Service discovery in mobile social networks

Abstract

We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. SIDEMAN takes advantage of mobile social networking characteristics, such as user membershIP to a restricted number of communities and interest for similar services among users in the same community. We evaluated the performance of SIDEMAN via simulations in a scenario based on traces collected at the IEEE conference Infocom in 2006. Our algorithm has been compared to the social version of two popular data dissemination techniques, namely, flooding and gossIPing. We have measured how proactive an algorithm is in distributing services of interest (Recall), how many services are already with a user when they are needed (Gain), the energy cost for service discovery, and the time needed to reply a service query. We show that SIDEMAN obtains perfect Recall and a Gain that is always comparable to that of the other algorithms. Furthermore, most services are retrieved in reasonable time and at a lower energy cost than that of the flooding and gossIPing-based solutions

Collaborative Service Discovery in Mobile Social Networks

“…In our previous works [9,10] we proposed SIDEMAN, a service discovery protocol for MSN that, similarly to CORDIAL, leverage on human behavior, in particular on membership to communities and mobility, to build opportunistic and social-aware mechanisms of service discovery. In particular, both algorithms diffuse service queries and advertisements in the network by exploiting the similarity of the users' interests and the membership to common communities, aiming at limiting this diffusion to nodes that are not interested in.…”

“…The first simulation (presented in Section 5.1) concerned the use of a large number of traces to achieve a 97.5% confidence limits and a 0.025% probability of error for all the parameters under study. The second simulation (presented in Section 5.2), is aimed at analyzing the behavior of CORDIAL, S-Flood and of another service discovery algorithm called SIDEMAN [10] over a long time period of 12 days in a specific sample trace of the datasets.…”

“…To demonstrate the effectiveness of CORDIAL in discovering and advertising services, we analysed its behaviour against S-Flood [33] and another algorithm for service discovery in MSN, namely SIDEMAN [10]. SIDEMAN represents a useful term of comparison for CORDIAL since it is also designed to cope with the social features of MSN.…”

“…Furthermore, CORDIAL implements the advertisement dissemination phase (which communicates to the other nodes the availability of a service in the network) by proactively disseminating service advertisements among devices that might be more interested in accessing the service. In our preliminary work [8], we evaluated the performance of CORDIAL with respect to metrics like Proactivity, message delivery rate, Query Response Time and number of answered queries in a simplified simulation scenario and against a social-based flooding strategy and SIDEMAN (ServIce DiscovEry in Mobile sociAl Networks) [9,10]. In this work, we provide a detailed description of CORDIAL and an extended evaluation of its performance.…”

An Event Search Platform Using Machine Learning

“…Finding digital content related to events is challenging, requiring searching at different sources and sites [5] and sometimes, the data is ambiguous and incomplete.…”