Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Interference charecterisation, location and bandwidth estimation in emerging WiFi networks

Wireless LAN technology based on the IEEE 802.11 standard, commonly referred to as WiFi, has been hugely successful not only for the last hop access to the Internet in home, office and hotspot scenarios but also for realising wireless backhaul in mesh networks and for point -to -point long- distance wireless communication. This success can be mainly attributed to two reasons: low cost of 802.11 hardware from reaching economies of scale, and operation in the unlicensed bands of wireless spectrum.The popularity of WiFi, in particular for indoor wireless access at homes and offices, has led to significant amount of research effort looking at the performance issues arising from various factors, including interference, CSMA/CA based MAC protocol used by 802.11 devices, the impact of link and physical layer overheads on application performance, and spatio-temporal channel variations. These factors affect the performance of applications and services that run over WiFi networks. In this thesis, we experimentally investigate the effects of some of the above mentioned factors in the context of emerging WiFi network scenarios such as multi- interface indoor mesh networks, 802.11n -based WiFi networks and WiFi networks with virtual access points (VAPs). More specifically, this thesis comprises of four experimental characterisation studies: (i) measure prevalence and severity of co- channel interference in urban WiFi deployments; (ii) characterise interference in multi- interface indoor mesh networks; (iii) study the effect of spatio-temporal channel variations, VAPs and multi -band operation on WiFi fingerprinting based location estimation; and (iv) study the effects of newly introduced features in 802.11n like frame aggregation (FA) on available bandwidth estimation.With growing density of WiFi deployments especially in urban areas, co- channel interference becomes a major factor that adversely affects network performance. To characterise the nature of this phenomena at a city scale, we propose using a new measurement methodology called mobile crowdsensing. The idea is to leverage commodity smartphones and the natural mobility of people to characterise urban WiFi co- channel interference. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployed WiFi APs via the mobile crowdsensing approach. These show that few channels in 2.4GHz are heavily used and there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that co- channel interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city -scale are similar to those of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.For mesh networks, the use of multiple radio interfaces is widely seen as a practical way to achieve high end -to -end network performance and better utilisation of available spectrum. However this gives rise to another type of interference (referred to as coexistence interference) due to co- location of multiple radio interfaces. We show that such interference can be so severe that it prevents concurrent successful operation of collocated interfaces even when they use channels from widely different frequency bands. We propose the use of antenna polarisation to mitigate such interference and experimentally study its benefits in both multi -band and single -band configurations. In particular, we show that using differently polarised antennas on a multi -radio platform can be a helpful counteracting mechanism for alleviating receiver blocking and adjacent channel interference phenomena that underlie multi -radio coexistence interference. We also validate observations about adjacent channel interference from previous studies via direct and microscopic observation of MAC behaviour.Location is an indispensable information for navigation and sensing applications. The rapidly growing adoption of smartphones has resulted in a plethora of mobile applications that rely on position information (e.g., shopping apps that use user position information to recommend products to users and help them to find what they want in the store). WiFi fingerprinting is a popular and well studied approach for indoor location estimation that leverages the existing WiFi infrastructure and works based on the difference in strengths of the received AP signals at different locations. However, understanding the impact of WiFi network deployment aspects such as multi -band APs and VAPs has not received much attention in the literature. We first examine the impact of various aspects underlying a WiFi fingerprinting system. Specifically, we investigate different definitions for fingerprinting and location estimation algorithms across different indoor environments ranging from a multi- storey office building to shopping centres of different sizes. Our results show that the fingerprint definition is as important as the choice of location estimation