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Special Issue "Smart IoT Sensing"

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

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 30397

Special Issue Editors

Prof. Rajiv Ranjan
E-Mail Website
Guest Editor
School of Computing, Urban Sciences Building, Newcastle University, 1 Science Square, Newcastle Helix, Newcastle upon Tyne NE4 5TG, UK
Interests: cloud computing; internet of things; big data; distributed systems; peer-to-peer networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michael Sheng
E-Mail Website
Guest Editor
Prof. Dr. Prem Prakash Jayaraman
E-Mail Website
Guest Editor
Faculty of Science, Engineering & Technology, Swinburne University of Technology, 1 Alfred Street, Hawthorn, VIC 3122, Australia
Interests: internet of things; distributed computing; mobile and cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is the latest Internet evolution that incorporates billions of Internet-connected devices that range from cameras, sensors, RFIDs, smart phones, and wearables, to smart meters, vehicles, medication pills, signs and industrial machines. Such IoT things are often owned by different organizations and people who are deploying and using them for their own purposes. Federations of such IoT devices (referred to as IoT things) can also deliver timely and accurate information that is needed to solve internet-scale problems that have been too difficult to tackle before.

To realize its enormous potential, IoT must provide IoT solutions for discovering needed IoT devices, collecting and integrating their data, and distilling the high value information each application needs. Such IoT solutions must be capable of filtering, aggregating, correlating, and contextualizing IoT information in real-time, on the move, in the edge and the cloud, and securely and must be capable of introducing data-driven changes to the physical world.

The MDPI Sensors solicits paper submissions and aim to bring together researchers and application developers working on the intersection of IoT with next-generation sensor development, distributed, cloud, internet, mobile, ambient, semantic, real-time, secure and privacy-preserving computing. We also aim to explore the application of novel IoT computing results and describe and assess their impact.

Topics of Interest

Research topics of interest track include (but not necessary limited to these):

  • Development of smart IoT devices and robots that provide information from and perform actions in the IoT cyber-physical ecosystem
  • Discovery and integration of IoT devices and their data permitting the use of billions of IoT devices that have been deployed, owned, and controlled by others – one of the major benefits of IoT
  • Real-time IoT data analysis on the cloud, at the edge, and on the move, including localisation, personalisation, and contextualisation of IoT data
  • IoT Security and privacy for IoT devices with limited computing resource and connectivity
  • IoT Actuation via IoT devices, robots and process-based automation
  • Lower-power and longer-range IoT networking for IoT devices
  • Wearable IoT devices and systems
  • Human performance monitoring, human/IoT integration, and IoT information visualisation

Application topics of interest include all aspects of, but not limited to:

  • Smart city services, including transportation, utilities, pollution management, and disaster management
  • Smart grids, including hybrid power stations and demand side management
  • Smart farming, including crop irrigation, fertilisation, and crop assessment and recommendation
  • Smart manufacturing including Industry 4.0 and the Industrial Internet
  • Smart retail chains, including supply chain management and probability marketing,

Prof. Dimitrios Georgakopoulos
Prof. Rajiv Ranjan
Prof. Michael Sheng
Dr. Prem Prakash Jayaraman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2400 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.

Published Papers (9 papers)

