Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks

Cram\'er-Rao Bounds for Polynomial Signal Estimation using Sensors with AR(1) Drift

We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively studied in the literature, the loss of estimation performance due to systematic errors like drift have rarely been looked into. In this paper, we derive closed-form Fisher Information matrix and subsequently Cram\'er-Rao bounds (upto reasonable approximation) for the estimation accuracy of drift-corrupted signals. We assume a polynomial time-series as the representative signal and an autoregressive process model for the drift. When the Markov parameter for drift \rho<1, we show that the first-order effect of drift is asymptotically equivalent to scaling the measurement noise by an appropriate factor. For \rho=1, i.e., when the drift is non-stationary, we show that the constant part of a signal can only be estimated inconsistently (non-zero asymptotic variance). Practical usage of the results are demonstrated through the analysis of 1) networks with multiple sensors and 2) bandwidth limited networks communicating only quantized observations.Comment: 14 pages, 6 figures, This paper will appear in the Oct/Nov 2012 issue of IEEE Transactions on Signal Processin

Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Comment: 12 pages, 11 figure

Cooperative Detection and Network Coding in Wireless Networks

In cooperative communication systems, multiple terminals in wireless networks share their antennas and resources for information exchange and processing. Recently, cooperative communications have been shown to achieve significant performance improvements in terms of transmission reliability, coverage area extension, and network throughput, with respect to existing classical communication systems. This dissertation is focused on two important applications of cooperative communications, namely: (i) cooperative distributed detection in wireless sensor networks, and (ii) many-to-many communications via cooperative space-time network coding. The first application of cooperative communications presented in this dissertation is concerned with the analysis and modeling of the deployment of cooperative relay nodes in wireless sensor networks. Particularly, in dense wireless sensor networks, sensor nodes continuously observe and collect measurements of a physical phenomenon. Such observations can be highly correlated, depending on the spatial separation between the sensor nodes as well as how the physical properties of the phenomenon are evolving over time. This unique characteristic of wireless sensor networks can be effectively exploited with cooperative communications and relays deployment such that the distributed detection performance is significantly improved as well as the energy efficiency. In particular, this dissertation studies the Amplify-and-Forward (AF) relays deployment as a function of the correlation of the observations and analyzes the achievable spatial diversity gains as compared with the classical wireless sensor networks. Moreover, it is demonstrated that the gains of cooperation can be further leveraged to alleviate bandwidth utilization inefficiencies in current sensor networks. Specifically, the deployment of cognitive AF cooperative relays to exploit empty/under-utilized time-slots and the resulting energy savings are studied, quantified and compared. The multiple terminal communication and information exchange form the second application of cooperative communications in this dissertation. Specifically, the novel concept of Space-Time-Network Coding (STNC) that is concerned with formulation of the many-to-many cooperative communications over Decode-and-Forward (DF) nodes is studied and analyzed. Moreover, the exact theoretical analysis as well as upper-bounds on the network symbol error rate performance are derived. In addition, the tradeoff between the number of communicating nodes and the timing synchronization errors is analyzed and provided as a network design guideline. With STNC, it is illustrated that cooperative diversity gains are fully exploited per node and significant performance improvements are achieved. It is concluded that the STNC scheme serves as a potential many-to-many cooperative communications scheme and that its scope goes much further beyond the generic source-relay-destination communications

Measure of covertness based on the imperfect synchronization of an eavesdropper in Random Communication Systems

10th International Conference on Electrical and Electronics Engineering, ELECO 2017; Bursa; Turkey; 29 November 2017 through 2 December 2017Random Communication Systems (RCSs) given in the literature have assumed perfectly synchronized transmitter and receiver. However in this paper, instead of assuming perfect synchronization approach in RCSs, the effects of imperfect synchronization (IS) on Skewed Alpha-Stable Noise Shift Keying (SkaS-NSK) based RCS have been observed through simulations. The Bit Error Rate (BER) performance of the eavesdropper with respect to his synchronization error in SkaS-NSK based RCS, has been analyzed. An expression for the probability of an eavesdropper to decode the binary information (i.e., Eavesdropping Probability) in SkaS-NSK based RCS, has been derived. The criterion (i.e., Covertness Value) to measure the covertness level of RCSs has also been proposed. The BER performance of an eavesdropper provides an approximate margin of synchronization error if it can be overcome by an eavesdropper then he can achieve the decoding (i.e., eavesdropping) process

