This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption

Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation recognition in the communication field and the rapid development of DL, by adding a well-designed adversarial perturbation to the input signal, this article explores the performance of attack methods on modulation recognition, measures the effectiveness of adversarial attacks on signals, and provides the empirical evaluation of the reliabilities of CNNs. The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, the accuracy of the model could drop by about 50 ${\%}$ on average. Among them, iterative methods show greater attack performances than that of one-step method. In addition, the consistency of the waveform before and after the perturbation is examined, to consider whether the added adversarial examples are small enough (i.e., hard to distinguish by human eyes). This article also aims at inspiring researchers to further promote the CNNs reliabilities against adversarial attacks.

Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks

Structural health monitoring (SHM) and damage assessment of civil engineering infrastructure are complex tasks. Structural health and strength of structures are influenced by various factors, such as the material production stage, transportation, placement, workmanship, formwork removal, and concrete curing. Technological advancements and the widespread availability of Wi-Fi networks has resulted in SHM shifting from traditional wire-based methods to Internet of Things (IoT)-based real-time wireless sensors. Comprehensive structural health assessment can be performed through the efficient use of real-time test data on structures obtained from various types of IoT sensors, which monitor several health parameters of structures, available on cloud-based data storage systems. The sensor data may be subsequently used for various applications, such as forecasting masonry construction deterioration, predicting the early-stage compressive strength of concrete, forecasting the optimum time for the removal of formwork, vibration and curing quality control, crack detection in buildings, pothole detection on roads, determination of the construction quality, corrosion diagnosis, identification of various damage typologies and seismic vulnerability assessment. This review paper summarizes the applications of the wireless IoT technology in the monitoring of civil engineering infrastructure. In addition, several case studies on real structures and laboratory investigations for monitoring the structural health of civil engineering constructions are discussed. 

Structural health monitoring of civil engineering structures by using the internet of things: A review

http://www.cell.com/article/S2405844022006879/pdf

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Device to Device communication is an important aspect of the fifth-generation(5G) and beyond fifth-generation (B5G) wireless networks. 5G facilitates network connectivity among a large number of devices. This tremendous growth in the number of devices requires a large number of spectrum resources to support a variety of applications and also lays a huge burden on the Base Station. D2D skips the need to forward the data to the Base Station and helps the devices to take part in direct Peer-to-Peer (P2P) transmission. This enables high-speed data transmission, efficient information transmission with improved latency and most importantly is used to offload the traffic that is laid on the Base Station. D2D has many practical issues and challenges that are briefly explained in this paper, out of which resource allocation is the main area of focus as it plays an important role in the performance of the system. The optimal allocation of resources such as power, time and spectrum can improve the system performance. Therefore, in order to identify the open research issues in the field of resource allocation in D2D communication, a detailed survey is needed. In this paper, various resource allocation algorithms and methodologies have been seriously analysed and evaluated based on the degree of involvement of the Base Station to figure out the research gap and to provide a strong theoretical basis for the research problems related to resource allocation in D2D communication.

A review on resource allocation techniques in D2D communication for 5G and B5G technology

Cognitive radio networks improve spectrum efficiency by employing vigilant and accurate spectrum management techniques. This is done to enable an unlicensed user to use the underutilized spectrum. Spectrum sensing determines if a spectrum hole exists for the unlicensed user, this is accomplished using various techniques such as energy detection, minimum Eigen value detection, and matched filter technique. Conventional energy detection techniques do not achieve high values of detection; hence an improved adaptive energy detection technique has been proposed to improve the efficiency. The proposed methodology exhibits better numerical results than conventional techniques. Another important spectrum management procedure is spectrum handoff. The concept of multiple attributes for decision making was implemented, under which, the simple additive weights method and the technique for order preference by similarity to ideal solution method were compared based on a performance involving the triple play of services.

/pdf/efficient-spectrum-management-techniques-for-cognitive-radio-4zn65tf3bo.pdf

Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.

Structural health monitoring of railway tracks using IoT-based multi-robot system

Recent studies have shown that utilizing a mobile sink to harvest and carry data from a Wireless Sensor Network (WSN) can improve network operational efficiency as well as maintain uniform energy consumption by the sensor nodes in the network. Due to Sink mobility, the path between two sensor nodes continuously changes and this has a profound effect on the operational longevity of the network and a need arises for a protocol which utilizes minimal resources in maintaining routes between the mobile sink and the sensor nodes. Swarm Intelligence based techniques inspired by the foraging behavior of ants, termites and honey bees can be artificially simulated and utilized to solve real wireless network problems. The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.

https://iopscience.iop.org/article/10.1088/1757-899X/263/5/052026/pdf

A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks

Computational complexity and error propagation phenomenon are important drawbacks of existing Decision Feedback Equalizers (DFE) for dispersive channels. A new Iterative Block DFE (IBDFE) is considered where the equalization is performed iteratively on blocks of received signal in the frequency domain i.e. both signal processing and filter design are in frequency domain. Thus computational complexity is reduced and error propagation is limited to one block. The feed forward and feedback filters of DFE are designed with the minimization of Mean Square Error (MSE) at detector input as the parameter for effective detection. Two design methods have been solved and simulated for a Rayleigh fading channel. Channel is assumed to be time in-variant during one block of data (128 symbols) transmission. In the first method, the hard detected data are used as the input to the feedback, and filters are designed according to the correlation between detected and transmitted data. In the second method, the feedback signal is directly designed from soft detection of the equalized signal at the previous iteration. Estimates of the parameters involved in the FF and FB filters are also solved and used to evaluate the filter coefficients. From simulation, it was found that the IBDFE as claimed in the research literature performs better than the time domain DFE.

http://ieeexplore.ieee.org/iel5/5191366/5204362/05204497.pdf

S. Nandakumar

Papers

A review on resource allocation techniques in D2D communication for 5G and B5G technology

Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

Structural health monitoring of railway tracks using IoT-based multi-robot system

A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks

Frequency domain equalization for single carrier wireless systems