Reverse Nearest Neighbor (RNN) queries are of particular interest in a wide range of applications such as decision support systems, profile based marketing, data streaming, document databases, and bioinformatics. The earlier approaches to solve this problem mostly deal with two dimensional data. However most of the above applications inherently involve high dimensions and high dimensional RNN problem is still unexplored. In this paper, we propose an approximate solution to answer RNN queries in high dimensions. Our approach is based on the strong correlation in practice between k-NN and RNN. It works in two phases. In the first phase the k-NN of a query point is found and in the next phase they are further analyzed using a novel type of query Boolean Range Query (BRQ). Experimental results show that BRQ is much more efficient than both NN and range queries, and can be effectively used to answer RNN queries. Performance is further improved by running multiple BRQ simultaneously. The proposed approach can also be used to answer other variants of RNN queries such as RNN of order k, bichromatic RNN, and Matching Query which has many applications of its own. Our technique can efficiently answer NN, RNN, and its variants with approximately same number of I/O as running a NN query.

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Internet of Things (IoT) has grown rapidly in the last decade and continue to develop in terms of dimension and complexity, offering wide range of devices to support diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology along with artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance and convenience to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of contemporary technological deployments especially to contain the wide spread of this disease. IoT and smart connected technologies together with data-driven applications can play a crucial role not only in the prevention, mitigation or continuous remote monitoring of patients, but also enable prompt enforcement of guidelines, rules and administrative orders to contain such future outbreaks. In this paper, we envision an IoT and data supported connected ecosystem designed for intelligent monitoring, pro-active prevention and control, and mitigation of COVID-19 and similar epidemics. We propose a gamut of synergistic applications and technology systems for various smart infrastructures including E-Health, smart home, supply chain management, transportation, and city, which will work in convergence to develop ‘pandemic-proof’ future smart communities. We also present a generalized cloud-enabled IoT implementation framework along with scientific solutions, which can be adapted and extended to deploy smart connected ecosystem scenarios using widely used Amazon Web Services (AWS) cloud infrastructures. In addition, we also implement an E-Health RPM use case scenario to demonstrate the need and practicality for smart connected communities. Finally, we highlight challenges and research directions which need thoughtful consideration and across the board cooperation among stakeholders to build resilient communities against future pandemics.

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