| 1. |
- Nilsson, Tommy, et al.
(författare)
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Applying Seamful Design in Location-Based Mobile Museum Applications
- 2016
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Ingår i: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP). - : Association for Computing Machinery (ACM). - 1551-6857 .- 1551-6865. ; 12:4
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Tidskriftsartikel (refereegranskat)abstract
- The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention. In combination with the introduction of power-efficient wireless communication technologies, such as Bluetooth Low Energy (BLE), designers are today increasingly empowered to shape the way we interact with our physical surroundings and thus build entirely new experiences. However, our evaluations of BLE and its abilities to facilitate mobile location-based experiences in public environments revealed a number of potential problems. Most notably, the position and orientation of the user in combination with various environmental factors, such as crowds of people traversing the space, were found to cause major fluctuations of the received BLE signal strength. These issues are rendering a seamless functioning of any location-based application practically impossible. Instead of achieving seamlessness by eliminating these technical issues, we thus choose to advocate the use of a seamful approach, that is, to reveal and exploit these problems and turn them into a part of the actual experience. In order to demonstrate the viability of this approach, we designed, implemented, and evaluated the Ghost Detector-an educational location-based museum game for children. By presenting a qualitative evaluation of this game and by motivating our design decisions, this article provides insight into some of the challenges and possible solutions connected to the process of developing location-based BLE-enabled experiences for public cultural spaces.
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| 2. |
- Perera, Charith, et al.
(författare)
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A Knowledge-Based Resource Discovery for Internet of Things
- 2016
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Ingår i: Knowledge-Based Systems. - : Elsevier BV. - 0950-7051 .- 1872-7409. ; 109, s. 122-136
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Tidskriftsartikel (refereegranskat)abstract
- In the sensing as a service paradigm, Internet of Things (IoT) Middleware platforms allow data consumers to retrieve the data they want without knowing the underlying technical details of IoT resources (i.e. sensors and data processing components). However, configuring an IoT middleware platform and retrieving data is a significant challenge for data consumers as it requires both technical knowledge and domain expertise. In this paper, we propose a knowledge driven approach called Context Aware Sensor Configuration Model (CASCOM) to simplify the process of configuring IoT middleware platforms, so the data consumers, specifically non-technical personnel, can easily retrieve the data they required. In this paper, we demonstrate how IoT resources can be described using semantics in such away that they can later be used to compose service work-flows. Such automated semantic-knowledge-based IoT resource composition approach advances the current research. We demonstrate the feasibility and the usability of our approach through a prototype implementation based on an IoT middleware called Global Sensor Networks (GSN), though our model can be generalized to any other middleware platform.
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| 3. |
- Perera, Charith, et al.
(författare)
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Fog Computing for Sustainable Smart Cities : A Survey
- 2017
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Ingår i: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 50:3
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, especially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build a sustainable IoT infrastructure for smart cities. In this article, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges toward implementing them, to shed light on future research directions on realizing Fog computing for building sustainable smart cities.
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| 4. |
- Perera, Charith, et al.
(författare)
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Guest Editorial Special Section on Engineering Industrial Big Data Analytics Platforms for Internet of Things
- 2018
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 14:2, s. 744-747
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.
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| 5. |
- Perera, Charith, et al.
(författare)
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Privacy Mindset for Developing Internet of Things Applicationsfor Social Sensing : Software Engineering Challenges
- 2017
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Ingår i: SocialSens'17. - New York : ACM Digital Library. - 9781450349772 ; , s. 103-
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Konferensbidrag (refereegranskat)abstract
- Social sensing aims to collect sensory data by using human population as sensor carriers (e.g., location), sensor operators (e.g., taking photos), and sensors themselves (e.g., Twitter). The Internet of Things (IoT) applications facilitate social sensing tasks. However, designing and developing IoT applications is much more complicated than designing and developing desktop, mobile, or web applications. The IoT applications require both software and hardware (e.g., sensors and actuators) to work together on multiple different type of nodes (e.g., micro-controllers, system-on-chips, mobile phones, single-board computers, cloud platforms) with different capabilities under different conditions.
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