DISTRIBUTED OBJECT-LOCALIZATION USING RFID CROWDSOURCING
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Internet of Things (IoT) refers to an evolution of the current Internet in which a large number of “smart objects” sense their surroundings and communicate amongst themselves and to data analytic servers. IoT applications are rooted in our physical world to offer users more convenient context and location-aware services, for which a common requirement is the ability to locate objects. Two approaches are proposed to localize IoT objects based on Radio Frequency IDentification (RFID) technology: localize mobile/stationary tagged objects through a set of coordinated readers that report to a central server and localize mobile reader based on connectivity information with a set of tags deployed at known locations. The former is based on a centralized and fixed infrastructure which provides limited scalability while the latter is not cost effective for IoT settings as a large number of objects have to be equipped with RFID readers. In a typical IoT environment, there are considerable RFID crowdsourcing resources in terms of a large number of tags attached to objects and a considerable group of ad hoc mobile readers which are possibly heterogeneous and un-coordinated and can be used for locating objects. We investigate this promising direction and devise distributed localization schemes that leverage heterogeneous and independent mobile RFID readers along with RFID tags’ residual memories to cooperatively localize passive-tagged objects, while maintaining high scalability. In estimating object location, Multilateration is a commonly used technique that estimates object location based on the intersection of all plausible areas where the object is expected to exist. This technique requires at least three concurrent readings about an object to estimate its location which is a challenge under IoT settings. We address ways to overcome this challenge and provide better location accuracy in the absence of sufficient concurrent readings. We propose location information dissemination strategies that work on providing high location information availability with low overhead. We validate our schemes via extensive simulation and field experiments and show that our approach has the potential to provide localization service in typical IoT environments.