Latency-sensitive applications, such as autonomous driving in smart cities and smart industries, require a networking and computing infrastructure to support their operations. Cloud-to-edge continuum represents a promising architecture to provide computational capability close to edge devices. However, deploying latency-sensitive applications in the continuum is challenging due to the heterogeneity and the geographical distribution of the computing nodes. In this paper, we address the deployment problem in a tele-operated autonomous driving scenario, formulating the orchestration task as a Virtual Network Function Placement Problem (VNFPP) with multi-tier performance levels, enabling vertical scaling of computational resources per microservice. Our MILP model, MORAL, minimizes node centrality-based deployment costs while satisfying resource and end-to-end latency constraints. We tested our approach through extensive simulations on realistic network topologies and synthetic applications, showing that the proposed model improves deployment feasibility, latency compliance, and resource efficiency compared to single performance tier versions and baseline strategies.

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems.

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations.

The fifth generation (5G) of mobile network offers a remarkable degree of flexibility to mobile operators, enabling them to provide users with effective and tailored network services. Software Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing have given the operator the opportunity to easily bring computational capacity to the edge and to support latency-sensitive services. While 5G standards have defined the technological and architectural frameworks to orchestrate services, finding effective resources management and QoS optimization policies is still an open research issue. In this paper, we propose an online orchestration methodology for a multi-user edge service. The orchestrator goal is to simultaneously maximize the QoS, and minimize the amount of resources needed. We provide a mathematical formulation to compute an optimal offline policy and derive an online approach based on a model-free Deep Reinforcement Learning (DRL) framework. As a novel feature, the DRL agent action is modeled as a parametric combinatorial problem. A tailored multi-objective reward function leads the agent towards an effective choice of parameters for such a model. Our models are built, trained and fine-tuned by exploiting real data. Extensive simulations in diverse scenarios show that our DRL online approach produces solutions with small gaps to the optimal offline ones, enabling the operator to both save resources and grant the users an adequate QoS level.

This paper presents an approach to create high fidelity Digital-Twin models for distributed multi-agent cyber-physical systems based on the combination of simulating components, generated from different modeling languages, each tailored for the specific domain of the subsystem. The approach specifically addresses the wireless communication domain, exploiting a Python module as a simulating component to evaluate the impact of network delay among the distributed elements of the system under analysis. A case study with a platoon of four vehicles following a leading car, all modeled in Simulink, is used to show the applicability of the approach, allowing the comparison between a Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm.

Platooning of cars or trucks is one of the most relevant applications of autonomous driving, since it has the potential to greatly improve efficiency in road utilization and fuel consumption. Traditional proposals of vehicle platooning were based on distributed architectures with computation on board platoon vehicles and direct vehicle-to-vehicle (V2V) communications (or Dedicated Short Range Communication - DSRC), possibly with the support of roadside units. However, with the introduction of the 5G technology and of computing elements at the edge of the network, according to the multi-access edge computing (MEC) paradigm, the possibility emerges of a centralized control of platoons through MEC, with several significant advantages with respect to the V2V approach. For this reason, in this paper we investigate the feasibility of vehicle platooning in a centralized scenario where the control of vehicle speed and acceleration is managed by the network through its MEC facilities, possibly with a platooning-as-a-service (PaaS) paradigm. Using a detailed simulator, we show that, with realistic values of latency and packet loss probability, large platoons can be effectively controlled by MEC hosts.

We present an approach to the discovery and characterization of relevant locations and related mobility patterns in symbolic trajectories built on call detail records - CDRs - of mobile phones (telco trajectories). While the discovery of relevant locations has been widely investigated for continuous spatial trajectories (e.g., stay points detection methods), it is not clear how to deal with the problem when the movement is defined over a discrete space and the locations are symbolic, noisy and irregularly sampled, such as in telco trajectories. In this paper, we propose a methodological approach structured in two steps, called trajectory summarization and summary trajectories analysis, respectively, the former for removing noise and irrelevant locations; the latter to synthesize key mobility features in a few novel indicators. We evaluate the methodology over a dataset of approx 17,000 trajectories with 55 million points and spanning a period of 67 days. We find that trajectory summarization does not compromise data utility, while significantly reducing data size. Moreover, the mobility indicators provide novel insights into human mobility behavior.

