In this thesis, we consider the distributed classification problem in wireless sensor networks (WSNs). Sensor nodes in WSNs detect environmental variations and make their decisions individually, after which their decisions, possibly in the presence of faults, are transmitted to a fusion center. In literature, the distributed classification fusion using error correcting codes has been shown to have good sensor fault-tolerance capability. In this thesis, we extend the fault-tolerant classification system using error correcting code by using two-dimensional channel coding. We also extend the binary coding in literature to the M-ary code. This thesis then suggests a code construction method with low computational complexity. Based on the suggest code construction method, this thesis then conducts a series experiment to investigate the performance of the suggested method.

We introduce the notion of diversity order as a performance measure for distributed radar systems. We define the diversity order of a radar network as the slope of the probability of detection (PD) versus SNR evaluated at PD =0.5. We prove that the communication bandwidth between the sensors and the fusion center does not affect the growth in diversity order. We also prove that the OR rule leads to the best performance and its diversity order grows as (log K). We then introduce the notion of a random radar network to study the effect of geometry on overall system performance. We approximate the distribution of the SINR at each sensor by an exponential distribution, and we derive the moments for a specific system model. We then analyze multistatic systems and prove that each sensor should be large enough to cancel the interference in order to exploit the available spatial diversity.

We introduce the notion of diversity order as a performance measure for distributed radar systems. We define the diversity order of a radar network as the slope of the probability of detection (PD) versus SNR evaluated at PD =0.5. We prove that the communication bandwidth between the sensors and the fusion center does not affect the growth in diversity order. We also prove that the OR rule leads to the best performance and its diversity order grows as (log K). We then introduce the notion of a random radar network to study the effect of geometry on overall system performance. We approximate the distribution of the SINR at each sensor by an exponential distribution, and we derive the moments for a specific system model. We then analyze multistatic systems and prove that each sensor should be large enough to cancel the interference in order to exploit the available spatial diversity.

This work considers the sampling design for detection problems.Firstly,we focus on studying the effect of signal shape on sampling design for Gaussian detection problem.We then investigate the sampling design for distributed detection problems and compare the performance with the single sensor context. We also propose a sampling design scheme for the cluster-based wireless sensor networks.The cluster head employs a linear combination fusion to reduce the dimension of the sampled observation.Mathematical verification and simulation result show that the performance loss caused by the dimensionality reduction is exceedingly small as compared with the benchmark scheme,which is the sampling scheme without dimensionality reduction.In particular,there is no performance loss when the identical sampling points are employed at all sensor nodes.

The energy efficiency issue, which is subjected to an energy constraint, is important for the applications in wireless sensor network. For the distributed detection problem considered in this thesis, the sensor makes a local decision based on its observation and transmits a one-bit message to the fusion center. We consider the local sensors employing a censoring scheme, where the sensors are silent and transmit nothing to fusion center if their observations are not very informative. The goal of this thesis is to achieve an energy efficiency design when the distributed detection employs the censoring scheme. Simulation results show that we can have the same error probabilities of decision fusion while conserving more energy simultaneously as compared with the detection without using censoring schemes. In this thesis, we also demonstrate that the error probability of decision fusion is a convex function of the censoring probability.

Recently, distributed detection has been intensively studied. The prevailing model for distributed detection (DD) is a system involving both distributed local sensors and a fusion center. In a DD system, multiple sensors work collaboratively to distinguish between two or more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD problem is reexamined in the context of wireless sensor network applications. For minimize the error probability at the fusion center, we consider the conventional method that designs the optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the transmission channel characteristics for find the optimal binary local sensor decision threshold to minimize the error probability at the fusion center. And there have different optimal local sensor decision thresholds for different channel state information. Because of optimal multi-bit (soft) local sensor decision is more practical than optimal binary local sensor decision. Allowing for multi-bit local sensor output, we also consider another conventional method that designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system. However, to design the optimal local sensor decision rule, both of two conventional methods are easily trapped into local optimal thresholds, which are depended on the pre-selected initialization values. To overcome this difficulty, we consider several modified Simulated Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional methods, we propose two novel SA algorithms for implementing the optimal local sensor decision rule. Computer simulation results show that the employments of two novel SA algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor decision problem and optimal multi-bit local sensor decision problem. And two novel SA algorithms offer superior performance with lower search points compared to conventional SA algorithm.

The introduction of programmability and dynamic protocol deployment in routers, there would be an increase in the potential vulnerabilities and attacks . The next- generation Internet promises to provide a fundamental shift in the underlying architecture to support dynamic deployment of network protocols. In this thesis, we consider the problem of detecting malicious packet drops in routers. Specifically, we focus on an attack scenario, where a router selectively drops packets destined for another node. Detecting such an attack is challenging since it requires differentiating malicious packet drops from congestion-based packet losses. We propose a controller- based malicious packet detection technique that effectively detects malicious routers using delayed sampling technique and verification of the evidence. The verification involves periodically determining congestion losses in the network and comparing the forwarding behaviors of the adjoining routers to affirm the state of a router in the network. We provide a performance analysis of the detection accuracy and quantify the communication overhead of our system. Our results show that our technique provides accurate detection with low performance overhead.

