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Special Issue "Context-Awareness in the Internet of Things"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editor

Guest Editor
Prof. Dr. Daniele Riboni

Dept. of Mathematics and Computer Science, University of Cagliari, Italy
Website | E-Mail
Interests: Sensor-based activity recognition, Hybrid activity recognition methods, Recognition of behavioral anomalies, Pervasive computing and context-awareness, Context modeling techniques, Privacy in location-based services, Privacy in pervasive computing

Special Issue Information

Dear Colleagues,

The emerging integration of objects able to sense, reason and communicate into people's daily lives is enabling a new generation of services, promising to assist users in a number of tasks. In order to be effective, these services must be aware of the user’s context, including not only his/her position, but also the current activity, situation, mood, social context, task and goals, just to name a few. Luckily, objects connected to the Internet of Things (IoT) provide extensive low-level data that can be mined for capturing a fine-grained picture of the user’s context. Integrating and mining IoT data for context-awareness is a hot research topic, which involve challenging issues regarding artificial intelligence, knowledge representation and reasoning, big data analysis, security, trust, ethics, and privacy. The goal of this Special Issue is to collect high-quality research outcomes that may advance the state of the art about context-awareness in IoT. Original, high quality contributions that have not yet been published, submitted, or are not currently under review by other journals or peer-reviewed conferences are sought. Topics of interest include, but are not limited to, the following topics:

  • Statistical, symbolic, and hybrid methods for context-awareness based on sensor data
  • Recognition of activities, moods, and social context in IoT
  • Big data analysis for context awareness
  • IoT-centered methods, applications and middlewares for context-awareness
  • Security, privacy, trust, and ethical issues in context-awareness

Assoc. Prof. Dr. Daniele Riboni
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Context-awareness in IoT
  • Activity and mood recognition
  • Big data analysis for context-awareness
  • Security and privacy issues in context-awareness
  • Sensor-based context modeling and reasoning

Published Papers (4 papers)

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Research

Open AccessArticle
Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges
Sensors 2019, 19(14), 3208; https://doi.org/10.3390/s19143208
Received: 29 April 2019 / Revised: 17 June 2019 / Accepted: 21 June 2019 / Published: 21 July 2019
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Abstract
Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility [...] Read more.
Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility with IoT devices with limited computational resources. In this paper, we investigate the maturity of this technology and the challenges toward PUF utilization in IoT that still need to be addressed. Full article
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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Open AccessArticle
Action Graphs for Performing Goal Recognition Design on Human-Inhabited Environments
Sensors 2019, 19(12), 2741; https://doi.org/10.3390/s19122741
Received: 29 March 2019 / Revised: 4 June 2019 / Accepted: 14 June 2019 / Published: 18 June 2019
PDF Full-text (2330 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Goal recognition is an important component of many context-aware and smart environment services; however, a person’s goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations [...] Read more.
Goal recognition is an important component of many context-aware and smart environment services; however, a person’s goal often cannot be determined until their plan nears completion. Therefore, by modifying the state of the environment, our work aims to reduce the number of observations required to recognise a human’s goal. These modifications result in either: Actions in the available plans being replaced with more distinctive actions; or removing the possibility of performing some actions, so humans are forced to take an alternative (more distinctive) plan. In our solution, a symbolic representation of actions and the world state is transformed into an Action Graph, which is then traversed to discover the non-distinctive plan prefixes. These prefixes are processed to determine which actions should be replaced or removed. For action replacement, we developed an exhaustive approach and an approach that shrinks the plans then reduces the non-distinctive plan prefixes, namely Shrink–Reduce. Exhaustive is guaranteed to find the minimal distinctiveness but is more computationally expensive than Shrink–Reduce. These approaches are compared using a test domain with varying amounts of goals, variables and values, and a realistic kitchen domain. Our action removal method is shown to increase the distinctiveness of various grid-based navigation problems, with a width/height ranging from 4 to 16 and between 2 and 14 randomly selected goals, by an average of 3.27 actions in an average time of 4.69 s, whereas a state-of-the-art approach often breaches a 10 min time limit. Full article
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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Graphical abstract

Open AccessArticle
Cross-Device Computation Coordination for Mobile Collocated Interactions with Wearables
Sensors 2019, 19(4), 796; https://doi.org/10.3390/s19040796
Received: 3 January 2019 / Revised: 12 February 2019 / Accepted: 12 February 2019 / Published: 15 February 2019
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Abstract
Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices [...] Read more.
Mobile devices, wearables and Internet-of-Things are crammed into smaller form factors and batteries, yet they encounter demanding applications such as big data analysis, data mining, machine learning, augmented reality and virtual reality. To meet such high demands in the multi-device ecology, multiple devices should communicate collectively to share computation burdens and stay energy-efficient. In this paper, we present a cross-device computation coordination method for scenarios of mobile collocated interactions with wearables. We formally define a cross-device computation coordination problem and propose a method for solving this problem. Lastly, we demonstrate the feasibility of our approach through experiments and exemplar cases using 12 commercial Android devices with varying computation capabilities. Full article
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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Open AccessArticle
Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring
Sensors 2019, 19(3), 646; https://doi.org/10.3390/s19030646
Received: 10 January 2019 / Revised: 30 January 2019 / Accepted: 1 February 2019 / Published: 4 February 2019
Cited by 4 | PDF Full-text (4908 KB) | HTML Full-text | XML Full-text
Abstract
Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians [...] Read more.
Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians with indicators for improving a patient’s health. To be successful, such system has to reason about the person’s actions and goals. To address this problem, we introduce a symbolic behaviour recognition approach, called Computational Causal Behaviour Models (CCBM). CCBM combines symbolic representation of person’s behaviour with probabilistic inference to reason about one’s actions, the type of meal being prepared, and its potential health impact. To evaluate the approach, we use a cooking dataset of unscripted kitchen activities, which contains data from various sensors in a real kitchen. The results show that the approach is able to reason about the person’s cooking actions. It is also able to recognise the goal in terms of type of prepared meal and whether it is healthy. Furthermore, we compare CCBM to state-of-the-art approaches such as Hidden Markov Models (HMM) and decision trees (DT). The results show that our approach performs comparable to the HMM and DT when used for activity recognition. It outperformed the HMM for goal recognition of the type of meal with median accuracy of 1 compared to median accuracy of 0.12 when applying the HMM. Our approach also outperformed the HMM for recognising whether a meal is healthy with a median accuracy of 1 compared to median accuracy of 0.5 with the HMM. Full article
(This article belongs to the Special Issue Context-Awareness in the Internet of Things)
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