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Sensor Networks: Physical and Social Sensing in the IoT

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

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 62060

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Dr. Suparna De

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Guest Editor

Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH, UK
Interests: Internet of Things; data analytics; social computing; semantics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Klaus Moessner

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Guest Editor

Technische Universität Chemnitz, Str. der Nationen 62, 09111 Chemnitz, Germany
Interests: context and Situation awareness; sensor and actuator network; wireless communication systems
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Advances made in the Internet of Things and other disruptive technological trends such as Big Data analytics and edge computing are contributing enabling solutions to the numerous challenges affecting modern communities. With Gartner reporting 14.2 billion devices in 2019 and, according to some reports, a projected 75 billion IoT devices that will be deployed by 2025 in areas like environment monitoring, smart agriculture, smart healthcare or smart cites, one could think that most issues are already resolved. However, there remain practical challenges in large-scale and rapid deployment of sensors for diverse applications, such as siting optimization methods and participant recruitment and incentive mechanisms. On a higher level, the deluge of data sources that drive the IoT phenomenon grows every day with the rise of smartphone-enabled citizen sensing data on social networks and personal health devices, as well as with increasing connectedness, be it in the transport, logistics, utilities, or manufacturing domains, this range and complexity of the available data calls for even more advanced data processing, mining and fusion methods than those already applied.

Dr. Suparna De
Prof. Dr. Klaus Moessner
Guest Editors

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Keywords

Rapid deployment of sensor networks
Context-awareness for smart IoT environments
Cloud virtual sensors in IoT
Virtual sensors modelling using neural networks and/or deep learning
Crowd sensing and related issues for IoT applications
Techniques for spatio-temporal big data analysis of IoT datasets
Large-scale IoT data fusion techniques with computational intelligence
Multi-sensor fusion approaches
Pattern derivation through visualization
Correlation between physical and social data streams
Applications of IoT sensor networks, including analytics and visualization
Applications based on heterogeneous data in IoT networks, such as eHealth, traffic, infrastructures, environment monitoring, etc.

Published Papers (12 papers)

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Editorial

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4 pages, 192 KiB

Open AccessEditorial

Sensor Networks: Physical and Social Sensing in the IoT

by Suparna De and Klaus Moessner

Sensors 2023, 23(3), 1451; https://doi.org/10.3390/s23031451 - 28 Jan 2023

Viewed by 1348

Abstract

Advances made in the Internet of Things (IoT) and other disruptive technological trends, including big data analytics and edge computing methods, have contributed enabling solutions to the numerous challenges affecting modern communities [...] Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

Research

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26 pages, 590 KiB

Open AccessArticle

A Novel Privacy Preserving Scheme for Smart Grid-Based Home Area Networks

by Wajahat Ali, Ikram Ud Din, Ahmad Almogren and Byung-Seo Kim

Sensors 2022, 22(6), 2269; https://doi.org/10.3390/s22062269 - 15 Mar 2022

Cited by 10 | Viewed by 2802

Abstract

Despite the benefits of smart grids, concerns about security and privacy arise when a large number of heterogeneous devices communicate via a public network. A novel privacy-preserving method for smart grid-based home area networks (HAN) is proposed in this research. To aggregate data from diverse household appliances, the proposed approach uses homomorphic Paillier encryption, Chinese remainder theorem, and one-way hash function. The privacy in Internet of things (IoT)-enabled smart homes is one of the major concerns of the research community. In the proposed scheme, the sink node not only aggregates the data but also enables the early detection of false data injection and replay attacks. According to the security analysis, the proposed approach offers adequate security. The smart grid distributes power and facilitates a two-way communications channel that leads to transparency and developing trust. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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20 pages, 1602 KiB

