This article presents a multichannel cryptosystem based on the proposed shared buffer techniques which enhance security for several Internet of Things (IoT) sensors and devices by using block cipher with feedback operation modes. Information security and its performance are both important characteristics for modern networking devices. In cryptography, the block cipher, for example, advanced encryption standard (AES), using feedback operation modes can enhance the security level but, unfortunately, the encryption cannot be performed in parallel owing to data dependency. Therefore, the pipeline and unrolling techniques are not applicable to increase the throughput of hardware designs. Under this situation, the round-based architecture is popular and regarded as an area-efficient solution. However, this method inherently limits its throughput. The proposed cryptosystem aims to provide many resource-constrained IoT devices with high-speed centralized encryption service to enhance their security levels, which are applicable for various scenarios, such as vehicular network, home network, and network function virtualization (NFV)/software-defined networking (SDN) IoT. Beyond that, the proposed design introduces the shared buffer technique based on linked lists and presents a novel queuing structure to enhance the memory utilization so that it can reduce 72.9% memory requirement of the naïve implementation while achieving the same speedup. According to the implementation result, an aggregate throughput of 130.91 Gb/s for encrypting ten IoT devices in cipher block chaining (CBC), cipher feedback (CFB), and output feedback (OFB) modes can be achieved on TSMC 40 nm. The area efficiency of this work significantly outperforms the state-of-the-art works.

Abstract This work introduces an efficient neuron design for fixed‐point artificial neural networks with the rectified linear unit (ReLU) activation function for energy‐constrained wireless applications. The fixed‐point binary numbers and ReLU activation function are used in most application‐specific integrated circuit designs and artificial neural networks (ANN), respectively. It is well known that, owing to involved computation intensive tasks, the computational burden of ANNs is ultra heavy. Consequently, many practitioners and researchers are discovering the ways to reduce implementation complexity of ANNs, particularly for battery‐powered wireless applications. For this, a low‐complexity neuron to predict the sign bit of the input of the non‐linear activation function, ReLU, by employing the saturation characteristics of the activation function is proposed. According to our simulation results based on random data, computation overhead of a neuron using the proposed technique can be saved by a ratio of 29.6% compared to the conventional neuron using a word length of 8 bits without apparently increasing the prediction error. A comparison of the proposed algorithm with the popular 16‐bit fixed‐point format of the convolutional network, AlexNet, indicates that the computation can be saved by 48.58% as well.

An efficient algorithm for the heartbeat detection in the Internet of Things (IoT) health-care system remains a challenging issue due to incurred random variations. The QRS complex reflects the electrical activity within the heart during the ventricular contraction. Although recently many QRS complex detection methods have been proposed with different features, their real-time implementations in low-cost portable platforms are still problems due to limited hardware resources. As a result, it is difficult to provide the accuracy level required for medical applications. By contrast, this paper focuses on developing a new method based on the Bayesian framework to provide a real-time and accurate QRS complex detector. More specifically, we propose a new algorithm with two stages, i.e., variance-based detection (VBD) and maximum-likelihood estimation (MLE), to detect QRS complexes. Furthermore, simulations with the benchmark MIT-BIH arrhythmia and QT databases verify the advantage of being easily portable to different databases using the proposed approach.

This paper introduces a time-domain nondata-aided maximum-likelihood (ML) estimation of Doppler frequency for orthogonal frequency-division multiplexing (OFDM) over doubly selective fading channels with the Clarke-Jakes-Doppler spectrum. The proposed approach adopts the repetitive signal structure of OFDM, such as the cyclic prefix, which can be used for synchronization purpose. The originally derived ML estimation results in a high computational cost; therefore, after gaining insights into the log-likelihood function, we further design an algorithm with reduced complexity. The proposed algorithm only has a complexity ~O(QNd), where Q and Nddenote the numbers of repetitive parts and samples in a repetitive part, respectively. In contrast to conventional works based on (data aided) channel estimates and/or assuming that channels are flat fading, the proposed method employs the cyclostationary feature of received signals and is not sensitive to channel estimation error and unknown channel phases. Furthermore, the Cramer-Rao lower bounds of the Doppler frequency estimate over nonline-of-sight (NLOS) and line-of-sight multipath fading channels are analyzed. Simulations confirm the advantages of the proposed estimator.

