Special Issue "Sensors and Actuators in Smart Cities"

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708).

Deadline for manuscript submissions: closed (31 October 2017)

Special Issue Editors

Guest Editor
Dr. Mohammad Hammoudeh

Manchester Metropolitan University, Manchester, UK
Website | E-Mail
Phone: +44 (0)161 247 2845
Interests: wireless sensor networks; Internet of things; wireless ad hoc communications; mobile communications; network security; sensor/actuator networks; cyber–physical systems
Guest Editor
Dr. Mounir Arioua

Abdelmalek Essaadi University, Tetuan-Tangier, Morocco
E-Mail
Phone: +212 (0)539 688 027
Interests: wireless sensor networks; wireless networking and communication; wireless communications and mobile computing; Internet of things; real-time processing and embedded systems

Special Issue Information

Dear Colleagues,

With the city, from its earliest emergence in the Near East between 4500 and 3100 BCE, came a wide range of new discoveries and inventions, from synthetic materials to wheeled vehicles. Through its dense population, irrigation, social continuity and physical security, emerged civil engineering, monumental construction, sculpture, mathematics, arts and law. Today, there is an enormous set of ideas and notions with respect to our ways of living, e.g., the ramp and the lever, which are still fundamental to cities’ environmental, social, and economic structures. Modern-day smart cities compete for the introduction of smart technologies and applications to improve key areas of urban communities, such as system automation, sustainability, and quality of life. Technology research experts paint thrilling images of futuristic cities. What’s glossed over, however, is the sensor and actuator technologies that enable these smart cities; in particular, the reliable, heterogeneous, wireless networks specifically designed to provision communication across a countless number of sensors embedded in almost everything.

The world is on the verge of a new epoch of innovation and change with the emergence of Wireless Sensor Networks (WSN). The convergence of smaller, more powerful processors, smart mobile devices, low-cost sensing, big data analytics, cloud hosting and new levels of connectivity allowed by the Internet is fuelling the latest wave of Machine-to-Machine (M2M) technology. The merits of this marriage of machines and the digital world are multiple and significant. It holds the potential to dramatically alter the way in which most global industries, such as buildings, rail transportation, power grids and healthcare operate on daily basis. WSNs expand to include our vehicles and homes, as well as newly developed wearable and implanted sensors, which brings fundamental transformations to many aspects of daily life.

WSN innovations promise to integrate and optimise smart buildings, autonomous vehicles, power grids, etc., to enable a successful transition towards smart, user-driven and demand-focused city infrastructures and services. There is a wide range of current smart cities applications, which make our lives easier and more efficient, e.g., a smart phone application that let users find free parking spaces in the centre of town. However, cities are notoriously inefficient. As populations grow, everything from garbage collection and public transport becomes more expensive and complex. Away from increasing spending, there is also a demand among citizens for smarter services driven by sensor- and actuator-based infrastructure.

In this Special Issue, we are seeking submissions that focus on implementing intelligent sensing infrastructure to solve the smart cities conundrum. This Special Issue invites academic researchers in computer science, communication engineering and physics, as well as information technology industry consultants and practitioners, to contribute original contributions in all aspects of sensors and actuators for smart cities. Authors of selected outstanding papers in the International Conference of Future Networks and Distributed Systems will be invited to submit extended versions of their papers for consideration in this Special Issue.

Contributions may include, but are not limited to:

  • Smart city architecture and infrastructure
  • WSN and Internet of Things (IoT) architectures, protocols, platforms and algorithms
  • Smart city technologies and applications
  • Enabling wireless and mobile technologies for smart cities
  • Intelligent sensors and actuators for homes, buildings and infrastructures
  • Embedded sensing and actuating
  • WSN for enabling technologies for precision healthcare
  • Intelligent transportation systems and technologies
  • Smart sewage and water
  • Smart electricity, grids and meters
  • Smart city community connectivity and solutions
  • City environment monitoring and analysis
  • Smart city sensing and IoT
  • Smart city big data and open data
  • Smart city system security and privacy
  • Inclusive design of smart cities and smart environments
  • Application, deployment, testbed, experiment experiences and innovative applications for smart cities
  • Successful case studies in development or deployment of WSN applications and services in the area of accessibility

