicles cannot be satisfied solely by GNSS and thus a combination of positioning and navigation approaches need to be explored. This work investigates how reliable relative positioning can be achieved in GNSS-challenged application scenarios using a combination of signals of opportunity (SOPs), as well as inertial and vision data. The proposed cooperative relative positioning system (CRPS) exploits this data for real-time positioning, and employs a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time without the use of any GNSS information. Experiments conducted in an outdoor setting demonstrate the applicability of the proposed CRPS and its performance against standalone positioning approaches including GNSS.

In this article, we propose a novel deep reinforcement learning (DRL) approach for controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking multiple first responders (FRs) in challenging 3-D environments in the presence of obstacles and occlusions. We assume that the UAVs receive noisy distance measurements from the FRs which are of two types, i.e., Line of Sight (LoS) and non-LoS (NLoS) measurements and which are used by the UAV agents in order to estimate the state (i.e., position) of the FRs. Subsequently, the proposed DRL-based controller selects the optimal joint control actions according to the Cramér–Rao lower bound (CRLB) of the joint measurement likelihood function to achieve high tracking performance. Specifically, the optimal UAV control actions are quantified by the proposed reward function, which considers both the CRLB of the entire system and each UAV's individual contribution to the system, called global reward and difference reward, respectively. Since the UAVs take actions that reduce the CRLB of the entire system, tracking accuracy is improved by ensuring the reception of high quality LoS measurements with high probability. Our simulation results show that the proposed DRL-based UAV controller provides a highly accurate target tracking solution with a very low runtime cost.

Over the last few years there has been an unprecedented interest in unmanned aerial vehicles (UAVs). However, drones potentially pose great threats to security and public safety, especially when their malicious use involves critical infrastructures and public spaces. This work proposes a multiagent counter-drone system where a team of pursuer drones cooperate in order to track and jam multiple rogue drones. Specifically, a cooperative multi-agent approach is proposed in which the best joint mobility and power control actions of each agent are chosen so that the rogue drones are optimally tracked and jammed over time. Two variants of the joint optimization problem are developed and extensive simulations are conducted so as to evaluate the performance of the proposed approach.

Intersections are among the most challenging sections of our road infrastructure and a clear bottleneck for traffic flows. Key aspects of the 5G cellular network, e.g., the Multi Access Edge Computational (MEC) platform and the reduced network latency, act as enablers for the utilization of Connected Autonomous Vehicles (CAVs) that can ultimately bring about drastic changes in the management of intersection crossings and transportation networks in general. To date, there exist extensive research on the problem of autonomous intersection crossings facilitated by CAVs, but the majority of these works assumes that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized Intersection Manager (IM) that also considers vehicle location uncertainties. Building upon the proposed optimization framework, AVOID is presented, a real-time, near-optimal algorithm that maximizes intersection throughput with probabilistic collision avoidance guarantees. Extensive simulations assess the performance of AVOID, in terms of safe distance between vehicles and intersection throughput, and the effects of the update communication frequency (from the vehicles to the IM) on the gains of the proposed framework.

This work investigates the problem of simultaneous tracking and jamming of a rogue drone in 3D space with a team of cooperative unmanned aerial vehicles (UAVs). We propose a decentralized estimation, decision and control framework in which a team of UAVs cooperate in order to a) optimally choose their mobility control actions that result in accurate target tracking and b) select the desired transmit power levels which cause uninterrupted radio jamming and thus ultimately disrupt the operation of the rogue drone. The proposed decision and control framework allows the UAVs to reconfigure themselves in 3D space such that the cooperative simultaneous tracking and jamming (CSTJ) objective is achieved; while at the same time ensures that the unwanted inter-UAV jamming interference caused during CSTJ is kept below a specified critical threshold. Finally, we formulate this problem under challenging conditions i.e., uncertain dynamics, noisy measurements and false alarms. Extensive simulation experiments illustrate the performance of the proposed approach.

Drones or Unmanned Aerial Vehicles (UAVs) have become a reliable and efficient tool for road traffic monitoring. Compared to loop detectors and bluetooth receivers (with high capital and operational expenditure), drones are a low-cost alternative that offers great flexibility and high quality data.In this work, we derive optimized tour plans that a fleet of drones can follow for rapid traffic monitoring across particular regions of the transportation network. To derive these tours, we first identify monitoring locations over which drones should fly through and then compute minimum travel-time tours based on realistic resource constrains. Evaluation results are presented over a real road network topology to demonstrate the applicability of the proposed approach.

Efficient traffic monitoring is playing a fundamental role in successfully tackling congestion in transportation networks. Congestion is strongly correlated with two measurable characteristics, the demand and the network density that impact the overall system behavior. At large, this system behavior is characterized through the fundamental diagram of a road segment, a region or the network. In this paper we devise an innovative way to obtain the fundamental diagram through aerial footage obtained from drone platforms. The derived methodology consists of 3 phases: vehicle detection, vehicle tracking and traffic state estimation. We elaborate on the algorithms developed for each of the 3 phases and demonstrate the applicability of the results in a real-world setting.