Orthogonal frequency-division multiplexing (OFDM) is a popular transmission technology in cognitive radio (CR) networks, because the correlation of cyclic prefix (CP) in OFDM signals can be utilized to improve the reliability of spectrum sensing of secondary users (SUs). However, the optimal spectrum sensing over multipath fading channels remains an important and challenging issue. Therefore, this work proposes an optimal Neyman- Pearson (NP) detector for spectrum sensing using CP. To detect the OFDM signal of primary users (PUs), the log- likelihood ratio (LR) test is formulated by using the correlation characteristics of the redundancy of CP. Analytical results indicate that the LR of received samples is equivalent to their log- likelihood function (LF) plus LR of an energy detector (ED), subsequently allowing us to gain insights on the optimal NP detector. Since many unknown parameters need to be resolved, a practical generalized log-likelihood ratio test (GLRT) is presented. Moreover, to achieve a good performance over multipath fading channels, a channel- independent GLRT (CI-GLRT) is employed to derive an estimation of correlation coefficient independent of multipath channel profiles. Simulations confirm the advantages of the proposed detectors compared with state-of-the-art detectors.

In this work, a channel-power profile estimation for orthogonal frequency-division multiplexing systems, based on the cyclic prefix (CP), is introduced. By knowing the delay of each path, the time-dispersion information can be derived. The proposed method, considering long intersymbol interference (ISI) fading channels, requires only the coarse symbol timing information. More specifically, quasi-stationary fading channels are considered. The basic contribution is to obtain the maximum-likelihood estimation of the correlation coefficient based on the CP. Subsequently, the relationship between the correlation coefficient and the channel-tap powers is explored. With the estimate of correlation coefficient, the least-square solution of the channel-tap powers can be determined. The proposed method is suitable for both short and long ISI channels. Furthermore, the Cramer–Rao lower bound of the channel-power profile estimation is analyzed, and simulations confirm the advantages of the proposed estimator. Copyright © 2011 John Wiley & Sons, Ltd.

Recently, cognitive radio (CR) has recently emerged as a useful technology to improve the efficiency of spectrum utilization. However, wireless networks are accompanied with an important security flaw that they are much easier to be attacked than any wired network. Many security issues are discovered. Within which, the most important one is primary user emulation attack (PUEA). In this work, we propose a method by using the characteristics of wireless channels to identify the PUEA. In the wireless environment, the statistical property of the wireless channel between the receiver and transmitter is unique; therefore, we can use this feature as a radio fingerprint. By employing the capability of spectrum sensing in CR, we can identify primary user emulation attackers via the uniqueness of wireless channels. Compared with conventional security schemes based on higher layer protocols, whose information must be passed to the upper layers, the proposed scheme using physical layer is more efficient in terms of the detection time. Simulations confirm the advantages of the proposed scheme.

Due to the ability of sensor nodes to collaborate, time synchronization is essential for many sensor network operations. This work presents a novel clock synchronization method for energy-constrained wireless sensor networks (WSNs). To conserve WSN energy, this study adopts the flooding time synchronization scheme based on one-way timing messages. Via the proposed approach, the maximum-likelihood (ML) estimation of clock parameters, such as clock skew and clock offset, can be obtained for clock synchronization. Additionally, with the proposed scheme, the clock skew and offset estimation problem will be transformed into a problem independent of random delay and propagation delay. In addition to good performance, the proposed method has low complexity.

This work presents a symbol time (ST) synchronization algorithm for orthogonal frequency-division multiplexing systems based on a metric of signal-to-interference-and-noise ratio (SINR) which drops drastically with the increasing ST error. The ST is intuitively estimated by maximizing the SINR metric that exploits the correlation results of the separated-by-Nsamples. Unlike most existing techniques considering only AWGN or time-invariant multipath channels, the proposed maximum-SINR (MSINR) technique considers the time-variant multipath channels. Compared with conventional techniques, the proposed MSINR technique is less sensitive to the carrier frequency offset and doubly-selective channel effects.

