Annalisa Socievole

ML-SOR

Opportunistic networks are a generalization of DTNs in which disconnections are frequent and encounter patterns between mobile devices are unpredictable. In such scenarios, message routing is a fundamental issue. Social-based routing protocols usually exploit the social information extracted from the history of encounters between mobile devices to find an appropriate message relay. Protocols based on encounter history, however, take time to build up a knowledge database from which to take routing decisions. While contact information changes constantly and it takes time to identify strong social ties, other types of ties remain rather stable and could be exploited to augment available partial contact information. In this paper, we start defining a multi-layer social network model combining the social network detected through encounters with other social networks and investigate the relationship between these social network layers in terms of node centrality, community structure, tie strength and link prediction. The purpose of this analysis is to better understand user behavior in a multi-layered complex network combining online and offline social relationships. Then, we propose a novel opportunistic routing approach ML-SOR (Multi-layer Social Network based Routing) which extracts social network information from such a model to perform routing decisions. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. Trace driven simulations show that a routing metric combining social information extracted from multiple social network layers allows users to achieve good routing performance with low overhead cost.

Wireless contacts, Facebook friendships and interests: Analysis of a multi-layer social network in an academic environment

Human mobility traces have drawn increasing attention in recent years due to their usefulness for constructing mobility models and evaluating mobile opportunistic communication systems. Even if human mobility provides us insights into the social behavior of mobile users, there is a growing awareness that human sociality is expressed simultaneously on multiple layers. The multilayered complex network composed by the social network constructed on wireless contacts and other types of social network layers needs still to be analyzed and understood in depth. In this paper, we describe the experiment we performed in a campus environment to trace the wireless contacts in terms of Bluetooth encounters, occurring between nodes inside and outside the group of experimenters carrying smartphones, and to gather the profiles, Facebook friendships, and interests of the participants. By analyzing the multilayer social network constructed on this dataset, we contribute to novel understanding of human behavior at different social dimensions. We study the relationship between offline encounters detected through mobile devices, Facebook friendships and shared interests in terms of closeness between the corresponding social graphs, matching between strong offline ties and the other social ties, and similarity between communities. We show that Bluetooth contacts network layer and Facebook friendships network layer are similar.

Evaluating the impact of energy consumption on routing performance in delay tolerant networks

Delay tolerant networks (DTNs) are sparse wireless networks where most of the time there does not exist a complete path from the source node to the destination node. In this context, where connectivity is intermittent, but where the possibility of communication is still desirable, conventional routing protocols are unsuitable to deliver messages between nodes. The design of efficient routing protocols is a fundamental problem in DTNs. Many different routing approaches for DTNs have been proposed in the literature, each one based on different characteristics and properties. In most cases, their performance have been evaluated considering aspects like delivery ratio and delivery latency, without considering energy consumption constraint. In this work we focus on the energy consumption issues of the routing protocols. We present a performance comparison of the Epidemic, Spray and Wait, PROPHET, MaxProp and Bubble Rap routing protocols with respect to energy consumption, evaluating how the energy consumption impacts the performance of the protocols and how the different forwarding algorithms affect the energy usage in the mobile devices.

Routing approaches and performance evaluation in delay tolerant networks

In this paper the problem of routing in Delay Tolerant Networks (DTNs) is considered. In these networks, where nodes are sparse and the links between them are intermittent, there is no guarantee that a path between source and destination nodes exists at any time, rendering traditional routing protocols unsuitable to deliver messages between nodes. Different DTN routing protocols for stochastic or dynamic networks, where the time-evolving topology is random and not known, are examined. These protocols depend on decisions regarding where and when to forward messages. Performance metrics such as message delivery ratio, average latency, buffer occupancy and average hop count are analyzed in order to compare Direct Delivery, First Contact, epidemic routing and the VACCINE recovery mechanism, Spray and Wait protocols, PROPHET and MaxProp. Simulations show that MaxProp and epidemic protocol combined with VACCINE are able to deliver more messages than the other protocols, ensuring also a lower delivery latency and a lower percentage of buffer occupancy.

