IoT-basedenvironmentsmayinferanomaliesbasedonthedataprocessedfromtheirheterogeneous sensors. Within the technologies evolving the IoT concept, nowadays the Radio Frequency Identification(RFID)technologyisadefactostandardinareaslikeretailorlogistics.Forinstance, mostretailersattachRFID-labelstotheiritemstoavoidstock-outintheinventoryorspeedupcash processes.Besidesidentification,RFIDprovidesfurtherRFdatawhichcanbeusedforinformation management like anomaly detection (i.e. a shoplifting in a RFID loss prevention system). This manuscriptpresentstwoIoTscenariostodetectanomaliesusingmultivariateoutlierdetectionmethods, uniquelyusingRFIDdata.Thisresearchempiricallyevaluatestheauthors'proposedmethodsby reproducingaRFID-enabledstore,andthetwoproposedscenarios.TheevaluationachievedaFalse PositiveRatearound0.1%andaTruePositiveRatearound87%.

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People with physical or cognitive disabilities lack independence in their everyday activities. Deviceless activity detection through RFID-enabled ambient assisted living technologies can improve the lives of such people, especially in their shopping experiences.

Radio Frequency Identification (RFID) offers an extraordinary opportunity to enhance the shopping experience of customers in a retail store. There are two types of possible enhancements: increasing the efficiency of traditional processes, or offering new use cases at the store. RFID offers a great opportunity in both cases. RFID can be used to improve the availability of products, reducing stock outs, to streamline the check-out process, reducing the lines, or to substitute the typical Electronic Article Surveillance (EAS) "horse gates" by hidden antennas, freeing the entrance to the store from intimidating barriers. Besides these operational improvements, RFID can also be used to offer shoppers new and enticing functionalities, such as a "magic mirror" to virtually try garments on, or an interactive screen in the fitting room that displays information and offers functionalities related to the particular garments brought in by the customer. This paper describes an actual installation in an apparel retail store in Barcelona, and presents some of the initial conclusions after several months of operation with real customers.

Abstract:The number of connected devices is increasing worldwide. Not only in contexts like the Smart City, but also in rural areas, to provide advanced features like smart farming or smart logistics. Thus, wireless network technologies to efficiently allocate Internet of Things (IoT) and Machine to Machine (M2M) communications are necessary. Traditional cellular networks like Global System for Mobile communications (GSM) are widely used worldwide for IoT environments. Nevertheless, Low Power Wide Area Networks (LP-WAN) are becoming widespread as infrastructure for present and future IoT and M2M applications. Based also on a subscription service, the LP-WAN technology SIGFOX TM may compete with cellular networks in the M2M and IoT communications market, for instance in those projects where deploying the whole communications infrastructure is too complex or expensive. For decision makers to decide the most suitable technology for each specific application, signal coverage is within the key features. Unfortunately, besides simulated coverage maps, decision-makers do not have real coverage maps for SIGFOX TM , as they can be found for cellular networks. Thereby, we propose Internet of THings Area Coverage Analyzer (ITHACA), a signal analyzer prototype to provide automated signal coverage maps and analytics for LP-WAN. Experiments performed in the Gran Canaria Island, Spain (with both urban and complex topographic rural environments), returned a real SIGFOX TM service availability above 97% and above 11% more coverage with respect to the company-provided simulated maps. We expect that ITHACA may help decision makers to deploy the most suitable technologies for future IoT and M2M projects.