Cecilia Mascolo

CARISMA: context-aware reflective middleware system for mobile applications

Mobile devices, such as mobile phones and personal digital assistants, have gained wide-spread popularity. These devices will increasingly be networked, thus enabling the construction of distributed applications that have to adapt to changes in context, such as variations in network bandwidth, battery power, connectivity, reachability of services and hosts, etc. In this paper, we describe CARISMA, a mobile computing middleware which exploits the principle of reflection to enhance the construction of adaptive and context-aware mobile applications. The middleware provides software engineers with primitives to describe how context changes should be handled using policies. These policies may conflict. We classify the different types of conflicts that may arise in mobile computing and argue that conflicts cannot be resolved statically at the time applications are designed, but, rather, need to be resolved at execution time. We demonstrate a method by which policy conflicts can be handled; this method uses a microeconomic approach that relies on a particular type of sealed-bid auction. We describe how this method is implemented in the CARISMA middleware architecture and sketch a distributed context-aware application for mobile devices to illustrate how the method works in practice. We show, by way of a systematic performance evaluation, that conflict resolution does not imply undue overheads, before comparing our research to related work and concluding the paper.

Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks

Applications involving the dissemination of information directly relevant to humans (e.g., service advertising, news spreading, environmental alerts) often rely on publish-subscribe, in which the network delivers a published message only to the nodes whose subscribed interests match it. In principle, publish- subscribe is particularly useful in mobile environments, since it minimizes the coupling among communication parties. However, to the best of our knowledge, none of the (few) works that tackled publish-subscribe in mobile environments has yet addressed intermittently-connected human networks. Socially-related people tend to be co-located quite regularly. This characteristic can be exploited to drive forwarding decisions in the interest-based routing layer supporting the publish-subscribe network, yielding not only improved performance but also the ability to overcome high rates of mobility and long-lasting disconnections. In this paper we propose SocialCast, a routing framework for publish-subscribe that exploits predictions based on metrics of social interaction (e.g., patterns of movements among communities) to identify the best information carriers. We highlight the principles underlying our protocol, illustrate its operation, and evaluate its performance using a mobility model based on a social network validated with real human mobility traces. The evaluation shows that prediction of colocation and node mobility allow for maintaining a very high and steady event delivery with low overhead and latency, despite the variation in density, number of replicas per message or speed.

Adaptive routing for intermittently connected mobile ad hoc networks

The vast majority of mobile ad hoc networking research makes a very large assumption - that communication can only take place between nodes that are simultaneously accessible within the same connected cloud (i.e., that communication is synchronous). In reality, this assumption is likely to be a poor one, particularly for sparsely or irregularly populated environments. We present the context-aware routing (CAR) algorithm. CAR is a novel approach to the provision of asynchronous communication in partially-connected mobile ad hoc networks, based on the intelligent placement of messages. We discuss the details of the algorithm, and then present simulation results demonstrating that it is possible for nodes to exploit context information in making local decisions that lead to good delivery ratios and latencies with small overheads.

A community based mobility model for ad hoc network research

Validation of mobile ad hoc network protocols relies almost exclusively on simulation. The value of the validation is, therefore, highly dependent on how realistic the movement models used in the simulations are. Since there is a very limited number of available real traces in the public domain, synthetic models for movement pattern generation must be used. However, most widely used models are currently very simplistic, their focus being ease of implementation rather than soundness of foundation. As a consequence, simulation results of protocols are often based on randomly generated movement patterns and, therefore, may differ considerably from those that can be obtained by deploying the system in real scenarios. Movement is strongly affected by the needs of humans to socialise or cooperate, in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled and that have been studied in social sciences for years.In this paper we propose a new mobility model founded on social network theory. The model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is then mapped to a topographical space, with movements influenced by the strength of social ties that may also change in time. We have validated our model with real traces by showing that the synthetic mobility traces are a very good approximation of human movement patterns.