algorithm and there is no single combination of these two that works across all environments or even all floors of a given environment. We then consider the effect of WiFi frequency bands (e.g., 2.4GHz and 5GHz) and the presence of virtual access points (VAPs) on location accuracy with WiFi fingerprinting. Our results demonstrate that lower co- channel interference in the 5GHz band yields more accurate location estimation. We show that the inclusion of VAPs has a significant impact on the location accuracy of WiFi fingerprinting systems; we analyse the potential reasons to explain the findings.End -to -end available bandwidth estimation (ABE) has a wide range of uses, from adaptive application content delivery, transport-level transmission rate adaptation and admission control to traffic engineering and peer node selection in peer -to- peer /overlay networks [ 1, 2]. Given its importance, it has been received much research attention in both wired data networks and legacy WiFi networks (based on 802.11 a/b /g standards), resulting in different ABE techniques and tools proposed to optimise different criteria and suit different scenarios. However, effects of new MAC/PHY layer enhancements in new and next generation WiFi networks (based on 802.11n and 802.11ac standards) have not been studied yet. We experimentally find that among different new features like frame aggregation, channel bonding and MIMO modes (spacial division multiplexing), frame aggregation has the most harmful effect as it has direct effect on ABE by distorting the measurement probing traffic pattern commonly used to estimate available bandwidth. Frame aggregation is also specified in both 802.11n and 802.1 lac standards as a mandatory feature to be supported. We study the effect of enabling frame aggregation, for the first time, on the performance of the ABE using an indoor 802.11n wireless testbed. The analysis of results obtained using three tools - representing two main Probe Rate Model (PRM) and Probe Gap Model (PGM) based approaches for ABE - led us to come up with the two key principles of jumbo probes and having longer measurement probe train sizes to counter the effects of aggregating frames on the performance of ABE tools. Then, we develop a new tool, WBest+ that is aware of the underlying frame aggregation by incorporating these principles. The experimental evaluation of WBest+ shows more accurate ABE in the presence of frame aggregation.Overall, the contributions of this thesis fall in three categories - experimental characterisation, measurement techniques and mitigation/solution approaches for performance problems in emerging WiFi network scenarios. The influence of various factors mentioned above are all studied via experimental evaluation in a testbed or real - world setting. Specifically, co- existence interference characterisation and evaluation of available bandwidth techniques are done using indoor testbeds, whereas characterisation of urban WiFi networks and WiFi fingerprinting based location estimation are carried out in real environments. New measurement approaches are also introduced to aid better experimental evaluation or proposed as new measurement tools. These include mobile crowdsensing based WiFi monitoring; MAC/PHY layer monitoring of co- existence interference; and WBest+ tool for available bandwidth estimation. Finally, new mitigation approaches are proposed to address challenges and problems identified throughout the characterisation studies. These include: a proposal for crowd - based interference management in large scale uncoordinated WiFi networks; exploiting antenna polarisation diversity to remedy the effects of co- existence interference in multi -interface platforms; taking advantage of VAPs and multi -band operation for better location estimation; and introducing the jumbo frame concept and longer probe train sizes to improve performance of ABE tools in next generation WiFi networks

Spectrum handoff strategy for cognitive radio-based Mac in industrial wirless sensor and actuator networks

In this thesis, a Cognitive Radio(CR)-based MAC for Industrial Wireless Sensor and Actuator Network (IWSAN) applications is proposed. IWSANs are typically used for closed-loop control applications, and they demand strict requirements in terms of time and robustness. Low latency and low error rates are required in order not to endanger persons or machinery. Moreover, these applications operate in industrial environments such as factories or transport scenarios (as aeronautics or railway) where multipath fading and shadowing are present due to metal surfaces. Furthermore, interference from other communication systems or industrial machinery is also common in these environments. The proposed MAC, based on the CR paradigm, is capable of ensuring time and robustness requirements in industrial channels. In the process of designing the CR-based MAC for IWSAN applications, a comparison between several non-CR-based MACs