Distributed Knowledge Discovery in Large Scale Peer-to-Peer Networks

Explosive growth in the availability of various kinds of data in distributed locations has resulted in unprecedented opportunity to develop distributed knowledge discovery (DKD) techniques. DKD embraces the growing trend of merging computation with communication by performing distributed data analysis and modeling with minimal communication of data. Most of the current state-of-the-art DKD systems suffer from the lack of scalability, robustness and adaptability due to their dependence on a centralized model for building the knowledge discovery model. Peer-to-Peer networks offer a better scalable and fault-tolerant computing platform for building distributed knowledge discovery models than client-server based platforms. Algorithms and communication protocols have been developed for file search and discovery services in peer-to-peer networks. The file search algorithms are concerned with identification of a peer and discovery of a file on that specified peer, so most of the current peer-to-peer networks for file search act as directory services. The problem of distributed knowledge discovery is different from file search services, however new issues and challenges have to be addressed. The algorithms and communication protocols for knowledge discovery deal with implementing algorithms by which every peer in the network discovers the correct knowledge discovery model, as if it were given the combined database. Therefore, algorithms and communication protocols for DKD mainly deal with distributed computing. The distributed computations are entirely asynchronous, impose very little communication overhead, transparently tolerate network topology changes and peer failures and quickly adjust to changes in the data as they occur. Another important aspect of the distributed computations in a peer-to-peer network is that most of the communication between peer nodes is local i.e. the knowledge discovery model is learned at each peer using information gathered from a very small neighborhood, whose size is independent of the size of the peer-to-peer network. The peer-to-peer constraints on data and/or computing are the hard ones, so the challenge is to show that it is still possible to extract useful information from the distributed data effectively and dependably. The implementation of a distributed algorithm in an asynchronous and decentralized environment is the hardest challenge. DKD in a peer-to-peer network raises issues related to impracticality of global communications and global synchronization, on-the-fly data updates, lack of control, accuracy of computation, the need to share resources with other applications, and frequent failure and recovery of resources. We propose a methodology based on novel distributed algorithms and communication protocols to perform DKD in a peer-to-peer network. We investigate the performance of our algorithms and communication protocols by means of analysis and simulations

Diffusion Tensor Imaging as a novel technique in early detection of cervical spondylotic myelopathy

Introduction: Diffusion tensor imaging (DTI) is an advanced MR imaging technique which helps in early detection of cervical spondylotic myelopathy (CSM). Decompressive surgery performed during early stages of the disease was reported to be more successful when compared with later stages. Aim: To evaluate the usefulness of diffusion tensor imaging (DTI) in early stages of cervical spondylotic myelopathy (CSM) and to aid in better surgical outcome. Materials and methods: This prospective observational study included 25 patients with clinical diagnosis of cervical spondylotic myelopathy who underwent routine MRI of the cervical spine. Conventional MRI sequences along with diffusion tensor imaging (DTI) were performed. Quantitative fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values were compared at stenotic and nonstenotic segments. Results: A statistically significant difference in mean FA and ADC values were seen at stenotic and nonstenotic segments. In the most stenotic segments, the mean FA value was 0.415 ± 0.203 and in the nonstenotic segment, the mean FA value was 0.717 ± 0.160, which was statistically significant (P < 0.001). The mean ADC value in the most stenotic segments was 1.777 ± 1.005 x 10-3 mm2/s and that of the nonstenotic segments was 1.010 ± 0.458 x 10-3 mm2/s. The difference in the mean ADC value was statistically significant (p <0.001). Conclusion: Use of diffusion tensor imaging (DTI) along with conventional MRI sequences enables early detection of the disease and helps in appropriate timing of surgery. Keywords: Diffusion tensor imaging (DTI), cervical spondylotic myelopathy (CSM), apparent diffusion coefficient (ADC), fractional anisotropy (FA)

Detection of Rouge Drones based on Radio Frequency Classification

The current classification of RF signals from the drone is achieved by leveraging raw signal information of a specific band. The modulation scheme that was found prevailing in commercial drones is Orthogonal Frequency Division Multiplexing (OFDM). OFDM can demodulated to provide information about a raw drone signal. This extracted data is coupled with a machine learning algorithm that is used to classify the signal. Testing of this research is needed to identify better equipment and an optimized test scenario that captures quality data that can be used to train a machine learning algorithm

Security of Internet of Things (IoT) Using Federated Learning and Deep Learning — Recent Advancements, Issues and Prospects

