Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Spectrum Sensing and Security Challenges and Solutions: Contemporary Affirmation of the Recent Literature

Cognitive radio (CR) has been recently proposed as a promising technology to improve spectrum utilization by enabling secondary access to unused licensed bands. A prerequisite to this secondary access is having no interference to the primary system. This requirement makes spectrum sensing a key function in cognitive radio systems. Among common spectrum sensing techniques, energy detection is an engaging method due to its simplicity and efficiency. However, the major disadvantage of energy detection is the hidden node problem, in which the sensing node cannot distinguish between an idle and a deeply faded or shadowed band. Cooperative spectrum sensing (CSS) which uses a distributed detection model has been considered to overcome that problem. On other dimension of this cooperative spectrum sensing, this is vulnerable to sensing data falsification attacks due to the distributed nature of cooperative spectrum sensing. As the goal of a sensing data falsification attack is to cause an incorrect decision on the presence/absence of a PU signal, malicious or compromised SUs may intentionally distort the measured RSSs and share them with other SUs. Then, the effect of erroneous sensing results propagates to the entire CRN. This type of attacks can be easily launched since the openness of programmable software defined radio (SDR) devices makes it easy for (malicious or compromised) SUs to access low layer protocol stacks, such as PHY and MAC. However, detecting such attacks is challenging due to the lack of coordination between PUs and SUs, and unpredictability in wireless channel signal propagation, thus calling for efficient mechanisms to protect CRNs. Here in this paper we attempt to perform contemporary affirmation of the recent literature of benchmarking strategies that enable the trusted and secure cooperative spectrum sensing among Cognitive Radios

Error models for digital channels and applications to wireless communication systems

Digital wireless channels are extremely prone to errors that appear in bursts or clusters. Error models characterise the statistical behaviour of bursty profiles derived from digital wireless channels. Generative error models also utilise those bursty profiles in order to create alternatives, which are more efficient for experimental purposes. Error models have a tremendous value for wireless systems. They are useful for the design and performance evaluation of error control schemes, in addition to higher layer protocols in which the statistical properties of the bursty profiles are greatly functional. Furthermore, underlying wireless digital channels can be substituted by generated error profiles. Consequently, computational load and simulation time can be significantly reduced when executing experiments and performing evaluation simulations for higher layer communications protocols and error control strategies. The burst error statistics are the characterisation metrics of error models. These statistics include: error-free run distribution; error-free burst distribution; error burst distribution; error cluster distribution; gap distribution; block error probability distribution; block burst probability distribution; bit error correlation function; normalised covariance function; gap correlation function; and multigap distribution. These burst error statistics scrutinise the error models and differentiate between them, with regards to accuracy. Moreover, some of them are advantageous for the design of digital components in wireless communication systems. This PhD thesis aims to develop accurate and efficient error models and to find applications for them. A thorough investigation has been conducted on the burst error statistics. A breakdown of this thesis is presented as follows. Firstly, an understanding of the different types of generative error models, namely, Markovian based generative models, context-free grammars based generative models, chaotic models, and deterministic process based generative models, has been presented. The most widely used models amongst the generative models have been compared with each other consulting the majority of burst error statistics. In order to study generative error models, error burst profiles were obtained mainly from the Enhanced General Packet Radio Service (EGPRS) system and also the Long Term Evolution (LTE) system. Secondly, more accurate and efficient generative error models have been proposed. Double embedded processes based hidden Markov model and three-layered processes based hidden Markov model have been developed. The two types of error profiles, particularly the bit-level and packet-level error profiles were considered. Thirdly, the deterministic process based generative models’ parameters have been tuned or modified in order to generate packet error sequences rather than only bit error sequences. Moreover, a modification procedure has been introduced to the same models to enhance their generation process and to make them more desirable. Fourthly, adaptive generative error models have been built in order to accommodate widely used generative error models to different digital wireless channels with different channel conditions. Only a few reference error profiles have been required in order to produce additional error profiles in various conditions that are beneficial for the design and performance evaluation of error control schemes and higher layer protocols. Finally, the impact of the Hybrid Automatic Repeat reQuest (HARQ) on the burst error statistics of physical layer error profiles has been studied. Moreover, a model that can generate predicted error sequences with burst error statistics similar to those of error profiles when HARQ is included has been proposed. This model is constructive in predicting the behaviour of the HARQ in terms of a set of higher order statistics rather than only predicting a first order statistic. Moreover, the whole physical layer is replaced by adaptively generated error profiles in order to check the performance of the HARQ protocol. The developed generative error models as well as the developed adaptive generative error models are expected to benefit future research towards the testing of many digital components in the physical layer as well as the wireless protocols of the link and transport layers for many existing and emerging systems in the field of wireless communications

Packet Loss in Terrestrial Wireless and Hybrid Networks

The presence of both a geostationary satellite link and a terrestrial local wireless link on the same path of a given network connection is becoming increasingly common, thanks to the popularity of the IEEE 802.11 protocol. The most common situation where a hybrid network comes into play is having a Wi-Fi link at the network edge and the satellite link somewhere in the network core. Example of scenarios where this can happen are ships or airplanes where Internet connection on board is provided through a Wi-Fi access point and a satellite link with a geostationary satellite; a small office located in remote or isolated area without cabled Internet access; a rescue team using a mobile ad hoc Wi-Fi network connected to the Internet or to a command centre through a mobile gateway using a satellite link. The serialisation of terrestrial and satellite wireless links is problematic from the point of view of a number of applications, be they based on video streaming, interactive audio or TCP. The reason is the combination of high latency, caused by the geostationary satellite link, and frequent, correlated packet losses caused by the local wireless terrestrial link. In fact, GEO satellites are placed in equatorial orbit at 36,000 km altitude, which takes the radio signal about 250 ms to travel up and down. Satellite systems exhibit low packet loss most of the time, with typical project constraints of 10−8 bit error rate 99% of the time, which translates into a packet error rate of 10−4, except for a few days a year. Wi-Fi links, on the other hand, have quite different characteristics. While the delay introduced by the MAC level is in the order of the milliseconds, and is consequently too small to affect most applications, its packet loss characteristics are generally far from negligible. In fact, multipath fading, interference and collisions affect most environments, causing correlated packet losses: this means that often more than one packet at a time is lost for a single fading even