Thesis (M.S.)--Auburn University, 2007. / Abstract. Vita. Includes bibliographic references (ℓ. 48-50)

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references. Also available in print.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2002. / Thesis advisor(s): Tri Ha, Roberto Cristi. Includes bibliographical references (p. 69). Also available online.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 101-105).

Thesis (M.S.)--Rutgers University, 2008. / "Graduate Program in Electrical and Computer Engineering." Includes bibliographical references (p. 59-60).

Thesis (M.E.)--University of Canterbury, 2009. / Typescript (photocopy). "December 2009." Includes bibliographical references (p. [95)-102). Also available via the World Wide Web.

Zugl.: Duisburg, Essen, Univ., Diss., 2009

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 74-75)

Thesis (MScEng)--University of Stellenbosch, 2004. / ENGLISH ABSTRACT: The high pace of technological advancement enables the realisation of ever more advanced mobile communications standards with more functionality than simple voice communications. The hardware that is used to implement the radio sections of these systems generally require long design cycles, much longer than the design cycles of the other components of a communications system. Another problem is that, once new communications standards are introduced, the current hardware platforms used in the terminal equipment becomes obsolete because they can generally not be used with the new standards. This has serious cost implications for both the service provider and the consumer, because both parties have to acquire new equipment to be able to use the new standards. An elegant solution to the above issues is to use software-defined radio sections to replace the hardware radio components. New communications standards can then be supported by simply loading new software onto the equipment, provided the maximum processing capacity of the processor(s) that the software runs on can accommodate the bandwidth requirements of that specific standard. This thesis investigates the ideas behind software defined radio and also describes the design and implementation of a software architecture that can be used to implement software defined radios on general-purpose platforms such as personal computers. / AFRIKAANSE OPSOMMING: Die hoë tempo van tegnologiese vordering maak dit moontlik om baie gevorderde mobiele kommunikasie standaarde te implementeer wat meer funksionaliteit bied as blote spraakkommunikasie. Die hardeware wat gebruik word om die radios van sulke stelsels te implementeer neem gewoonlik langer om te ontwikkel as die ander komponente van die stelsels. Die ander probleem is dat hierdie hardeware gewoonlik nie hergebruik kan word wanneer nuwe kommunikasie standaarde in gebruik geneem word nie omdat die standaarde nie versoenbaar is nie. Dit het tot gevolg dat beide die verbruiker en die diensverskaffer groot bedrae geld moet spandeer om die nuwe tegnologie te kan gebruik. 'n Elegante oplossing vir hierdie probleme is om gebruik te maak van radios waarvan die funksionaliteit in sagteware gedefiniëer word. Nuwe kommunikasie standaarde kan dan gebruik word deur slegs die nodige sagteware op die toerusting te laai, solank die verwerkingskapasiteit van die mikroverwerkers in die stelsel die benodigde bandwydte kan akkommodeer. Hierdie tesis ondersoek die konsepte van sagteware-gedefiniëerde radio en beskryf die ontwerp en implementering van 'n sagteware argitektuur vir die implementering van sagteware-gedefiniëerde radios op veeldoelige platforms soos persoonlike rekenaars.

Many large-scale distributed Multi-Agent Systems (MAS) exchange information over low- power communication networks. In such scenarios, agents communicate intermittently with each other, often with limited power and over unlicensed spectrum bands that are susceptible to interference, eavesdropping, and Denial-of-Service (DoS) attacks. In this work, we consider a popular low-power, long-range communication protocol known as LoRa. Despite LoRa's high tolerance for noise and interference, it was found vulnerable to interference from particular chirp-type signals. State-of-the-art signal jamming techniques that exploit this property require the knowledge of two sensitive parameters - Bandwidth (BW) and Spreading Factor (SF). However, such information is available only to authenticated parties on the network and not to an eavesdropping adversary. We expose LoRa's vulnerability to DoS attacks by designing an intelligent jammer that surpasses the need for prior knowledge of these parameters. Exploiting a structural pattern in LoRa signals, we propose a Neural Network (NN) implementation for jointly inferring the two parameters by eavesdropping. Through simulation and experimentation, we analyze the detection vulnerability of LoRa for each combination of these parameters at various Signal to Noise Ratio (SNR) values. This work also presents a Radio Frequency (RF) dataset of LoRa signals, which is used to validate our inference model through experimentation. / Master of Science / When many independent devices (or agents) work together in a large system, they often need to communicate with each other. They do so using low-powered networks and often in an intermittent manner. These networks operate on unlicensed radio frequencies, which are open to interference, unwanted snooping, and 'denial-of-service' attacks that could shut down communication. In our study, we focus on a popular low-power, long-distance communication protocol called LoRa. Despite being designed to handle interference and noise well, related literature revealed that LoRa is vulnerable to a specific type of interference caused by 'chirp' signals. Current techniques to jam these signals and disrupt communication require the knowledge of two important factors - bandwidth and spreading factor. Normally, only authorized parties in the network would know these details, not any outsiders looking to interfere. However, we exploit LoRa's vulnerability without knowing these two parameters. By identifying a pattern in LoRa signals, we designed an artificial intelligence model that can determine these two parameters just by listening in. We then ran simulations and conducted experiments to understand how susceptible LoRa is to being detected under various levels of signal strength and noise. We also prepared a dataset of LoRa signals and used this data to confirm the effectiveness of our model.