Franck Legendre

WiFi-Opp: ad-hoc-less opportunistic networking

Opportunistic networking offers many appealing application perspectives from local social-networking applications to supporting communications in remote areas or in disaster and emergency situations. Yet, despite the increasing penetration of smartphones, opportunistic networking is not feasible with most popular mobile devices. There is still no support for WiFi Ad-Hoc and protocols such as Bluetooth have severe limitations (short range, pairing). We believe that WiFi Ad-Hoc communication will not be supported by most popular mobile OSes (i.e., iOS and Android) and that WiFi Direct will not bring the desired features. Instead, we propose WiFi-Opp, a realistic opportunistic setup relying on (i) open stationary APs and (ii) spontaneous mobile APs (i.e., smartphones in AP or tethering mode), a feature used to share Internet access, which we use to enable opportunistic communications. We compare WiFi-Opp to WiFi Ad-Hoc by replaying real-world contact traces and evaluate their performance in terms of capacity for content dissemination as well as energy consumption. While achieving comparable throughput, WiFi-Opp is up to 10 times more energy efficient than its Ad-Hoc counterpart. Eventually, a proof of concept demonstrates the feasibility of WiFi-Opp, which opens new perspectives for opportunistic networking.

Social Trust in Opportunistic Networks

Opportunistic networks enable mobile users to participate in various social interactions with applications such as content distribution and micro-blogs. Because of their distributed nature, securing user interactions relies rather on trust than hard cryptography. Trust is often based on past user interactions such as in reputation systems relying on ratings. Yet, a more fundamental trust, social trust - assessing a user is genuine with honest intentions - must be established beforehand as many identities can be created easily (i.e., sybils). By leveraging the social network structure and its dynamics (conscious secure pairing and wireless contacts), we propose two complementary approaches for social trust establishment: explicit social trust and implicit social trust. Complexity, trust propagation and security issues are evaluated using real world complex graphs, synthetic mobility models and mobility traces. We show how our approach limits the maximum number of sybils independently of the network size and is more robust against manipulation attacks compared to state-of-the-art approaches such as PGP- like certification chains and distributed community detection algorithms.

Putting contacts into context: mobility modeling beyond inter-contact times

Realistic mobility models are crucial for the simulation of Delay Tolerant and Opportunistic Networks. The long standing benchmark of reproducing realistic pairwise statistics (e.g., contact and inter-contact time distributions) is today mastered by state-of-the-art models. However, mobility models should also reflect the macroscopic community structure of who meets whom. While some existing models reproduce realistic community structure - reflecting groups of nodes who work or live together - they fail in correctly capturing what happens between such communities: they are often connected by few bridging links between nodes who socialize outside of the context and location of their home communities. In a first step, we analyze the bridging behavior in mobility traces and show how it differs to that of mobility models. By analyzing the context and location of contacts, we then show that it is the social nature of bridges which makes them differ from intra-community links. Based on these insights, we propose a Hypergraph to model time-synchronized meetings of nodes from different communities as a social overlay. Applying this as an extension to two existing mobility models we show that it reproduces correct bridging behavior while keeping other features of the original models intact.

A complex network analysis of human mobility

Opportunistic networks use human mobility and consequent wireless contacts between mobile devices, to disseminate data in a peer-to-peer manner. To grasp the potential and limitations of such networks, as well as to design appropriate algorithms and protocols, it is key to understand the statistics of contacts. To date, contact analysis has mainly focused on statistics such as inter-contact and contact distributions. While these pair-wise properties are important, we argue that structural properties of contacts need more thorough analysis. For example, communities of tightly connected nodes, have a great impact on the performance of opportunistic networks and the design of algorithms and protocols.

Generic mobility simulation framework (GMSF)

Vehicular ad-hoc networks with inter-vehicular communications are a prospective technology which contributes to safer and more efficient roads and offers information and entertainment services to mobile users. Since large real-world testbeds are not feasible, research on vehicular ad-hoc networks depends mainly on simulations. Therefore, it is crucial that realistic mobility models are employed. We propose a generic and modular mobility simulation framework (GMSF). GMSF simplifies the design of new mobility models and their evaluation. Besides, new functionalities can be easily added. GMSF also propose new vehicular mobility models, GIS-based mobility models. These models are based on highly detailed road maps from a geographic information system (GIS) and realistic microscopic behaviors (car-following and traffic lights management). We perform an extensive comparison of our new GIS-based mobility models with popular mobility models (Random Waypoint, Manhattan) and realistic vehicular traces from a proprietary traffic simulator. Our findings leverages important issues the networking community still has to address.

