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.

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.

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today's cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to characterize in detail traffic hotspots in today's cellular networks using real data.

In the era of pervasive mobile computing, human encounters can be leveraged to enable new forms of social interactions mediated by the personal devices of individuals. In this framework, emerging needs, such as content dissemination, social discovery and question&answering, advocate the raising of novel communication paradigms where the binding content-recipients is not provided by the sender (in the classical IP addressing style), but directly executed by specific recipients with interest on it. This allows tagged contents to be freely advertised on the network according to a content-driven approach; human encounters drive the information towards potential recipients that extract it from the stream when content type and personal interest match. This very active research area has recently produced a few preliminary solutions to this networking problem; they inherently confine message delivery inside a specific location and/or community. This covers only a part of users needs, as emerging from everyday life experience and recent studies in human sciences. This paper proposes a novel communication protocol, named InterestCast, or ICast, solving the problem for a wide range of social scenarios and applying to a delay tolerant ad hoc network whose nodes are the personal device of moving individuals, possibly interacting with fixed road-side devices. The protocol is able to chase users interests decoupling content tags from locations and social communities. The main advantages the proposal achieves are: it ensures remarkable performance results; it is simple and, thus, it is feasible and keeps computational and networking costs low; it preserves users privacy.

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.

ABSTRACT The concepts of location and community are rapidly becoming key points in the design of new communication paradigms and in deploying emerging mobile computing services. The need of reliable and quantitative knowledge and predictions of some relevant information, such as which locations are enjoyed by people in their daily lives and how people aggregate within communities, advocates a realistic mobility model able to describe both the human mobility throughout locations and the human attitude to socialize within communities. Unfortunately, so far, neither the concept of location nor the concept of community has been univocally defined. In this paper, we approach the problem from the most basic of starting points, namely by analyzing the real Global Positioning System datasets of human mobility traces. On this elementary basis, the paper provides a few relevant contributions. We firstly derive a deep understanding of the term “location” and at the same time of the notion of community strictly related to it. Secondly, we merge the two concepts into what we call geo‐community. By proceeding from real spatial data rather than from a priori reasonings, we are able to quantitatively describe geo‐communities and infer the probability distributions of all the features of human behavior. Finally, not to lose social implications, we present the method to derive people sociality from geo‐communities. Copyright © 2012 John Wiley & Sons, Ltd.

Rank-based policies represent a promising approach for designing message forwarding algorithms that meet the needs of opportunistic networks. In fact, they combine low computation and communication costs with good performance in terms of both latency and delivery rates. Nonetheless, they highly depend on the mobility scenario relevant to the user, and a forwarding policy with good performances in heterogeneous settings has yet to be designed. In this paper, we propose to provide each mobile device with novel autonomic observation and reasoning components according to the following objectives: enable the device (i) to achieve awareness about the behavior of the mobility scenario it is moving in, and (ii) to identify the role played by the device within the set of other moving devices. These components are combined into a self-configuring forwarding algorithm that uses them to locally install both the utility function and the relevant settings suitable for the sensed configuration. Through of extensive simulations, this paper shows that by properly discriminating between roles it is possible to derive a self-configuring forwarding mechanism that constantly performs well in different mobility settings.

Mobile computing services are becoming highly personalized and influenced by user location, social attitudes and interests. This trend is often synthesized with the term behavior-awareness. Behavioral services characterize most of the applications of Opportunistic Networks and advocate a single communication paradigm: multicast. Despite that, multicast is less studied than unicast and broadcast. We believe that there is not correspondence between problem relevance and research attention. This paper focuses on the multicast best effort service in opportunistic networks with the aim of better understanding the problem and defining the guidelines for the design of a novel multicast protocol.

