Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

A distributed scheme to detect wormhole attacks in mobile wireless sensor networks

Due to mostly being unattended, sensor nodes become open to physical attacks such as wormhole attack, which is our focus in this paper. Various solutions are proposed for wormhole attacks in sensor networks, but only a few of them take mobility of sensor nodes into account. We propose a distributed wormhole detection scheme for mobile wireless sensor networks in which mobility of sensor nodes is utilized to estimate two network features (i.e. network node density, standard deviation in network node density) through using neighboring information in a local manner. Wormhole attack is detected via observing anomalies in the neighbor nodes’ behaviors based on the estimated network features and the neighboring information. We analyze the performance of proposed scheme via simulations. The results show that our scheme achieves a detection rate up to 100% with very small false positive rate (at most 1.5%) if the system parameters are chosen accordingly. Moreover, our solution requires neither additional hardware nor tight clock synchronization which are both costly for sensor networks

Robust control tools for traffic monitoring in TCP/AQM networks

Several studies have considered control theory tools for traffic control in communication networks, as for example the congestion control issue in IP (Internet Protocol) routers. In this paper, we propose to design a linear observer for time-delay systems to address the traffic monitoring issue in TCP/AQM (Transmission Control Protocol/Active Queue Management) networks. Due to several propagation delays and the queueing delay, the set TCP/AQM is modeled as a multiple delayed system of a particular form. Hence, appropriate robust control tools as quadratic separation are adopted to construct a delay dependent observer for TCP flows estimation. Note that, the developed mechanism enables also the anomaly detection issue for a class of DoS (Denial of Service) attacks. At last, simulations via the network simulator NS-2 and an emulation experiment validate the proposed methodology