Face-to-face with facebook friends: Using online friendlists for routing in opportunistic networks

Opportunistic networks are a generalization of Delay Tolerant Networks (DTNs) where communication is challenged by frequent disconnections and encounter patterns between mobile devices are unpredictable. Opportunistic routing protocols attempt to enable the forwarding of messages via encounters between devices, while dealing with the problems of large delays and lack of end-to-end connectivity. To save energy in mobile environments, such routing protocols must minimize unnecessary message replication. This paper presents FSF (Friendlist-based Social Forwarding), an opportunistic routing scheme that exploits both social network information detected through encounters between mobile devices and pre-existing online social network information. To select an effective forwarding node, FSF measures the forwarding capability of a node when compared to an encountered node in terms of node degree cen-trality measured on the temporal encounter network and online tie strength. Simulations with experimental datasets containing encounter records with corresponding Facebook friendlists of the participants show that the proposed routing scheme allows mobile users to achieve good delivery ratio, while forwarding fewer messages than other existing algorithms.

Exploiting online and offline activity-based metrics for opportunistic forwarding

Opportunistic networks are challenged wireless networks of handheld mobile devices that use contact opportunities to allow users to communicate without network infrastructure.The highly dynamic nature of these networks requires efficient forwarding mechanisms as disconnections are frequent and an end-to-end communication paradigm is not applicable. Consequently, many existing routing protocols for opportunistic networks make use of social behavior characteristics to perform hop-by-hop routing and select an appropriate relay node. Social network information is commonly extracted from encounters detected between mobile devices. However, Internet added online social interaction techniques which reflect user’s online behavior and are not based on physical meetings. In this paper we present a social-based forwarding strategy for opportunistic networks that exploits both offline and online user’s social network information. By proposing a model of dynamic online social network that uses information extracted from offline and online user behavior, we show that routing centrality metrics combining node centrality extracted from the dynamic online social network and centrality extracted from the social network detected through encounters between mobile devices are able to improve delivery ratio and even reduce the number of message replicas to be injected into the network.

Cyber-physical systems for Mobile Opportunistic Networking in Proximity (MNP)

h 1 The pervasive presence of mobile personal devices like smarthones, tablets, and similar smart devices, together with the masive use of online social networking services (e.g., Facebook, Twiter, Google Plus, LinkedIn, etc.) are increasingly creating a cyberhysical space where users can interact exploiting and generatng information. Enriched with several sensing capabilities and etworking interfaces, today’s portable devices are enabling new ays of communication including Mobile Networking in Proximity MNP) [1] . This network mode complements the classic scenario ith Internet coverage by enlarging the range of functionalities f these devices through short-range communications (e.g., Blueooth, Wi-Fi Direct, etc.). Even with the wide coverage of the Interet, there are still some situations where the traditional network is verloaded, unavailable, or too expensive. In such situations, when wo devices are in proximity, they could still share information by ust exploiting the meeting opportunities and activating an opporunistic hop-by-hop forwarding towards the destination. Due to the dynamic and intermittent nature of encounters beween mobile devices, MNP needs to operate under a specific netork paradigm to manage the sparse topologies often created by hese devices. Specifically, a store-carry-forward mechanism is used, hich allows mobile nodes to store messages, carry them while oving, and forward those messages to other suitable nodes (nextop or destination nodes) when encounter opportunities arise. Reent studies have shown that a social-based logic is the most romising choice for selecting the intermediate nodes to which orward a packet during an encounter [2] . The basic idea is that xploring human behavior, the meeting patterns between mobile odes can be predicted with a certain accuracy thus maximizing he chances of delivering a message to its destination. In this view, ocial-inspired mobile networking research is increasingly underining the benefits and the need of having knowledge of the social ehavior of users for the MNP-based design. On one hand, the soial behavior of mobile nodes can be extracted from the mobility f nodes. On the other hand, also online social network interacions may provide information about user’s social behavior [3] . As uch, the convergence between these two worlds, the physical ofine one made by mobile devices and the cyber online one made y the online social networks is becoming increasingly important, pening a novel and interesting research field with many challengng issues. The purpose of this special issue is to depict the current statef-the-art of the research closely related to mobile and social netorking, and in particular, to cyber-physical MNP, by reporting adP

