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Multi-Agent Sensors for e-Healthcare
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Multi-Agent Sensors for e-Healthcare

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 23150

Special Issue Editor

Dr. Iván García-Magariño

E-Mail Website1 Website2
Guest Editor
Department of Computer Science and Engineering of Systems, University of Zaragoza, 50001 Teruel, Spain
Interests: mobile applications for health and well-being; Internet of things; wearable sensors; big data; datamining; agent-based simulation and multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Managing Wireless Sensor Network Resources with Multicriteria Decision Methods

Multicriteria decision-making (MCDM) techniques consist of structuring an extensive selection of information from a set of alternatives, assessed using several criteria. MCDM techniques are therefore useful in evaluating a set of data against multiple and often conflicting criteria, and determining the optimum values or standards against which these can be considered. MCDM techniques can be used in various situations, from the development of hierarchical structures to problem representation and for the development of priorities for the given alternatives. Other techniques are also applicable in the situation of fuzziness and ambiguity, which are well-known characteristics in most decision problems. MCDM techniques mainly structure and solve an objective by first defining the problem, followed by the hierarchical structure, and then finally evaluating the alternatives. Customarily, decisions are made on an ad hoc basis, and decision makers may not be able to make a decision in complex, uncertain, and vague scenarios.

MCDM techniques are becoming increasingly important in the field of wireless sensor networks (WSNs), and decision making in WSNs has become a challenging task. For the best option to be selected, it would be ideal to have a technique that supports the decision maker in the selection of any suitable option from available alternatives. A good decision can contribute to the amplification of productivity and lead to a successful system. If done scientifically, the process of obtaining the best selection from existing choices will have many benefits.

This Special Issue invites submissions from the research areas in WSNs where selection of the best option from available choices is challenging. We invite high-quality original research and systematic literature reviews that address multicriteria decision-making problems for managing resources. Potential topics include but are not limited to:

  • Multicriteria decision making in WSNs;
  • Managing resources with multicriteria decision methods;
  • Decision making in uncertain, vague, and complex situations;
  • Ranking, selection, and prioritizing techniques for WSNs;
  • Goal based prioritization/selection techniques for WSNs;
  • Compromise programming for making efficient selection in WSNs;
  • Decision making for secure WSNs;
  • Decision making for WSN application maintenance services;
  • MCDM-based clustering techniques for WSNs;
  • Applications of multicriteria decision making with suitable case studies;
  • Multiagent based multicriteria decisions in WSNs;
  • Security-related aspects in multi-decisions in WSNs.

Dr. Iván García-Magariño
Guest Editor

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 submissions that pass pre-check are 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 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

  • wireless sensor networks
  • smart sensing
  • hierarchical routing
  • resource management
  • multiattribute utility
  • multiobjective optimization

Published Papers (5 papers)

