A Lossless Data-Hiding based IoT Data Authenticity Model in Edge-AI for Connected Living

Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to hamper data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.

ASPECTS REGARDING OPTIMAL DESIGN FOR THE WORM-GEAR DRIVE

It is known that, from the point of view of the accuracy of a machine-tool, at its design, the dynamic behaviour of each element of the kinematic chains prevails. Worm-gear drives are widely used in the different machine-tools and robots. Therefore, it is important that during meshing, as far as possible, there are no vibrations, shocks, power losses, noise and low durability. These requirements can be met if, for example, the gear ratio is constant during meshing, without transmission errors, which means that the worm-gear drive should have a high accuracy. The accuracy improvement of the worm-gear drive has long been a focus of attention for machine-tools designers. Thus, this paper presents various approaches to solving such problems, based on modelling and simulation, such as: estimating the load share of worm-gear drives and to calculate the instantaneous tooth meshing stiffness and loaded transmission errors; the desired worm-gear drive design configuration by altering the optimum set of worm-gear drive design parameters which are suitable for the required performance by associating it with SVM (Support Vector Machine); optimization approach for design of worm-gear drive based on Genetic Algorithm; design optimization of worm-gear drive with reduced power loss; etc. The optimization of the worm-gear design is an important problem for the research because the design variables are correlated to each other. An optimal design algorithm developed by the authors of this paper, for worm-gear drive, is also presented.

Determination of probabilistic characteristics of random values of estimates of the Lyapunov function when describing a physical process

The applied application of the Lyapunov characteristic function is determined by the properties of its estimates. Probabilistic characteristics of estimates of the Lyapunov characteristic function are described for the first time. The probabilistic characteristics of random values of estimates of the Lyapunov function are empirically estimated using statistical methods. The Matlab package has developed a model of a special device for obtaining estimates of the characteristic function by a direct method. A quasi-deterministic signal is fed to the input of the model, the instantaneous values of which are distributed according to the arcsine law, and an array of values of estimates of the Lyapunov function is obtained at the output, which is used to estimate the probabilistic characteristics of these estimates. Statistical estimation was performed by an indirect method. It is established that the values of the estimates of the Lyapunov characteristic function are distributed according to the normal law. The results of the research will be useful in engineering calculations, for example, when detecting message transmission errors in modems with a modulated characteristic function.

Experimental investigation into the implications of transmission errors for rack-and-pinion drives

Rack-and-pinion drives are the preferred option in the machine tool sector when long ranges of motion and high loads are involved. However, their shortcomings particularly include deficiencies in the achievable positioning and path accuracy. The backlash as one of the main issues is well described in the literature and numerous solutions to reduce its negative effects exist. In contrast, there is a lack of literature regarding the scientific and systematic analysis of the transmission errors in rack-and-pinion drives. In this paper, the displacements originating in the drive train of a system with industrial components are measured under different operating conditions. The observed transmission errors are thoroughly analyzed in no-load operation and their sources are discussed. Subsequent investigations show significant load-dependent alterations of the transmission errors and direction-dependent characteristics, the causes of which are explained. It is shown, that transmission errors negatively affect the path accuracy of position controlled drives, which is amplified by excitation of the machine structure in certain operating conditions. To address this issue, different error compensation concepts are presented.

Design and implementation of a diagnostic system for measuring high-precision reducers

At present, the industry is in a phase where there is an effort to maximize the automation of production processes. In many places, human power is being replaced by automated machines and industrial robots. Automation makes it possible to increase work efficiency, significantly reduce production costs and also increase the quality of the final product. A precondition for increasing the quality of production is to achieve high accuracy of specialized machines and industrial robots, resp. the accuracy of positioning of individual parts. Due to the drive system and the achieved speed, the gear unit includes a gearbox. Reducers used in robotic joints are the most complex subsystems of robots. For very precise applications, the designers will reach for the so-called backlash-free reducers for their characteristic properties (minimum values of backlash in teeth, angular transmission errors, hysteresis and others). Despite many positive properties, high-precision reducers also show their characteristic nonlinearities, which influence the behavior of the whole system and it is so important to know their behavior. Given these facts, this article deals with the design and implementation of mechatronic diagnostic equipment for the identification of nonlinearities, static and dynamic parameters, vibrodiagnostic measurements and measurements of the efficiency of bearing reducers.

A Framework for Analyzing Neighbor Discovery Protocols under Non-Ideal Conditions

Neighbor Discovery (ND) protocols are crucial to achieving the paradigm of interconnecting thousands of small nodes (sensors or things) to the Internet, also known as the IoT. These protocols usually assume that nodes operate with few energy resources. Therefore, they cannot be fully active all the time. The vast majority of these protocols focus on increasing the probability that two nodes become active simultaneously, thus enabling mutual discovery. In addition, these protocols assume that successful discovery is guaranteed once two nodes are simultaneously active, with very few exceptions. However, many problems can disrupt the discovery, such as channel errors, collisions, synchronization mismatches, energy availability, and so forth. Most ND protocols did not consider these factors, making them vulnerable to severe performance degradation when transmission errors occur. This paper proposes a new framework to evaluate the performance of deterministic neighbor discovery protocols when transmission errors are present. The proposed framework facilitates obtaining an analytical CDF of the discovery time of such protocols with transmissions errors without having to implement the protocol in a simulator, since is time-consuming and prone to implementation errors. We applied the framework to analyze the effect of transmission errors on the discovery time in four of the most representative ND protocols in the literature. Finally, we validate the framework accuracy for the selected protocols using extensive simulations. The results show that the CDF of discovery times provided by the framework closely matches the performance results obtained through simulating these protocols. In general, neighbor discovery protocols are deeply affected as a result of transmission errors.