Dr. Mohammad Hammoudeh
Dr. Mounir Arioua
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 papers will be 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. Journal of Sensor and Actuator Networks is an international peer-reviewed open access quarterly 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 350 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 (10 papers)

View options order results:
result details:
Displaying articles 1-10
Export citation of selected articles as:

Editorial

Jump to: Research, Review

Open AccessEditorial Sensors and Actuators in Smart Cities
J. Sens. Actuator Netw. 2018, 7(1), 8; doi:10.3390/jsan7010008
Received: 9 February 2018 / Revised: 11 February 2018 / Accepted: 14 February 2018 / Published: 16 February 2018
PDF Full-text (147 KB)
Abstract
With the city, from its earliest emergence in the Near East between 4500 and 3100 BCE, came a wide range of new discoveries and inventions, from synthetic materials to wheeled vehicles.[...] Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)

Research

Jump to: Editorial, Review

Open AccessArticle Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks
J. Sens. Actuator Netw. 2018, 7(1), 2; doi:10.3390/jsan7010002
Received: 31 October 2017 / Revised: 25 December 2017 / Accepted: 29 December 2017 / Published: 2 January 2018
PDF Full-text (2569 KB) | HTML Full-text | XML Full-text
Abstract
We propose a new peak searching algorithm (PSA) that uses Bayesian optimization to find probability peaks in a dataset, thereby increasing the speed and accuracy of clustering algorithms. Wireless sensor networks (WSNs) are becoming increasingly common in a wide variety of applications that
[...] Read more.
We propose a new peak searching algorithm (PSA) that uses Bayesian optimization to find probability peaks in a dataset, thereby increasing the speed and accuracy of clustering algorithms. Wireless sensor networks (WSNs) are becoming increasingly common in a wide variety of applications that analyze and use collected sensing data. Typically, the collected data cannot be directly used in modern data analysis problems that adopt machine learning techniques because such data lacks additional information (such as data labels) specifying its purpose of users. Clustering algorithms that divide the data in a dataset into clusters are often used when additional information is not provided. However, traditional clustering algorithms such as expectation–maximization (EM) and k - m e a n s algorithms require massive numbers of iterations to form clusters. Processing speeds are therefore slow, and clustering results become less accurate because of the way such algorithms form clusters. The PSA addresses these problems, and we adapt it for use with the EM and k - m e a n s algorithms, creating the modified P S E M and P S k - m e a n s algorithms. Our simulation results show that our proposed P S E M and P S k - m e a n s algorithms significantly decrease the required number of clustering iterations (by 1.99 to 6.3 times), and produce clustering that, for a synthetic dataset, is 1.69 to 1.71 times more accurate than it is for traditional EM and enhanced k - m e a n s ( k - m e a n s ++) algorithms. Moreover, in a simulation of WSN applications aimed at detecting outliers, P S E M correctly identified the outliers in a real dataset, decreasing iterations by approximately 1.88 times, and P S E M was 1.29 times more accurate than EM at a maximum. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessArticle Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
J. Sens. Actuator Netw. 2018, 7(1), 1; doi:10.3390/jsan7010001
Received: 31 October 2017 / Revised: 5 December 2017 / Accepted: 11 December 2017 / Published: 2 January 2018
PDF Full-text (3506 KB) | HTML Full-text | XML Full-text
Abstract
One of the main design aspects of the Tactile Internet system is the 1 ms end-to-end latency, which is considered as being the main challenge with the system realization. Forced by recent development and capabilities of the fifth generation (5G) cellular system, the
[...] Read more.
One of the main design aspects of the Tactile Internet system is the 1 ms end-to-end latency, which is considered as being the main challenge with the system realization. Forced by recent development and capabilities of the fifth generation (5G) cellular system, the Tactile Internet will become a real. One way to overcome the 1 ms latency is to employ a centralized controller in the core of the network with a global knowledge of the system, together with the concept of network function virtualization (NFV). This is the idea behind the software defined networking (SDN). This paper introduces a Tactile Internet system structure, which employs SDN in the core of the cellular network and mobile edge computing (MEC) in multi-levels. The work is mainly concerned with the structure of the core network. The system is simulated over a reliable environment and introduces a round trip latency of orders of 1 ms. This can be interpreted by the reduction of intermediate nodes that are involved in the communication process. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessArticle Using Sensors to Study Home Activities
