E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Mobile Sensing: Platforms, Technologies and Challenges"

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

Deadline for manuscript submissions: 30 September 2019.

Special Issue Editors

Guest Editor
Dr. Marco Picone

Adjunct Professor, Department of Engineering and Architecture,University of Parma, Italy
Website | E-Mail
Interests: distributed systems; Internet of Things; edge computing; vehicular networks (Internet of Vehicles); pervasive and mobile computing
Guest Editor
Prof. Dr. Simone Cirani

-Co-Founder and Head of Internet of Things at Caligoo s.r.l, Caligoo Srl: Via Don Minzoni, 112. 42043 Taneto di Gattatico, RE. Italy
-Adjunct Professor, Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A 43124 Parma, Italy
Website | E-Mail
Interests: distributed systems, Internet of Things, edge computing, security, pervasive and mobile computing
Guest Editor
Prof. Dr. Andrea Prati

Associate Professor, University of Parma, Italy. Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A 43124 Parma, Italy
Website 1 | Website 2 | E-Mail
Phone: 00390521906223
Interests: video surveillance, mobile vision, visual sensor networks, machine vision, multimedia and video processing, performance analysis of multimedia computer architectures

Special Issue Information

Dear Colleagues,

The widespread diffusion and global popularity of mobile devices (e.g., smartphones, tablets, single board computers, etc.) has significantly changed the market, making them extremely attractive as enablers of an endless number of always-connected applications and services. Their extended sensing capabilities, combined with a dramatic improvement in their performance, have fostered new services that take full advantage of the huge amount of heterogeneous data sensed and collected, such as audio, video, motion, and geo-related information. Moreover, in the Internet of Things the role of mobile devices will be even more centric, as they will also serve as entry points to IoT applications by users and thus become the link between people and things.

This Special Issue addresses the innovative developments, technologies, and challenges related to Mobile Sensing. It seeks the latest findings from research and ongoing projects. Additionally, review articles that provide readers with current research trends and solutions are also welcome. Potential topics include, but are not limited to, the following:

  • New emerging architectures for mobile sensing and data processing
  • Mobile sensor networks
  • Security and privacy for mobile sensing applications
  • Mobile sensing and Internet of Things
  • Mobile vision
  • Mobile devices as smart objects
  • Participatory sensing and mobile crowd sensing
  • Software platforms and frameworks for mobile sensing
  • Mobile data processing and analytics
  • Geo-spatial and location-based sensing
  • Network architectures to support distributed mobile sensing

Prof. Dr. Marco Picone
Prof. Dr. Simone Cirani
Prof. Dr. Andrea Prati
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. 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 1800 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 sensing
  • Internet of Things
  • Mobile applications
  • Mobile vision
  • Participatory sensing
  • Crowd sensing
  • Mobile sensor networks
  • Urban sensing
  • Algorithms
  • Architectures

Published Papers (9 papers)

