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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 (31 May 2019) | Viewed by 68014

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Dr. Antonio Guerrieri

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ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Internet of Things; wireless sensor and actuator networks; WSN frameworks; multi agent systems
Special Issues, Collections and Topics in MDPI journals

Dr. Franco Cicirelli

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Guest Editor

ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Software Engineering tools and methodologies for the modeling; analysis and implementation of complex time-dependent systems; agent-based systems; distributed simulation; parallel and distributed systems; real-time systems; workflow management systems; Internet of Things and cyber-physical systems; smart cities; Petri Nets; Timed Automata and the DEVS formalism
Special Issues, Collections and Topics in MDPI journals

Dr. Andrea Vinci

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Guest Editor

ICAR-CNR, Institute of High Performance Computing and Networking of the Italian National Research Council, 87036 Rende, Cosenza, Italy
Interests: Internet of Things and Cyber-Physical Systems; definitions of platforms and methodologies for the design and implementation of cyber-physical systems; distributed algorithms for the efficient management of urban facilities; swarm intelligence and peer-to-peer techniques; and Data Mining; Ambient Intelligence; edge computing; GPU computing; smart cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all the things can cooperate among them and can be managed from anywhere via the Internet, to allow tight integration between physical and cyber worlds, thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies are successfully exploited in several domains providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance: the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of the IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data.

This Special Issue aims at covering state-of-the-art and advancements in technologies, methodologies and applications for IoT, together with emerging standards and research topics which would lead to realization of the Future Internet of Things.

Dr. Antonio Guerrieri
Dr. Franco Cicirelli
Dr. Andrea Vinci
Guest Editors

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Keywords

Internet of Things and Cyber–Physical Systems
Architectures, Methodologies, and Infrastructures for IoT Systems
Cognitive and Social Internet of Things
Future Internet of Things
Smart Computing Technologies and Applications
Networked Sensing and Control
Standards and Protocols for IoT
Cloud, Fog, and Edge Computing for IoT
Big Data Analytics and Ambient Intelligence
Consciousness, Awareness, and Artificial Intelligence in the IoT

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Published Papers (13 papers)

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Editorial

Jump to: Research

4 pages, 165 KiB

Open AccessEditorial

Smart Monitoring and Control in the Future Internet of Things

by Franco Cicirelli, Antonio Guerrieri and Andrea Vinci

Sensors 2022, 22(1), 27; https://doi.org/10.3390/s22010027 - 22 Dec 2021

Viewed by 2757

Abstract

The Internet of Things (IoT) and related technologies are promising in terms of realizing pervasive and smart applications, which, in turn, have the potential to improve the quality of life of people living in a connected world [...] Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

Research

Jump to: Editorial

14 pages, 3654 KiB

Open AccessArticle

A Quantum Ant Colony Multi-Objective Routing Algorithm in WSN and Its Application in a Manufacturing Environment

by Fei Li, Min Liu and Gaowei Xu

Sensors 2019, 19(15), 3334; https://doi.org/10.3390/s19153334 - 29 Jul 2019

Cited by 29 | Viewed by 4001

Abstract

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR’s improvement in performance. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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15 pages, 1502 KiB

Open AccessArticle

Computational Efficiency-Based Adaptive Tracking Control for Robotic Manipulators with Unknown Input Bouc–Wen Hysteresis

by Kan Xie, Yue Lai and Weijun Li

Sensors 2019, 19(12), 2776; https://doi.org/10.3390/s19122776 - 20 Jun 2019

Cited by 4 | Viewed by 2859

Abstract

In order to maintain robotic manipulators at a high level of performance, their controllers should be able to address nonlinearities in the closed-loop system, such as input nonlinearities. Meanwhile, computational efficiency is also required for real-time implementation. In this paper, an unknown input Bouc–Wen hysteresis control problem is investigated for robotic manipulators using adaptive control and a dynamical gain-based approach. The dynamics of hysteresis are modeled as an additional control unit in the closed-loop system and are integrated with the robotic manipulators. Two adaptive parameters are developed for improving the computational efficiency of the proposed control scheme, based on which the outputs of robotic manipulators are driven to track desired trajectories. Lyapunov theory is adopted to prove the effectiveness of the proposed method. Moreover, the tracking error is improved from ultimately bounded to asymptotic tracking compared to most of the existing results. This is of important significance to improve the control quality of robotic manipulators with unknown input Bouc–Wen hysteresis. Numerical examples including fixed-point and trajectory controls are provided to show the validity of our method. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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20 pages, 1627 KiB