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. In this context, this review focuses on the implementation of federated learning (FL) and deep learning (DL) algorithms for IoT security. Unlike conventional ML techniques, FL models can maintain the privacy of data while sharing information with other systems. The study suggests that FL can overcome the drawbacks of conventional ML techniques in terms of maintaining the privacy of data while sharing information with other systems. The study discusses different models, overview, comparisons, and summarization of FL and DL-based techniques for IoT security

Vind: A Blockchain-Enabled Supply Chain Provenance Framework for Energy Delivery Systems

Enterprise-level energy delivery systems (EDSs) depend on different software or hardware vendors to achieve operational efficiency. Critical components of these systems are typically manufactured and integrated by overseas suppliers, which expands the attack surface to adversaries with additional opportunities to infiltrate into EDSs. Due to this reason, the risk management of the EDS supply chain is crucial to ensure that we are knowledgeable about the vulnerabilities in software and hardware components that comprise any critical part, quantifiable risk metrics to assess the severity and exploitability of the attack, and provide remediation solutions that can influence a prioritized mitigation plan. There is a need to realize cyber supply chain risk management for industrial control systems\u27 hardware, software, and computing and networking services associated with bulk electric system (BES) operations. This article proposes a blockchain-based cyber supply chain provenance platform ( Vind ) for EDSs to realize data provenance in a cyber supply chain ecosystem

A Review of IoT Security and Privacy Using Decentralized Blockchain Techniques

IoT security is one of the prominent issues that has gained significant attention among the researchers in recent times. The recent advancements in IoT introduces various critical security issues and increases the risk of privacy leakage of IoT data. Implementation of Blockchain can be a potential solution for the security issues in IoT. This review deeply investigates the security threats and issues in IoT which deteriorates the effectiveness of IoT systems. This paper presents a perceptible description of the security threats, Blockchain based solutions, security characteristics and challenges introduced during the integration of Blockchain with IoT. An analysis of different consensus protocols, existing security techniques and evaluation parameters are discussed in brief. In addition, the paper also outlines the open issues and highlights possible research opportunities which can be beneficial for future research

Open reduction internal fixation with triceps-sparing approach in distal humerus fracture: an experience from a tertiary level hospital in Mangalore

Background: Fractures of the distal humerus represent challenging problems to an orthopaedic surgeon. The present study aimed to assess the range of movement after performing open reduction and internal fixation of distal humerus fractures treated with triceps sparing approach.Methods: This prospective study included all skeletally mature patients with distal humerus fractures and operated at our center with open reduction and internal fixation of distal humerus with triceps on or triceps sparing approach were included in the study. During the study period 30 cases underwent surgery and were included in the final analysis. Fractures were classified according to the AO/OTA classification. Patients will be followed up at 6 weeks, 12 weeks and at 6 months. Mean range of motion of the fractured elbow at different follow up points were compared.Results: The mean age of the total population was 37.7±13.8 years, 57% males and left side was affected in 60% of the patients. Majority of the patients had a range of motion in normal elbow in the range 0 to 140 degrees. There was an increase in the mean range of movement from 63.4±14.2 at 6th week to 120±6 at 24th week, and this change was statistically significant (p<0.001).Conclusions: Future multicentric randomized studies, specially comparing triceps-sparing with olecranon osteotomy, are needed to support the results of our study

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communicatio

A Tutorial and Future Research for Building a Blockchain-Based Secure Communication Scheme for Internet of Intelligent Things

The Internet of Intelligent Things (IoIT) communication environment can be utilized in various types of applications (for example, intelligent battlefields, smart healthcare systems, the industrial internet, home automation, and many more). Communications that happen in such environments can have different types of security and privacy issues, which can be resolved through the utilization of blockchain. In this paper, we propose a tutorial that aims in desiging a generalized blockchain-based secure authentication key management scheme for the IoIT environment. Moreover, some issues with using blockchain for a communication environment are discussed as future research directions. The details of different types of blockchain are also provided. Some of the widely-accepted consensus algorithms are then discussed. Next, we discuss different types of applications in blockchain-based IoIT communication environments. The details of the associated system models are provided, such as, the network and attack models for the blockchain-based IoIT communication environment, which are helpful in designing a security protocol for such an environment. A practical demonstration of the proposed generalized scheme is provided in order to measure the impact of the scheme on the performance of the essential parameters. Finally, some of the future research challenges in the blockchain-based IoIT communication environment are highlighted, which will also be helpful to the researchers