Evan Welbourne, Nodira Khoussainova, J. Letchner, Yang Li, M. Balazinska, G. Borriello, Dan Suciu
Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an intuitive visual language based on direct manipulation. Second, it provides an API that facilitates the development of applications which rely on RFID-based events. Third, it automatically detects the specified events, forwards them to registered applications and stores them for later use (e.g., for historical queries). We present the design and implementation of Cascadia along with an evaluation that includes both a user study and measurements on traces collected in a building-wide RFID deployment. To demonstrate how Cascadia facilitates application development, we built a simple digital diary application in the form of a calendar that populates itself with RFID-based events. Cascadia copes with ambiguous RFID data and limitations in an RFID deployment by transforming RFID readings into probabilistic events. We show that this approach outperforms deterministic event detection techniques while avoiding the need to specify and train sophisticated models.
{"title":"Cascadia","authors":"Evan Welbourne, Nodira Khoussainova, J. Letchner, Yang Li, M. Balazinska, G. Borriello, Dan Suciu","doi":"10.1145/1378600.1378631","DOIUrl":"https://doi.org/10.1145/1378600.1378631","url":null,"abstract":"Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data. Cascadia provides three important services. First, it allows application developers and even users to specify events using either a declarative query language or an intuitive visual language based on direct manipulation. Second, it provides an API that facilitates the development of applications which rely on RFID-based events. Third, it automatically detects the specified events, forwards them to registered applications and stores them for later use (e.g., for historical queries). We present the design and implementation of Cascadia along with an evaluation that includes both a user study and measurements on traces collected in a building-wide RFID deployment. To demonstrate how Cascadia facilitates application development, we built a simple digital diary application in the form of a calendar that populates itself with RFID-based events. Cascadia copes with ambiguous RFID data and limitations in an RFID deployment by transforming RFID readings into probabilistic events. We show that this approach outperforms deterministic event detection techniques while avoiding the need to specify and train sophisticated models.","PeriodicalId":435472,"journal":{"name":"Proceeding of the 6th international conference on Mobile systems, applications, and services - MobiSys '08","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122720060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cory Cornelius, Apu Kapadia, David Kotz, D. Peebles, Minho Shin, Nikos Triandopoulos
Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.
{"title":"Anonysense","authors":"Cory Cornelius, Apu Kapadia, David Kotz, D. Peebles, Minho Shin, Nikos Triandopoulos","doi":"10.1145/1378600.1378624","DOIUrl":"https://doi.org/10.1145/1378600.1378624","url":null,"abstract":"Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications.\u0000 We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.","PeriodicalId":435472,"journal":{"name":"Proceeding of the 6th international conference on Mobile systems, applications, and services - MobiSys '08","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115717368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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