Multi-layer sociality in opportunistic networks: an extensive analysis of online and offline node social behaviors

The large diffusion of mobile devices able to derive human sociality from wireless encounters and the growing use of online social networks is increasingly driving the research community on opportunistic networks towards social-based techniques and hence, towards the analysis of user social behavior. Within these challenged networks where node connectivity is highly intermittent and contact opportunities are exploited to communicate without network infrastructure, node mobility is basically driven by human sociality. As such, understanding the social behavior of nodes within these networks is of paramount importance, especially for finding suitable relays in message forwarding. In this paper, we focus on the analysis of a collection of multi-layer social networks derived from six different datasets containing mobility data and Facebook friendships of nodes moving in opportunistic network environments. Analyzing egocentric and sociocentric node behaviors on the opportunistic social network detected through wireless encounters and on the corresponding Facebook social network, we show that online and offline degree centralities are significantly correlated on most datasets. On the contrary, betweenness, closeness and eigenvector centralities show medium-low correlation values. Considering that opportunistic networks are highly dynamic and in the bootstrapping phase of the network having a clear social behavior of nodes in terms of offline centrality is one of the main issues, these results show that in some cases, online centrality which is easier to compute can predict offline node centrality. Moreover, we show that in most datasets, most of the strong ties correspond to Facebook friendships.

Meta-Heuristics Techniques and Swarm Intelligence in Mobile Ad Hoc Networks

The infrastructure-less and the dynamic nature of mobile ad hoc networks (MANETs) demands new set of networking strategies to be implemented in order to provide efficient end-to-end communication. MANETs employ the traditional TCP/IP structure to provide end-to-end communication between nodes. However, due to their mobility and the limited resource in wireless networks, each layer in the TCP/IP model requires redefinition or modifications to work efficiently in MANETs. One interesting research area in MANETs is routing. Routing is a challenging task and has received huge attention from researches. Due to the adaptive and dynamic nature of these networks, the Swarm Intelligence approach is considered a successful design paradigm to solve the routing problem. Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviours of insects and of other animals. In particular, the collective behaviour of ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as Ant Colony Optimization (ACO) meta-heuristic. ACO takes inspiration from the foraging behaviour of some ant species. These ants deposit a chemical substance called pheromone on the ground in order to mark some favourable path that should be followed by other members of the colony. This behaviour has led to development of many different ant based routing protocols for MANETs. In this chapter, a description of swarm intelligence approach and ACO meta-heuristic is given, an overview of a wide range of ant based routing protocols in the literature is proposed and finally other applications related to ACO in MANETs and new directions are discussed.

Simulating node selfishness in opportunistic networks

In situations where Internet connectivity is not available, opportunistic networks exploit the encounters between mobile human-carried devices for exchanging information. When people encounter each other, their handheld devices can communicate wirelessly and in a cooperative way using the encounter opportunities for forwarding their messages. However, in the real world, most of the nodes exhibit selfish behaviors mostly to conserve the limited buffer and power resources. As such, for ensuring a fair delivery, node selfishness should be taken into account. In this paper, we study the impact of node selfishness on the system throughput and delay of opportunistic communications. Through extensive simulations, we evaluate the system performances by utilizing experimental human mobility traces with different communication patterns. Our results show that the message delivery ratio decreases and the end-to-end average latency increases as the percentage of selfish nodes increases for all the routing protocols considered. However, the social-based forwarding rules are able to better manage node selfishness.