Open AccessArticle

An Architecture for Service Integration to Fully Support Novel Personalized Smart Tourism Offerings

by Andrea Sabbioni, Thomas Villano and Antonio Corradi

Sensors 2022, 22(4), 1619; https://doi.org/10.3390/s22041619 - 18 Feb 2022

Cited by 7 | Viewed by 2157

Abstract

The continuous evolution of IT (information technology) technologies is radically transforming many technical areas and social aspects, also reshaping the way we behave and looking for entertainment and leisure services. In that context, tourism experiences request to enhance the level of user involvement and integration and to create an ever more personalized and connected experience, by leveraging on the differentiated tourist services and information locally present in the territory, by pushing active participation of customers, and by taking advantage of the ever-increasing presence of sensors and IoT (Internet of Things) devices deployed in many realities. However, the deep fragmentation of services and technologies adopted in tourism context characterizes the whole information provided also by customer sensing and IoTs (Internet of Things) heterogeneity and deep clashes with an effective organization of smart tourism. This article presents APERTO5.0 (an Architecture for Personalization and Elaboration of services and data to Reshape Tourism Offers 5.0), an innovative architecture aiming at a whole integration and deep facilitation of tourism service and information organization and blending, to enable the re-provisioning of novel services as advanced aggregates or re-elaborated ones. The proposed solution will demonstrate its effectiveness in the context of Smart Tourism by choosing the real use case of the “Francigena way” (a pilgrim historical path), the Italian part. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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33 pages, 760 KiB

Open AccessArticle

Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts

by Riki Murakami and Basabi Chakraborty

Sensors 2022, 22(3), 852; https://doi.org/10.3390/s22030852 - 23 Jan 2022

Cited by 11 | Viewed by 3753

Abstract

With the rapid proliferation of social networking sites (SNS), automatic topic extraction from various text messages posted on SNS are becoming an important source of information for understanding current social trends or needs. Latent Dirichlet Allocation (LDA), a probabilistic generative model, is one of the popular topic models in the area of Natural Language Processing (NLP) and has been widely used in information retrieval, topic extraction, and document analysis. Unlike long texts from formal documents, messages on SNS are generally short. Traditional topic models such as LDA or pLSA (probabilistic latent semantic analysis) suffer performance degradation for short-text analysis due to a lack of word co-occurrence information in each short text. To cope with this problem, various techniques are evolving for interpretable topic modeling for short texts, pretrained word embedding with an external corpus combined with topic models is one of them. Due to recent developments of deep neural networks (DNN) and deep generative models, neural-topic models (NTM) are emerging to achieve flexibility and high performance in topic modeling. However, there are very few research works on neural-topic models with pretrained word embedding for generating high-quality topics from short texts. In this work, in addition to pretrained word embedding, a fine-tuning stage with an original corpus is proposed for training neural-topic models in order to generate semantically coherent, corpus-specific topics. An extensive study with eight neural-topic models has been completed to check the effectiveness of additional fine-tuning and pretrained word embedding in generating interpretable topics by simulation experiments with several benchmark datasets. The extracted topics are evaluated by different metrics of topic coherence and topic diversity. We have also studied the performance of the models in classification and clustering tasks. Our study concludes that though auxiliary word embedding with a large external corpus improves the topic coherency of short texts, an additional fine-tuning stage is needed for generating more corpus-specific topics from short-text data. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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20 pages, 1220 KiB

Open AccessArticle

Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications

by Elena Simona Lohan, Viktoriia Shubina and Dragoș Niculescu

Sensors 2022, 22(2), 687; https://doi.org/10.3390/s22020687 - 17 Jan 2022

Cited by 7 | Viewed by 2641

Abstract

Future social networks will rely heavily on sensing data collected from users’ mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user’s location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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23 pages, 6851 KiB

Open AccessArticle

Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

by Alberto Gascón, Roberto Casas, David Buldain and Álvaro Marco

Sensors 2022, 22(2), 586; https://doi.org/10.3390/s22020586 - 13 Jan 2022

Cited by 1 | Viewed by 2127

Abstract

Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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38 pages, 12954 KiB

Open AccessArticle

Energy Management Expert Assistant, a New Concept

by Matias Linan-Reyes, Joaquin Garrido-Zafra, Aurora Gil-de-Castro and Antonio Moreno-Munoz

