In recent years, electric scooters (e-scooters) have rapidly spread all over the world, becoming a new major paradigm of micro- mobility. Their success can be mainly attributed to their easiness of riding and parking, providing an agile, cheap and sustainable option for last-mile trips. Following this success, many e-scooter sharing companies started to operate in many cities. However, the rapid diffusion of these sharing services has not only brought evident benefits, but also critical issues: a substantial part of the users has soon shown a tendency to ride and park mindless of road rules, causing safety risks for riders and pedestrians and reducing urban decorum. Consequently, municipalities have introduced bans, fines and stricter regulations. In this work, we study the reformed e-sharing scooter system of Rome, the Italian capital city, following the introduction of new regulations in 2023, after the end of the experimental phase started in 2019. Major changes have been a) reducing the number of operators to only three, b) limiting the size of fleets (decreasing the total number of deployed e-scooter by 40%), c) extending services to outer districts of the city. All the changes were aimed to favour a closer control of the sharing services, safer rides and more well-ordered parking, plus a fairer access to services also in suburbs. In our GIS-based study, we focus on identifying and analyzing the service coverage in this new Rome setting, in terms of the geofences defined by the operators. After having spatially identified the service areas, we analyze their new features, especially in terms of the new fairer distribution over the territory. We also analyze how service areas and geofences have evolved over time, in order to reflect modifications and updates in the regulations passed by the municipality.

Time-Sensitive Networking (TSN) is a toolbox of technologies that enable deterministic communication over Ethernet. A key area has been TSN's time-aware traffic shaping (TAS), which supports stringent end-to-end latency and reliability requirements. Configuration of TAS requires the computation of a network-wide traffic schedule, which is particularly challenging with integrated wireless networks (e.g., 5G, Wi-Fi) due to the stochastic nature of wireless links. This paper introduces a novel method for configuring TAS, focusing on cyclic traffic patterns and jitter of wireless links. We formulate a linear program that computes a network-wide time-aware schedule, robust to wireless performance uncertainties. The given method enables robust scheduling of multiple TSN frames per transmission window using a tunable robustness parameter (Γ). To reduce computational complexity, we also propose a sequential batch-scheduling heuristic that runs in polynomial time. Our approach is evaluated by using different network topologies and wireless link characteristics, demonstrating that the heuristic can schedule 90

Dense and resource-constrained wireless networks, such as the Internet of Things (IoT), pose significant challenges for Medium Access Control (MAC) protocols in addressing communication collisions and Quality of Service (QoS) issues. Interference and message collisions within communication channels are primary culprits behind network faults and energy wastage. Recent research has explored methods for enabling large-scale networks to communicate efficiently, utilizing local information and avoiding collisions through distributed and parallel distance-2 TDMA (Time-Division Multiple Access) coloring. However, a critical aspect that has been largely unexplored is how highly active nodes can utilize the time slots of less active neighbors without message exchanges, thereby enhancing network performance, especially in broadcasting emergency messages. In this paper, we introduce a distributed protocol designed to enhance communication and energy efficiency in wireless networks by effectively utilizing underused time slots from neighboring nodes. Our protocol enables more active nodes to efficiently recover these underused time slots, facilitating the rapid broadcast of urgent messages. We conclude by presenting and discussing experimental results to demonstrate the efficacy of our proposed solution.

Electric scooter sharing mobility services have recently spread in major cities all around the world. However, the bad parking behavior of users has become a major source of issues, provoking accidents and compromising urban decorum of public areas. Reducing wild parking habits can be pursued by setting reserved parking spaces. In this work, we consider the problem faced by a municipality that hosts e-scooter sharing services and must choose which locations in its territory may be rented as reserved parking lots to sharing companies, with the aim of maximizing a return on renting and while taking into account spatial consideration and parking needs of local residents. Since this problem may result difficult to solve even for a state-of-the-art optimization software, we propose a hybrid metaheuristic solution algorithm combining a quantum-inspired ant colony optimization algorithm with an exact large neighborhood search. Results of computational tests considering realistic instances referring to the Italian capital city of Rome show the superior performance of the proposed hybrid metaheuristic.

The passage to a second generation of broadcasting Single Frequency Networks has generated the need for reconfiguring and redesigning existing networks. In this work, we present a robust optimization model for the green design of such networks based on the digital television DVB-T2 standard. Our robust model pursues protection against uncertainty of signal propagation in a complex real-world environment. As reference model, we adopt Multiband Robustness and we propose to solve the resulting model by a hybrid metaheuristic that combines mathematically strong formulations of the optimization model, with an exact large neighborhood search. We report computational tests based on realistic instances, showing that the multiband model grants highly protected solutions without reducing service coverage and without leading to a high price of robustness.

