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

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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 (20 July 2023) | Viewed by 28888

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Special Issue Editors

Dr. Ramon Alcarria

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Guest Editor

School of Geomatic Engineering, Technical University of Madrid, 28040 Madrid, Spain
Interests: service composition; prosumer; VGI; machine learning; Internet of Things; blockchain
Special Issues, Collections and Topics in MDPI journals

Dr. Borja Bordel

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Co-Guest Editor

Department of Information Systems, Universidad Politécnica de Madrid, 28031 Madrid, Spain
Interests: cybersecurity; blockchain; cyberphysical systems; 5G; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

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

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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.

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Keywords

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

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Published Papers (8 papers)

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Research

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27 pages, 572 KiB

Open AccessArticle

A Framework for Continuous Authentication Based on Touch Dynamics Biometrics for Mobile Banking Applications

by Priscila Morais Argôlo Bonfim Estrela, Robson de Oliveira Albuquerque, Dino Macedo Amaral, William Ferreira Giozza and Rafael Timóteo de Sousa Júnior

Sensors 2021, 21(12), 4212; https://doi.org/10.3390/s21124212 - 19 Jun 2021

Cited by 19 | Viewed by 4092

Abstract

As smart devices have become commonly used to access internet banking applications, these devices constitute appealing targets for fraudsters. Impersonation attacks are an essential concern for internet banking providers. Therefore, user authentication countermeasures based on biometrics, whether physiological or behavioral, have been developed, including those based on touch dynamics biometrics. These measures take into account the unique behavior of a person when interacting with touchscreen devices, thus hindering identitification fraud because it is hard to impersonate natural user behaviors. Behavioral biometric measures also balance security and usability because they are important for human interfaces, thus requiring a measurement process that may be transparent to the user. This paper proposes an improvement to Biotouch, a supervised Machine Learning-based framework for continuous user authentication. The contributions of the proposal comprise the utilization of multiple scopes to create more resilient reasoning models and their respective datasets for the improved Biotouch framework. Another contribution highlighted is the testing of these models to evaluate the imposter False Acceptance Error (FAR). This proposal also improves the flow of data and computation within the improved framework. An evaluation of the multiple scope model proposed provides results between 90.68% and 97.05% for the harmonic mean between recall and precision (F1 Score). The percentages of unduly authenticated imposters and errors of legitimate user rejection (Equal Error Rate (EER)) are between 9.85% and 1.88% for static verification, login, user dynamics, and post-login. These results indicate the feasibility of the continuous multiple-scope authentication framework proposed as an effective layer of security for banking applications, eventually operating jointly with conventional measures such as password-based authentication. Full article

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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16 pages, 949 KiB

Open AccessArticle

Learning History with Location-Based Applications: An Architecture for Points of Interest in Multiple Layers

by Samuli Laato, Sampsa Rauti, Antti Laato, Teemu H. Laine, Erkki Sutinen and Erno Lehtinen

Sensors 2021, 21(1), 129; https://doi.org/10.3390/s21010129 - 28 Dec 2020

Cited by 3 | Viewed by 3003

Abstract

Location-based applications (LBAs) capture the user’s physical location via satellite navigation sensors and integrate it as part of the digital application. Because of this connection, the real-world environment needs to be accounted for in LBA design. In this work, we focused on creating a database of geographically distributed points of interest (PoIs) that is optimal for learning local history. First, we conducted a requirements elicitation study at three outdoor archaeological sites and identified issues in existing solutions. Second, we designed a multi-layered prototype solution. Third, we evaluated the solution with nine experts who had prior experience with LBAs or similar systems. We incorporated their feedback to our design to iteratively improve it. As a whole, our work contributes to the LBA design literature by proposing a solution that is optimized for the learning of local history. Full article

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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19 pages, 580 KiB