and CR-based MACs has been carried out. This comparison, which allows stating the benefits of CR for these applications, is presented in this thesis. The performance of every MAC is determined theoretically using Network Calculus, and it is validated through OPNET simulations. CR solutions, due to their adaptability characteristics, are capable of avoiding interference and ensuring robustness in industrial environments. However, none of the selected MACs are capable of ensuring robustness without comprising time requirements, since interference is avoided but not in a bounded time. On the other hand, the MAC proposed in this thesis is capable of avoiding interference ensuring time and robustness requirements at the same time. This MAC is therefore suitable for IWSAN applications. To ensure a deterministic behavior against interference, a novel handoff algorithm, which detects interference and hops to another channel, has been proposed. This algorithm has been designed to be used jointly with one of the evaluated MACs. The detection of the interference and the hop to another channel is done in a bounded time, because the proposed algorithm detects interference while the system is transmitting. The performance of this proposal is evaluated using Network Calculus and OPNET simulations, and the results are compared with the system without the proposed handoff algorithm. The comparison of the results shows how the evaluated MAC is only capable of ensuring both time and robustness requirements when the proposed handoff strategy is used. Moreover, the spectrum sensing algorithm used to obtain information about the environment is delved and its performance is measured through MATLAB simulations. An energy detector has been chosen due to its simplicity. Also, a cyclostationary Modulation Classifier is presented and a simplification has been carried out allowing its implementation on real hardware. The Modulation Classifier is capable of distinguishing between OFDM, QPSK and GFSK signals. The performance of the algorithm is presented in this thesis for different signals and for different receiver impairments such as frequency offset, DC offset and I/Q imbalance. Finally, a cognitive platform to validate the spectrum sensing algorithms is presented. This platform has been designed using a Xilinx Virtex 6 FPGA by a working group composed of researchers from IK4-Ikerlan and Mondragon Unibertsitatea. The platform, which uses both spectrum sensing algorithms, is an Ethernet-to-RF bridge. It has been designed to replace an Ethernet wired link by a wireless one for IWSAN applications. The proposed platform ensures a reliable communication link against interference. In the proposed implementation, the energy detector is used by the transmitter in order to find a free channel to transmit data, whereas the modulation classifier is used by the receiver in order to distinguish between the signal transmitted by the RF-Ethernet bridge and other signals. In this way the receiver can find the channel where the transmitter is carrying out the communication.En esta tesis se propone una MAC basada en el paradigma de la Radio Cognitiva (RC) para redes de sensores y actuadores inalámbricos industriales. Estas redes se suelen utilizar para aplicaciones de control en lazo cerrado, que exigen requisitos estrictos de tiempo y robustez. Para no poner en peligro la salud de las personas o la maquinaria es necesario que la red asegure una baja latencia y una tasa baja de errores. Además, al trabajar en ambientes industriales como fábricas o transportes (trenes, aviones, etc.), estas redes tienen que hacer frente a canales con mucho desvanecimiento por multitrayecto y efecto sombra debido a las superficies metálicas. También es común en estos entornos que haya interferencias de otros sistemas de comunicaciones o de la propia maquinaria industrial. La MAC propuesta en esta tesis es capaz de asegurar los requisitos temporales y de robustez demandados trabajando en este tipo de entornos. En el proceso de diseño de la MAC basada en RC para redes de sensores y actuadores inalámbricos industriales, se ha llevado a cabo una comparación de diferentes MACs diseñadas para estas redes. Se han evaluado tanto MACs basadas en RC como no basadas en ella, estableciendo las ventajas de la RC para estas aplicaciones. La evaluación se ha llevado a cabo haciendo un estudio teórico mediante Network Calculus, cuyos resultados se han validado mediante simulaciones en OPNET. Los resultados muestran como la RC es capaz de evitar interferencias y asegurar robustez en ambientes industriales. Sin embargo, ninguna de las MACs seleccionadas ha conseguido asegurar ambos requisitos, temporales y de robustez, al mismo tiempo; se puede evitar las interferencias pero no sin comprometer los requisitos temporales de la aplicación. Sin embargo, la MAC propuesta es capaz de evitar interferencias asegurando al mismo tiempo los requisitos temporales y de robustez. Por lo tanto, la