Smartphone sensing research has been advancing at a brisk pace. Yet, current social networking services often only take advantage of location sensing: applications like Foursquare use the phone's GPS and Wi-Fi radios to infer the user's location to simplify checking-in to a place. However, smartphone sensing could be exploited to considerably expand the spectrum of information a user can share with a few clicks with friends: not only the location of an event but activities such as “cooking dinner” or “waiting for a bus” can be predicted and suggested to the user to ease the check-in process. In this paper we show how mobile phone sensing can be used in this sense. For this prediction process to be accurate however, sensors need to be sampled often, with a considerable impact on the phone battery. To alleviate this issue, we explore streams of phone usage data (soft sensors), such as application usage, messages, and phone calls for predicting the user's activity in a more efficient fashion for augmenting mobile social check-in apps. We have deployed our application and collected a dataset of over 2700 check-ins to 48 activities from 20 users. Our analysis shows a prediction accuracy of 75% when offering 5 check-in suggestions to users. Furthermore, we show that when using only soft sensors we can achieve very similar performance to that obtained with real sensors, thereby significantly reducing the impact on the phone battery. This finding might have a potentially high impact on smartphone based activity check-in apps.

The exponential growing demand of mobile Internet data traffic requires a rethinking of current infrastructures and calls for innovative approaches that provide online-streaming of content to interested subscribed mobile users. We introduce a mobile system called HyCloud for device-to-device content distribution. HyCloud delivers the content from a cloud backend, and it can reduce the total amount of data transferred through the cellular network by exploiting the similarity of interests among multiple mobile users. Our preliminary real-life experiment with video streaming with a simple content scheduler shows that the system can save in average 24% (and up to 38%) of data downloaded by the cellular connection.

Opportunistic networks are formed by people carrying mobile devices with wireless capabilities. When in mutual transmission range, the nodes of such networks use device-to-device communication to automatically exchange data, without requiring fixed infrastructure. To solve challenging opportunistic networking problems like routing, nodes exchange information about whom they have met in the past and form a contact graph, which encodes the social structure of past meetings. This contact graph is then used to assign a utility to each node (e.g., based on their centrality), thereby defining a ranking of the nodes' values for carrying a message. However, while being a useful tool, the contact graph represents a privacy risk to the users, as it allows an attacker to learn about social links. In this paper, we investigate the trade-off of privacy and utility in the contact graph. By transforming the graph through adding and removing edges, we are able to control the amount of link privacy. The evaluation of a greedy approach shows that it maintains the node ranking very well, even if many links are changed.

In opportunistic networks, resource constrained mobile devices must continuously scan their environment for potential peers with whom they can exchange messages. This is an extremely energy consuming process and prior work has focused typically on how to adaptively changing the probing frequency by predicting future contact opportunities. However, without setting an energy budget for such probing activities beforehand, the standby time of smart phones cannot be guaranteed. In this paper, we provide a method to improve the probing efficiency given a certain energy budget. First, we discuss the characteristics of several real-life contact traces and motivate the need for an energy-efficient probing strategy in sparse scenarios. Subsequently, we propose an algorithm based on a Constrained Markov Decision Process, which maximizes the overall probing efficiency within a given energy budget. Simulating the algorithm on measured contact traces, we show that given a certain energy budget, the number of interesting contacts captured can be improved by 50% to 100% compared to a uniform probing policy.

Epidemic spreading is one of the most popular bio-inspired principles, which has made its way into computer networking. This principle naturally applies to Opportunistic or Delay Tolerant Networks (DTNs), where nodes probabilistically meet their neighbors thanks to mobility. Epidemic-based algorithms are often the only choice for DTN problems such as broadcast and unicast routing, distributed estimation etc. Existing analyses of epidemic spreading in various contexts only consider specific graph geometries (complete, random, regular etc) and/or homogeneous exponential node meeting rates. In addition, in wired networks, synchronous communication is usually assumed. In this paper, we relax these assumptions and provide a detailed analysis of epidemic spreading in DTNs with heterogeneous node meeting rates. We observe the special properties of a Markov model, describing the epidemic process and use them to derive bounds for the delay (expectation and distribution). We apply our analysis to epidemic-based DTN algorithms for routing and distributed estimation and validate the bounds against simulation results, using various real and synthetic mobility scenarios. Finally, we empirically show that the delay distribution is relatively concentrated, and that, depending on graph properties (communities, scale-freeness), the delay scales very well with network size.