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Research

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21 pages, 366 KiB  
Article
A Survey for the Ranking of Trajectory Prediction Algorithms on Ubiquitous Wireless Sensors
by Muhammad Daud Kamal, Ali Tahir, Muhammad Babar Kamal, Faisal Moeen and M. Asif Naeem
Sensors 2020, 20(22), 6495; https://doi.org/10.3390/s20226495 - 13 Nov 2020
Cited by 3 | Viewed by 2146
Abstract
The number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used [...] Read more.
The number of wireless sensors in use—for example, the global positioning system (GPS) intelligent sensor—has increased in recent years. These intelligent sensors generate a vast amount of spatiotemporal data, which can assist in finding patterns of movements. These movement patterns can be used to predict the future location of moving objects; for example, the movement of an emergency vehicle can be predicted for health care decision-making. Although there is a body of research work regarding motion trajectory prediction, there are no guidelines for choosing algorithms best suited for individual needs in uncertain and complex situations and as per the application domains. In this paper, we surveyed existing trajectory prediction algorithms. These algorithms are further ranked scientifically in terms of accuracy (performance), ease of use, and best fit as per the available datasets. Our results show three top algorithms, namely NextPlace, the Markov model, and the hidden Markov model. This study can be beneficial for multicriteria decision-making for various disciplines, including health care. Full article
(This article belongs to the Special Issue Multi-Agent Sensors for e-Healthcare)
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23 pages, 3135 KiB  
Article
Quantum Diffie–Hellman Extended to Dynamic Quantum Group Key Agreement for e-Healthcare Multi-Agent Systems in Smart Cities
by Vankamamidi S. Naresh, Moustafa M. Nasralla, Sivaranjani Reddi and Iván García-Magariño
Sensors 2020, 20(14), 3940; https://doi.org/10.3390/s20143940 - 15 Jul 2020
Cited by 14 | Viewed by 2868
Abstract
Multi-Agent Systems can support e-Healthcare applications for improving quality of life of citizens. In this direction, we propose a healthcare system architecture named smart healthcare city. First, we divide a given city into various zones and then we propose a zonal level three-layered [...] Read more.
Multi-Agent Systems can support e-Healthcare applications for improving quality of life of citizens. In this direction, we propose a healthcare system architecture named smart healthcare city. First, we divide a given city into various zones and then we propose a zonal level three-layered system architecture. Further, for effectiveness we introduce a Multi-Agent System (MAS) in this three-layered architecture. Protecting sensitive health information of citizens is a major security concern. Group key agreement (GKA) is the corner stone for securely sharing the healthcare data among the healthcare stakeholders of the city. For establishing GKA, many efficient cryptosystems are available in the classical field. However, they are yet dependent on the supposition that some computational problems are infeasible. In light of quantum mechanics, a new field emerges to share a secret key among two or more members. The unbreakable and highly secure features of key agreement based on fundamental laws of physics allow us to propose a Quantum GKA (QGKA) technique based on renowned Quantum Diffie–Hellman (QDH). In this, a node acts as a Group Controller (GC) and forms 2-party groups with remaining nodes, establishing a QDH-style shared key per each two-party. It then joins these keys into a single group key by means of a XOR-operation, acting as a usual group node. Furthermore, we extend the QGKA to Dynamic QGKA (DQGKA) by adding join and leave protocol. Our protocol performance was compared with existing QGKA protocols in terms of Qubit efficiency (QE), unitary operation (UO), unitary operation efficiency (UOE), key consistency check (KCC), security against participants attack (SAP) and satisfactory results were obtained. The security analysis of the proposed technique is based on unconditional security of QDH. Moreover, it is secured against internal and external attack. In this way, e-healthcare Multi-Agent System can be robust against future quantum-based attacks. Full article
(This article belongs to the Special Issue Multi-Agent Sensors for e-Healthcare)
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21 pages, 530 KiB  
Article
Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data
by Amin Ul Haq, Jian Ping Li, Jalaluddin Khan, Muhammad Hammad Memon, Shah Nazir, Sultan Ahmad, Ghufran Ahmad Khan and Amjad Ali
Sensors 2020, 20(9), 2649; https://doi.org/10.3390/s20092649 - 06 May 2020
Cited by 96 | Viewed by 6694
Abstract
Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role [...] Read more.
Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient’s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment. Full article
(This article belongs to the Special Issue Multi-Agent Sensors for e-Healthcare)
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11 pages, 781 KiB  
Article
A Collocation Method for Numerical Solution of Nonlinear Delay Integro-Differential Equations for Wireless Sensor Network and Internet of Things
by Rohul Amin, Shah Nazir and Iván García-Magariño
Sensors 2020, 20(7), 1962; https://doi.org/10.3390/s20071962 - 31 Mar 2020
Cited by 26 | Viewed by 3013
Abstract
Wireless sensor network and industrial internet of things have been a growing area of research which is exploited in various fields such as smart home, smart industries, smart transportation, and so on. There is a need of a mechanism which can easily tackle [...] Read more.
Wireless sensor network and industrial internet of things have been a growing area of research which is exploited in various fields such as smart home, smart industries, smart transportation, and so on. There is a need of a mechanism which can easily tackle the problems of nonlinear delay integro-differential equations for large-scale applications of Internet of Things. In this paper, Haar wavelet collocation technique is developed for the solution of nonlinear delay integro-differential equations for wireless sensor network and industrial Internet of Things. The method is applied to nonlinear delay Volterra, delay Fredholm and delay Volterra–Fredholm integro-differential equations which are based on the use of Haar wavelets. Some examples are given to show the computational efficiency of the proposed technique. The approximate solutions are compared with the exact solution. The maximum absolute and mean square roots errors for distant number of collocation points are also calculated. The results show that Haar method is efficient for solving these equations for industrial Internet of Things. The results are compared with existing methods from the literature. The results exhibit that the method is simple, precise and efficient. Full article
(This article belongs to the Special Issue Multi-Agent Sensors for e-Healthcare)
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Review

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32 pages, 834 KiB  
Review
A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems
by Adnan Tahir, Fei Chen, Habib Ullah Khan, Zhong Ming, Arshad Ahmad, Shah Nazir and Muhammad Shafiq
Sensors 2020, 20(18), 5392; https://doi.org/10.3390/s20185392 - 21 Sep 2020
Cited by 35 | Viewed by 7791
Abstract
As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out [...] Read more.
As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment. Full article
(This article belongs to the Special Issue Multi-Agent Sensors for e-Healthcare)
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