MAC propuesta es apropiada para este tipo de redes. Para asegurar el comportamiento determinista del sistema, se ha propuesto un novedoso algoritmo de handoff que es capaz de detectar una interferencia y saltar a otro canal. Este algoritmo se ha diseñado para ser utilizado conjuntamente con una de las MACs previamente evaluadas. La detección de la interferencia y el salto a otro canal se hace en un tiempo determinado de tiempo, ya que es posible detectar interferencias mientras el sistema está transmitiendo. Su rendimiento se ha evaluado mediante Network Calculus y simulaciones en OPNET, y se ha comparado con los resultados obtenidos con la MAC cuando no se utiliza el esquema propuesto. De la comparación se deduce que el esquema de handoff añade a la MAC la capacidad de asegurar a la vez los requisitos temporales y de robustez. Además, en la tesis se explica el algoritmo de spectrum sensing que la MAC utiliza para obtener información del entorno, y su rendimiento se ha estudiado mediante simulaciones en MATLAB. Debido a su simplicidad, se ha optado por un detector de energía para este propósito. También se presenta un clasificador de modulaciones cicloestacionario. Este clasificador ha sido simplificado todo lo posible para posibilitar su implementación en hardware real. El clasificador de modulaciones es capaz de distinguir entre señales OFDM, QPSK y GFSK. Su rendimiento se detalla para diferentes señales y para diferentes deficiencias presentes en el receptor, como son offset de frecuencia, offset de continua o desequilibrios I/Q. Por último, se presenta una plataforma cognitiva que se ha utilizado para validar los algoritmos de spectrum sensing. Un grupo de trabajo compuesto por investigadores de IK4-Ikerlan y Mondragon Unibertsitatea ha diseñado esta plataforma sobre una FPGA Virtex 6 de Xilinx. La plataforma, que utiliza los dos algoritmos de spectrum sensing, es un puente Ethernet-RF. Su objetivo es reemplazar un enlace cableado de Ethernet por uno inalámbrico para aplicaciones de redes de sensores y actuadores industriales. Gracias a los algoritmos de spectrum sensing, la plataforma es capaz de asegurar un enlace robusto ante interferencias. El detector de energía se utiliza en el transmisor para encontrar los posibles canales libres donde transmitir la información. Mientras que el clasificador de modulaciones se utiliza en el receptor para distinguir entre la señal del transmisor y otras posibles señales. Esto permite al receptor saber en qué canal de todos los posibles está el transmisor.Tesi honetan proposatzen da Irrati Kognitiboaren (IK) paradigman oinarritutako MAC bat industriako haririk gabeko sentsore eta eragingailuen sareetarako. Sare horiek begizta itxiko kontrol aplikazioetarako erabili ohi dira, denbora eta sendotasunaren aldetik baldintza ugari eskatzen dute eta. Pertsonen osasuna eta makinak arriskuan ez jartzeko, beharrezkoa da sareak latentzia eta hutsegite tasa txikiak bermatzea. Gainera, industri giroetan lan egiteko direnez, esaterako, lantegietan edo garraioetan (trenak, hegazkinak, etab.), sare horiek gai izan behar dira gainazal metalikoek eragiten dituzten ibilbide aniztunaren eta itzal efektuaren ondorioz asko barreiatzen diren kanalei aurre egiteko. Ingurune horien ohiko ezaugarria da, baita ere, beste komunikazio sistema batzuen edo industriako makinen beraien interferentziak egotea. Tesi honetan proposatzen den MACa gai da honelako inguruetan lan egiteko denborari eta sendotasunari dagokienez eskatzen dituen baldintzak ziurtatzeko. IKan oinarrituta haririk gabeko sentsore eta eragingailu industrialen sareetarako MACa diseinatzeko prozesuan, horrelako sareetarako aurkeztu diren hainbat MAC alderatu dira. IKan oinarritutako MACak zein bestelakoak ebaluatu dira, eta IKak aplikazio hauetarako dituen abantailak ezarri dira. Ebaluaziorako Network Calculus erabili da, zeinaren bidez azterketa teoriko bat egin baita, eta azterketaren emaitzak OPNETen simulazioak eginda baliozkotu dira. Emaitzek erakusten dutenez, IKa gai da industriako inguruneetan interferentziak ekidin eta sendotasuna ziurtatzeko. Halere, aukeratu diren MACetatik batek ere ez du lortu baldintza biak, denborari buruzkoa zein sendotasunari buruzkoa, aldi berean ziurtatzea; interferentziak ekidin daitezke, baina ez aplikazioaren denborari buruzko baldintzak arriskuan jarri gabe. Dena dela, proposatu den MACak portaera determinista bat ziurtatzen du interferentziekiko, eta aldi berean denborari eta sendotasunari buruzko baldintzak ere ziurtatzen ditu. Hortaz, MAC hau egokia da sare mota honetarako. Sistemaren portaera determinista ziurtatzeko, handoff algoritmo berritzailea proposatu da, zeina interferentzia bat antzeman eta beste kanal bat igarotzeko gai den. Algoritmo hori aurretik ebaluatutakoa MACetako batekin batera erabiltzeko diseinatu da. Interferentzia antzeman eta beste kanal batera salto egitea denbora jakin batean egiten da, izan ere, sistema transmititzen ari dela antzeman baitaitezke interferentziak. Network