For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically all modern positioning systems cannot satisfy this requirement for heighten autonomy levels and positioning is becoming a decisive factor for their proliferation. This work investigates how relative positioning can be achieved using signals that are already accessible in the environment, and derives an online procedure for the exploitation of these signals for localization in GNSS-challenged areas. The proposed relative positioning system (RPS) explores the signal properties over a large spectrum of frequency bands, and derives a vehicle tracking algorithm to accurately estimate the vehicle's trajectory in space and time using an arbitrary set of unknown reference positions. Experimental results demonstrate the applicability of RPS and investigate its performance over the different parameter values.

In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents. The targets appear and disappear at random times inside a surveillance region and their positions are random and unknown. The agents have limited sensing range and receive noisy measurements from the targets. A decision and control problem arises, where the mode of operation (i.e., search or track) as well as the mobility control action for each agent, at each time instance, must be determined so that the collective goal of searching and tracking is achieved. We build our approach upon the theory of random finite sets (RFS) and we use Bayesian multi-object stochastic filtering to simultaneously estimate the time-varying number of targets and their states from a sequence of noisy measurements. We formulate the above problem as a non-linear binary program (NLBP) and show that it can be approximated by a genetic algorithm. Finally, to study the effectiveness and performance of the proposed approach we have conducted extensive simulation experiments.

Accurate arrival-time predictions in public transportation systems can improve the perceived quality-of-service offered and increase usage of these systems. To date, predictions were primarily based on periodic updates of mobility information that however exhibit a tradeoff between deterministic performance and system efficiency. To further improve on this tradeoff, event-triggering is emerging as a promising operations paradigm. This work describes an innovative design of a public transportation tracking system within which arrival-time predictions are made utilizing an event-triggering framework. As a first step towards this direction, behavior models are derived through extensive analysis of real mobility data. Thereafter, an event-triggering algorithm is developed to detect changes in the model in an online fashion. The efficiency and applicability of the proposed data-driven event-triggering paradigm is demonstrated through a real-world transit scenario that compares event triggering updating techniques.

Secure logging is essential for the integrity and accountability of cyber-physical systems (CPS). To prevent modification of log files the integrity of data must be ensured. In this work, we propose a solution for secure event in cyber-physical systems logging based on the blockchain technology, by encapsulating event data in blocks. The proposed solution considers the real-time application constraints that are inherent in CPS monitoring and control functions by optimizing the heterogeneous resources governing blockchain computations. In doing so, the proposed blockchain mechanism manages to deliver events in hard-to-tamper ledger blocks that can be accessed and utilized by the various functions and components of the system. Performance analysis of the proposed solution is conducted through extensive simulation, demonstrating the effectiveness of the proposed approach in delivering blocks of events on time using the minimum computational resources.

Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.

Supporting effective load balancing is paramount for increasing network utilization efficiency and improving the perceivable user experience in emerging and future cellular networks. At the same time, it is becoming increasingly alarming that current communication practices lead to excessive energy wastes both at the infrastructure side and at the terminals. To address both these issues, this paper discusses an innovative communication approach enabled by the implementation of device-to-device (d2d) communication over cellular networks. The technique capitalizes on the delay tolerance of a significant portion of Internet applications and the inherent mobility of the nodes to achieve significant performance gains. For delay-tolerant messages, a mobile node can postpone message transmission-in a store-carry and forward manner-for a later time to allow the terminal to achieve communication over a shorter range or to postpone communication to when the terminal enters a cooler cell, before engaging in communication. Based on this framework, a theoretical model is introduced to study the generalized multihop d2d forwarding scheme where mobile nodes are allowed to buffer messages and carry them while in transit. Thus, a multiobjective optimization problem is introduced where both the communication cost and the varying load levels of multiple cells are to be minimized. We show that the mathematical programming model that arises can be efficiently solved in time. Furthermore, extensive numerical investigations reveal that the proposed scheme is an effective approach for both energy-efficient communication and offering significant gains in terms of load balancing in multicell topologies.

Vehicular connectivity is considered as one of the most highly anticipated emerging technologies since it promises to transform the automotive sector and have a significant impact on all related markets. Data gathered (and information generated) within and around vehicles will be used to improve road safety, travelling efficiency, and passenger comfort and convenience. However, delivering such data to the infrastructure (to process information and generate intelligence) is a challenging task, mainly due to the very large volume of data traffic produced. A promising approach to support these communication needs is to deliver data traffic opportunistically through the available WiFi APs. Evidently, the intermittent connectivity of these hotspots and the inherent mobility of the vehicles severely limit the volume of traffic sent at any one instance in time. The latter limitation is studied in this paper, where decision policies are derived for vehicle-to-vehicle-assisted offloading to maximize the transmission opportunities and thus expedite data traffic delivery. As illustrated in this paper, these policies are easy to implement in practice and offer significant improvement in vehicular data traffic offloading as compared with opportunistic offloading and basic relaying practices.

