Mobile Ad hoc NETworks (MANETs)

Terminal's latency, connectivity, energy and memory are the main characteristics of today's mobile environments whose performance may be improved by caching. In this paper, we present an adaptive scheme for mobile Web data caching, which accounts for congestion of the wireless network and energy limitation of mobile terminals. Our main design objective is to minimize the energy cost of peer-to-peer communication among mobile terminals so as to allow for unexpensive Web access when a fixed access point is not available in the communication range of the mobile terminal. We propose a collaborative cache management strategy among mobile terminals interacting via an ad hoc network. We further provide evaluation of the proposed solution in terms of energy consumption on mobile devices.

Enabling the ambient intelligence vision means that consumers will be provided with universal and immediate access to available content and services, together with ways of effectively exploiting them. Concentrating on the software system development aspect, this means that the actual implementation of any ambient intelligence application requested by a user can only be resolved at runtime according to the user's specific situation. This paper introduces a base declarative language and associated core middleware, which supports the abstract specification of Ambient Intelligence applications together with their dynamic composition according to the environment. The proposed solution builds on the Web services architecture, whose pervasiveness enables both services availability in most environments, and specification of applications supporting automated retrieval and composition. In addition, dynamic composition of applications is dealt in a way that enforces the quality of service of deployed applications in terms of security and performance.

While bringing massive-scale sensing at low cost, mobile participatory sensing is challenged by the low accuracy of the sensors embedded in and/or connected to the smartphones. The mobile measurements that are collected need to be corrected so as to accurately match the phenomena being observed. This paper addresses this challenge by introducing a multi-hop, multiparty calibration method that operates in the background in an automated way. Using our method, sensors that are within a relevant sensing (and communication) range coordinate so that the observations of the participating (previously) calibrated sensors serve calibrating the other participants. As a result, our method is particularly well suited for participatory sensing within crowd meetings, as as for instance within public spaces. Our solution leverages multivariate linear regression, together with robust regression so as to discard the measurements that are of too low quality for being meaningful. To the best of our knowledge, we are the first to introduce a multiparty calibration algorithm, while previous work in the area focused on pairwise calibration. The paper further introduces a supporting prototype implemented over Android, and related experiment in the context of noise sensing. We show that the proposed multiparty calibration system enhances the accuracy of the mobile noise sensing application.

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Terminal 's latency, connectivity, energy and

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