Calculusen bitartez eta OPNETeko simulazioen bitartez ebaluatu da sistemaren errendimendua, eta proposatutako eskema erabiltzen ez denean MACak ematen dituen emaitzekin alderatu da. Alderaketa horretatik ondorioztatzen denez, handoff eskemak denborari eta sendotasunari buruzko baldintzak batera ziurtatzeko ahalmena ematen dio MACari. Gainera, tesiak azaltzen du inguruneari buruzko informazioa eskuratzeko MACak erabiltzen duen spectrum sensing algoritmoa, eta bere errendimendua MATLABen simulazioak eginez aztertu da. Bere sinpletasuna dela eta, energia detektore bat aukeratu da asmo honetarako. Modulazio sailkatzaile zikloegonkor bat ere aurkezten da. Sailkapen hori ahalik eta gehien sinplifikatu da benetako hardwarean inplementatu ahal izateko. Modulazioen sailkatzaileak OFDM, QPSK eta GFSK seinaleak bereizi ditzake. Bere errendimendua hargailuan dauden seinale eta akats desberdinetarako zehazten da, esaterako maiztasunaren offset-a,zuzenaren offset-a edo I/Q desorekak. Bukatzeko, spectrum sensing-eko algoritmoak baliozkotzeko erabili den plataforma kognitibo bat aurkezten da. IK4-Ikerlaneko eta Mondragon Unibertsitateko ikertzailez osatutako lantalde batek diseinatu du plataforma hori Xilinxen Virtex 6 FPGA baten oinarrutz. Plataformak spectrum sensing-eko bi algoritmo erabiltzen ditu eta Ethernet-RF zubi bat da. Bere helburua da Etherneteko kable bidezko lotura bat haririk gabeko batekin ordeztea industriako sentsore eta eragingailuen sareetan aplikatzeko. Spectrum sensing-eko algoritmoei esker, plataformak lotura sendoa bermatu dezake interferentziak gertatzen direnean. Energia detektorea transmisorean erabiltzen da informazioa transmititzeko erabilgarri egon daitezkeen kanalak aurkitzeko. Modulazioen sailkatzailea, berriz, hargailuan erabiltzen da transmisorearen seinalea eta egon daitezkeen beste seinale batzuk bereizteko. Horri esker, hargailuak badaki posible diren kanal guztietatik non dagoen transmisorea

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Localization and security algorithms for wireless sensor networks and the usage of signals of opportunity

In this dissertation we consider the problem of localization of wireless devices in environments and applications where GPS (Global Positioning System) is not a viable option. The _x000C_rst part of the dissertation studies a novel positioning system based on narrowband radio frequency (RF) signals of opportunity, and develops near optimum estimation algorithms for localization of a mobile receiver. It is assumed that a reference receiver (RR) with known position is available to aid with the positioning of the mobile receiver (MR). The new positioning system is reminiscent of GPS and involves two similar estimation problems. The _x000C_rst is localization using estimates of time-di_x000B_erence of arrival (TDOA). The second is TDOA estimation based on the received narrowband signals at the RR and the MR. In both cases near optimum estimation algorithms are developed in the sense of maximum likelihood estimation (MLE) under some mild assumptions, and both algorithms compute approximate MLEs in the form of a weighted least-squares (WLS) solution. The proposed positioning system is illustrated with simulation studies based on FM radio signals. The numerical results show that the position errors are comparable to those of other positioning systems, including GPS. Next, we present a novel algorithm for localization of wireless sensor networks (WSNs) called distributed randomized gradient descent (DRGD), and prove that in the case of noise-free distance measurements, the algorithm converges and provides the true location of the nodes. For noisy distance measurements, the convergence properties of DRGD are discussed and an error bound on the location estimation error is obtained. In contrast to several recently proposed methods, DRGD does not require that blind nodes be contained in the convex hull of the anchor nodes, and can accurately localize the network with only a few anchors. Performance of DRGD is evaluated through extensive simulations and compared with three other algorithms, namely the relaxation-based second order cone programming (SOCP), the simulated annealing (SA), and the semi-de_x000C_nite programing (SDP) procedures. Similar to DRGD, SOCP and SA are distributed algorithms, whereas SDP is centralized. The results show that DRGD successfully localizes the nodes in all the cases, whereas in many cases SOCP and SA fail. We also present a modi_x000C_cation of DRGD for mobile WSNs and demonstrate the e_x000E_cacy of DRGD for localization of mobile networks with several simulation results. We then extend this method for secure localization in the presence of outlier distance measurements or distance spoo_x000C_ng attacks. In this case we present a centralized algorithm to estimate the position of the nodes in WSNs, where outlier distance measurements may be present

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model