Sensors 2021, 21(17), 5915; https://doi.org/10.3390/s21175915 - 02 Sep 2021

Cited by 4 | Viewed by 2787

Abstract

In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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32 pages, 2379 KiB

Open AccessArticle

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

by Thorben Iggena, Eushay Bin Ilyas, Marten Fischer, Ralf Tönjes, Tarek Elsaleh, Roonak Rezvani, Narges Pourshahrokhi, Stefan Bischof, Andreas Fernbach, Josiane Xavier Parreira, Patrik Schneider, Pavel Smirnov, Martin Strohbach, Hien Truong, Aurora González-Vidal, Antonio F. Skarmeta, Parwinder Singh, Michail J. Beliatis, Mirko Presser, Juan A. Martinez, Pedro Gonzalez-Gil, Marianne Krogbæk and Sebastian Holmgård Christophersen Show full author list Hide full author list

Sensors 2021, 21(5), 1559; https://doi.org/10.3390/s21051559 - 24 Feb 2021

Cited by 15 | Viewed by 4865

Abstract

Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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23 pages, 798 KiB

Open AccessArticle

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

by Nicolas Zurbuchen, Adriana Wilde and Pascal Bruegger

Sensors 2021, 21(3), 938; https://doi.org/10.3390/s21030938 - 30 Jan 2021

Cited by 27 | Viewed by 4443

Abstract

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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26 pages, 1680 KiB

Open AccessArticle

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals

by Ivan Rodrigues de Moura, Ariel Soares Teles, Markus Endler, Luciano Reis Coutinho and Francisco José da Silva e Silva

Sensors 2021, 21(1), 86; https://doi.org/10.3390/s21010086 - 25 Dec 2020

Cited by 5 | Viewed by 3236

Abstract

Traditionally, mental health specialists monitor their patients’ social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson’s correlation coefficient >70%) with individuals’ social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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21 pages, 2510 KiB

Open AccessArticle

egoDetect: Visual Detection and Exploration of Anomaly in Social Communication Network

by Jiansu Pu, Jingwen Zhang, Hui Shao, Tingting Zhang and Yunbo Rao

Sensors 2020, 20(20), 5895; https://doi.org/10.3390/s20205895 - 18 Oct 2020

Cited by 2 | Viewed by 2515

Abstract

The development of the Internet has made social communication increasingly important for maintaining relationships between people. However, advertising and fraud are also growing incredibly fast and seriously affect our daily life, e.g., leading to money and time losses, trash information, and privacy problems. Therefore, it is very important to detect anomalies in social networks. However, existing anomaly detection methods cannot guarantee the correct rate. Besides, due to the lack of labeled data, we also cannot use the detection results directly. In other words, we still need human analysts in the loop to provide enough judgment for decision making. To help experts analyze and explore the results of anomaly detection in social networks more objectively and effectively, we propose a novel visualization system, egoDetect, which can detect the anomalies in social communication networks efficiently. Based on the unsupervised anomaly detection method, the system can detect the anomaly without training and get the overview quickly. Then we explore an ego’s topology and the relationship between egos and alters by designing a novel glyph based on the egocentric network. Besides, it also provides rich interactions for experts to quickly navigate to the interested users for further exploration. We use an actual call dataset provided by an operator to evaluate our system. The result proves that our proposed system is effective in the anomaly detection of social networks. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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Review

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36 pages, 810 KiB

Open AccessReview

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

by Mamoona Majid, Shaista Habib, Abdul Rehman Javed, Muhammad Rizwan, Gautam Srivastava, Thippa Reddy Gadekallu and Jerry Chun-Wei Lin

Sensors 2022, 22(6), 2087; https://doi.org/10.3390/s22062087 - 08 Mar 2022

Cited by 246 | Viewed by 26564

Abstract

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions. Full article

(This article belongs to the Special Issue Sensor Networks: Physical and Social Sensing in the IoT)

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