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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 27761

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Special Issue Editor

Dr. Tamer M. Nadeem

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Guest Editor

Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
Interests: smart wireless systems; mobile and edge computing; software-defined networks; network security and privacy; Internet-of-Things and smart city systems; vehicular networks; intelligent transportation systems; location determination systems
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Special Issue Information

Dear Colleagues,

Sensors play a pivotal role in modern technology, particularly with the advent of the Internet of Things. The Internet of things (IoT) is becoming the key enabler for highly intelligent data-rich applications and is the major technology behind smart-computing domains like smart homes, connected health, connected cars, automated enterprise workflows, smart cities, and smart grids. Ericsson predicts the number of connected IoT devices to be around 18 billion by 2022.

With the current extreme miniaturization of components of many sensor systems, sensors today are growing increasingly complex and becoming smarter. Smart sensors are capable of customizing outputs and providing interpretive data that significantly improve the capability and performance of the sensor systems. Integrating the advances in smart sensors with the recent advancements in wireless technologies, edge computing, Internet of things, blockchain, cybersecurity, artificial intelligence, and so on, will enable novel mobile and sensor systems, applications, and services. The future of the role of smart mobile and sensor systems in the world is limitless and it's expected to revolutionize the future of the world within the next few years.

For example, with almost 55% of the current world population concentrated in an urban setting, a proportion that the UN expects to increase to 70% by 2050, smart mobile and sensor systems will play a significant role in improving the smartness and modernizing city infrastructure creating a stronger and more sustainable place to live and work. Smart mobile and sensor systems are crucial components of smart cities as the data they gather are fundamental for the large scope of services related to health, transportation, sustainability, economy, law enforcement, community, and others affecting the overall wellbeing of the residents and businesses.

In this Special Issue, the Guest Editors welcome submissions on novel sensing technologies, innovative mobile and sensor system designs, experiments that deploy prototype systems, and the exploration of new mobile and sensor applications and services. Relevant topics include, but are not limited to:

Algorithms for mobile ad hoc and wireless sensor networks
Clustering, topology control, coverage, and connectivity
Cooperative sensing, compressive sensing, sensing from communications
Cloud, crowd-sourced, participatory and (mobile) social sensing
Cyber-physical systems and applications
Data collection and analytic techniques for sensor systems
Edge computing for sensor systems
Experiences in real-world sensing applications and deployments
Human factors for sensor systems
Internet of things (IoT) devices, complex systems, and systems-of-systems
Security and privacy for sensor systems
Localization and location-based services
Machine learning techniques for sensing and sensor systems
Measurements, experimental systems, and test-beds of sensor systems
Mobility modeling and management
Energy-efficient architectures, algorithms, and protocols
QoS and Resource management
Robotic networks
Scalability, stability, and robustness of networks and sensor systems
Sensor application and services
Sensor-enabled drones, UAV, UUV systems
Smart grid, healthcare, transportation applications
Vehicular networks and protocols
Wearable and human-centric devices and networks
Wireless Communication for sensing

Dr. Tamer Nadeem
Guest Editor

Manuscript Submission Information

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Mobile networks and systems
Sensor systems
Internet of things
Smart cities
Wireless sensor networks
Smart sensing applications

Published Papers (7 papers)

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Research

25 pages, 7808 KiB

Open AccessArticle

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

by Abayomi Otebolaku, Timibloudi Enamamu, Ali Alfoudi, Augustine Ikpehai, Jims Marchang and Gyu Myoung Lee

Sensors 2020, 20(13), 3803; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133803 - 07 Jul 2020

Cited by 6 | Viewed by 3952

Abstract

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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21 pages, 4974 KiB

Open AccessArticle

Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions

by Jan M. Kelner and Cezary Ziółkowski

Sensors 2020, 20(9), 2575; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092575 - 01 May 2020

Cited by 9 | Viewed by 2769

Abstract

This paper focused on assessing the effectiveness of the signal Doppler frequency (SDF) method to locate a mobile emitter using a swarm of unmanned aerial vehicles (UAVs). Based on simulation results, we showed the impact of various factors such as the number of UAVs, the movement parameters of the emitter and the sensors on location effectiveness. The study results also showed the dependence of the accuracy and continuity of the emitter coordinate estimation on the type of propagation environment, which was determined by line-of-sight (LOS) or non-LOS (NLOS) conditions. The applied research methodology allowed the selection of parameters of the analyzed location system that would minimize the error and maximize the monitoring time of the emitter position. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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18 pages, 421 KiB

Open AccessArticle

From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

by Pawel Zalewski, Letizia Marchegiani, Atis Elsts, Robert Piechocki, Ian Craddock and Xenofon Fafoutis

