Goto

Collaborating Authors

Results


Telecom Trends: 5G most mentioned term on Twitter Q4 2021

#artificialintelligence

Telecom: Verdict lists the top five terms tweeted on telecommunications in Q4 2021 based on data from GlobalData's Technology Influencer Platform. The top trends are the most mentioned terms or concepts among Twitter discussions of more than 150 telecommunications experts tracked by GlobalData's Technology Influencer platform during the fourth quarter (Q4) of 2021. India's 5G network roll out plans, a partnership between wireless voice and data services provider DISH Wireless and technology company Cisco, and new 5G testing capabilities for non-terrestrial networks (NTNs) were among the popular discussions on 5G in Q4 2021. Madhav Seth, vice president of smartphone company realme, shared an article on India's Department of Telecommunication (DoT) confirming its plans to roll out 5G network in the country in 2022. The network will initially be launched in 13 cities where 5G trials were conducted by telecom operators Airtel, Jio and Vi to perform end-to-end testing of 5G and develop 5G products and services.


How Telecom Companies Can Leverage Machine Learning To Boost Their Profits

#artificialintelligence

The number of smartphone users across the world has skyrocketed over the last decade and promises to do so in the future too. Additionally, most business functions can now be executed on mobile devices. However, despite the mobile surge, telecom operators around the world are still not that profitable, with average net profit margins hovering around the 17% mark. The main reasons for the middling profit rates are the high number of market rivals vouching for the same customer base and the high overhead expenses associated with the sector. Communication Service Providers (CSPs) need to become more data-driven to reduce such costs and, automatically, improve their profit margins.


A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

arXiv.org Artificial Intelligence

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.


Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

arXiv.org Artificial Intelligence

The unprecedented surge of data volume in wireless networks empowered with artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by collecting datasets and training models centrally. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost due to increased data communication, (ii) threatened data privacy by allowing untrusted parties to utilise this information. Recently, in light of these limitations, a new emerging technique, coined as federated learning (FL), arose to bring ML to the edge of wireless networks. FL can extract the benefits of data silos by training a global model in a distributed manner, orchestrated by the FL server. FL exploits both decentralised datasets and computing resources of participating clients to develop a generalised ML model without compromising data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of FL. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth generation (B5G) and sixth generation (6G) communication systems. The purpose of this survey is to provide an overview of the state-of-the-art of FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.


Rootkits: evolution and detection methods

#artificialintelligence

A rootkit is a program (or set of programs) that allows you to hide the presence of malware in the system. Rootkits are often part of multifunctional malware that could have multiple abilities, such as providing attackers with remote access to compromised hosts, intercepting network traffic, spying on users, recording keystrokes, stealing authentication information, or using the host as a base to mine cryptocurrencies and aid in DDoS attacks. The task of the rootkit is to mask this illegitimate activity on the compromised machine. Some rootkits, such as Necurs, Flame and DirtyMoe, are designed to combine both modes of operation and thus work at both levels. They accounted for 31% of the sample.


Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs

arXiv.org Artificial Intelligence

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multiple iIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in ...


IT & Telecommunications Technology Trends 2021 - Coderus

#artificialintelligence

In today's world, technology advances at an extremely fast pace, with new innovations and advancements made every day. Things change quickly in this industry so it's vital to keep up with the latest IT and telecommunications technology trends. Cognitive technology is a very exciting area of technology that we have already seen huge breakthroughs in, and facial recognition is a clear example of this. The developments being made in cognitive technology today will lead to drastic changes in humanity's relationship with machines and their understanding of humans. Today, we see these technologies commonly used in virtual assistants and smart speakers but there is huge potential for them to have a huge impact on a wide range of industries and sectors.


Could 2021 be the year for technology? Here are some trends to watch out for

#artificialintelligence

The icing on the cake is that the action takes place in the PUBG universe. Some of the most exciting inventions in TV will be in 2021. LG has hinted at ditching the E-Series OLED and bringing in Gallery Series. On the other hand, Samsung might unveil a rotating Sero TV. This year will be bigger and mightier with TV screens measuring above 75-inch becoming mainstream.


Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

arXiv.org Artificial Intelligence

The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G)networks. 5G networks have transitioned to software-defined infrastructures, thereby reducing their dependence on hardware-based network functions. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This has resulted in significant improvements in efficiency, performance, and robustness of the networks. However, this has also made the core network more vulnerable, as software systems are generally easier to compromise than hardware systems. In this article, we present a comprehensive security analysis framework for the 5GCN. The novelty of this approach lies in the creation and analysis of attack graphs of the software-defined and virtualized 5GCN through machine learning. This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate that these possible exploits of 5GCN vulnerabilities generate five novel attacks on the 5G Authentication and Key Agreement protocol. We combine the attacks at the network, protocol, and the application layers to generate complex attack vectors. In a case study, we use these attack vectors to find four novel security loopholes in WhatsApp running on a 5G network.


Loss Tolerant Federated Learning

arXiv.org Artificial Intelligence

Federated learning has attracted attention in recent years for collaboratively training data on distributed devices with privacy-preservation. The limited network capacity of mobile and IoT devices has been seen as one of the major challenges for cross-device federated learning. Recent solutions have been focusing on threshold-based client selection schemes to guarantee the communication efficiency. However, we find this approach can cause biased client selection and results in deteriorated performance. Moreover, we find that the challenge of network limit may be overstated in some cases and the packet loss is not always harmful. In this paper, we explore the loss tolerant federated learning (LT-FL) in terms of aggregation, fairness, and personalization. We use ThrowRightAway (TRA) to accelerate the data uploading for low-bandwidth-devices by intentionally ignoring some packet losses. The results suggest that, with proper integration, TRA and other algorithms can together guarantee the personalization and fairness performance in the face of packet loss below a certain fraction (10%-30%).