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

In recent years, electric scooters (e-scooters) have become a major successful expression of micro and shared mobility and have widely spread across the world. Because of this ample success, numerous e-scooter sharing companies have started their business in an increasing number of cities. A typical company puts a fleet of vehicles at disposal of its users, allowing to rent them by a smartphone application and paying a per-minute fee. Each company identifies a service area, namely a portion of the territory of the city where shared e-scooters may be rented, ridden and parked. The service area is commonly provided under the form of a geofence, namely a virtual limit that can be set through Global Positioning System (GPS) technology and is drawn on the map provided by the smartphone application of the company. In this work, we study the e-scooter sharing systems that have been created by a number of private companies in Rome, the capital city of Italy. Specifically, we focus on identifying and analyzing the service coverage guaranteed by the companies through their service areas and the overlap of areas managed by different sharing companies. We also provide an overview of the e-scooter sharing regulation of Rome, identifying some first recommendations that could lead to a more effective deployment of e-scooter sharing services.

Notwithstanding the introduction of brand new 5G-based wireless services, single frequency networks supporting digital television and radio broadcasting still represent a major source of telecommunications services in modern smart cities. In this work, we propose a robust optimization model for the green design of second generation single frequency networks based on the digital television DVB-T standard, whose ongoing adoption requires to reconfigure and redesign existing networks. Our robust model aims at protecting design solutions against the data uncertainty that naturally affect propagation of signals in a real environment. For reducing conservatism of solutions, we refer to a heuristic min-max regret paradigm and to solve the resulting problem we propose to adopt a hybrid exact-heuristic algorithm based on the combination of an Ant Colony Optimization-like learning procedure, exploiting tight formulations of the optimization model, with an exact large neighborhood search. Results of computational tests considering realistic instances show that the heuristic min-max regret approach can produce solutions characterized by a substantially lower price of robustness without sacrificing protection against data uncertainty.

Small Cells (SCs) mounted on top of Unmanned Aerial Vehicles (UAVs) can be used to boost the radio capacity in hotspot zones.However, UAV-SCs are subject to tight battery constraints, resulting in frequent recharges operated at the ground sites.To meet the UAV-SCs energy demanded to the ground sites, the operator leverages a set of Solar Panels (SPs) and grid connection.In this work, we demonstrate that both i) the level of throughput provided to a set of areas and ii) the amount of energy that is exchanged with the grid by the ground sites play a critical role in such UAV-aided cellular network.We then formulate the J-MATE model to jointly optimize the energy and throughput through revenue and cost components.In addition, we design the BBSR algorithm, which is able to retrieve a solution even for large problem instances.We evaluate J-MATE and BBSR over a realistic scenario composed of dozens of areas and multiple ground sites, showing that: i) both J-MATE and BBSR outperform previous approaches targeting either the throughput maximization or the energy minimization, and ii) the computation time and the memory occupation of BBSR are reduced up to five orders of magnitude compared to J-MATE.

Electric scooter (e-scooter) sharing has recently known a wide success in many cities all around the world. Nevertheless, it has also generated issues due to risky and improper behaviour of its users. Wild parking, namely parking without caring about the rules of the road, has in particular become a major issue and has induced an increasing number of cities to impose bans and fines to e-scooter sharing. To tackle wild parking, we introduced the figure of the beautificator, an agent hired by a sharing company with the specific task to reposition e-scooters for guaranteeing urban decorum. In this paper, we propose to increase the effectiveness of the beautificators by integrating Unmanned Aerial Vehicles (UAVs) in their activities: remotely controlled UAVs equipped with cameras are deployed to fly across the sharing service area for identifying e-scooters that require beautification with priority. Thanks to the UAVs, the beautificators do not have to operate blindly, touring locations of parked e-scooters without knowing their parking condition, but can readily learn which e-scooters require their immediate attention. We formulate the problem of optimally scheduling the joint actions of beautificators and UAVs, taking into account beautification constraints, battery limits of UAVs and the possibility of swapping exhausted UAV batteries. For tackling this problem, we propose a mixed integer programming model and a heuristic for accelerating the convergence to the optimum of a state-of-the-art optimization solver, reporting results of computational tests over realistic instances.

In recent years, many electric scooters (e-scooters) sharing companies have appeared around the world. However, a major issue that has soon become apparent is that a consistent part of the users is prone to park the e-scooters without caring about the rules of the road, abandoning them in locations and positions that greatly reduce urban decorum and may interfere with pedestrians and other vehicles. To cope with the issue of bad parking and to not compromise acceptance of e-scooters by city residents, some sharing companies have started to include correcting the position of wrongly parked scooters as an important part of their operations. In this work, we address the problem of optimally managing the actions of a set of agents who are hired by a sharing company expressly for repositioning e-scooters in order to guarantee urban decorum. We call these agents beautificators, since their fundamental task is to reposition scooters over short distances (even just a few meters), so to fix inappropriate and disordered parking made by users. We stress that such repositioning must not be confounded with traditional relocation made in vehicle-sharing systems to rebalance fleets in the service area: rebalancing is made over medium and long city distances and is primarily aimed at guaranteeing a balanced distribution of vehicles in the service area, better satisfying the demand and increasing the overall profit. To the best of our knowledge, such optimization problem has not yet been considered in literature and we propose to model it by Integer Linear Programming and solve it by means of a matheuristic, which offers a good performance on realistic data instances defined in collaboration with e-scooter sharing professionals.