In the realm of cooperative Intelligent Transportation Systems, vehicles are able to communicate with each other and with the available telecommunications infrastructure to support various safety-related services, traffic-efficiency solutions, and a wide variety of infotainment applications. As the number of mobile devices and information they need to exchange steadily increases, this will further strain mobile networks. It is for that reason that communication over wireless local area networks (WLANs) is becoming the primary means of transporting data to/from vehicles. However, due to their limit range WLANs only offer intermittent connectivity, and due to node mobility the established connections are only available for a short period of time. The present paper investigates how inter-vehicle communication can help minimize the upload/download delivery times in the context of delay tolerant traffic and intermittent communication. To do so, each vehicular station distributes part of its message to neighboring nodes prior to accessing the infrastructure. In turn, vehicles transmit their buffered messages when passing by the infrastructure terminals to complete the file transfer. This paper presents a thorough study of the aforementioned strategy providing a mathematical framework and optimized forwarding techniques. Performance results validate the applicability of the proposed solution.

This work is concerned with the problem of efficient and intelligent message forwarding in wireless networks. This problem arises in many diverse scenarios within ad-hoc networks and especially networks formed during and in the aftermath of a disaster in which infrastructure-based communication systems have been damaged or completely destroyed. Within this setting, mobile devices need to support critical message exchanges in order to offer user reassurance and aid first responders' search-and-rescue operations. Notably, the dissemination of alert messages has to be done in a way that achieves sufficient dissemination while ensuring network longevity. Under the proposed explore and exploit (EnE) framework, this paper derives innovative networking heuristics that capitalizes on locally-calculated metrics (including the Local Connectivity (LC) centrality metric) to make message forwarding/replication decisions. The proposed heuristics exhibit excellent features with regards to the aforementioned performance objectives and are shown to greatly outperform current popular alternative solutions.

The provision of reliable location estimates in WiFi fingerprinting localization is challenging, mainly because users typically carry heterogeneous devices that report Received Signal Strength (RSS) measurements from surrounding Access Points (AP) very differently. This may render the user-carried device incompatible with the fingerprinting system, in case the RSS radiomap was collected with a different device, thus incurring high localization errors. To this end, we introduce a novel differential fingerprinting method that computes the difference between the RSS value of each AP and the mean RSS value across all APs in the original fingerprint. We show that the new fingerprints are robust to device heterogeneity, as opposed to traditional RSS fingerprints. In addition, we derive analytical results and demonstrate with simulations and experimental data that the proposed approach performs considerably better than existing differential fingerprinting solutions, in terms of localization accuracy and computational overhead.

In this paper, store-carry and forward (SCF) decision policies for relaying within the cell are developed. The key motivation of SCF relaying stems from the fact that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. More specifically, we show how the actual mobility of relay nodes can be incorporated as an additional resource in the system to achieve savings in the required communication energy levels. To this end, we provide a mathematical programming formulation on the aforementioned problem and find optimal routing and scheduling policies to achieve maximum energy savings. By investigating structural properties of the proposed mathematical program we show that optimal solutions can be computed efficiently in time. The tradeoffs between energy and delay in the system are meticulously studied and Pareto efficient curves are derived. Numerical investigations show that the achievable energy gains by judiciously storing and carrying information from mobile relays can grow well above 70 percent for the macrocell scenario when compared to a baseline multihop wireless relaying scheme that uses shortest path routes to the base station.

Long range multi-hop wireless broadband services are an alternative to satellite for the provision of Internet connectivity. In a maritime network scenario, they can potentially offer solutions close to ports or relatively narrow water channels; over such a multi-hop network one could enable various services ranging from simple file transfers to real-time communication. Concentrating on the latter case, which poses the biggest challenges, we develop and evaluate strategies for dynamic and predictive network resource allocation in order to support Voice over IP. We analyse how the ability to monitor the mobility of seaborne vessels through the Automatic Identification System provides us with a critical advantage in anticipating topology changes. The algorithms developed maintain high resource utilization levels but also offer significant enhancements in Quality of Service by withholding resources for arriving vessels in order to satisfy their imminent data traffic demands.

