Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications

Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection. The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms. Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features. The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability. In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources. On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators. Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults

Emergency message dissemination schemes based on congestion avoidance in VANET and vehicular FoG computing

With the rapid growth in connected vehicles, FoG-assisted vehicular ad hoc network (VANET) is an emerging and novel field of research. For information sharing, a number of messages are exchanged in various applications, including traffic monitoring and area-specific live weather and social aspects monitoring. It is quite challenging where vehicles' speed, direction, and density of neighbors on the move are not consistent. In this scenario, congestion avoidance is also quite challenging to avoid communication loss during busy hours or in emergency cases. This paper presents emergency message dissemination schemes that are based on congestion avoidance scenario in VANET and vehicular FoG computing. In the similar vein, FoG-assisted VANET architecture is explored that can efficiently manage the message congestion scenarios. We present a taxonomy of schemes that address message congestion avoidance. Next, we have included a discussion about comparison of congestion avoidance schemes to highlight the strengths and weaknesses. We have also identified that FoG servers help to reduce the accessibility delays and congestion as compared to directly approaching cloud for all requests in linkage with big data repositories. For the dependable applicability of FoG in VANET, we have identified a number of open research challenges. © 2013 IEEE

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Multi Sensor Fusion Based Framework For Efficient Mobile Robot Collision Avoidance and Path Following System

The field of autonomous mobile robotics has recently gained the interests of many researchers. Due to the specific needs required by various applications of mobile robot systems (especially in navigation), designing a real-time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. Therefore, an efficient collision avoidance and path following methodology is needed to develop an intelligent and effective autonomous mobile robot system. Mobile robots are equipped with various types of sensors (such as GPS, camera, infrared and ultrasonic sensors); these sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. A new technique for line following and collision avoidance in the mobile robotic systems is introduced. The proposed technique relies on the use of infrared sensors and involves a reasonable level of calculations, to be easily used in real-time control applications. In addition, a fusion model based on fuzzy logic is proposed. Eight distance sensors and a range finder camera are used for the collision avoidance approach, where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs (which are the eight distance sensors and the camera), two outputs (which are the left and right velocities of the mobile robot’s wheels), and twenty four fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and robot to show the ability of the robot to follow a path, detect obstacles, and navigate around them to avoid collision. It also shows that the robot has been successfully following extremely congested curves and has avoided any obstacle that emerged on its path. The proposed methodology which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real-time experiments. Various scenarios have been presented with static and dynamic obstacles, using one and multiple robots while avoiding obstacles in different shapes and sizes. The proposed methodology reduced the traveled distance of the mobile robot, as well as minimized the energy consumption and the distance between the robot and the obstacle detected as compared to a non-fuzzy logic approach

A novel energy efficient wireless sensor network framework for object tracking

Object tracking is a typical application of Wireless Sensor Networks (WSNs), which refers to the process of locating a moving object (or multiple objects) over time using a sensor network. Object tracking in WSNs can be a time consuming and resource hungry process due to factors, such as the amount of data generated or limited resources available to the sensor network. The traditional centralised approaches where a number of sensors transmit all information to a base station or a sink node, increase computation burden. More recently static or dynamic clustering approaches have been explored. Both clustering approaches suffer from certain problems, such as, large clusters, redundant data collection and excessive energy consumption. In addition, most existing object tracking algorithms mainly focus on tracking an object instead of predicting the destination of an object. To address the limitations of existing approaches, this thesis presents a novel framework for efficient object tracking using sensor networks. It consists of a Hierarchical Hybrid Clustering Mechanism (HHCM) with a Prediction-based Algorithm for Destinationestimation (PAD). The proposed framework can track the destination of the object without prior information of the objects movement, while providing significant reduction in energy consumption. The costs of computation and communication are also reduced by collecting the most relevant information and discarding irrelevant information at the initial stages of communication. The contributions of this thesis are: Firstly, a novel Prediction-based Algorithm for Destination-estimation (PAD) has been presented, that predicts the final destination of the object and the path that particular object will take to that destination. The principles of origin destination (OD) estimation have been adopted to create a set of trajectories that a particular object could follow. These paths are made up of a number of mini-clusters, formed for tracking the object, combined together. PAD also contains a Multi-level Recovery Mechanism (MRM) that recovers tracking if the object is lost. MRM minimises the number of nodes involved in the recovery process by initiating the process at local level and then expanding to add more nodes till the object is recovered. Secondly, a network architecture called Hierarchical Hybrid Clustering Mechanism (HHCM) has been developed, that forms dynamic mini-clusters within and across static clusters to reduce the number of nodes involved in the tracking process and to distribute the initial computational tasks amoung a larger number of mini-cluster heads. Lastly, building upon the HHCM to create a novel multi-hierarchy aggregation and next-step prediction mechanism to gather the most relevant data about the movement of the tracked object and its next-step location, a Kalman-filter based approach for prediction of next state of an object in order to increase accuracy has been proposed. In addition, a dynamic sampling mechanism has been devised to collect the most relevant data. Extensive simulations were carried out and results were compared with the existing approaches to prove that HHCM and PAD make significant improvements in energy conservation. To the best of my knowledge the framework developed in unique and novel, which can predicts the destination of the moving object without any prior historic knowledge of the moving object