Open AccessArticle

Approaching the Communication Constraints of Ethereum-Based Decentralized Applications

by Matevž Pustišek, Anton Umek and Andrej Kos

Sensors 2019, 19(11), 2647; https://doi.org/10.3390/s19112647 - 11 Jun 2019

Cited by 20 | Viewed by 6363

Abstract

Those working on Blockchain technologies have described several new innovative directions and novel services in the Internet of things (IoT), including decentralized trust, trusted and verifiable execution of smart contracts, and machine-to-machine communications and automation that reach beyond the mere exchange of data. However, applying blockchain principles in the IoT is a challenge due to the constraints of the end devices. Because of fierce cost pressure, the hardware resources in these devices are usually reduced to the minimum necessary for operation. To achieve the high coverage needed, low bitrate mobile or wireless technologies are frequently applied, so the communication is often constrained, too. These constraints make the implementation of blockchain nodes for IoT as standalone end-devices impractical or even impossible. We therefore investigated possible design approaches to decentralized applications based on the Ethereum blockchain for the IoT. We proposed and evaluated three application architectures differing in communication, computation, storage, and security requirements. In a pilot setup we measured and analyzed the data traffic needed to run the blockchain clients and their applications. We found out that with the appropriate designs and the remote server architecture we can strongly reduce the storage and communication requirements imposed on devices, with predictable security implications. Periodic device traffic is reduced to 2400 B/s (HTTP) and 170 B/s (Websocket) from about 18 kB/s in the standalone-device full client architecture. A notification about a captured blockchain event and the corresponding verification resulted in about 2000 B of data. A transaction sent from the application to the client resulted in an about 500 B (HTTP) and 300 B message (Websocket). The key store location, which affects the serialization of a transaction, only had a small influence on the transaction-related data. Raw transaction messages were 45 B larger than when passing the JSON transaction objects. These findings provide directions for fog/cloud IoT application designers to avoid unrealistic expectations imposed upon their IoT devices and blockchain technologies, and enable them to select the appropriate system design according to the intended use case and system constraints. However, for very low bit-rate communication networks, new communication protocols for device to blockchain-client need to be considered. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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20 pages, 524 KiB

Open AccessArticle

A Feature-Based Model for the Identification of Electrical Devices in Smart Environments

by Andrea Tundis, Ali Faizan and Max Mühlhäuser

Sensors 2019, 19(11), 2611; https://doi.org/10.3390/s19112611 - 8 Jun 2019

Cited by 20 | Viewed by 5278

Abstract

Smart Homes (SHs) represent the human side of a Smart Grid (SG). Data mining and analysis of energy data of electrical devices in SHs, e.g., for the dynamic load management, is of fundamental importance for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and reduction of the carbon dioxide emission, by knowing how the energy demand of a building is composed in the SG. Advanced monitoring and control mechanisms are necessary to deal with the identification of appliances. In this paper, a model for their automatic identification is proposed. It is based on a set of 19 features that are extracted by analyzing energy consumption, time usage and location from a set of device profiles. Then, machine learning approaches are employed by experimenting different classifiers based on such model for the identification of appliances and, finally, an analysis on the feature importance is provided. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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13 pages, 3451 KiB

Open AccessArticle

An IoT Platform with Monitoring Robot Applying CNN-Based Context-Aware Learning

by Moonsun Shin, Woojin Paik, Byungcheol Kim and Seonmin Hwang

Sensors 2019, 19(11), 2525; https://doi.org/10.3390/s19112525 - 2 Jun 2019

Cited by 25 | Viewed by 5697

Abstract

Internet of Things (IoT) technology has been attracted lots of interests over the recent years, due to its applicability across the various domains. In particular, an IoT-based robot with artificial intelligence may be utilized in various fields of surveillance. In this paper, we propose an IoT platform with an intelligent surveillance robot using machine learning in order to overcome the limitations of the existing closed-circuit television (CCTV) which is installed fixed type. The IoT platform with a surveillance robot provides the smart monitoring as a role of active CCTV. The intelligent surveillance robot, which has been built with its own IoT server, and can carry out line tracing and acquire contextual information through the sensors to detect abnormal status in an environment. In addition, photos taken by its camera can be compared with stored images of normal state. If an abnormal status is detected, the manager receives an alarm via a smart phone. For user convenience, the client is provided with an app to control the robot remotely. In the case of image context processing it is useful to apply convolutional neural network (CNN)-based machine learning (ML), which is introduced for the precise detection and recognition of images or patterns, and from which can be expected a high performance of recognition. We designed the CNN model to support contextually-aware services of the IoT platform and to perform experiments for learning accuracy of the designed CNN model using dataset of images acquired from the robot. Experimental results showed that the accuracy of learning is over 0.98, which means that we achieved enhanced learning in image context recognition. The contribution of this paper is not only to implement an IoT platform with active CCTV robot but also to construct a CNN model for image-and-context-aware learning and intelligence enhancement of the proposed IoT platform. The proposed IoT platform, with an intelligent surveillance robot using machine learning, can be used to detect abnormal status in various industrial fields such as factory, smart farms, logistics warehouses, and public places. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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14 pages, 3735 KiB