GeOpps: Geographical Opportunistic Routing for Vehicular Networks

Vehicular networks can be seen as an example of hybrid delay tolerant network where a mixture of infostations and vehicles can be used to geographically route the information messages to the right location. In this paper we present a forwarding protocol which exploits both the opportunistic nature and the inherent characteristics of the vehicular network in terms of mobility patterns and encounters, and the geographical information present in navigator systems of vehicles. We also report about our evaluation of the protocol over a simulator using realistic vehicular traces and in comparison with other geographical routing protocols.

A tale of many cities: universal patterns in human urban mobility.

The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users. Surprisingly, while there are variations in human movement in different cities, our analysis shows that those are predominantly due to different distributions of places across different urban environments. Moreover, a universal law for human mobility is identified, which isolates as a key component the rank-distance, factoring in the number of places between origin and destination, rather than pure physical distance, as considered in some previous works. Building on our findings, we also show how a rank-based movement model accurately captures real human movements in different cities.

Designing mobility models based on social network theory

Validation of mobile ad hoc network protocols relies almost exclusively on simulation. The value of the validation is, therefore, highly dependent on how realistic the movement models used in the simulations are. Since there is a very limited number of available real traces in the public domain, synthetic models for movement pattern generation must be used. However, most widely used models are currently very simplistic, their focus being ease of implementation rather than soundness of foundation. Simulation results of protocols are often based on randomly generated movement patterns and, therefore, may differ considerably from those that can be obtained by deploying the system in real scenarios. Movement is strongly affected by the needs of humans to socialise or cooperate, in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled and that have been studied in social sciences for years. In this paper we propose a new mobility model founded on social network theory. The model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This clustering is then mapped to a topographical space, with movements influenced by the strength of social ties that may also change in time. We have validated our model with real traces by showing that the synthetic mobility traces are a very good approximation of human movement patterns. The impact of the adoption of the proposed algorithm on the performance of AODV and DSR is also presented and discussed.

CAR: Context-Aware Adaptive Routing for Delay-Tolerant Mobile Networks

Most of the existing research work in mobile ad hoc networking is based on the assumption that a path exists between the sender and the receiver. On the other hand, applications of decentralised mobile systems are often characterised by network partitions. As a consequence delay tolerant networking research has received considerable attention in the recent years as a means to obviate to the gap between ad hoc network research and real applications. In this paper we present the design, implementation and evaluation of the context-aware adaptive routing (CAR) protocol for delay tolerant unicast communication in intermittently connected mobile ad hoc networks. The protocol is based on the idea of exploiting nodes as carriers of messages among network partitions to achieve delivery. The choice of the best carrier is made using Kalman filter based prediction techniques and utility theory. We discuss the implementation of CAR over an opportunistic networking framework, outlining possible applications of the general principles at the basis of the proposed approach. The large scale performance of the CAR protocol are evaluated using simulations based on a social network founded mobility model, a purely random one and real traces from Dartmouth College.

XMIDDLE: A Data-Sharing Middleware for Mobile Computing

An increasing number of distributed applications will be written for mobilehosts, such as laptop computers, third generation mobile phones, personaldigital assistants, watches and the like. Application engineers have to dealwith a new set of problems caused by mobility, such as low bandwidth, contextchanges or loss of connectivity. During disconnection, users will typicallyupdate local replicas of shared data independently from each other. Theresulting inconsistent replicas need to be reconciled upon re-connection. Tosupport building mobile applications that use both replication andreconciliation over ad-hoc networks, we have designed xmiddle, a mobilecomputing middleware. In this paper we describe xmiddle and show how it usesreflection capabilities to allow application engineers to influencereplication and reconciliation techniques. xmiddle enables the transparentsharing of XML documents across heterogeneous mobile hosts, allowing on-lineand off-line access to data. We describe xmiddle using a collaborativee-shopping case study on mobile clients.

NextPlace: a spatio-temporal prediction framework for pervasive systems

Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future. In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.