Sensors 2020, 20(6), 1655; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061655 - 16 Mar 2020

Cited by 8 | Viewed by 4270

Abstract

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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14 pages, 2882 KiB

Open AccessArticle

An RSSI-Based Low-Power Vehicle-Approach Detection Technique to Alert a Pedestrian

by Yoshito Watanabe and Yozo Shoji

Sensors 2020, 20(1), 118; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010118 - 24 Dec 2019

Cited by 2 | Viewed by 2647

Abstract

Information about an approaching vehicle is helpful for pedestrians to avoid traffic accidents while most of the past studies related to collision avoidance systems have focused on alerting drivers and controlling vehicles. This paper proposes a technique to detect an approaching vehicle aiming at alerting a pedestrian by observing the variation of the received signal strength indicator (RSSI) of the repeatedly radiated beacons from a vehicle, called the alert beacons. A linear regression algorithm is first applied to RSSI samples. The decision about whether a vehicle is approaching or not is made by the Student’s t-test for the linear regression coefficient. A passive method, where the pedestrian’s device behaves only as a receiver, is first described. The neighbor-discovery-based (ND-based) method, in which the pedestrian’s device repeatedly broadcasts advertising beacons and the moving vehicle in the vicinity returns the alert beacon when it receives the advertising beacon, is then proposed to improve the detection performance as well as reduce the device’s energy consumption. The theoretical detection error rate under Rayleigh fading is derived. It is revealed that the proposed ND-based method achieves a lower detection error rate when compared with the passive method under the same delay. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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19 pages, 2093 KiB

Open AccessArticle

Integrated Robotic and Network Simulation Method

by Daniel Ramos, Luis Almeida and Ubirajara Moreno

Sensors 2019, 19(20), 4585; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204585 - 21 Oct 2019

Cited by 1 | Viewed by 2961

Abstract

The increasing use of mobile cooperative robots in a variety of applications also implies an increasing research effort on cooperative strategies solutions, typically involving communications and control. For such research, simulation is a powerful tool to quickly test algorithms, allowing to do more exhaustive tests before implementation in a real application. However, the transition from an initial simulation environment to a real application may imply substantial rework if early implementation results do not match the ones obtained by simulation, meaning the simulation was not accurate enough. One way to improve accuracy is to incorporate network and control strategies in the same simulation and to use a systematic procedure to assess how different techniques perform. In this paper, we propose a set of procedures called Integrated Robotic and Network Simulation Method (IRoNS Method), which guide developers in building a simulation study for cooperative robots and communication networks applications. We exemplify the use of the improved methodology in a case-study of cooperative control comparison with and without message losses. This case is simulated with the OMNET++/INET framework, using a group of robots in a rendezvous task with topology control. The methodology led to more realistic simulations while improving the results presentation and analysis. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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20 pages, 2787 KiB

Open AccessArticle

RMLNet—A Reliable Wireless Network for a Multiarea TDOA-Based Localization System

by Yuan Xue, Wei Su, Dong Yang, Hongchao Wang and Weiting Zhang

Sensors 2019, 19(20), 4374; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204374 - 10 Oct 2019

Cited by 5 | Viewed by 2545

Abstract

Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of target node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing target nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor nodes. In addition, an approach for target area determination is proposed, which can approximately determine the region of the target node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the target node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing target nodes. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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17 pages, 8616 KiB

Open AccessArticle

Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone

by Qiong Wu, Mengfei Sun, Changjie Zhou and Peng Zhang

Sensors 2019, 19(9), 2189; https://0-doi-org.brum.beds.ac.uk/10.3390/s19092189 - 11 May 2019

Cited by 114 | Viewed by 7943

Abstract

The update of the Android system and the emergence of the dual-frequency GNSS chips enable smartphones to acquire dual-frequency GNSS observations. In this paper, the GPS L1/L5 and Galileo E1/E5a dual-frequency PPP (precise point positioning) algorithm based on RTKLIB and GAMP was applied to analyze the positioning performance of the Xiaomi Mi 8 dual-frequency smartphone in static and kinematic modes. The results showed that in the static mode, the RMS position errors of the dual-frequency smartphone PPP solutions in the E, N, and U directions were 21.8 cm, 4.1 cm, and 11.0 cm, respectively, after convergence to 1 m within 102 min. The PPP of dual-frequency smartphone showed similar accuracy with geodetic receiver in single-frequency mode, while geodetic receiver in dual-frequency mode has higher accuracy. In the kinematic mode, the positioning track of the smartphone dual-frequency data had severe fluctuations, the positioning tracks derived from the smartphone and the geodetic receiver showed approximately difference of 3–5 m. Full article

(This article belongs to the Special Issue Smart Mobile and Sensor Systems)

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