This work analyzes combined effects of major synchronization errors, including the symbol time offset (STO), carrier frequency offset (CFO) and sampling clock frequency offset (SCFO) of orthogonal frequency-division multiplexing (OFDM) systems. Such errors degrade the performance of an OFDM receiver by introducing inter-carrier interference (ICI) and inter-symbol interference (ISI) into the systems. Traditionally, designing an OFDM receiver needs plenty of Monte Carlo simulations because the synchronization errors are simultaneously inevitable in practical environments. Therefore, we formulate the theoretical signal-to-interference-and-noise ratio (SINR) to assist the design of OFDM receivers. By knowing the required SINR of specific application, all combinations of allowable errors can be derived. Then, cost-effective algorithms could be easily designed.

This work presents a blind synchronizer for orthogonal frequency-division multiplexing (OFDM) systems based on signal-to-interference-and-noise-ratio (SINR) maximization. Due to the incurred losses from inter-symbol interference (ISI) and inter-carrier interference (ICI) introduced by synchronization errors, the SINR of the received data drops drastically. By taking advantage of this characteristic, both the symbol time and carrier frequency offsets are intuitively estimated by maximizing the SINR metric. For the SINR metric, we propose a blind SINR estimation, which does not need prior knowledge of the channel profiles and transmitted data. As such, the proposed maxi-mum-SINR (MSINR) synchronization algorithm is non-data-aided (NDA) so that the transmission efficiency can be improved. Moreover, to reduce the computational complexity, the early-late gate (ELG) technique is proposed for the implementation of the synchronizer. Simulation results exhibit better performance for the MSINR algorithm than conventional techniques in multipath fading channels.

Conventional symbol time (ST) synchronization algorithms for orthogonal frequency-division multiplexing (OFDM) systems mostly are based on maximum correlation result of the cyclic prefix. Due to the channel effect, one needs to further identify the channel impulse response (CIR) so as to obtain a better ST estimation. Overall, the required computational complexity is high because it involves correlation operation, as well as the fast Fourier transform (FFT) and inverse FFT (IFFT) operations. In this work, without the FFT/IFFT operations and the knowledge of CIR, a low-complexity time-domain ST estimation is proposed. We first characterize the frequency-domain interference effect as a function of the ST by deriving some analytical equations considering the channel effect. Based on the derivation, the new method locates the symbol boundary at the sampling point with the minimum interference in the frequency-domain. Moreover, for reducing the computational complexity, the proposed frequency-domain minimum-interference metric is converted into a low-complexity time-domain metric by utilizing the Parseval’s theorem and the sampling theory. Simulation results exhibit high performances for the proposed algorithm in the multipath fading channels.

Few papers investigate synchronization error effects for the uplink of orthogonal frequency-division multiple access (OFDMA) systems. By contrast, this paper simultaneously analyzes the joint effects of major synchronization errors, including symbol time (ST) offset (STO), carrier frequency offset (CFO) and sampling clock frequency offset (SCFO) for the uplink of OFDMA systems in time-variant multipath fading channels. Such errors degrade the performance of an OFDMA receiver by introducing inter-carrier interference (ICI), inter-symbol interference (ISI) and multiple-access interference (MAI) into the systems. A theoretical signal-to-interference-and-noise ratio (SINR) is formulated to characterize the losses due to synchronization errors in time-variant multipath fading channels. The results provide designers a useful reference in designing suitable synchronization algorithms for the OFDMA applications.

This work presents a joint maximum-likelihood (ML) synchronization algorithm for symbol time (ST) offset (STO), carrier frequency offset (CFO) and sampling clock frequency offset (SCFO) for OFDM systems. Unlike most existing techniques which are time-domain approaches considering only AWGN and/or static multipath conditions, the proposed algorithm is developed in frequency-domain under time-variant multipath channels. By analyzing the received frequency- domain data, a mathematical model for the joint effects of STO, CFO and SCFO is derived. The results are used to formulate a log-likelihood function of two consecutive symbols. Based on the function, a joint ML algorithm is proposed. The joint algorithm is both efficient and robust, because the three main synchronization issues are treated altogether. Simulation results exhibit high performances in time-variant multipath fading channels.