A novel Received Signal Strength (RSS) rank based fingerprinting algorithm for indoor positioning is presented. Because RSS rank is invariant to bias and scaling, the algorithm provides the same accuracy for any receiver device, without the need for RSS calibration. Similarity measures to compare ranked vectors are introduced and their impact on positioning accuracy is investigated in experiments. Experimental results shown that proposed algorithm can achieve better accuracy than commonly used NN and WKNN fingerprinting algorithms.

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Next to climate change on the list of challenges faced by humankind in today’s technological age is energy management. While “smart” ideas continue to gather momentum as some of the ways earmarked to combat the menace of a changing climate, coupled with efficient management of energy, research and development in the blockchain is not retracting, recently giving rise to digital currencies capable of fueling massive energy consumption via mining of “crypto-coins”. Given that sustainability is a crucial goal in the design of smart cities nowadays, there are currently no assurances of sustainable cities where cryptocurrency mining is at full scale. Nevertheless, alternative energy sources may come to the rescue in no distant time. In this paper, we contextualize energy-use in smart cities through mining of virtual currencies, in order to predict whether or not smart cities can truly be sustainable if crypto-mining is sustained. An attempt is also made to emphasize the possible ways of reducing energy use and all activities involving digital currencies by seeking to replace “Proof of Work” (PoW) with improved alternatives.

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