Ahmed Mohamed Habelroman B M Badawy

Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey

Structural health monitoring (SHM) using wireless sensor networks (WSNs) has gained research interest due to its ability to reduce the costs associated with the installation and maintenance of SHM systems. SHM systems have been used to monitor critical infrastructure such as bridges, high-rise buildings, and stadiums and has the potential to improve structure lifespan and improve public safety. The high data collection rate of WSNs for SHM pose unique network design challenges. This paper presents a comprehensive survey of SHM using WSNs outlining the algorithms used in damage detection and localization, outlining network design challenges, and future research directions. Solutions to network design problems such as scalability, time synchronization, sensor placement, and data processing are compared and discussed. This survey also provides an overview of testbeds and real-world deployments of WSNs for SH.

Non-data-aided SNR Estimation for QPSK Modulation in AWGN Channel

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal-to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms.

Unleashing the secure potential of the wireless physical layer

Within the paradigm of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. This key is then used to encrypt the data to hide information from eavesdroppers. In this paper, we survey the most recent common sources of randomness used to generate the secret key. We present the steps used to extract the secret key from the estimated common source of randomness. We describe the metrics used to evaluate the strength of the generated key. We follow that with a qualitative comparison between different common sources of randomness along with a proposed new direction which capitalizes on hybridization of sources of randomness. We conclude by a discussion about current open research problems in secret key generation.

Secret key generation based on channel and distance measurements

Within the paradigm of physical layer secrecy, typically a physical layer specific characteristic is used as key generator to guarantee information hiding from eavesdroppers. In this paper, we propose a novel secret key generation algorithm based on two reciprocal physical layer parameters; the channel measurements and the distances between the two communicating nodes. The two parameters are estimated experimentally using implementations of our algorithm on three FPGA-based WARP kits emulating the two communicating nodes and the eavesdropper. The parameters are used as common sources of randomness to generate the secret key. We evaluate the performance of our algorithm through extensive iterations. We compare the bit mismatch rate as well as the entropy of the generated secret key of our algorithm versus classical channel only and distance only based algorithms. Our results reveal that even in the worst case scenarios, our algorithm outperforms the two other algorithms and overcomes their vulnerabilities.

On the performance of spectrum sensing based on GLR for full-duplex cognitive radio networks

In cognitive radio networks, secondary users (SUs) utilize the unused spectrum slots in the assigned band for the primary users (PUs). Conventional cognitive radio networks operate in half-duplex (HD) mode. Recently, full-duplex (FD) communication has become feasible. SUs with full-duplex capabilities can sense the spectrum and transmit simultaneously, which improves the efficiency of cognitive radio networks. In this paper, we study the performance of spectrum sensing based on general likelihood ratio (GLR) when the SU is operating in FD mode. We compare our results to the HD GLR case. We present the effect of residual self interference on the performance of the spectrum sensing technique. Moreover, we consider uncertainty in estimating the variance of the combined residual self interference and noise and show its effect on the performance of the FD GLR.

Robust secret key extraction from channel secondary random process

The vast majority of existing secret key generation protocols exploit the inherent randomness of the wireless channel as a common source of randomness. However, independent noise added at the receivers of the legitimate nodes affects the reciprocity of the channel. In this paper, we propose a new simple technique to generate the secret key that mitigates the effect of noise. Specifically, we exploit the estimated channel to generate a secondary random process SRP that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We study the properties of our generated SRP and derive a closed form expression for the probability mass function of the realizations of our SRP. We simulate an orthogonal frequency division multiplexing system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate between the legitimate nodes when compared with the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques. Moreover, we compute the conditional probabilities used to estimate the secret key capacity. Copyright

Channel secondary random process for robust secret key generation

The broadcast nature of wireless communications imposes the risk of information leakage to adversarial users or unauthorized receivers. Therefore, information security between intended users remains a challenging issue. Most of the current physical layer security techniques exploit channel randomness as a common source between two legitimate nodes to extract a secret key. In this paper, we propose a new simple technique to generate the secret key. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We simulate an orthogonal frequency division multiplexing (OFDM) system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate (BMR) between the legitimate nodes when compared to the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques.

A novel peak search & save cyclostationary feature detection algorithm

Spectrum sensing is a key task in any cognitive radio network. On the other hand, a literature survey in this topic shows a lack of implementation and testbeds for spectrum sensing techniques. In this paper, we plot the ROC curves for the cyclostationary detection through an extensive Monte Carlo simulation for different detection times. Then we implement the cyclostationary feature detection algorithm on an FPGA based WARP kit. We compare its implementation complexity to the conventional energy detection technique as well as our newly developed and implemented quickest detection algorithm. We then propose a peak search based FAM algorithm that speeds up the detection time.

Efficient FPGA-based implementation of a novel dual mode spectrum sensing technique

Cognitive radio networks require an efficient and reliable method of sensing the spectrum. In this paper, we introduce and implement a new dual mode spectrum sensing technique that is based on combining Quickest Detection with Energy Detection. Our dual mode system introduces a compromise between speed and complexity. When the SNR goes below the SNR wall for the low complexity energy detection, our system switches to the more expensive quickest detection based algorithm. A practical prototype of our system is implemented on the WARP FPGA-based nodes to study its efficiency and complexity. In addition to practical experimental results, we derive theoretical closed form expressions for the probability of false alarm and the probability of detection for our new approach. We also numerically compute the SNR wall for our quickest detection based technique.

A Simple Angle of Arrival Estimation System

We propose a practical, simple and hardware friendly, yet novel and very efficient, angle of arrival (AoA) estimation system. Our intuitive, two-phases cross-correlation based system requires a switched beam antenna array with a single radio frequency chain. Our system cross correlates a reference omni-directional signal with a set of received directed signals to determine the AoA. Practicality and high efficiency of our system are demonstrated through performance and complexity comparisons with multiple signal classification algorithm.