Bidding in the day-ahead market encompasses uncertainty on market prices. To properly address this issue, dedicated optimal bidding models are constructed. Traditionally, these models have been derived for generating units, in particular thermal generators. Recently, optimal bidding models have been updated to account for specifics of energy storage, foremost battery storage. Batteries are significantly different devices than generators. On one hand, a battery can both purchase and sell electricity with practically instant change in its output power. On the other hand, a battery is energy-limited, which makes its profit very sensitive to optimal scheduling. In this paper, we examine the existing and derive new robust optimization-based optimal bidding models individually for a thermal generator and a battery storage. The models are examined in terms of the expected profit by applying the obtained bidding curves and (dis)charging schedules to actual realizations of uncertainty. Moreover, we examine the effect of the range of uncertainty caused by the selection of input scenarios. Based on the presented case studies, we form conclusions on the effectiveness of the robust optimization approach for this type of problems.

Because of the introduction and spread of the second generation of the Digital Video Broadcasting—Terrestrial standard (DVB-T2), already active television broadcasters and new broadcasters that have entered in the market will be required to (re)design their networks. This is generating a new interest for effective and efficient DVB optimization software tools. In this work, we propose a strengthened binary linear programming model for representing the optimal DVB design problem, including power and scheduling configuration, and propose a new matheuristic for its solution. The matheuristic combines a genetic algorithm, adopted to efficiently explore the solution space of power emissions of DVB stations, with relaxation-guided variable fixing and exact large neighborhood searches formulated as integer linear programming (ILP) problems solved exactly. Computational tests on realistic instances show that the new matheuristic performs much better than a state-of-the-art optimization solver, identifying solutions associated with much higher user coverage.

Carsharing represents a major example of smart mobility service that allows a customer to rent a vehicle for a limited amount of time paying a per-minute fee. It may relieve people of the costly and non-sustainable burden of owning a car, especially when residing in a city. Though the spread of carsharing may bring significative benefits to (smart) cities, its penetration can be obstructed by non-up-to-date regulations, which can be still tied to a non-smart vision of mobility. In this study, we provide an overview of remarkable city regulations for carsharing, particularly highlighting the importance that parking policies can have in favouring the diffusion and use of carsharing services. Given such importance, we characterize the optimization problem of a local government that wants to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. To model and solve the problem we propose a new Binary Linear Programming problem and genetic-based matheuristic. Finally, we present results from computational tests referring to realistic data of the Italian city of Rome, showing that our optimization approach can return a fair territorial distribution of the parking slots, satisfying various families of constraints limiting the distribution.

This paper addresses the problem of communication in resource-limited broadcast/receive wireless networks. In large scale and resource-limited wireless networks, such as the Internet of Things (IoT), a massive amount of data is becoming increasingly available. Therefore, implementing protocols achieving error-free communication channels presents an important challenge. Indeed, in this new kind of network, the prevention of message conflicts and message collisions is a crucial issue. In terms of graph theory, solving this issue amounts to solve the distance-2 coloring problem on the network. This paper presents a first study on dynamic management in distance-2 coloring in resource-limited wireless networks. We propose a distributed distance-2 coloring in a dynamic network where (one) new node can join the network. Our protocol assigns to the new node a correct color without re-running the whole algorithm of time slot assigning. Our protocol is time-efficient and uses only local information with a high probability.

Smart mobility systems represent a new generation of transport systems that are strongly supported by information and communications technologies, allowing a continuous connection between the system administrators, the customers/users, the transport infrastructures and the vehicles. A major example of these systems is represented by carsharing. Carsharing can relieve people from the costly and non-sustainable burden of owning a car, especially when residing in a city. Furthermore, it can reduce pollution and traffic congestion and has been worldwide recognized as a fundamental component of smart cities by policy-makers In this study, we provide an overview of relevant regulations for carsharing, highlighting in particular the importance of parking policies. Given this importance, we propose a mathematical optimization model that can be used by a local government to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. We test the model on realistic data of the city of Rome, showing that we can obtain a fair territorial distribution of the parking slots that satisfies population needs. The data were defined on the basis of our collaboration with professionals of the electric utility company Enel within E-Go Car Sharing, an electrical vehicle carsharing service established at the University Roma Tre.

We address the question of defining a robust optimization approach to model and solve a DVB-T network design problem, while taking into account the uncertainty that naturally affects the propagation of wireless signals. The robust counterpart of the Mixed Integer Linear Programming model that represents the design problem exploits multiband uncertainty, a cardinality-constrained uncertainty model that employs multiple deviation bands. Since the robust counterpart may prove challenging to solve also for state-of-the-art optimization solvers, we propose a matheuristic for its solution. The matheuristic combines a variable fixing procedure exploiting suitable (tight) linear relaxations of the model with exact large neighborhood search. Results of computational tests considering realistic instances are reported to assess the performance of the approach, showing that the matheuristic can generate solutions of higher value than a commercial optimization solver within the available time budget.