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    @Article{OJIOT_2015v1i2n02_Gaur,
        title     = {Evidential Sensor Data Fusion in a Smart City Environment},
        author    = {Aditya Gaur and
                     Bryan W. Scotney and
                     Gerard P. Parr and
                     Sally I. McClean},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2015},
        volume    = {1},
        number    = {2},
        pages     = {1--18},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201704244969},
        urn       = {urn:nbn:de:101:1-201704244969},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Wireless sensor networks have increasingly become contributors of very large amounts of data. The recent deployment of wireless sensor networks in Smart City infrastructures have led to very large amounts of data being generated each day across a variety of domains, with applications including environmental monitoring, healthcare monitoring and transport monitoring. The information generated through the wireless sensor nodes has made possible the visualization of a Smart City environment for better living. The Smart City offers intelligent infrastructure and cogitative environment for the elderly and other people living in the Smart society. Different types of sensors are present that help in monitoring inhabitants' behaviour and their interaction with real world objects. To take advantage of the increasing amounts of data, there is a need for new methods and techniques for effective data management and analysis, to generate information that can assist in managing the resources intelligently and dynamically. Through this research a Smart City ontology model is proposed, which addresses the fusion process related to uncertain sensor data using semantic web technologies and Dempster-Shafer uncertainty theory. Based on the information handling methods, such as Dempster-Shafer theory (DST), an equally weighted sum operator and maximization operation, a higher level of contextual information is inferred from the low-level sensor data fusion process. In addition, the proposed ontology model helps in learning new rules that can be used in defining new knowledge in the Smart City system.}
    }
    @Article{OJIOT_2015v1i2n03_Cho,
        title     = {Accurate Distance Estimation between Things: A Self-correcting Approach},
        author    = {Ho-sik Cho and
                     Jianxun Ji and
                     Zili Chen and
                     Hyuncheol Park and
                     Wonsuk Lee},
        journal   = {Open Journal of Internet Of Things (OJIOT)},
        issn      = {2364-7108},
        year      = {2015},
        volume    = {1},
        number    = {2},
        pages     = {19--27},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201704244959},
        urn       = {urn:nbn:de:101:1-201704244959},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {This paper suggests a method to measure the physical distance between an IoT device (a Thing) and a mobile device (also a Thing) using BLE (Bluetooth Low-Energy profile) interfaces with smaller distance errors. BLE is a well-known technology for the low-power connectivity and suitable for IoT devices as well as for the proximity with the range of several meters. Apple has already adopted the technique and enhanced it to provide subdivided proximity range levels. However, as it is also a variation of RSS-based distance estimation, Apple's iBeacon could only provide immediate, near or far status but not a real and accurate distance. To provide more accurate distance using BLE, this paper introduces additional self-correcting beacon to calibrate the reference distance and mitigate errors from environmental factors. By adopting self-correcting beacon for measuring the distance, the average distance error shows less than 10\% within the range of 1.5 meters. Some considerations are presented to extend the range to be able to get more accurate distances.}
    }