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Research

Article
Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development
Sensors 2019, 19(11), 2430; https://doi.org/10.3390/s19112430 - 28 May 2019
Cited by 11 | Viewed by 1636
Abstract
Smart city is an emerging initiative for integrating Information and Communication Technologies (ICT) in effective ways to support development of smart cities with enhanced quality of life for its citizens through safe and secure context-aware services. Major technical challenges to realize smart cities [...] Read more.
Smart city is an emerging initiative for integrating Information and Communication Technologies (ICT) in effective ways to support development of smart cities with enhanced quality of life for its citizens through safe and secure context-aware services. Major technical challenges to realize smart cities include resource use optimization, service delivery without interruption at all times in all aspects, minimization of costs, and reduction of resource consumption. To address these challenges, new techniques and technologies are required for modeling and processing the big data generated and used through the underlying Internet of Things (IoT). To this end, we propose a data-centric approach to IoT in conceptualizing the “things” from a service-oriented perspective and investigate efficient ways to identify, integrate, and manage big data. The data-centric approach is expected to better support efficient management of data with complexities inherent in IoT-generated big data. Furthermore, it supports efficient and scalable query processing and reasoning techniques required in development of smart city applications. This article redresses the literature and contributes to the foundations of smart cities applications. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City
Sensors 2019, 19(9), 2048; https://doi.org/10.3390/s19092048 - 02 May 2019
Cited by 65 | Viewed by 5197
Abstract
The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s aim was to design and evaluate [...] Read more.
The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens’ privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework
Sensors 2019, 19(9), 2025; https://doi.org/10.3390/s19092025 - 30 Apr 2019
Cited by 18 | Viewed by 3143
Abstract
A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of [...] Read more.
A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
Object Tracking for a Smart City Using IoT and Edge Computing
Sensors 2019, 19(9), 1987; https://doi.org/10.3390/s19091987 - 28 Apr 2019
Cited by 19 | Viewed by 2191
Abstract
As the Internet-of-Things (IoT) and edge computing have been major paradigms for distributed data collection, communication, and processing, smart city applications in the real world tend to adopt IoT and edge computing broadly. Today, more and more machine learning algorithms would be deployed [...] Read more.
As the Internet-of-Things (IoT) and edge computing have been major paradigms for distributed data collection, communication, and processing, smart city applications in the real world tend to adopt IoT and edge computing broadly. Today, more and more machine learning algorithms would be deployed into front-end sensors, devices, and edge data centres rather than centralised cloud data centres. However, front-end sensors and devices are usually not so capable as those computing units in huge data centres, and for this sake, in practice, engineers choose to compromise for limited capacity of embedded computing and limited memory, e.g., neural network models being pruned to fit embedded devices. Visual object tracking is one of many important elements of a smart city, and in the IoT and edge computing context, high requirements to computing power and memory space severely prevent massive and accurate tracking. In this paper, we report on our contribution to object tracking on lightweight computing including (1) using limited computing capacity and memory space to realise tracking; (2) proposing a new algorithm region proposal correlation filter fitting for most edge devices. Systematic evaluations show that (1) our techniques can fit most IoT devices; (2) our techniques can keep relatively high accuracy; and (3) the generated model size is much less than others. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
Sensors 2019, 19(8), 1788; https://doi.org/10.3390/s19081788 - 14 Apr 2019
Cited by 103 | Viewed by 6505
Abstract
The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the [...] Read more.
The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications
Sensors 2019, 19(6), 1372; https://doi.org/10.3390/s19061372 - 19 Mar 2019
Cited by 11 | Viewed by 5404
Abstract
Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and [...] Read more.
Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
Towards Semantic Sensor Data: An Ontology Approach
Sensors 2019, 19(5), 1193; https://doi.org/10.3390/s19051193 - 08 Mar 2019
Cited by 13 | Viewed by 1974
Abstract
In order to optimize intelligent applications driven by various sensors, it is vital to properly interpret and reuse sensor data from different domains. The construction of semantic maps which illustrate the relationship between heterogeneous domain ontologies plays an important role in knowledge reuse. [...] Read more.
In order to optimize intelligent applications driven by various sensors, it is vital to properly interpret and reuse sensor data from different domains. The construction of semantic maps which illustrate the relationship between heterogeneous domain ontologies plays an important role in knowledge reuse. However, most mapping methods in the literature use the literal meaning of each concept and instance in the ontology to obtain semantic similarity. This is especially the case for domain ontologies which are built for applications with sensor data. At the instance level, there is seldom work to utilize data of the sensor instances when constructing the ontologies’ mapping relationship. To alleviate this problem, in this paper, we propose a novel mechanism to achieve the association between sensor data and domain ontology. In our approach, we first classify the sensor data by making them as SSN (Semantic Sensor Network) ontology instances, and map the corresponding instances to the concepts in the domain ontology. Secondly, a multi-strategy similarity calculation method is used to evaluate the similarity of the concept pairs between the domain ontologies at multiple levels. Finally, the set of concept pairs with a high similarity is selected by the analytic hierarchy process to construct the mapping relationship between the domain ontologies, and then the correlation between sensor data and domain ontologies are constructed. Using the method presented in this paper, we perform sensor data correlation experiments with a simulator for a real world scenario. By comparison to other methods, the experimental results confirm the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
A Type-Aware Approach to Message Clustering for Protocol Reverse Engineering
Sensors 2019, 19(3), 716; https://doi.org/10.3390/s19030716 - 10 Feb 2019
Cited by 7 | Viewed by 1887
Abstract
Protocol Reverse Engineering (PRE) is crucial for information security of Internet-of-Things (IoT), and message clustering determines the effectiveness of PRE. However, the quality of services still lags behind the strict requirement of IoT applications as the results of message clustering are often coarse-grained [...] Read more.
Protocol Reverse Engineering (PRE) is crucial for information security of Internet-of-Things (IoT), and message clustering determines the effectiveness of PRE. However, the quality of services still lags behind the strict requirement of IoT applications as the results of message clustering are often coarse-grained with the intrinsic type information hidden in messages largely ignored. Aiming at this problem, this study proposes a type-aware approach to message clustering guided by type information. The approach regards a message as a combination of n-grams, and it employs the Latent Dirichlet Allocation (LDA) model to characterize messages with types and n-grams via inferring the type distribution of each message. The type distribution is finally used to measure the similarity of messages. According to this similarity, the approach clusters messages and further extracts message formats. Experimental results of the approach against Netzob in terms of a number of protocols indicate that the correctness and conciseness can be significantly improved, e.g., figures 43.86% and 3.87%, respectively for the CoAP protocol. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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Article
Indoor Pedestrian Self-Positioning Based on Image Acoustic Source Impulse Using a Sensor-Rich Smartphone
Sensors 2018, 18(12), 4143; https://doi.org/10.3390/s18124143 - 26 Nov 2018
Cited by 4 | Viewed by 1520
Abstract
The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free [...] Read more.
The ubiquity of sensor-rich smartphones provides opportunities for a low-cost method to track indoor pedestrians. In this situation, pedestrian dead reckoning (PDR) is a widely used technology; however, its cumulative error seriously affects its accuracy. This paper presents a method of combining infrastructure-free indoor acoustic self-positioning with PDR self-positioning, which verifies the rationality of PDR results through the acoustic constraint between a sound source and its image sources. We further determine the first-order echo delay measurements, thus obtaining the mobile user position. We verify that the proposed method can achieve a continuous self-positioning median error of 0.19 m, and the error probability below 0.12 m is 54.46%, which indicates its ability to eliminate PDR error, as well as its adaptability to environmental disturbances. Full article
(This article belongs to the Special Issue Smart IoT Sensing)
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