A well-known result in social science states that, although people maintain a large number of social relationships, in practice, their interactions are concentrated mostly on a small portion of their neighbors. According to Dunbar's hypothesis, this phenomenon is due to a limited human cognitive capacity to handle too many social relationships, together with a few time for socializing. These constraints result in an organization of people's ego-network into four or five groups depending on the strength of the interactions, the so called Dunbar's circles. The verification of the Dunbar's hypothesis, the identification of social circles and their characterization are still open questions, although researchers have found evidence of its validity on different social networks, from small offline networks to online social networks, such as Twitter and Facebook. In addition, little is known about the semantic aspects of the circles, i.e. who are the members of each circle. In this paper, we cope with these issues by analyzing a mobile phone graph where people's interactions are expressed by both voice calls and text messages. We firstly compare two methods for the identification of the circles, which rely on the different definitions of strength proposed in literature. Both methods confirm the subdivision of ego-networks into four or five circles, each characterized by a specific interaction strength. Then, we validate the Dunbar's hypothesis and, by leveraging some powerful features of the mobile phone dataset in use, we provide a first semantic characterization of social circles. We show, for instance, that people maintain relationships with the members of the closest circles by combining calls and texts. In addition, by detecting home and work locations of each individual, we highlight that a semantic aspect, such as the role of the alters, impacts on the ego-network circles. In particular, family members and workmates are located in different circles: the former mainly form the closest circle, i.e. the support clique, while the latter are distributed among the outer circles.

The dichotomy between two opposite propensities, exploration and exploitation, characterizes and drives many human behaviors, from decision making to social learning. Recently, this dichotomy has been found also in human mobility where people can be divided into two basic types: returners, those who are very regular in their daily mobility; and explorers, those who are inclined to break out of their daily mobility routine and explore new places. While the former attitude has been widely studied in literature and results in the well-known tendency to frequently visit a few locations (e.g. home and workplace), the latter trait remains an unexplored aspect and deserves further investigation. In this work we focus on the characterization of the places that an individual visits when she is driven by her propensity for exploration, i.e. non-routine places which are outside her usual daily mobility patterns. To this end, we mine an anonymized mobile phone dataset which integrates call, text and data activities of about one million subscribers in Milan, to detect and characterize the non-routine places. Moreover, we complement it with Foursquare venues along with their category to semantically characterize the reasons driving the choice of the places to explore. The analysis of the non-routine places and the mobility patterns during the exploration phase brings to light some interesting findings: i) to a greater or lesser extent, all individuals are explorers since they visit a significant number of non-routine places; ii) due to the exceptionality of a visit, non-routine places are farther from home and workplace than frequently visited places; iii) we are explorers in our leisure time; iv) we get to a non-routine place leaving our home, then we return home later; and v) in Milan, shopping, in particular at fashion and clothing stores, is the main interest behind the need to explore non-routine places.

Within a 5G mobile network scenario, this paper adopts Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technological frameworks to evaluate the gain in terms of user Quality of Experience (QoE) that can be achieved when contents and server of a video delivery service move from the cloud at the edge of the Radio Access Network. The application scenario we set up envisions a video streaming service geared a small group of individuals who have strong social ties and who move in an urban space. The scaling up of this emergent type of social interaction is set to become in the near future highly demanding for the resources of mobile operators. The paper recreates the network infrastructure of a mobile operator in the city of Milano. We model network conditions through Mininet, and manage (computing, network and storage) resources by means of an orchestrator named OpenVolcano. The paper shows that, compared to the traditional cloud approach, a MEC one provides better QoE to group members simply taking advantage both of the proximity and distribution of mini data centers and of proper resource orchestration.