Optimization of capacity in non-Gaussian noise models with and without fading channels for sustainable communication systems

The highest rate at which information may be reliably sent via a communication link is known as its capacity. In the case of non-Gaussian noise, the capacity of the channel depends on the specific characteristics of the noise, which can cause severe errors and reduce the reliability of communication systems over a fading channel. The Gaussian mixture impulsive noise model (GMINM), which is a more general and flexible non-Gaussian model for impulsive noise, has been compared in this paper with the Middleton Class-A impulsive noise model (MCAINM) in terms of derived channel capacity normalized by channel bandwidth (C/BW) with and without Rayleigh fading (Rf) channels. It also investigated the trade-off between complexity and accuracy in modeling the impulsive noise using two simplified Middleton Class-A impulsive noise models based on derived C/BW. The derived C/BW of these models under various conditions, such as different signal-to-noise ratios and impulsive noise parameters and models, have been performed and evaluated using two different scenarios: the exact method and the semi-analytical method. When the impulsive noise parameters  and A are both near 0 in GMINM and MCAINM, respectively, the capacity of the impulsive noise channel is found to be equivalent to that of the Gaussian channel sustainable, as shown by the findings based on Monte-Carlo simulations. We have shown that when the impulsive noise decreases, the capacity increases in all models; however, the capacity of Gaussian noise is higher than the capacity of non-Gaussian noise, which in turn is higher than the capacity of non-Gaussian noise over the Rf channel overall values of SNR in dB. Moreover, multi-channel configuration introduces spatial diversity and multiplexing gains that have been proposed to sustainably optimize the ergodic capacity for the challenge case when the channel state information (CSI) is unknown at the transmitter in non-Gaussian noise over Rf channel. In today's rapidly evolving world, sustainable communication systems play a crucial role in ensuring efficient and responsible utilization of resources. As the demand for wireless communication continues to rise, it becomes imperative to optimize the capacity of communication channels, especially in scenarios involving non-Gaussian noise models and fading channels.

Spectrum sensing, spectrum monitoring, and security in cognitive radios

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. In this dissertation, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user’s signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. In-band spectrum sensing in overlay cognitive radio networks requires that the secondary users (SU) periodically suspend their communication in order to determine whether the primary user (PU) has started to utilize the channel. In contrast, in spectrum monitoring the SU can detect the emergence of the PU from its own receiver statistics such as receiver error count (REC). We investigate the problem of spectrum monitoring in the presence of fading where the SU employs diversity combining to mitigate the channel fading effects. We show that a decision statistic based on the REC alone does not provide a good performance. Next we introduce new decision statistics based on the REC and the combiner coefficients. It is shown that the new decision statistic achieves significant improvement in the case of maximal ratio combining (MRC). Next we consider the problem of cooperative spectrum sensing in cognitive radio networks (CRN) in the presence of misbehaving radios. We propose a novel approach based on the iterative expectation maximization (EM) algorithm to detect the presence of the primary users, to classify the cognitive radios, and to compute their detection and false alarm probabilities. We also consider the problem of centralized binary hypothesis testing in a cognitive radio network (CRN) consisting of multiple classes of cognitive radios, where the cognitive radios are classified according to the probability density function (PDF) of their received data (at the FC) under each hypotheses

Spectrum Awareness in Cognitive Radio Systems

The paper addresses the issue of the Electromagnetic Environment Situational Awareness techniques. The main focus is put on sensing and the Radio Environment Map. These two dynamic techniques are described in detail. The Radio Environment Map is considered the essential part of the spectrum management system. It is described how the density and deployment of sensors affect the quality of maps and it is analysed which methods are the most suitable for map construction. Additionally, the paper characterizes several sensing methods