View options order results:
result details:
Displaying articles 1-9
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
On-Device Deep Learning Inference for Efficient Activity Data Collection
Sensors 2019, 19(15), 3434; https://doi.org/10.3390/s19153434
Received: 9 July 2019 / Revised: 25 July 2019 / Accepted: 1 August 2019 / Published: 5 August 2019
PDF Full-text (2794 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them [...] Read more.
Labeling activity data is a central part of the design and evaluation of human activity recognition systems. The performance of the systems greatly depends on the quantity and “quality” of annotations; therefore, it is inevitable to rely on users and to keep them motivated to provide activity labels. While mobile and embedded devices are increasingly using deep learning models to infer user context, we propose to exploit on-device deep learning inference using a long short-term memory (LSTM)-based method to alleviate the labeling effort and ground truth data collection in activity recognition systems using smartphone sensors. The novel idea behind this is that estimated activities are used as feedback for motivating users to collect accurate activity labels. To enable us to perform evaluations, we conduct the experiments with two conditional methods. We compare the proposed method showing estimated activities using on-device deep learning inference with the traditional method showing sentences without estimated activities through smartphone notifications. By evaluating with the dataset gathered, the results show our proposed method has improvements in both data quality (i.e., the performance of a classification model) and data quantity (i.e., the number of data collected) that reflect our method could improve activity data collection, which can enhance human activity recognition systems. We discuss the results, limitations, challenges, and implications for on-device deep learning inference that support activity data collection. Also, we publish the preliminary dataset collected to the research community for activity recognition. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing
Sensors 2019, 19(14), 3158; https://doi.org/10.3390/s19143158
Received: 24 June 2019 / Revised: 13 July 2019 / Accepted: 16 July 2019 / Published: 18 July 2019
PDF Full-text (3610 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection [...] Read more.
With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
PUEGM: A Method of User Revenue Selection Based on a Publisher-User Evolutionary Game Model for Mobile Crowdsensing
Sensors 2019, 19(13), 2927; https://doi.org/10.3390/s19132927
Received: 6 May 2019 / Revised: 24 June 2019 / Accepted: 28 June 2019 / Published: 2 July 2019
PDF Full-text (1925 KB) | HTML Full-text | XML Full-text
Abstract
Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a [...] Read more.
Mobile crowdsensing (MCS) is a way to use social resources to solve high-precision environmental awareness problems in real time. Publishers hope to collect as much sensed data as possible at a relatively low cost, while users want to earn more revenue at a low cost. Low-quality data will reduce the efficiency of MCS and lead to a loss of revenue. However, existing work lacks research on the selection of user revenue under the premise of ensuring data quality. In this paper, we propose a Publisher-User Evolutionary Game Model (PUEGM) and a revenue selection method to solve the evolutionary stable equilibrium problem based on non-cooperative evolutionary game theory. Firstly, the choice of user revenue is modeled as a Publisher-User Evolutionary Game Model. Secondly, based on the error-elimination decision theory, we combine a data quality assessment algorithm in the PUEGM, which aims to remove low-quality data and improve the overall quality of user data. Finally, the optimal user revenue strategy under different conditions is obtained from the evolutionary stability strategy (ESS) solution and stability analysis. In order to verify the efficiency of the proposed solutions, extensive experiments using some real data sets are conducted. The experimental results demonstrate that our proposed method has high accuracy of data quality assessment and a reasonable selection of user revenue. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
Understanding Collective Human Mobility Spatiotemporal Patterns on Weekdays from Taxi Origin-Destination Point Data
Sensors 2019, 19(12), 2812; https://doi.org/10.3390/s19122812
Received: 17 May 2019 / Revised: 15 June 2019 / Accepted: 19 June 2019 / Published: 24 June 2019
PDF Full-text (12454 KB) | HTML Full-text | XML Full-text
Abstract
With the availability of large geospatial datasets, the study of collective human mobility spatiotemporal patterns provides a new way to explore urban spatial environments from the perspective of residents. In this paper, we constructed a classification model for mobility patterns that is suitable [...] Read more.