The city's social structure is mainly based on the activities and behavior of small groups of people whose cohesion is preserved by their tight social relations. The interactions that people have among themselves and with the places/services of the city ensure strength and duration to these social ties. Among the many media at disposal today to carry on social activities, on-phone and off-line interactions are those mainly involved in the creation of the strongest social links. In this paper we leverage mobile data to detect groups of people with strong ties and to identify the urban places where they are used to gather. We apply this methodology to the city of Milan, by means of a large anonymized dataset of Call Detail Records (CDRs) collecting phone activities (calls, texts, and Internet traffic) of about 1 million mobile users. The paper shows that mobile phone data enables to detect socially cohesive groups gathering in city's places.

This paper presents a simple policy to immediately reduce the overall core network's interpersonal voice/data traffic of nearly 15% on average in highly populated urban areas. This remarkable result can be achieved without resorting to complementary radio technologies, but by adding a few basic functionalities that simply rely on enabling the mobile operator to be aware of the fact that people are interacting by mobile phone in proximity. As a first contribution of this paper we give empirical evidence of the role played by these short-range social interactions through an extensive analysis of the large anonymized datasets of Call Detail Records (CDR) of two different mobile operators. Then, we describe a NFV/SDN-based approach that a mobile operator should adopt to detect whether a voice/text activity is directed to a nearby person, to negotiate with adjacent cells the establishment of the communication channel and to divert the traffic over it to offload the core network.

Unlike virtual sociality, in their daily social behavior individuals are used to communicate with a limited number of persons and periodically meet their inner social circle in specific city locations to perform common social activities. Physical encounters among a restricted number of people interestingly give rise to a significant amount of in-proximity voice/data traffic on the cellular network and advocate the provisioning of a new class of services supporting it. This paper gives empirical evidence of the role played by these location-centered social interactions through the extensive analysis of a large anonymized dataset of Call Detail Records (CDR) relying on the phone activities of nearly 1 million people in the city of Milano. The analysis and understanding of these human interactions have inspired the design of a new mobile service that detects, after user's consent, proximity with a person in my inner social circle and autonomously deploys the mobile social network supporting proximity interactions. The approach we propose brings together a few important contributions: first, it concretely shows that the current NFV-enabled trend of placing cloud services at the edge of the operator's network has a payoff in terms of traffic offloading and improved user's experience; secondly, it demonstrates for the first time that a few typical cloud-based services can actually be directly performed by the mobile network operator by simply leveraging the rich amount of data they possess and never exploit.

In this paper we focus on mobile data offloading and propose a solution for the placement of offloading infrastructures that optimizes the trade-off between reducing deployed resources and increasing traffic breakout. We base our analysis on a real dataset of Internet accesses generated, in the city of Milano, by some 50,000 users of an important mobile network operator. The target application we consider is the distribution of digital contents (such as MP3 songs, videos and newspapers) over an urban area. The paper's contributions is showing that offloading of digital contents can be achieved by leveraging people's regular mobility patterns.

In these last years the growing availability of personal mobile devices has boosted the demand for mobile services offered based on location and context. Mobile gaming services are, of course, no exception. Unfortunately, the 3G networks infrastructure struggles to keep pace due to scalability limitations and a pull-based service model. One feasible solution, also for gaming, is to adopt an Opportunistic Network (ON) to exploit unplanned contact opportunities between players to perform data exchange and forwarding in ad hoc mode. When designing a game for an ON, the main issue is about forecasting the feasibility of a given gameplay when applied to a real - and complex - environment. In this paper we address this issue proposing an opportunistic game that is meaningful for the category of collaborative puzzle games. The game is evaluated by means of simulations on the topology of a real city in order to understand its scalability and setback on the gameplay.