SCAMPI: service platform for social aware mobile and pervasive computing

Allowing mobile users to find and access resources available in the surrounding environment opportunistically via their smart devices could enable them to create and use a rich set of services. Such services can go well beyond what is possible for a mobile phone acting alone. In essense, access to diverse resources such as raw computational power, social networking relationships, or sensor readings across a set of different devices calls for distributed task execution. In this paper, we discuss the SCAMPI architecture designed to support distributed task execution in opportunistic pervasive networks. The key elements of the architecture include leveraging human social behavior for efficient opportunistic interaction between a variety of sensors, personal communication devices and resources embedded in the local environment. The SCAMPI architecture abstracts resources as service components following a service-oriented model. This enables composing rich applications that utilize a collection of service components available in the environment.

Collection and analysis of multi-dimensional network data for opportunistic networking research

Opportunistic networks use human mobility and consequent wireless contacts between mobile devices to disseminate data in a peer-to-peer manner. Designing appropriate algorithms and protocols for such networks is challenging as it requires understanding patterns of (1) mobility (who meets whom), (2) social relations (who knows whom) and (3), communication (who communicates with whom). To date, apart from few small test setups, there are no operational opportunistic networks where measurements could reveal the complex correlation of these features of human relationships. Hence, opportunistic networking research is largely based on insights from measurements of either contacts, social networks, or communication, but not all three combined. In this paper we analyze two datasets comprising social, mobility and communication ties. The first dataset we have collected with Stumbl, a Facebook application that lets participating users report their daily face-to-face meetings with other Facebook friends. It also logs user interactions on Facebook (e.g. comments, wall posts, likes). For the second dataset, we use data from two online social networks (Twitter and Gowalla) on the same set of nodes to infer social, communication and mobility ties. We look at the interplay of the different dimensions of relationships on a pairwise level and analyze how the network structures compare to each other.

From Contacts to Graphs: Pitfalls in Using Complex Network Analysis for DTN Routing

Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions are made using locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict which nodes are likely to deliver a content or bring it closer to the destination. One promising way of predicting future contact opportunities is to aggregate contacts seen in the past to a social graph and use metrics from complex network analysis (e.g., centrality and similarity) to assess the utility of a node to carry a piece of content. This aggregation presents an inherent tradeoff between the amount of time-related information lost during this mapping and the predictive capability of complex network analysis in this context. In this paper, we use two recent DTN routing algorithms that rely on such complex network analysis, to show that contact aggregation significantly affects the performance of these protocols. We then propose simple contact mapping algorithms that demonstrate improved performance up to a factor of 4 in delivery ratio, and robustness to various connectivity scenarios for both protocols.

WLAN-Opp

Opportunistic networking enables many appealing applications including local social-networking, communication in emergency situations, and circumventing censorship. The increasing penetration of smartphones should, in theory, foster opportunistic networking. In practice, current candidate technologies for opportunistic networking, such as Wi-Fi ad-hoc, Bluetooth, and Wi-Fi Direct, are either not available on current smartphones, or require undesired user interaction to establish connectivity.To overcome these shortcomings, we propose WLAN-Opp for smartphones. This IEEE 802.11-based technology leverages the tethering mode of smartphones, a feature originally used to share Internet access, which allows smartphones to become WLAN-based access points that provide networks for other smartphones operating as stations. The transitions between WLAN-Opp access point and station mode are randomized as a function of the number of other co-located networks and stations, and depend on duty cycling intervals. We optimize the probabilistic operations in a simulation study and provide a parametrized implementation of WLAN-Opp for out of the box smartphones. By replaying real contact traces in simulation, we find that WLAN-Opp can utilize up to 80% of the contact time while saving up to 90% of the energy Wi-Fi ad-hoc would consume. Finally, we demonstrate in a field trial with 34 users over 5days that WLAN-Opp can provide a practical solution in a realistic setting.

Stumbl: Using Facebook to collect rich datasets for opportunistic networking research

Opportunistic networks use human mobility and consequent wireless contacts between mobile devices to disseminate data in a peer-to-peer manner. Designing appropriate algorithms and protocols for such networks is challenging as it requires understanding patterns of (1) mobility (who meets whom), (2) social relations (who knows whom) and (3), communication (who communicates with whom). To date, apart from few small test setups, there are no operational opportunistic networks where measurements could reveal the complex correlation of these features of human relationships. Hence, opportunistic networking research is largely based on insights from measurements of either contacts, social networks, or communication, but not all three combined. In this paper we report an experiment called Stumbl, as a step towards collecting rich datasets comprising social, mobility and communication ties. Stumbl is a Facebook application that provides participating users with a user-friendly interface to report their daily face-to-face meetings with other Facebook friends. It also logs user interactions on Facebook (e.g. comments, wall posts, likes). This way the contact graph, social graph, and activity graphs for the same set of users could be compared and analyzed. We report here preliminary results and analyses of a first experiment we have performed.