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.

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.

Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, forwarding decisions are generally made using locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict future contact opportunities. The use of complex network analysis has been recently suggested to perform this prediction task and improve the performance of DTN routing. Contacts seen in the past are aggregated to a social graph, and a variety of metrics (e.g., centrality and similarity) or algorithms (e.g., community detection) have been proposed to assess the utility of a node to deliver a content or bring it closer to the destination. In this paper, we argue that it is not so much the choice or sophistication of social metrics and algorithms that bears the most weight on performance, but rather the mapping from the mobility process generating contacts to the aggregated social graph. We first study two well-known DTN routing algorithms - SimBet and BubbleRap - that rely on such complex network analysis, and show that their performance heavily depends on how the mapping (contact aggregation) is performed. What is more, for a range of synthetic mobility models and real traces, we show that improved performances (up to a factor of 4 in terms of delivery ratio) are consistently achieved for a relatively narrow range of aggregation levels only, where the aggregated graph most closely reflects the underlying mobility structure. To this end, we propose an online algorithm that uses concepts from unsupervised learning and spectral graph theory to infer this 'correct' graph structure; this algorithm allows each node to locally identify and adjust to the optimal operating point, and achieves good performance in all scenarios considered.

A majority of network architectures aim at solving specific shortcomings of the original Internet architecture. While providing solutions for the particular problems, they often lack in flexibility and do not provide general concepts for future networking requirements. In contrast, we introduce a network architecture that aims to be versatile enough to serve as a foundation for the future Internet. The main pillars of our architecture are communication pivots called information dispatch points (IDPs) which embed the concept of modularity at all levels of the architecture. IDPs completely decouple functional entities by means of indirection thus enabling evolving protocol stacks. Our architecture also provides a consistent application programming interface (API) to access node-local or network-wide functionality. In addition to the description of this architecture, we report about a working prototype of the architecture and we give examples of its application.

As mobile devices are getting more and more ubiquitous, the paradigm of wireless Mobile Ad hoc NETworks (MANETs) is gaining popularity. Especially distributed real-time applications are attractive for their users in this environment. To help manage these applications in MANETs, previously we developed a distributed algorithm called PBS (priority based selection). PBS divides the ad hoc network into zones and selects and maintains a set of nodes, which act as zone servers, to manage these zones. The zone server selection applies node priority comparison, which reflects the node's capabilities to act as zone server. In this paper, we propose a mechanism called NWC (node weight computation) to be applied in the node priority comparison of PBS. NWC computes the node weight, on which the priority comparison is based, as the weighted linear combination of the node parameters. The parameter weights are extracted from the so-called service profile which reflects the characteristics and requirements of the given service. To create this profile we developed a procedure using factorial design via simulations and multi-objective optimization. Our NWC mechanism, applying the created service profile, assigns the highest priorities to the best suited nodes for a given service type and thus designates the most powerful nodes to be selected as zone servers by PBS. Moreover, the weight computation of NWC can be used by other clustering algorithms in MANETs, too.

As mobile devices are getting more ubiquitous, the paradigm of wireless mobile ad hoc networks (MANETs) is gaining popularity. However, MANETs impose new challenges because of their self-organizing, mobile and error-prone nature. Mobility prediction can mitigate the problems emerging from node mobility.In this paper, we propose an approach called XCoPred to predict link quality variations based on pattern matching which can be exploited for mobility prediction. XCoPred doesn't require the use of any external hardware or reference point. Each MANET node monitors the Signal to Noise Ratio (SNR) of its links to obtain a time series of SNR measurements. When a prediction is required, the node tries to detect patterns similar to the current situation in the history of the SNR values of its links by applying the normalized cross-correlation function. The found matches are then used as the base of the prediction. Simulations have shown that fairly accurate predictions around $2~dB$ of absolute average prediction error can be achieved with XCoPred in case of appropriate parameter settings and scenarios showing clear node mobility patterns.