A New Approach to Detecting and Correcting Single and Multiple Errors in Wireless Sensor Networks

Transmission errors are commonplace in communication systems. Wireless sensor networks like many other communication systems are susceptible to various forms of errors arising from sheer noise, heat and interference in sensor circuitry and from other forms of distortions. Research efforts in WSN have attempted to guarantee reliable and accurate data transmission from a target environment in the midst of these unwanted exposures. Many techniques have appeared and employed over the years to deal with the issue of transmission errors in communication systems. In this paper we present a new approach for single and multiple error control in WSN relying on the inherent fault tolerant feature of the Redundant Residue Number System. As an off shoot of Residue Number System, RRNS's fault tolerant capabilities help in building robust systems required for reliable data transmission in WSN systems. The Chinese Remainder Theorem and the Manhattan Distance Heuristics are used during the integer recovery process when detecting and correcting error digit(s) in a transmitted data. The proposed method performs considerably better in terms of data retrieval time than similar approaches by needing a smaller number of iterations to recover an originally transmitted data from its erroneous form. The approach in this work is also less computationally intensive compared to recent techniques during the error correction steps. Evidence of utility of the technique is illustrated in numerical examples.

Orangutan information broadcast via consonant-like and vowel-like calls breaches mathematical models of linguistic evolution

The origin of language is one of the most significant evolutionary milestones of life on Earth, but one of the most persevering scientific unknowns. Two decades ago, game theorists and mathematicians predicted that the first words and grammar emerged as a response to transmission errors and information loss in language's precursor system, however, empirical proof is lacking. Here, we assessed information loss in proto-consonants and proto-vowels in human pre-linguistic ancestors as proxied by orangutan consonant-like and vowel-like calls that compose syllable-like combinations. We played back and re-recorded calls at increasing distances across a structurally complex habitat (i.e. adverse to sound transmission). Consonant-like and vowel-like calls degraded acoustically over distance, but no information loss was detected regarding three distinct classes of information ( viz. individual ID, context and population ID). Our results refute prevailing mathematical predictions and herald a turning point in language evolution theory and heuristics. Namely, explaining how the vocal–verbal continuum was crossed in the hominid family will benefit from future mathematical and computational models that, in order to enjoy empirical validity and superior explanatory power, will be informed by great ape behaviour and repertoire.

Finite Element/Contact Mechanics Analysis of Spur Gear Pairs With Tooth Root Cracks

This study analyzes the nonlinear static and dynamic response in spur gear pairs with tooth root crack damage. A finite element/contact mechanics (FE/CM) model is used that accurately captures the elastic deformations on the gear teeth due to kinematic motion, tooth and rim deformations, vibration, and localized increases in compliance due to a tooth root crack. The damage is modeled by releasing the connectivity of the finite element mesh at select nodes near a tooth crack. The sensitivity of the calculated static transmission errors and tooth mesh stiffnesses is determined for varying crack initial locations, final locations, and the path from the initial to final location. Gear tooth mesh stiffness is calculated for a wide range of tooth root crack lengths, including large cracks that extend through nearly all of the tooth. Mesh stiffnesses are meaningfully reduced due to tooth root crack damage. The dynamic response is calculated for cracks of varying length. Larger cracks result in increased peak dynamic transmission errors. For small tooth root cracks the spectrum of dynamic transmission error contains components near the natural frequency of the gear pair. The spectrum of dynamic transmission error has broadband frequency response for large tooth root cracks that extend further than one-half of the tooth’s thickness.

Self-Supervised Speech Enhancement for Arabic Speech Recognition in Real-World Environments

Mobile speech recognition attracts much attention in the ubiquitous context, however, background noises, speech coding, and transmission errors are prone to corrupt the incoming speech. Therein, building a robust speech recognizer requires the availability of a large number of real-world speech samples. Arabic language, like many other languages, lacks such resources; to overcome this limitation, we propose a speech enhancement step, before the recognition begins. For the speech enhancement purpose, we suggest the use of a deep autoencoder (DAE) algorithm. A two-step procedure is suggested: in the first step, an overcomplete DAE is trained in an unsupervised way, and in the second one, a denoising DAE is trained in a supervised way leveraging the clean speech produced in the previous step. Experimental results performed on a real-life mobile database confirmed the potentials of the proposed approach and show a reduction of the WER (Word Error Rate) of a ubiquitous Arabic speech recognizer. Further experiments show an improvement of the perceptual evaluation of speech quality (PESQ), and the short-time objective intelligibility (STOI) as well.