We introduce a wait-and-chase scheme that models the contact times between moving agents within a connectionist construct. The idea that elementary processors move within a network to get a proper position is borne out both by biological neurons in the brain morphogenesis and by agents within social networks. From the former, we take inspiration to devise a medium-term project for new artificial neural network training procedures where mobile neurons exchange data only when they are close to one another in a proper space (are in contact). From the latter, we accumulate mobility tracks experience. We focus on the preliminary step of characterizing the elapsed time between neuron contacts, which results from a spatial process fitting in the family of random processes with memory, where chasing neurons are stochastically driven by the goal of hitting target neurons. Thus, we add an unprecedented mobility model to the literature in the field, introducing a distribution law of the intercontact times that merges features of both negative exponential and Pareto distribution laws. We give a constructive description and implementation of our model, as well as a short analytical form whose parameters are suitably estimated in terms of confidence intervals from experimental data. Numerical experiments show the model and related inference tools to be sufficiently robust to cope with two main requisites for its exploitation in a neural network: the nonindependence of the observed intercontact times and the feasibility of the model inversion problem to infer suitable mobility parameters.

The study of complex networks led to the belief that the connectivity of network nodes generally follows a Power-law distribution. In this work, we show that modeling large-scale online social networks using a Power-law distribution produces significant fitting errors. We propose the use of a more accurate node degree distribution model based on the Pareto-Lognormal distribution. Using large datasets gathered from Facebook, we show that the Power-law curve produces a significant over-estimation of the number of high degree nodes, leading researchers to erroneous designs for a number of social applications and systems, including shortest-path prediction, community detection, and influence maximization. We provide a formal proof of the error reduction using the Pareto-Lognormal distribution, which we envision will have strong implications on the correctness of social systems and applications.

The differentiated services model is the emerging standard to provide Quality-of-Service (QoS) support for multimedia applications in the future Internet. This model involves bandwidth broker agents performing admission control and network configuration functionalities. A great deal of effort has been recently devoted to investigate viable approaches to the implementation of mechanisms that automatically perform the bandwidth broker functions, yet no standard policy has been proposed so far. In this paper we propose a distributed measurement-based protocol that performs admission control functionalities for multicast traffic in diff-serv networks. The protocol supports dynamic changes of the multicast group membership, operates on-demand, and supports the premium service. We prove that the proposed protocol performs an effective and efficient admission control function.

We describe a reliable broadcast protocol for mobile multihop packet radio networks.The reliable broadcast service ensures that all the hosts in a group deliver the same set of messages to the upper layer.It represents the building block to construct fault tolerant distributed applications,, whose demand is growing in a mobile environment too.The protocol is intended for use in networks where the host mobility arbitrarily changes the network connectivity and where the rate of change is not so fast as to impose flooding as the only option.In this context, our protocol is a iirst attempt to show that a mechanism more flexible than spanning tree and more efficient than flooding is possible and a service such as the reliable broadcast can be obtained.The protocol is constructed on top of a multi-hop, multicluster architecture.It provides an exactly once message delivery semantics and tolerates communication failures and host mobility.Temporaneous disconnections and network partitions are also tolerated under the assumption that they are eventually repaired.

A multicast protocol that is independent of the subnetwork and can operate on the top of a basic datagram network is described. The protocol provides reliable communication among the members of a group nd guarantees that each message sent to a group G is delivered to all active, i.e. both correct and faulty, processes in G or to none of them, and that all the members of G consistently decide on the same progressive order to process messages. The focus is mainly on the design of the protocol. The protocol architecture to which it belongs is described, and some simulation results are given. An outline of the protocol algorithm, which uses a centralized approach based on the rotating coordinator paradigm, allows processes to asynchronously decide on a given value, and recovers processes from failures through history buffers, is also given.>

In this paper we present a solution to the causal reliable multicast problem. User processes generate separate sequences of messages and specify the causal relation among them according to some application need; the algorithm ensures that the messages within the same sequence are delivered to all active, i.e. both correct and faulty, processes in the group, or to none of them, and are processed according to their causal order. Messages belonging to different sequences can be concurrently processed.This problem has few solutions presented in literature; in common with a part of them, the algorithm we describe has the centralized approach and the use of history buffers to recover from omission failures. The differences mainly concern the mechanism we devised to recover from crash failures, that avoids resorting to specialized protocols. As a consequence, under failure conditions, the algorithm performs better than other proposals in terms of both network load and throughput without affecting the performances under reliable conditions. Further, it allows to implement the most general interpretation of causality and it does not require any particular service to the underlying transport protocol.