Open AccessArticle

An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

by Yuanju Qu, Xinguo Ming, Siqi Qiu, Maokuan Zheng and Zengtao Hou

Sensors 2019, 19(10), 2338; https://doi.org/10.3390/s19102338 - 21 May 2019

Cited by 19 | Viewed by 4296

Abstract

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders’ functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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14 pages, 3163 KiB

Open AccessArticle

A Posture Recognition Method Based on Indoor Positioning Technology

by Xiaoping Huang, Fei Wang, Jian Zhang, Zelin Hu and Jian Jin

Sensors 2019, 19(6), 1464; https://doi.org/10.3390/s19061464 - 26 Mar 2019

Cited by 28 | Viewed by 5095

Abstract

Posture recognition has been widely applied in fields such as physical training, environmental awareness, human-computer-interaction, surveillance system and elderly health care. The traditional methods consist of two main variations: machine vision methods and acceleration sensor methods. The former has the disadvantages of privacy invasion, high cost and complex implementation processes, while the latter has low recognition rate for still postures. A new body posture recognition scheme based on indoor positioning technology is presented in this paper. A single deployed indoor positioning system is constructed by installing wearable receiving tags at key points of the human body. The distance measurement method with ultra-wide band (UWB) radio is applied to position the key points of human body. Posture recognition is implemented by positioning. In the posture recognition algorithm, least square estimation (LSE) method and the improved extended Kalman filtering (iEKF) algorithm are respectively adopted to suppress the noise of the distances measurement and to improve the accuracy of positioning and recognition. The comparison of simulation results with the two methods shows that the improved extended Kalman filtering algorithm is more effective in error performance. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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20 pages, 4988 KiB

Open AccessArticle

A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information

by Xiaochao Dang, Xiong Si, Zhanjun Hao and Yaning Huang

Sensors 2019, 19(4), 875; https://doi.org/10.3390/s19040875 - 20 Feb 2019

Cited by 48 | Viewed by 7707

Abstract

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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16 pages, 6699 KiB

Open AccessArticle

Computation of Traffic Time Series for Large Populations of IoT Devices

by Mikel Izal, Daniel Morató, Eduardo Magaña and Santiago García-Jiménez

Sensors 2019, 19(1), 78; https://doi.org/10.3390/s19010078 - 26 Dec 2018

Cited by 10 | Viewed by 5519

Abstract

The Internet of Things (IoT) contains sets of hundreds of thousands of network-enabled devices communicating with central controlling nodes or information collectors. The correct behaviour of these devices can be monitored by inspecting the traffic that they create. This passive monitoring methodology allows the detection of device failures or security breaches. However, the creation of hundreds of thousands of traffic time series in real time is not achievable without highly optimised algorithms. We herein compare three algorithms for time-series extraction from traffic captured in real time. We demonstrate how a single-core central processing unit (CPU) can extract more than three bidirectional traffic time series for each one of more than 20,000 IoT devices in real time using the algorithm DStries with recursive search. This proposal also enables the fast reconfiguration of the analysis computer when new IoT devices are added to the network. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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13 pages, 5073 KiB

Open AccessArticle

Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway

by Liming Xiao, Yonghong Zhang and Gongzhuang Peng

Sensors 2018, 18(12), 4436; https://doi.org/10.3390/s18124436 - 14 Dec 2018

Cited by 104 | Viewed by 6636

Abstract

The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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18 pages, 2685 KiB

Open AccessArticle

A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments

by Bruno Abade, David Perez Abreu and Marilia Curado

Sensors 2018, 18(11), 3953; https://doi.org/10.3390/s18113953 - 15 Nov 2018

Cited by 39 | Viewed by 6281

Abstract

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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16 pages, 6577 KiB

Open AccessArticle

Development and Application of an Atmospheric Pollutant Monitoring System Based on LoRa—Part I: Design and Reliability Tests

by Yushuang Ma, Long Zhao, Rongjin Yang, Xiuhong Li, Qiao Song, Zhenwei Song and Yi Zhang

Sensors 2018, 18(11), 3891; https://doi.org/10.3390/s18113891 - 12 Nov 2018

Cited by 17 | Viewed by 3941

Abstract

At present, as growing importance continues to be attached to atmospheric environmental problems, the demand for real-time monitoring of these problems is constantly increasing. This article describes the development and application of an embedded system for monitoring of atmospheric pollutant concentrations based on LoRa (Long Range) wireless communication technology, which is widely used in the Internet of Things (IoT). The proposed system is realized using a combination of software and hardware and is designed using the concept of modularization. Separation of each function into independent modules allows the system to be developed more quickly and to be applied more stably. In addition, by combining the requirements of the remote atmospheric pollutant concentration monitoring platform with the specific requirements for the intended application environment, the system demonstrates its significance for practical applications. In addition, the actual application data also verifies the sound application prospects of the proposed system. Full article

(This article belongs to the Special Issue Smart Monitoring and Control in the Future Internet of Things)

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