A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled

Secrecy Performance Analysis of SIMO Systems over Correlated κ-µ Shadowed Fading Channels

In this paper, the secrecy performance of single-input-multiple-output systems over correlated κ-μ shadowed fading channels is investigated. In particular, based on the classic Wyner's wiretap model, we derive analytical expressions for secure outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) over correlated κ-μ shadowed fading channels. In order to further study the impact of channel parameters on the secrecy performance, novel SOP and the probability of SPSC over independent and identically distributed κ-μ shadowed fading channels are also obtained. In addition, we discuss the asymptotic expressions of the SOP and the SPSC. The match between the analytical results and simulations is excellent for all parameters under considerations. It is interesting to find that the results show that when the signal-to-noise ratio of the main channel is lower than that of the eavesdropping channel, the larger value of correlation coefficient is helpful to improve the secrecy performance and vice versa

A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems

Compared with orthogonal frequency division multiplexing (OFDM) systems, orthogonal time frequency space systems based on bi-orthogonal frequency division multiplexing (OTFS-BFDM) have lower out-of-band emission (OOBE) and better robustness to high-mobility scenarios, but suffer from a higher peak-to-average ratio (PAPR) in large data packets. In this paper, one-iteration clipping and filtering (OCF) is adopted to reduce the PAPR of OTFS-BFDM signals. However, the extra noise introduced by the clipping process, i.e., clipping noise, will distort the desired signal and increase the bit error rate (BER). We propose a message passing (MP)-assisted iterative cancellation (MP-AIC) method to cancel the clipping noise based on the traditional MP decoding at the receiver, which incorporates with the (OCF) at the transmitter to keep the sparsity of the effective channel matrix. The main idea of MP-AIC is to extract the residual signal fed to the MP detector by iteratively constructing reference clipping noise at the receiver. During each iteration, the variance of residual signal and channel noise are taken as input parameters of MP decoding to improve the BER. Moreover, the convergence probability of the modulation alphabet after MP decoding in the current iteration is used as the initial probability of MP decoding in the next iteration to accelerate the convergence rate of MP decoding. Simulation results show that the proposed MP-AIC method significantly improves MP-decoding accuracy while accelerating the BER convergence in the clipped OTFS-BFDM system. In the clipped OTFS-BFDM system with rectangular pulse shaping, the BER of MP-AIC with two iterations can be reduced by 72% more than that without clipping noise cancellation

Secrecy Analysis of Cognitive Radio Networks over Generalized Fading Channels

At present, the fifth generation (5G) communication networks are in the time of large-scale deployment principally because its characteristics consists of large bandwidth, fast response, and high stability. As a partner of 5G, the Internet of Things (IoT) involves billions of devices around the world, which can make the wireless communication environment more intelligent and convenient. However, the problem that cannot be ignored is the physical layer security of 5G-IoT networks. Based on this, we perform a security analysis of cognitive radio networks (CRN) for IoT, where the CRN is the single-input multiple-output (SIMO) model experiencing κ-μ shadowed fading with multiple eavesdroppers. To analyze the confidentiality of the system under consideration, we analyze the security performance for the considered IoT systems with the help of the derived secure outage probability (SOP) and probability of strictly positive secrecy capacity (SPSC). As a verification of the theoretical formula, Monte Carlo simulation is also provided. The results of great interest are the factors that can produce better security performance in high SNRs region which consist of smaller M, smaller k, and larger N, and larger μ, smaller IP, and smaller Rth

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks

A duplex real-time NASBA assay targeting serotype-specific gene for rapid detection of viable S. enterica serovar Paratyphi C in retail foods of animal origin

Salmonella enterica serovars Paratyphi C is highly adapted to humans and can cause a typhoid-like disease with high mortality rates. In this study, three serovar-specific genes were determined for S. Paratyphi C, SPC_0871，SPC_0872, and SPC_0908, by comparative genomics method. Based on SPC_0908 and xcd gene for testing Salmonella spp., we have developed a duplex real-time nucleic acid sequence-based amplification (real-time NASBA) with molecular beacon approach for simultaneous detection of viable cells of Salmonella spp. and serotype Paratyphi C. The test selectively and consistently detected 53 Salmonella spp. (representing 31 serotypes) and 18 non-Salmonella strains. Additionally, the method showed high resistance to interference by natural background flora in pork and chicken samples. The sensitivity of the established approach was determined to be 4.89 CFU/25 g in artificially contaminated pork and chicken samples after pre-enrichment. We propose this NASBA-based protocol as a potential detection method for Salmonella spp. and serotype Paratyphi C in food of animal origin.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Pharmacodynamics and Medicinal Chemistry of an External Chinese Herbal Formula for Mammary Precancerous Lesions

Ruyan Neixiao Cream (RYNXC) is a traditional Chinese herbal formula for treating mammary precancerous disease. This study was carried out to investigate in vivo anticancer effect of RYNXC and multiple constituents. 32 virginal Sprague-Dawley rats were randomly divided into blank control group (BC), mammary precancer models group (MODEL), tamoxifen group (TAM), and Ruyan Neixiao Cream group (RYNXC). TAM was intervened by tamoxifen; RYNXC was intervened by Ruyan Neixiao Cream. The chromatographic separation was performed by high performance liquid chromatography (HPLC) coupled with mass spectrometry (MS). RYNXC showed significant improvement in erythrocyte aggregation index (EAI), hematocrit (HCT), fibrinogen (FIB), spleen coefficient, and uterus coefficient compared with MODEL. In RYNXC and TAM groups, atypical hyperplasia was observed in pathological mammary tissues; meanwhile in MODEL group, ductal carcinoma was observed in situ. Moreover, fifteen compounds were characterized according to HPLC-MS data, including organic acids, tannin, alkaloid, volatile oil, anthraquinones, and flavonoids. The study suggests that RYNXC was an effective Chinese herbal formula for mammary precancerous lesions and provides a scientific basis for the quality standard and the pharmacology of RYNXC. It will be beneficial to the future clinical application of RYNXC