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Special Issue "Distributed Algorithms for Wireless Sensor Networks"

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

Deadline for manuscript submissions: 1 October 2020.

Special Issue Editors

Prof. Mohamed Mosbah
Website
Guest Editor
LaBRI , Université de Bordeaux, 351, cours de la Libération, 33405 Talence Cedex, France
Interests: distributed algorithms; wireless sensor networks; intelligent transportation systems; communication and distributed systems; security and safety; network and mobile protocols
Special Issues and Collections in MDPI journals
Dr. Imen Jemili
Website
Guest Editor
Univ. Carthage, Faculty of sciences of Bizerte, Bizerte 7021, Tunisia
Interests: wireless communications, smart cities, Internet of vehicles, routing, MAC protocols
Special Issues and Collections in MDPI journals
Dr. Mohamed Tounsi
Website
Guest Editor
Common First Year deanship, Umm Al-Qura University, Makkah, Saudi Arabia
Interests: Distributed systems, Graph Relabeling Systems, Formal methods, Wireless Sensors Networks

Special Issue Information

Dear Colleagues,

Today, the growing interest in Wireless Sensor Networks has led to their deployment at large scale in several fields ranging from environmental monitoring to smart transportation, industrial systems, health and biomedical systems, intelligent environments, etc. Sensors being a key component of the Internet of Things, many IoT solutions entail their deployment in order to gather information from the surrounding environment, which will be forwarded for remote data processing (at sink level, fog/edge level or by a cloud-processing service). Sensor nodes have to cooperate in order to ensure a reliable forwarding for the different types of gathered data while respecting the QoS requirements. For some applications, other concerns have to be considered, such as security issues and energy limitation, as sensors have to operate for many years with limited batteries that cannot easily be replaced. To enhance network performances, additional contextual information can also be handled to adjust node and network behaviors. However, with the significant growth in the use of IoT and distributed sensor systems, many challenges arise and need to be addressed to support such large deployment, mainly in the context of smart cities and environments. In this context, the recourse to distributed algorithms in which computation is distributed among all sensor nodes allows overcoming inherent WSN limitations related to the short range communication over a wireless medium, resource limitations in terms of energy, computational resources, unreliability of links, etc. Further, distributed algorithms are required to comprehend the aspects of the large implementations that lead to certain characteristics, i.e., scalability, efficiency, and optimization. This Special Issue welcomes contributions dealing with distributed algorithms for Wireless Sensor Networks tackling all aspects, from sensor deployment to data management cloud.

  • Distributed Algorithms for Sensor Networks
  • Distributed communication and networking algorithms and protocols
  • Mobile and Wireless Network Computing for WSNs
  • Formal methods for Sensor Networks
  • Edge and fog computing
  • Artificial intelligence (or machine intelligence) in distributed sensor systems
  • Crowdsourcing and Wireless Sensors
  • Vehicle platoon networking
  • Distributed and federated Machine Learning
  • Internet of Things and cyber-physical systems
  • Sensor technologies and monitoring
  • Security, privacy, and trust in WSNs
  • MAC protocol
  • Routing protocols

Prof. Mohamed Mosbah
Dr. Imen Jemili
Dr. Mohamed Tounsi
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.

Published Papers (4 papers)

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Research

Open AccessArticle
Robust -Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations
Sensors 2020, 20(13), 3673; https://doi.org/10.3390/s20133673 - 30 Jun 2020
Abstract
Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling [...] Read more.
Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H controller alone, and the H with the FL compensator, but without the presence of the robust control law. Full article
(This article belongs to the Special Issue Distributed Algorithms for Wireless Sensor Networks)
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Open AccessArticle
Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method
Sensors 2020, 20(11), 3210; https://doi.org/10.3390/s20113210 - 05 Jun 2020
Abstract
Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by [...] Read more.
Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets. Full article
(This article belongs to the Special Issue Distributed Algorithms for Wireless Sensor Networks)
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Open AccessFeature PaperArticle
A Distributed Clustering Algorithm Guided by the Base Station to Extend the Lifetime of Wireless Sensor Networks
Sensors 2020, 20(8), 2312; https://doi.org/10.3390/s20082312 - 18 Apr 2020
Abstract
Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station [...] Read more.
Clustering algorithms are necessary in Wireless Sensor Networks to reduce the energy consumption of the overall nodes. The decision of which nodes are the cluster heads (CHs) greatly affects the network performance. The centralized clustering algorithms rely on a sink or Base Station (BS) to select the CHs. To do so, the BS requires extensive data from the nodes, which sometimes need complex hardware inside each node or a significant number of control messages. Alternatively, the nodes in distributed clustering algorithms decide about which the CHs are by exchanging information among themselves. Both centralized and distributed clustering algorithms usually alternate the nodes playing the role of the CHs to dynamically balance the energy consumption among all the nodes in the network. This paper presents a distributed approach to form the clusters dynamically, but it is occasionally supported by the Base Station. In particular, the Base Station sends three messages during the network lifetime to reconfigure the s k i p value of the network. The s k i p , which stands out as the number of rounds in which the same CHs are kept, is adapted to the network status in this way. At the beginning of each group of rounds, the nodes decide about their convenience to become a CH according to a fuzzy-logic system. As a novelty, the fuzzy controller is as a Tagaki–Sugeno–Kang model and not a Mandami-one as other previous proposals. The clustering algorithm has been tested in a wide set of scenarios, and it has been compared with other representative centralized and distributed fuzzy-logic based algorithms. The simulation results demonstrate that the proposed clustering method is able to extend the network operability. Full article
(This article belongs to the Special Issue Distributed Algorithms for Wireless Sensor Networks)
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Open AccessArticle
Distributed Robust Filtering for Wireless Sensor Networks with Markov Switching Topologies and Deception Attacks
Sensors 2020, 20(7), 1948; https://doi.org/10.3390/s20071948 - 31 Mar 2020
Abstract
This paper is concerned with the distributed full- and reduced-order l 2 - l state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology [...] Read more.
This paper is concerned with the distributed full- and reduced-order l 2 - l state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology model is proposed which uses homogeneous Markov chain to reflect the change of filtering networks communication modes. Then, the sector-bound deception attacks among the communication channels are taken into consideration, which could better characterize the filtering network communication security. Additionally, a random variable obeying the Bernoulli distribution is used to describe the phenomenon of the randomly occurring deception attacks. Furthermore, through an adjustable parameter E, we can obtain full- and reduced-order l 2 - l state estimator over sensor networks, respectively. Sufficient conditions are established for the solvability of the addressed switching topology-dependent distributed filtering design in terms of certain convex optimization problem. The purpose of solving the problem is to design a distributed full- and reduced-order filter such that, in the presence of deception attacks, stochastic external interference and switching topologies, the resulting filtering dynamic system is exponentially mean-square stable with prescribed l 2 - l performance index. Finally, a simulation example is provided to show the effectiveness and flexibility of the designed approach. Full article
(This article belongs to the Special Issue Distributed Algorithms for Wireless Sensor Networks)
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