J. Sens. Actuator Netw. 2017, 6(4), 32; doi:10.3390/jsan6040032
Received: 1 November 2017 / Revised: 3 December 2017 / Accepted: 13 December 2017 / Published: 16 December 2017
PDF Full-text (1741 KB) | HTML Full-text | XML Full-text
Abstract
Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and
[...] Read more.
Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessArticle Extended Batches Petri Nets Based System for Road Traffic Management in WSNs
J. Sens. Actuator Netw. 2017, 6(4), 30; doi:10.3390/jsan6040030
Received: 1 October 2017 / Revised: 21 November 2017 / Accepted: 28 November 2017 / Published: 4 December 2017
PDF Full-text (3899 KB) | HTML Full-text | XML Full-text
Abstract
One of the most critical issues in modern cities is transportation management. Issues that are encountered in this regard, such as traffic congestion, high accidents rates and air pollution etc., have pushed the use of Intelligent Transportation System (ITS) technologies in order to
[...] Read more.
One of the most critical issues in modern cities is transportation management. Issues that are encountered in this regard, such as traffic congestion, high accidents rates and air pollution etc., have pushed the use of Intelligent Transportation System (ITS) technologies in order to facilitate the traffic management. Seen in this perspective, this paper brings forward a road traffic management system based on wireless sensor networks; it introduces the functional and deployment architecture of the system and focuses on the analysis component that uses a new extension of batches Petri nets for modeling road traffic flow. A real world implementation of visualization and data analysis components were carried out. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessFeature PaperArticle Wearable-Based Human Activity Recognition Using an IoT Approach
J. Sens. Actuator Netw. 2017, 6(4), 28; doi:10.3390/jsan6040028
Received: 30 September 2017 / Revised: 15 November 2017 / Accepted: 17 November 2017 / Published: 24 November 2017
PDF Full-text (3970 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile,
[...] Read more.
This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessArticle A Social Environmental Sensor Network Integrated within a Web GIS Platform
J. Sens. Actuator Netw. 2017, 6(4), 27; doi:10.3390/jsan6040027
Received: 1 October 2017 / Revised: 5 November 2017 / Accepted: 15 November 2017 / Published: 21 November 2017
PDF Full-text (2810 KB) | HTML Full-text | XML Full-text
Abstract
We live in an era where typical measures towards the mitigation of environmental degradation follow the identification and recording of natural parameters closely associated with it. In addition, current scientific knowledge on the one hand may be applied to minimize the environmental impact
[...] Read more.
We live in an era where typical measures towards the mitigation of environmental degradation follow the identification and recording of natural parameters closely associated with it. In addition, current scientific knowledge on the one hand may be applied to minimize the environmental impact of anthropogenic activities, whereas informatics on the other, playing a key role in this ecosystem, do offer new ways of implementing complex scientific processes regarding the collection, aggregation and analysis of data concerning environmental parameters. Furthermore, another related aspect to consider is the fact that almost all relevant data recordings are influenced by their given spatial characteristics. Taking all aforementioned inputs into account, managing such a great amount of complex and remote data requires specific digital structures; these structures are typically deployed over the Web on an attempt to capitalize existing open software platforms and modern developments of hardware technology. In this paper we present an effort to provide a technical solution based on sensing devices that are based on the well-known Arduino platform and operate continuously for gathering and transmitting of environmental state information. Controls, user interface and extensions of the proposed project rely on the Android mobile device platform (both from the software and hardware side). Finally, a crucial novel aspect of our work is the fact that all herein gathered data carry spatial information, which is rather fundamental for the successful correlation between pollutants and their place of origin. The latter is implemented by an interactive Web GIS platform operating oversight in situ and on a timeline basis. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessArticle User-Generated Services Composition in Smart Multi-User Environments
