This correspondence presents a construction of quasicyclic (QC) low-density parity-check (LDPC) codes based on a special type of combinatorial designs known as block disjoint difference families (BDDFs). The proposed construction of QC-LDPC codes gives parity-check matrices with column weight three and Tanner graphs having a girth lower-bounded by 6. The proposed QC-LDPC codes provide an excellent performance with iterative decoding over an additive white Gaussian-noise (AWGN) channel. Performance analysis shows that the proposed short and moderate length QC-LDPC codes perform as well as their competitors in the lower signal-to-noise ratio (SNR) region but outperform in the higher SNR region. Also, the codes constructed are quasicyclic in nature, so the encoding can be done with simple shift-register circuits with linear complexity.

In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver’s activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.

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The design and implementation of energy-efficient routing protocols for next-generation wireless sensor networks is always a challenge due to limited power resource capabilities. Hierarchical (clustering) routing protocols appeared to be a remarkable solution for extending the lifetime of wireless sensor networks, particularly in application-aware (threshold-sensitive) and heterogeneity-aware cluster-based routing protocols. In this article, we propose a protocol, namely, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol. It is a heterogeneity-aware and threshold-based protocol that provides a better solution to existing problems in next-generation wireless sensor networks. During execution, the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol splits the entire network area into several zones to manage network traffic efficiently. In the first step, Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is designed for a homogeneous network where the initial energy of all the nodes is the same. Thereafter, we bring in heterogeneity in the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol execution environment to optimize its energy consumption. By investigating the performance of the various numbers of divisions, it is proved that the Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol with 9 zonal divisions has higher stability and throughput. The performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol is compared with those of Stable Election Protocol, Low-Energy Adaptive Clustering Hierarchy, Modified Low-Energy Adaptive Clustering Hierarchy, and Gateway-Based Energy-Efficient Routing Protocol through computer simulations. Simulation results verify the improved performance of the proposed Threshold-based Energy-aware Zonal Efficiency Measuring hierarchical routing protocol in terms of network stability, lifetime, and throughput.