Abstract-Industrial wireless sensor networks have to contend with environments that are usually harsh and time varying. Industrial wireless technology, such as WirelessHART and ISA 100.11a, also operates in a frequency spectrum utilised by many other wireless technologies, and with wireless applications rapidly growing it is possible that multiple heterogeneous wireless systems would need to operate in overlapping spatiotemporal regions. Interference such as noise or other wireless devices affects connectivity and reduces communication link quality. This negatively affects reliability and latency, which are core requirements of industrial communication. Building wireless networks that are resistant to noise in industrial environments and can coexist with competing wireless devices in an increasingly crowded frequency spectrum is challenging. To meet these challenges, we need to consider the benefits that approaches finding success in other application areas can offer industrial communication. Cognitive radio methods offer a potential solution to improve resistance of industrial wireless sensor networks to interference. Integrating cognitive radio principles into the lower layers of industrial wireless sensor networks can enable devices to detect and avoid interference and potentially opens the possibility of utilising free radio spectrum for additional communication channels. This improves resistance to noise and increases redundancy in terms of channels per network node or adding additional nodes. In this paper, we summarise cognitive radio methods relevant to industrial applications, covering cognitive radio architecture, spectrum access and interference management, spectrum sensing, dynamic spectrum access, game theory and cognitive radio network security.Index Terms-Cognitive radio, industrial wireless sensor networks, Internet of Things, spectrum management, spectrum sensing.

Wireless sensor networks are used in several multi-disciplinary areas covering a wide variety of applications. They provide distributed computing, sensing and communication in a powerful integration of capabilities. They have great long-term economic potential and have the ability to transform our lives. At the same time however, they pose several challenges -mostly as a result of their random deployment and non-renewable energy sources.Among the most important issues in wireless sensor networks are energy efficiency and radio interference. Topology control plays an important role in the design of wireless ad hoc and sensor networks; it is capable of constructing networks that have desirable characteristics such as sparser connectivity, lower transmission power and a smaller node degree.In this research a distributed topology control technique is presented that enhances energy efficiency and reduces radio interference in wireless sensor networks. Each node in the network makes local decisions about its transmission power and the culmination of these local decisions produces a network topology that preserves global connectivity.ii The topology that is produced consists of a planar graph that is a power spanner, it has lower node degrees and can be constructed using local information. The network lifetime is increased by reducing transmission power and the use of low node degrees reduces traffic interference.The approach to topology control that is presented in this document has an advantage over previously developed approaches in that it focuses not only on reducing either energy consumption or radio interference, but on reducing both of these obstacles. Results are presented of simulations that demonstrate improvements in performance. Van die belangrikste kwessies in draadlose sensor netwerke is energie-doeltreffendheid en radiosteuring. Topologie-beheer speel "n belangrike rol in die ontwerp van draadlose informele netwerke en sensor netwerke en dit is geskik om netwerke aan te bring wat gewenste eienskappe het soos verspreide koppeling, laer transmissiekrag en kleiner nodus graad.In hierdie ondersoek word "n verspreide topologie beheertegniek voorgelê wat energiedoeltreffendheid verhoog en radiosteuring verminder in draadlose sensor netwerke. Elke nodus in die netwerk maak lokale besluite oor sy transmissiekrag en die hoogtepunt van hierdie lokale besluite lewer "n netwerk-topologie op wat globale verbintenis behou.Die topologie wat gelewer word is "n tweedimensionele grafiek en "n kragsleutel; dit het laer nodus grade en kan gebou word met lokale inligting. Die netwerk-leeftyd word iv vermeerder deur transmissiekrag te verminder en verkeer-steuring word verminder deur lae nodus grade.Die benadering tot topologie-beheer wat voorgelê word in hierdie skrif het "n voordeel oor benaderings wat vroeër ontwikkel is omdat dit nie net op die vermindering van net energie verbruik of net radiosteuring fokus nie, maar op albei. Resultate van simulasies word voorgelê wat die verbetering in werkverrigting demonstreer.v ACK...

Cognitive radio and dynamic spectrum access can reform the way that radiofrequency spectrum is accessed. Problems of spectrum scarcity, coexistence and unreliable wireless communication that affect industrial wireless networks can be addressed. In this paper, a game theoretic dynamic spectrum access algorithm that improves upon on a hedonic coalition formation algorithm for spectrum sensing and access is presented. The modified algorithm is tailored for faster convergence and scalability and makes use of a novel simultaneous multi-channel sensing and access technique. Results to demonstrate the performance improvements of the adapted algorithm are presented and the use of different decision rules are investigated revealing that a conservative decision rule for exploiting spectrum opportunities performs better than an aggressive decision rule in most scenarios. The algorithm that was developed could be a key enabler for future cognitive radio networks.

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In this paper we present the design of Cognitiva, a cognitive radio protocol for industrial wireless networks. It is designed for reliable multi-band operation in the license free Industrial, Scientific and Medical band as well as in Television White Spaces. We also present details on how the testbed was implemented in software radio using GNU radio and the Universal Software Radio Peripheral. The software is Open Source allowing the testbed to be extended or adapted. The protocol has a modular, cross layer design and the testbed can be used to test the real world performance of different cooperative spectrum sensing and dynamic spectrum access schemes. The protocol covers the PHY and MAC layers and functionality such as spectrum sensing and dynamic frequency selection is included.Keywords-Cognitive radio networks, wireless sensor networks implementation of a Software Defined Radio (SDR) testbed for the protocol. Cognitiva can be used for reliable wireless communication and is designed with support for multichannel inter-band and intra-band operation involving TVWS and the 2.4 GHz ISM band. Cognitiva caters for the growing trend of Machine-to-Machine (M2M) communication and Internet-of-Things (IoT) type applications [9] which didn't exist a few years ago. This is borne out of a need to connect a wide variety of different devices running different applications using global communication solutions through the application of Internet Protocol Version 6 (IPv6). The testbed can be used for rapid prototyping and testing the real world performance of new communication algorithms such as cooperative spectrum sensing and Dynamic Spectrum Access (DSA). The implementation is Open Source and is available freely to students, researchers, engineers and wireless enthusiasts.

The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2. The genome sequences were transformed into dinucleotide relative frequencies and classified using the extreme gradient boosting (XGBoost) model. The classification models were trained to a) distinguish between the sequences of all eight species and b) distinguish between sequences of SARS-CoV-2 that originate from different geographic regions. Our method attained 100% in all performance metrics and for all tasks in the eight-species classification problem. Moreover, the models achieved 67% balanced accuracy for the task of classifying the SARS-CoV-2 sequences into the six continental regions and achieved 86% balanced accuracy for the task of classifying SARS-CoV-2 samples as either originating from Asia or not. Analysis of the dinucleotide genomic profiles of the eight species revealed a similarity between the SARS-CoV-2 and MERS-CoV viral sequences. Further analysis of SARS-CoV-2 viral sequences from the six continents revealed that samples from Oceania had the highest frequency of TT dinucleotides as well as the lowest CG frequency compared to the other continents. The dinucleotide signatures of AC, AG,CA, CT, GA, GT, TC, and TG were well conserved across most genomes, while the frequencies of other dinucleotide signatures varied considerably. Altogether, the results from this study demonstrate the utility of dinucleotide relative frequencies for discriminating and identifying similar species.INDEX TERMS alignment-free sequence analysis, COVID-19, dinucleotide frequencies, feature representations, genomic signatures, human pathogens, machine learning, XGBoost This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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