[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works.

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IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new 'Smart' healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently. 

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

As people spend significant parts of daily lives indoors, it is useful and important to measure indoor densities and find the dense regions in many indoor scenarios like space management and security control. In this paper, we propose a data-driven approach that finds top- $k$ indoor dense regions by using indoor positioning data. Such data is obtained by indoor positioning systems working at a relatively low frequency, and the reported locations in the data are discrete, from a preselected location set that does not continuously cover the entire indoor space. When a search is triggered, the object positioning information is already out-of-date and thus object locations are uncertain. To this end, we first integrate object location uncertainty into the definitions for counting objects in an indoor region and computing its density. Subsequently, we conduct a thorough analysis of the location uncertainty in the context of complex indoor topology, deriving upper and lower bounds of indoor region densities and introducing distance decaying effect into computing concrete indoor densities. Enabled by the uncertainty analysis outcomes, we design efficient search algorithms for solving the problem. Finally, we conduct extensive experimental studies on our proposals using synthetic and real data. The experimental results verify that the proposed search approach is efficient, scalable, and effective. The top- $k$ indoor dense regions returned by our search are considerably consistent with ground truth, despite that the search uses neither historical data nor extra knowledge about objects.

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In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data

Trajectory frequent pattern mining is an important branch of data mining The constraint of indoor space is between Euclid space and road network space, which makes it difficult to represent the approximate positions Grid partition method is a feasible way to solve this problem, but it will lead to a sharp problem of grid boundary Considering the indoor trajectory frequent pattern mining, this paper proposes a grid partition method based on vertical projection distance (VGS) and a trajectory frequent pattern mining algorithm based on vague grid sequence (VGS-PrefixSpan) At first, each grid is divided into explicit zones and vague zones according to vertical projection distance Then the trajectories are transformed into vague grid sequences At last, VGS-PrefixSpan is a PrefixSpan-like algorithm to mine trajectory frequent patterns from vague grid sequences Experimental results show that VGS-PrefixSpan has better performance than VSP-PrefixSpan under the same area ratio of explicit zones and covered zones, and has better mining results than VSP-PrefixSpan and GS-PrefixSpan under any value of Min _ Support  In terms of mining efficiency, the total time of VGS-PrefixSpan is close to GS-PrefixSpan and less than VSP-PrefixSpan about two orders of magnitude Therefore, VGS-PrefixSpan is an effective and efficient algorithm in mining frequent patterns of indoor trajectories As a research hotspot in Location Based Services (LBS), mining frequent patterns of indoor trajectories can protect the trajectory privacy of users from being leaked or mitigating the risk of leakage Therefore, the study of trajectory frequent patterns is of great significance to public security and personal information protection

An indoor trajectory frequent pattern mining algorithm based on vague grid sequence

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT.

We demonstrate a generic, user-configurable toolkit for generating different types of indoor mobility data for real-world buildings. Our prototype generates the desired data in a three-layer pipeline. The Infrastructure Layer accepts industry-standard digital building information (DBI) files to generate the host indoor environment, allowing users to configure the generation of a variety of positioning devices, such as Wi-Fi, Bluetooth, RFID, etc. The Moving Object Layer offers the functionality of defining objects or trajectories, with configurable indoor moving patterns, distribution models, and sampling frequencies. The Positioning Layer generates synthetic signal strength measurements known as raw RSSI1 measurements according to the positioning device data and trajectory data generated at relevant layers. It also generates different types of indoor positioning data through the customization of all typical indoor positioning methods on the raw RSSI data.

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Vita: a versatile toolkit for generating indoor mobility data for real-world buildings

Knowing popular indoor locations can benefit many applications like exhibition planning and location-based advertising, among others. In this work, we use uncertain historical indoor mobility data to find the top-$k$k popular indoor semantic locations with the highest flow values. In the data we use, an object positioning report contains a set of samples, each consisting of an indoor location and a corresponding probability. The problem is challenging due to the difficulty in obtaining reliable flow values and the heavy computational workload on probabilistic samples for large numbers of objects. To address the first challenge, we propose an indoor flow definition that takes into account both data uncertainty and indoor topology. To efficiently compute flows for individual indoor semantic locations, we design data structures for facilitating accessing the relevant data, a data reduction method that reduces the intermediate data to process, and an overall flow computing algorithm. Furthermore, we design search algorithms for finding the top-$k$k popular indoor semantic locations. All proposals are evaluated extensively on real and synthetic data. The evaluation results show that our data reduction method significantly reduces the data volume in computing, our search algorithms are efficient and scalable, and the top-$k$k popular semantic locations returned are in good accord with ground truth.

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Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

People have many activities indoors and there is an increasing demand of keyword-aware route planning for indoor venues. In this paper, we study the indoor top-k keyword-aware routing query (IKRQ). Given two indoor points s and t, an IKRQ returns k s-to-t routes that do not exceed a given distance constraint but have optimal ranking scores integrating keyword relevance and spatial distance. It is challenging to efficiently compute the ranking scores and find the best yet diverse routes in a large indoor space with complex topology. We propose prime routes to diversify top-k routes, devise mapping structures to organize indoor keywords and compute route keyword relevances, and derive pruning rules to reduce search space in routing. With these techniques, we design two search algorithms with different routing expansions. Experiments on synthetic and real data demonstrate the efficiency of our proposals.

Huan Li

Papers

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT.

In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data

Vita: a versatile toolkit for generating indoor mobility data for real-world buildings

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Indoor Top-k Keyword-aware Routing Query