One of the fundamental hurdles in realizing new spectrum sharing allocation policies is that of reliable spectrum sensing. In this thesis, three research thrusts are presented in order to further research in this critical area. The first of these research thrusts is the development of a novel asynchronous and noncoherent modulation classifier for PSK/QAM modulated signals in flat-fading channels. In developing this classifier, a novel estimator for the unknown channel gain and fractional time delay is proposed which uses a method-of-moments based estimation approach. For the second research thrust of this thesis, the developed method-of-moments based estimation approach is extended to estimate the signal-to-noise ratio of PSK/QAM modulated signals in flat-fading channels, in which no a priori knowledge of the modulation format and channel parameters is assumed. Finally, in the third research thrust, a distributed spectrum sensing approach is proposed in which a network of radios collaboratively detects the presence, as well as the modulation scheme, of a signal through the use of a combination of cyclic spectrum feature-based signal classification and an iterative algorithm for optimal data fusion. / Master of Science

[pt] Este trabalho estuda o problema de detecção de hipóteses binárias em sistemas distribuídos com centro de fusão operando em presença de canais seletivos em frequência. O uso de uma técnica de múltiplo acesso, referida aqui como CS-CDMA, é proposta para comunicação ortogonal entre os nós e o centro de fusão, assim como detector ótimo Bayesiano para fusão de dados em tais sistemas distribuídos é obtido. Como a complexidade do detector ótimo cresce exponencialmente com o número de nós sensores, um receptor sub-ótimo de baixa complexidade que realiza uma detecção casada multi-usuário seguida de decisão pela regra da maioria é proposto e examinado neste trabalho. Técnicas para estimação de canal, cega e assistida, necessárias para a implementação prática da detecção casada são também propostas. Simulações indicam que este receptor, de baixa complexidade, possui um desempenho próximo ao receptor ótimo. Com o objetivo de se ampliar o desempenho do detector casado do centro de fusão, é examinado o uso de cooperação na rede de sensores. Resultados de simulações mostraram que, como esperado, o uso de cooperação em sistema distribuídos utilizando o esquema de múltiplo acesso CS-CDMA melhora o desempenho do decisor do centro de fusão, entretanto esse ganho de desempenho mostrou-se mais significativo em ambientes com poucos multipercursos, uma vez que os sistemas distribuídos CS-CDMA não-cooperativos propostos exploram eficientemente a diversidade de multipercurso. Finalmente, este trabalho propõe um procedimento de fusão adaptativa não-assistida para sistemas distribuídos com fusão centralizada. Simulações mostram que a estratégia de fusão adaptativa possui desempenho muito próximo ao da regra de fusão ótima. / [en] This work studies the problem of detecting binary hypotheses in distributed systems with a fusion center operating in frequency selective channels. The use of a multiple access technique, referred herein as Chip Spread- Code Division Multiple Access (CS-CDMA), is proposed for orthogonal communication between the nodes and the fusion center and the Bayesian optimum detector for data fusion for such distributed systems is obtained. As the complexity of the optimal detector grows exponentially with the number of sensor nodes, a sub-optimal low-complexity receiver that performs a multi-user matched detection followed by the majority rule is proposed and examined in this work. Blind and assisted techniques for channel estimation necessary for the practical implementation of the matched detection have also been proposed. Simulations indicate that this low complexity receptor has a performance close to the optimal receiver. In order to increase the performance of the matched detector of the fusion center, it was examined the use of cooperation in this sensor network. Simulation results showed that, as expected, the use of cooperation in the distributed system with a multiple access scheme CS-CDMA improves the performance of the fusion center, however, this performance increasing was more significant in environments with few multipath, since the non-cooperative CS-CDMA distributed systems proposed here, efficiently exploits the multipath diversity. Finally, this paper proposes a non-assisted adaptive fusion for distributed systems with centralized fusion. Simulations show that the adaptive fusion strategy has a performance very close to the optimal fusion rule.

This dissertation studies a number of important issues that arise in multiple-input multiple-out wireless systems. First, wireless systems equipped with multiple-transmit multiple-receive antennas are considered where an energy-based antenna selection is performed at the receiver. Three different situations are considered: (i) selection over iid MIMO fading channel, (ii) selection over spatially correlated fading channel, and (iii) selection for space-time coded OFDM systems. In all cases, explicit upper bounds are derived and it is shown that using the proposed antenna selection, one can achieve the same diversity order as that attained by full-complexity MIMO systems. Next, joint source-channel coding problem for MIMO antenna systems is studied and a turbo-coded multiple description code for multiple antenna transmission is developed. Simulations indicate that by the proposed iterative joint source-channel decoding that exchanges the extrinsic information between the source code and the channel code, one can achieve better reconstruction quality than that can be achieved by the single-description codes at the same rate. The rest of the dissertation deals with wireless networks. Two problems are studied: channel coding for cooperative diversity in wireless networks, and distributed detection in wireless sensor networks. First, a turbo-code based channel code for three-terminal full-duplex wireless relay channels is proposed where both the source and the relay nodes employ turbo codes. An iterative turbo decoding algorithm exploiting the information arriving from both the source and relay nodes is proposed. Simulation results show that the proposed scheme can perform very close to the capacity of a wireless relay channel. Next the parallel and serial binary distributed detection problem in wireless sensor networks is investigated. Detection strategies based on single-bit and multiple-bit decisions are considered. The expressions for the detection and false alarm rates are derived and used for designing the optimal detection rules at all sensor nodes. Also, an analog approach to the distributed detection in wireless sensor networks is proposed where each sensor nodes simply amplifies-and-forwards its sufficient statistics to the fusion center. This method requires very simple processing at the local sensor. Numerical examples indicate that the analog approach is superior to the digital approach in many cases.