Open AccessArticle

Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent Networks

by Jia Liu, Mingchu Li, Yuanfang Chen, Sardar M. N. Islam and Noel Crespi

Sensors 2020, 20(20), 5939; https://doi.org/10.3390/s20205939 - 21 Oct 2020

Viewed by 2435

Abstract

With the rapid development of wireless sensor networks (WSNs) technology, a growing number of applications and services need to acquire the states of channels or sensors, especially in order to use these states for monitoring, object tracking, motion detection, etc. A critical issue in WSNs is the ability to estimate the source parameters from the readings of a distributed sensor network. Although there are several studies on channel estimation (CE) algorithms, existing algorithms are all flawed with their high complexity, inability to scale, inability to ensure the convergence to a local optimum, low speed of convergence, etc. In this work, we turn to variational inference (VI) with tempering to solve the channel estimation problem due to its ability to reduce complexity, ability to generalize and scale, and guarantee of local optimum. To the best of our knowledge we are the first to use VI with tempering for advanced channel estimation. The parameters that we consider in the channel estimation problem include pilot signal and channel coefficients, assuming there is orthogonal access between different sensors (or users) and the data fusion center (or receiving center). By formulating the channel estimation problem into a probabilistic graphical model, the proposed Channel Estimation Variational Tempering Inference (CEVTI) approach can estimate the channel coefficient and the transmitted signal in a low-complexity manner while guaranteeing convergence. CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of code division multiple access (CDMA) and uplink massive multi-input-multi-output (MIMO) easily. Simulations show that CEVTI has higher accuracy than state-of-the-art algorithms under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees a better local optimum. Therefore, the main contribution of the paper is the development of a new efficient, simple and reliable algorithm for channel estimation in WSNs. Full article

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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18 pages, 498 KiB

Open AccessArticle

Probabilistic Analysis of a Buffer Overflow Duration in Data Transmission in Wireless Sensor Networks

by Wojciech M. Kempa

Sensors 2020, 20(20), 5772; https://doi.org/10.3390/s20205772 - 12 Oct 2020

Cited by 3 | Viewed by 2211

Abstract

One of the most important problems of data transmission in packet networks, in particular in wireless sensor networks, are periodic overflows of buffers accumulating packets directed to a given node. In the case of a buffer overflow, all new incoming packets are lost until the overflow condition terminates. From the point of view of network optimization, it is very important to know the probabilistic nature of this phenomenon, including the probability distribution of the duration of the buffer overflow period. In this article, a mathematical model of the node of a wireless sensor network with discrete time parameter is proposed. The model is governed by a finite-buffer discrete-time queueing system with geometrically distributed interarrival times and general distribution of processing times. A system of equations for the tail cumulative distribution function of the first buffer overflow period duration conditioned by the initial state of the accumulating buffer is derived. The solution of the corresponding system written for probability generating functions is found using the analytical approach based on the idea of embedded Markov chain and linear algebra. Corresponding result for next buffer overflow periods is obtained as well. Numerical study illustrating theoretical results is attached. Full article

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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14 pages, 3845 KiB

Open AccessArticle

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

by Juan José Vinagre Díaz, Rubén Fernández Pozo, Ana Belén Rodríguez González, Mark R. Wilby and Carmen Sánchez Ávila

Sensors 2020, 20(12), 3550; https://doi.org/10.3390/s20123550 - 23 Jun 2020

Cited by 6 | Viewed by 2979

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 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

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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24 pages, 4287 KiB

Open AccessEditor’s ChoiceArticle

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks

by Dezhi Han, Yunping Yu, Kuan-Ching Li and Rodrigo Fernandes de Mello

Sensors 2020, 20(2), 343; https://doi.org/10.3390/s20020343 - 7 Jan 2020

Cited by 65 | Viewed by 4722

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 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

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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25 pages, 2491 KiB

Open AccessArticle

Self-Adaptive Filtering Approach for Improved Indoor Localization of a Mobile Node with Zigbee-Based RSSI and Odometry

by Anbalagan Loganathan, Nur Syazreen Ahmad and Patrick Goh

Sensors 2019, 19(21), 4748; https://doi.org/10.3390/s19214748 - 1 Nov 2019

Cited by 29 | Viewed by 3189

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 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

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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Review

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15 pages, 1253 KiB

Open AccessReview

Towards Security Joint Trust and Game Theory for Maximizing Utility: Challenges and Countermeasures

by Libingyi Huang, Guoqing Jia, Weidong Fang, Wei Chen and Wuxiong Zhang

Sensors 2020, 20(1), 221; https://doi.org/10.3390/s20010221 - 30 Dec 2019

Cited by 7 | Viewed by 4399

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 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

(This article belongs to the Special Issue Trends on Edge Computing and Artificial Intelligence for Next Generation Sensor Networks)

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