The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out new possibilities in boosting the service quality for the emerging applications through data sharing. Nevertheless, privacy concerns are major bottlenecks for data providers to share private data in traditional IoV networks. To this end, federated learning (FL) as an emerging learning paradigm, where data providers only send local model updates trained on their local raw data rather than upload any raw data, has been recently proposed to build a privacy-preserving data sharing models. Unfortunately, by analyzing on the differences of uploaded local model updates from data providers, private information can still be divulged, and performance of the system cannot be guaranteed when partial federated nodes executes malicious behavior. Additionally, traditional cloud-based FL poses challenges to the communication overhead with the rapid increase of terminal equipment in IoV system. All these issues inspire us to propose an autonomous blockchain empowered privacy-preserving FL framework in this paper, where the mobile edge computing (MEC) technology was naturally integrated in IoV system.
SK Telecom said on Thursday it has launched its 5G edge cloud service, called SKT 5GX Edge, embedded with Amazon Web Services (AWS) Wavelength in South Korea. The launch of the service will allow customers to build ultra-low latency mobile apps, the telco said, in areas such as machine learning, Internet of Things, gaming, and streaming. Use of the service will allow apps that are accessing the cloud to bypass the internet and regional websites, and quickly reach SK Telecom's data centre. The reduced step will allow customers to enjoy the full benefits offered by 5G network's low latency and bandwidth, the telco said. The first AWS Wavelength Zone has been launched in the city of Daejeon. It will expand to Seoul and other regions next year.
LG Uplus Corp, a major South Korean mobile carrier, said on Sunday that it is joining forces with Google to jointly develop 5G mobile edge computing (MEC) technology. Under the partnership, LG Uplus will work with Google Cloud, which will provide its artificial intelligence and machine learning technologies, to develop new services that utilize MEC on the telecom operator's 5G network. MEC is a key technology in delivering ultra-low latency data communication in 5G networks, and it is expected to boost upcoming services, such as smart factories, autonomous cars and cloud gaming. LG Uplus demonstrated the technology last October by transferring a vehicle's live-video feed to a car at its rear as part of its self-driving vehicle project. The new partnership comes as South Korean telecom operators have rushed to develop the budding 5G technology, reports Yonhap news agency.
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.
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Rodrigues, Diego O., Santos, Frances A., Filho, Geraldo P. Rocha, Akabane, Ademar T., Cabral, Raquel, Immich, Roger, Junior, Wellington L., Cunha, Felipe D., Guidoni, Daniel L., Silva, Thiago H., Rosário, Denis, Cerqueira, Eduardo, Loureiro, Antonio A. F., Villas, Leandro A.
The growing of cities has resulted in innumerable technical and managerial challenges for public administrators such as energy consumption, pollution, urban mobility and even supervision of private and public spaces in an appropriate way. Urban Computing emerges as a promising paradigm to solve such challenges, through the extraction of knowledge, from a large amount of heterogeneous data existing in urban space. Moreover, Urban Computing correlates urban sensing, data management, and analysis to provide services that have the potential to improve the quality of life of the citizens of large urban centers. Consider this context, this chapter aims to present the fundamentals of Urban Computing and the steps necessary to develop an application in this area. To achieve this goal, the following questions will be investigated, namely: (i) What are the main research problems of Urban Computing?; (ii) What are the technological challenges for the implementation of services in Urban Computing?; (iii) What are the main methodologies used for the development of services in Urban Computing?; and (iv) What are the representative applications in this field?
Oct 18: AI and the Evolution of Cloud Computing: Evaluating How Financial Data is Stored, Protected, and Maintained by Cloud Providers Congressional Hearing Friday, Oct 18, 9:30am EDT. Financial Services Committee Hearing -- Task Force on Artificial Intelligence: AI and the Evolution of Cloud Computing: Evaluating How Financial Data is Stored, Protected, and Maintained by Cloud Providers. Oct 17: US Stocks were UP Thursday, driven by solid Q3 earnings reports from Netflix, Morgan Stanley and others, as well as reports of a UK-EU Brexit deal. More than one-tenth of all the companies in the S&P 500 have reported Q3 earnings results so far, and most are coming in above analysts' expectations. Oct 16: US stocks end slightly lower after weak U.S. retail sales and tension with China offsets good corporate earnings, e.g., IBM (pdf): IBM reports solid earnings, but Q3 sales mixed, other info here; Netflix Q3 results (pdf) and other info here. Netflix $NFLX rises after earnings show subscriber ...
In the last five years, edge computing has attracted tremendous attention from industry and academia due to its promise to reduce latency, save bandwidth, improve availability, and protect data privacy to keep data secure. At the same time, we have witnessed the proliferation of AI algorithms and models which accelerate the successful deployment of intelligence mainly in cloud services. These two trends, combined together, have created a new horizon: Edge Intelligence (EI). The development of EI requires much attention from both the computer systems research community and the AI community to meet these demands. However, existing computing techniques used in the cloud are not applicable to edge computing directly due to the diversity of computing sources and the distribution of data sources. We envision that there missing a framework that can be rapidly deployed on edge and enable edge AI capabilities. To address this challenge, in this paper we first present the definition and a systematic review of EI. Then, we introduce an Open Framework for Edge Intelligence (OpenEI), which is a lightweight software platform to equip edges with intelligent processing and data sharing capability. We analyze four fundamental EI techniques which are used to build OpenEI and identify several open problems based on potential research directions. Finally, four typical application scenarios enabled by OpenEI are presented.
China's internet firms are getting pally with giant state-owned automakers as they look to deploy their artificial intelligence and cloud computing services across traditional industries. Ride-hailing startup Didi Chuxing, which owns Uber China, announced earlier this week a new joint venture with state-owned BAIC. Hot on the heels came another entity set up between Tencent and the GAC Group. GAC, which is owned by the Guangzhou municipal government in southern China, announced Thursday in a filing it will jointly establish a mobility company with social media and gaming behemoth Tencent, Guangzhou Public Transport Group and other investors. The announcement followed an agreement between Tencent and GAC in 2017 to team up on internet-connected cars and smart driving, a deal that saw the carmaker tapping into Tencent's expertise in mobile payments, social networking, big data and cloud services.
Google Cloud AI's Jia Li has left her position as head of research and development, a company spokesperson told VentureBeat in an email. Additional comment or details were not provided. Prior to joining Google in 2016, Li was head of research at Snap Inc., and before that led the visual computing and learning group at Yahoo Labs, according to her LinkedIn profile. During her time with the company, a number of AI services were made available through Google Cloud, including AutoML for automating the creation of AI models. She also acted as head of an AI center based in Beijing that Google opened in December 2017.