J. Sens. Actuator Netw. 2017, 6(3), 20; doi:10.3390/jsan6030020
Received: 6 July 2017 / Revised: 18 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
PDF Full-text (1622 KB) | HTML Full-text | XML Full-text
Abstract
The increasing complexity shown in Smart Environments, together with the spread of social networks, is increasingly moving the role of users from simple information and services consumers to actual producers. In this work, we focus on security issues raised by a particular kind
[...] Read more.
The increasing complexity shown in Smart Environments, together with the spread of social networks, is increasingly moving the role of users from simple information and services consumers to actual producers. In this work, we focus on security issues raised by a particular kind of services: those generated by users. User-Generated Services (UGSs) are characterized by a set of features that distinguish them from conventional services. To cope with UGS security problems, we introduce three different policy management models, analyzing benefits and drawbacks of each approach. Finally, we propose a cloud-based solution that enables the composition of multiple UGSs and policy models, allowing users’ devices to share features and services in Internet of Things (IoT) based scenarios. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Open AccessArticle Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
J. Sens. Actuator Netw. 2017, 6(3), 19; doi:10.3390/jsan6030019
Received: 30 June 2017 / Revised: 8 August 2017 / Accepted: 9 August 2017 / Published: 21 August 2017
Cited by 1 | PDF Full-text (3884 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we present a new enhancement for an emergency and disaster relief system called Critical and Rescue Operations using Wearable Wireless sensors networks (CROW2). We address the reliability challenges in setting up a wireless autonomous communication system in order
[...] Read more.
In this paper, we present a new enhancement for an emergency and disaster relief system called Critical and Rescue Operations using Wearable Wireless sensors networks (CROW 2 ). We address the reliability challenges in setting up a wireless autonomous communication system in order to offload data from the disaster area (rescuers, trapped victims, civilians, media, etc.) back to a command center. The proposed system connects deployed rescuers to extended networks and the Internet. CROW 2 is an end-to-end system that runs the recently-proposed Optimized Routing Approach for Critical and Emergency Networks (ORACE-Net) routing protocol. The system integrates heterogeneous wireless devices (Raspberry Pi, smart phones, sensors) and various communicating technologies (WiFi IEEE 802.11n, Bluetooth IEEE 802.15.1) to enable end-to-end network connectivity, which is monitored by a cloud Internet-of-Things platform. First, we present the CROW 2 generic system architecture, which is adaptable to various technologies integration at different levels (i.e., on-body, body-to-body, off-body). Second, we implement the ORACE-Net protocol on heterogeneous devices including Android-based smart phones and Linux-based Raspberry Pi devices. These devices act as on-body coordinators to collect information from on-body sensors. The collected data is then pushed to the command center thanks to multi-hop device-to-device communication. Third, the overall CROW 2 system performance is evaluated according to relevant metrics including end-to-end link quality estimation, throughput and end-to-end delay. As a proof-of-concept, we validate the system architecture through deployment and extracted experimental results. Finally, we highlight motion detection and links’ unavailability prevention based on the recorded data where the main factors (i.e., interference and noise) that affect the performance are analyzed. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Review

Jump to: Editorial, Research

Open AccessFeature PaperReview Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities
J. Sens. Actuator Netw. 2017, 6(4), 26; doi:10.3390/jsan6040026
Received: 20 October 2017 / Revised: 12 November 2017 / Accepted: 17 November 2017 / Published: 20 November 2017
PDF Full-text (1030 KB) | HTML Full-text | XML Full-text
Abstract
With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with
[...] Read more.
With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
Figures

Figure 1

Back to Top