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Special Issue "Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks"

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

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Ramon Alcarria
Website SciProfiles
Guest Editor
Associate Professor, School of Geospatial Engineering, Universidad Politécnica de Madrid, Spain
Interests: Service Architectures; Sensor Networks; Human-computer interaction and Prosumer Environments
Special Issues and Collections in MDPI journals
Dr. Borja Bordel
Website SciProfiles
Co-Guest Editor
Assistant Professor, School of Information Systems, Universidad Politécnica de Madrid, Spain
Interests: cyber-physical systems; internet of things; human-computer interaction, mobile technology, edge computing, sensor networks

Special Issue Information

Dear Colleagues,

In the last five years, technological sciences have been advancing and improving vertiginously due to the appearance and explosion of a large catalogue of new paradigms and solutions. This technological advancement is leading to a greater use of novel architectures and sensor networks in many fields, such as industry, agriculture, health, traffic management, etc. Large platforms connecting several different physical, social, and cyber “things” realize intelligent information transmission and processing in these next-generation networks, which include some relevant paradigms such as the Internet of Things (IoT), cyberphysical systems (CPS), Industry 4.0 or pervasive sensing and computing. However, as platforms and architectures turn larger, some important challenges and problems appear. First, data from ubiquitous platforms tend to be partial and redundant. Data aggregation, fusion, and compression are computationally expensive tasks for pervasive platforms. Moreover, in current engineered systems, as more elements (people, sensors, networks, etc.) are included in the platforms, data must be restructured to maintain the quality of service (QoS) regardless of the number of elements or users in the system. Thus, in order to extract valid knowledge and information, data transmission and processing must be supported by a new generation of sensors networks, in a more intelligent, organized, and distributed manner.      

All this context reveals that integrating edge computing architectures and artificial intelligence in next-generation sensor networks is a good driver to improve transmission and processing in these technological solutions. While edge computing (e.g., cloud services, edge devices, fog computing, dynamic capability distribution, etc.) could efficiently handle and communicate large amounts of unstructured data and “things” in these architectures, artificial intelligence technologies could simplify structuring sensor data and extracting useful information and knowledge.    

This Special Issue aims to solicit original papers with novel contributions on the integration of edge computing and artificial intelligence for next generation sensor networks. Novel computing schemes or applications by the integration of edge computing and artificial intelligence are particularly welcome.

We especially welcome authors of selected papers from 8th World Conference on Information Systems and Technologies (WorldCist'20) to be held in Budva, Montenegro, in April 2020 (http://worldcist.org/index.php/call-for-papers). However, we also welcome other papers related, but not only limited, to the topics as follows:

  • Edge computing algorithms and architectures for intelligent data processing in next-generation sensor networks;
  • Distributed artificial intelligence mechanisms for next-generation sensor networks;
  • Artificial intelligence for data integration in next-generation sensor networks;
  • Edge computing solutions for next-generation intelligent applications such as smart manufacturing or transportation;
  • Integration of edge computing and artificial intelligence for next-generation system protection;
  • Low energy consumption solutions for next-generation sensor networks, integrating edge computing and artificial intelligence;
  • Privacy and trust guaranteeing through artificial intelligence and edge computing technologies in next-generation sensor networks;
  • Artificial intelligence for data mining and knowledge extraction in next-generation sensor networks;
  • Novel and emerging computing applications of edge computing and artificial intelligence.

Dr. Ramon Alcarria
Dr. Borja Bordel
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 2000 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

  • Edge Computing
  • Sensor Networks
  • Artificial Intelligence
  • Low Energy Consumption
  • Next-Generation Networks
  • Data Mining
  • Knowledge Extraction

Published Papers (4 papers)

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Research

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Open AccessArticle
Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing
Sensors 2020, 20(12), 3550; https://doi.org/10.3390/s20123550 - 23 Jun 2020
Cited by 1
Abstract
Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies [...] Read more.
Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility. Full article
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Open AccessArticle
Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks
Sensors 2020, 20(2), 343; https://doi.org/10.3390/s20020343 - 07 Jan 2020
Cited by 2
Abstract
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless [...] Read more.
The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements. Full article
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Open AccessArticle
Self-Adaptive Filtering Approach for Improved Indoor Localization of a Mobile Node with Zigbee-Based RSSI and Odometry
Sensors 2019, 19(21), 4748; https://doi.org/10.3390/s19214748 - 01 Nov 2019
Cited by 1
Abstract
This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates [...] Read more.
This study presents a new technique to improve the indoor localization of a mobile node by utilizing a Zigbee-based received-signal-strength indicator (RSSI) and odometry. As both methods suffer from their own limitations, this work contributes to a novel methodological framework in which coordinates of the mobile node can more accurately be predicted by improving the path-loss propagation model and optimizing the weighting parameter for each localization technique via a convex search. A self-adaptive filtering approach is also proposed which autonomously optimizes the weighting parameter during the target node’s translational and rotational motions, thus resulting in an efficient localization scheme with less computational effort. Several real-time experiments consisting of four different trajectories with different number of straight paths and curves were carried out to validate the proposed methods. Both temporal and spatial analyses demonstrate that when odometry data and RSSI values are available, the proposed methods provide significant improvements on localization performance over existing approaches. Full article
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Review

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Open AccessReview
Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures
Sensors 2020, 20(1), 221; https://doi.org/10.3390/s20010221 - 30 Dec 2019
Abstract
The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security [...] Read more.
The widespread application of networks is providing a better platform for the development of society and technology. With the expansion of the scope of network applications, many issues need to be solved. Among them, the maximization of utility and the improvement of security have attracted much attention. Many existing attacks mean the network faces security challenges. The concept of trust should be considered to address these security issues. Meanwhile, the utility of the network, including efficiency, profit, welfare, etc., are concerns that should be maximized. Over the past decade, the concepts of game and trust have been introduced to various types of networks. However, there is a lack of research effort on several key points in distributed networks, which are critical to the information transmission of distributed networks, such as expelling malicious nodes quickly and accurately and finding equilibrium between energy assumption and high transmission rate. The purpose of this paper is to give a holistic overview of existing research on trust and game theory in networks. We analyzed that network utility can be maximized in terms of effectiveness, profits, and security. Moreover, a possible research agenda is proposed to promote the application and development of game theory and trust for improving security and maximizing utility. Full article
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