Subscribe
Issue No.06 - June (2013 vol.25)
pp: 1240-1253
Demetrios Zeinalipour-Yazti , University of Cyprus, Nicosia
Christos Laoudias , University of Cyprus, Nicosia
Constandinos Costa , University of Cyprus, Nicosia
Michail Vlachos , IBM Research Zurich, Zurich
Maria I. Andreou , Open University of Cyprus, Nicosia
Dimitrios Gunopulos , University of Athens, Athens
ABSTRACT
Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS, accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced computing services, which contribute to the solution of complex problems in a distributed manner. In this work, we leverage such a computing paradigm to solve efficiently the following problem: comparing a query trace $(Q)$ against a crowd of traces generated and stored on distributed smartphones. Our proposed framework, coined $({\rm SmartTrace}^+)$, provides an effective solution without disclosing any part of the crowd traces to the query processor. $({\rm SmartTrace}^+)$, relies on an in-situ data storage model and intelligent top-K query processing algorithms that exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to $(Q)$. We evaluate our algorithms on both synthetic and real workloads. We describe our prototype system developed on the Android OS. The solution is deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that computations over $({\rm SmartTrace}^+)$ result in substantial energy conservation; in addition, results can be computed faster than competitive approaches.
INDEX TERMS
Trajectory, Smart phones, Upper bound, NIST, Educational institutions, Time factors, IEEE 802.11 Standards, android OS, Crowdsourcing, trajectory similarity search, smartphones, longest common subsequence
CITATION
Demetrios Zeinalipour-Yazti, Christos Laoudias, Constandinos Costa, Michail Vlachos, Maria I. Andreou, Dimitrios Gunopulos, "Crowdsourced Trace Similarity with Smartphones", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 6, pp. 1240-1253, June 2013, doi:10.1109/TKDE.2012.55
REFERENCES
58 ms
(Ver 2.0)

Marketing Automation Platform