Cellular networks are currently facing significant challenges as mobile Internet access adoption continues to grow over the subscriber base. The challenge that network operators are facing is that Internet data traffic consumes considerably more resources than voice calls, necessitating in that respect significant capacity enhancements. Further, this rise in system utilization has caused a considerable increase in the energy consumption expenditure of both the access network components and the user terminals; in the former case causing a significant increase in cost, for the second case a reduction in the usability of battery operated terminals and for both cases a peak in the carbon footprint of telecoms equipment. Significant research effort has been placed recently in finding innovative solutions to support this boost in data usage demand. In this paper we detail a message forwarding strategy for cellular networks whereby capitalizing on the inherent delay tolerance of Internet type services and utilizing the mobility of nodes, intelligent information forwarding decisions can be made to achieve substantial reductions in the communication energy consumption. We devise optimal look-ahead strategies in which information on either the uplink/downlink is communicated only at the best locations within the system's coverage area to achieve a required performance target. Both analytical and experimental results are presented, showing that significant reductions on the aggregate energy consumption levels can be achieved by the proposed technique.

It is well known that the Internet infrastructure is experiencing an ever growing demand for large file/data transfers. To date, Delay Tolerant Networking (DTN) seems as the only economically viable over-the-sea communications paradigm to support bulk data transfers between seagoing vessels and the fixed Internet. In this work, we construct an architectural prototype of this DTN overlay to connect sailing vessels to the fixed Internet infrastructure. The proposed architecture integrates elements of the existing vessel Automatic Identification System (AIS) to fetch and manipulate mobility and trip related data of every vessel that could come into close contact (at any future instance in time) with a destination node and therefore assist in message forwarding. Considering both current and future contact opportunities all within the same optimization strategy allows for the maximization of routing performance. Ultimately, it is shown that the proposed architecture allows for computationally efficient routing solutions to be generated with variable optimization objectives. As an initial assessment of the performance of the routing functions of this architecture we illustrate the optimal minimum delay routing paths that deliver messages from an arbitrary infrastructure node to a target destination mobile host.

With the tremendous increase in mobile data traffic, system capacity considerations are no longer the primary and only concern to optimize for in cellular networks. The step increase in utilization of cellular networks not only has shorten the recharging cycle of mobile terminals but has further caused a considerable rise in the operators' energy bill. It has therefore become imperative for the sustainable proliferation of such systems to reduce the energy waste and maintain low operation energy cost. As we discuss in the sequel, mechanical relaying is purposefully envisioned to achieve the required performance gains that need to be realized in order to keep up with the exponential increase in data traffic demand. Via mechanical relaying, mobile nodes are able to postpone message communication while in transit and initiate communication only when found at locations within the cell with favorable channel gains. We show that such a scheme offers the possibility to realize innovating relaying strategies that reduce many-fold the system energy consumption and increase the resource utilization efficiency. In this work, both centralized and decentralized solutions that employ mechanical relaying are considered.

We bring to the fore a novel load balancing technique based on delay tolerant message forwarding that relies on the store-carry and forward paradigm by utilizing the mobility of vehicular nodes in multi-cell wireless networks. Considerations are made not only on the achievable load balancing performance but also on the en route energy consumption. That accounts for the optimal trade-offs between load balancing, communication energy consumption and message delivery delay in the cellular network. A mathematical program is formulated for finding optimal forwarding decision policies for the proposed store-carry and forward (SCF) relaying scheme for load balancing. Furthermore, a low complexity on-line algorithm for the proposed network setup is derived and its performance is compared with the optimal solution. To sharpen the understanding of the proposed message forwarding techniques, a wide set of numerical investigations are presented revealing that by trading-off message delivery delays, the variance of the load across cells can be dramatically reduced.

The integration of mobile relays in cellular networks has been recently envisioned as a way of increasing system capacity, coverage and reducing communication energy consumption. In we studied how store carry and forward (SCF) relaying within the cell can be utilized as an underlay message forwarding mechanism to achieve system wide energy savings. In this paper, by deriving optimal routing policies to reduce either the total energy consumption or the total transmit power, we show that the SCF relaying scheme reduces considerably inter-cell interference. We detail the factors affecting the maximum interference reductions, including the delay tolerance of elastic data traffic and flow characteristics of vehicles in the road network which in this case are used as mobile relay nodes.

In this paper we address the issue of store-carry and forward (SCF) relaying within a cell in a mobile network. The proposed scheme can be considered as a generalization of various multihop wireless relaying schemes, where storing and carrying an information message by a mobile relay node is not allowed. The key motivation of utilizing SCF relaying within the cell is that energy consumption levels can be dramatically reduced by capitalizing on the inherent mobility of nodes and the elasticity of Internet applications. In that respect, we consider a novel multihop cellular architecture to achieve energy savings both at the network side (i.e., Base Stations) and at the user terminals. The proposed scheme makes use of mobility information of relays (vehicles) while roaming inside the cell to device flows that could achieve maximum energy savings. We show that under SCF relaying large energy savings can be achieved by tolerating a controlled delay over the initiated service.