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G-Networks for Security, Low Energy Consumption and Quality of Service...
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G-Networks for Security, Low Energy Consumption and Quality of Service of the Internet of Things

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

Deadline for manuscript submissions: closed (15 August 2018) | Viewed by 8443

Special Issue Editor

Prof. Dr. Erol Gelenbe

E-Mail Website
Guest Editor
Intelligent Systems and Networks, Imperial College London, London SW7 2AZ, UK
Interests: energy optimization; energy packet networks; networked systems; physical and biological networks; probability models; natural computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Key probability models based on G-Networks and Diffusion Approximations allow the understanding, analysis and optimisation of the complex computer and communication systems that support the Internet of Things (IoT). Thus, this Special Issue will address the foundations of G-Networks for the analysis and control of computer and communication systems, and the applications of such models for Learning via the Random Neural Network including Deep Learning (for instance in security issues, intrusion and attack detection, as well as system management and control), for the design of energy aware systems including Energy Packet Networks, and for the dynamic control of systems. Many of these applications will be centred around the IoT, the modeling of wireless networks that use energy harvesting, the design of secure IOT gateways and IoT systems that are robust in situations where energy for operating the system is scarce.

Prof. Erol Gelenbe
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.

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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

  • G-Networks and Random Neural Networks
  • Diffusion Models
  • Secure IoT Systems
  • Energy and QoS Optimisation of the IoT and the Cloud
  • Performance Analysis with G-Networks
  • Cognitive Packet Networks to support the IoT

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Published Papers (3 papers)

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Research

10 pages, 289 KiB  
Article
G-Networks to Predict the Outcome of Sensing of Toxicity
by Ingrid Grenet, Yonghua Yin and Jean-Paul Comet
Sensors 2018, 18(10), 3483; https://doi.org/10.3390/s18103483 - 16 Oct 2018
Cited by 4 | Viewed by 2140
Abstract
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through the prediction [...] Read more.
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through the prediction of their bioactivity from the compounds’ physico-chemical structure, and propose that it be automated using machine learning (ML) techniques. Specifically the Random Neural Network is shown to be an effective analytical tool to this effect, and the approach is illustrated and compared with several ML techniques. Full article
(This article belongs to the Special Issue G-Networks for Security, Low Energy Consumption and Quality of Service of the Internet of Things)
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19 pages, 838 KiB  
Article
Interest Forwarding in Named Data Networking Using Reinforcement Learning
by Olumide Akinwande
Sensors 2018, 18(10), 3354; https://doi.org/10.3390/s18103354 - 8 Oct 2018
Cited by 16 | Viewed by 3332
Abstract
In-network caching is one of the key features of information-centric networks (ICN), where forwarding entities in a network are equipped with memory with which they can temporarily store contents and satisfy en route requests. Exploiting in-network caching, therefore, presents the challenge of efficiently [...] Read more.
In-network caching is one of the key features of information-centric networks (ICN), where forwarding entities in a network are equipped with memory with which they can temporarily store contents and satisfy en route requests. Exploiting in-network caching, therefore, presents the challenge of efficiently coordinating the forwarding of requests with the volatile cache states at the routers. In this paper, we address information-centric networks and consider in-network caching specifically for Named Data Networking (NDN) architectures. Our proposal departs from the forwarding algorithms which primarily use links that have been selected by the routing protocol for probing and forwarding. We propose a novel adaptive forwarding strategy using reinforcement learning with the random neural network (NDNFS-RLRNN), which leverages the routing information and actively seeks new delivery paths in a controlled way. Our simulations show that NDNFS-RLRNN achieves better delivery performance than a strategy that uses fixed paths from the routing layer and a more efficient performance than a strategy that retrieves contents from the nearest caches by flooding requests. Full article
(This article belongs to the Special Issue G-Networks for Security, Low Energy Consumption and Quality of Service of the Internet of Things)
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22 pages, 4314 KiB  
Article
Deep Learning Cluster Structures for Management Decisions: The Digital CEO
by Will Serrano
Sensors 2018, 18(10), 3327; https://doi.org/10.3390/s18103327 - 4 Oct 2018
Viewed by 2449
Abstract
This paper presents a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices by combining different learning algorithms. The proposed model is based on the Random Neural Network (RNN) Reinforcement Learning for fast local [...] Read more.
This paper presents a Deep Learning (DL) Cluster Structure for Management Decisions that emulates the way the brain learns and makes choices by combining different learning algorithms. The proposed model is based on the Random Neural Network (RNN) Reinforcement Learning for fast local decisions and Deep Learning for long-term memory. The Deep Learning Cluster Structure has been applied in the Cognitive Packet Network (CPN) for routing decisions based on Quality of Service (QoS) metrics (Delay, Loss and Bandwidth) and Cyber Security keys (User, Packet and Node) which includes a layer of DL management clusters (QoS, Cyber and CEO) that take the final routing decision based on the inputs from the DL QoS clusters and RNN Reinforcement Learning algorithm. The model has been validated under different network sizes and scenarios. The simulation results are promising; the presented DL Cluster management structure as a mechanism to transmit, learn and make packet routing decisions is a step closer to emulate the way the brain transmits information, learns the environment and takes decisions. Full article
(This article belongs to the Special Issue G-Networks for Security, Low Energy Consumption and Quality of Service of the Internet of Things)
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