With the availability of large geospatial datasets, the study of collective human mobility spatiotemporal patterns provides a new way to explore urban spatial environments from the perspective of residents. In this paper, we constructed a classification model for mobility patterns that is suitable for taxi OD (Origin-Destination) point data, and it is comprised of three parts. First, a new aggregate unit, which uses a road intersection as the constraint condition, is designed for the analysis of the taxi OD point data. Second, the time series similarity measurement is improved by adding a normalization procedure and time windows to address the particular characteristics of the taxi time series data. Finally, the DBSCAN algorithm is used to classify the time series into different mobility patterns based on a proximity index that is calculated using the improved similarity measurement. In addition, we used the random forest algorithm to establish a correlation model between the mobility patterns and the regional functional characteristics. Based on the taxi OD point data from Nanjing, we delimited seven mobility patterns and illustrated that the regional functions have obvious driving effects on these mobility patterns. These findings are applicable to urban planning, traffic management and planning, and land use analyses in the future. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
Drone Detection and Pose Estimation Using Relational Graph Networks
Sensors 2019, 19(6), 1479; https://doi.org/10.3390/s19061479
Received: 9 February 2019 / Revised: 13 March 2019 / Accepted: 22 March 2019 / Published: 26 March 2019
Cited by 1 | PDF Full-text (5269 KB) | HTML Full-text | XML Full-text
Abstract
With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on [...] Read more.
With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
Big Data-Driven Cellular Information Detection and Coverage Identification
Sensors 2019, 19(4), 937; https://doi.org/10.3390/s19040937
Received: 23 December 2018 / Revised: 11 February 2019 / Accepted: 15 February 2019 / Published: 22 February 2019
Cited by 1 | PDF Full-text (7157 KB) | HTML Full-text | XML Full-text
Abstract
As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is [...] Read more.
As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing
Sensors 2019, 19(4), 921; https://doi.org/10.3390/s19040921
Received: 21 December 2018 / Revised: 6 February 2019 / Accepted: 20 February 2019 / Published: 22 February 2019
Cited by 1 | PDF Full-text (1382 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, [...] Read more.
With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Open AccessArticle
A Cognitive-Inspired Event-Based Control for Power-Aware Human Mobility Analysis in IoT Devices
Sensors 2019, 19(4), 832; https://doi.org/10.3390/s19040832
Received: 31 December 2018 / Revised: 3 February 2019 / Accepted: 12 February 2019 / Published: 18 February 2019
Cited by 1 | PDF Full-text (1590 KB) | HTML Full-text | XML Full-text
Abstract
Mobile Edge Computing (MEC) relates to the deployment of decision-making processes at the network edge or mobile devices rather than in a centralized network entity like the cloud. This paradigm shift is acknowledged as one key pillar to enable autonomous operation and self-awareness [...] Read more.
Mobile Edge Computing (MEC) relates to the deployment of decision-making processes at the network edge or mobile devices rather than in a centralized network entity like the cloud. This paradigm shift is acknowledged as one key pillar to enable autonomous operation and self-awareness in mobile devices in IoT. Under this paradigm, we focus on mobility-based services (MBSs), where mobile devices are expected to perform energy-efficient GPS data acquisition while also providing location accuracy. We rely on a fully on-device Cognitive Dynamic Systems (CDS) platform to propose and evaluate a cognitive controller aimed at both tackling the presence of uncertainties and exploiting the mobility information learned by such CDS toward energy-efficient and accurate location tracking via mobility-aware sampling policies. We performed a set of experiments and validated that the proposed control strategy outperformed similar approaches in terms of energy savings and spatio-temporal accuracy in LBS and MBS for smartphone devices. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems
Sensors 2019, 19(14), 3071; https://doi.org/10.3390/s19143071
Received: 17 May 2019 / Revised: 26 June 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
PDF Full-text (1108 KB) | HTML Full-text | XML Full-text
Abstract
The development and uptake of field deployable hyperspectral imaging systems within environmental monitoring represents an exciting and innovative development that could revolutionize a number of sensing applications in the coming decades. In this article we focus on the successful miniaturization and improved portability [...] Read more.
The development and uptake of field deployable hyperspectral imaging systems within environmental monitoring represents an exciting and innovative development that could revolutionize a number of sensing applications in the coming decades. In this article we focus on the successful miniaturization and improved portability of hyperspectral sensors, covering their application both from aerial and ground-based platforms in a number of environmental application areas, highlighting in particular the recent implementation of low-cost consumer technology in this context. At present, these devices largely complement existing monitoring approaches, however, as technology continues to improve, these units are moving towards reaching a standard suitable for stand-alone monitoring in the not too distant future. As these low-cost and light-weight devices are already producing scientific grade results, they now have the potential to significantly improve accessibility to hyperspectral monitoring technology, as well as vastly proliferating acquisition of such datasets. Full article
(This article belongs to the Special Issue Mobile Sensing: Platforms, Technologies and Challenges)
Figures

Figure 1

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top