Editor’s Note

As the Internet of Things (IoT) further develops and expands to the Internet of Everything (IoE), high-speed multimedia streaming data processing, analysis, and shorter response times are increasingly becoming the demands of today. Driven by the Internet of Things (IoT), a new computing paradigm, Edge computing, is currently developing rapidly. Compared with traditional centralized generalpurpose computing, Edge computing is a distributed architecture. The operations of applications, data and services are moved from the central node of the network to the edge nodes on the network logic for processing. Under this structure, the analysis of data and the generation of knowledge are closer to the source of the data, so it is more suitable for processing. However, with the rapid development of 5G, IoT and other services and scenarios, there are more and more intelligent terminal devices. Multimedia streaming processing in IoT becomes a very prominent problem. To overcome this problem, the adoption of intelligent Edge or Artificial Intelligence (AI) powered Edge computing (Edge-AI) can achieve the goals of lower cost, higher security, lower latency, and ease of management. Recently, many network modeling methods, computing algorithms, and signal processing technologies have been successfully developed and applied to multimedia streaming processing in IoT with Edge Intelligence. A total of 13 papers are presented in this special issue for the purpose of collecting the latest developments and results on this research topic. We divide them into three categories: production and life applications, security, and text and image processing

Editor’s Note

As the Internet of Things (IoT) further develops and expands to the Internet of Everything (IoE), high-speed multimedia streaming data processing, analysis, and shorter response times are increasingly becoming the demands of today. Driven by the Internet of Things (IoT), a new computing paradigm, Edge computing, is currently developing rapidly. Compared with traditional centralized general- purpose computing, Edge computing is a distributed architecture. The operations of applications, data and services are moved from the central node of the network to the edge nodes on the network logic for processing. Under this structure, the analysis of data and the generation of knowledge are closer to the source of the data, so it is more suitable for processing. However, with the rapid development of 5G, IoT and other services and scenarios, there are more and more intelligent terminal devices. Multimedia streaming processing in IoT becomes a very prominent problem. To overcome this problem, the adoption of intelligent Edge or Artificial Intelligence (AI) powered Edge computing (Edge-AI) can achieve the goals of lower cost, higher security, lower latency, and ease of management

ArguteDUB: deep learning based distributed uplink beamforming in 6G-based IoV

In the last decade, MIMO spatial multiplexing and distributed beamforming play a significant role in improving datathroughput through cooperative transmission. It has been widely used in wireless communication, especially in 6G. However, thedistributed uplink beamforming is still an open problem in highly dynamic environments. However, the proposed 6G technologyrepresents the further integration of deep learning and wireless communication. In this paper, we propose Argute Distributed UplinkBeamforming (ArguteDUB), which uses a feedback algorithm with an offline-trained deep learning model to implement highly dynamicdistributed uplink beamforming for the Internet of Vehicles (IoV) in 6G. Specifically, each vehicle enables the base station (BS)/accesspoint (AP) to separate different channel state information (CSI) by inserting orthogonal sequences into the sending data. The BSadopts deep learning to filter the noise and predict the beamforming weight to achieve phase synchronization. Unlike traditionaldistributed uplink beamforming, ArguteDUB can be adapted to the highly dynamic time-varying channels. The simple network structureensures the fast response of ArguteDUB. In addition, we make ArguteDUB Orthogonal Frequency Division Multiplexing (OFDM)compatible so that it can be easily deployed in 6G networks. Our evaluation shows that ArguteDUB has an signal-to-noise ratio (SNR)gain of about 5dB to 5.3dB over the single vehicle transmission mod

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Performance Assessment of Fragmentation Mechanisms for Vehicular Delay-Tolerant Networks

[EN] Vehicular Delay-Tolerant Networks (VDTNs) are a new approach for vehicular communications where vehicles cooperate with each other, acting as the communication infrastructure, to provide low-cost asynchronous opportunistic communications. These communication technologies assume variable delays and bandwidth constraints characterized by a non-transmission control protocol/internet protocol architecture but interacting with it at the edge of the network. VDTNs are based on the principle of asynchronous communications, bundle-oriented communication from the DTN architecture, employing a store-carry-and-forward routing paradigm. In this sense, VDTNs should use the tight network resources optimizing each opportunistic contact among nodes. Given the limited contact times among nodes, fragmentation appears as a possible solution to improve the overall network performance, increasing the bundle delivery probability. This article proposes the use of several fragmentation approaches (proactive, source, reactive, and toilet paper) for VDTNs. They are discussed and evaluated through a laboratory testbed. Reactive and toilet paper approaches present the best results. It was also shown that only the source fragmentation approach presents worst results when compared with non-fragmentation approaches.This study was partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Euro-NF Network of Excellence of the Seventh Framework Programme of EU, in the framework of the Specific Joint Research Project VDTN, and by the INESC-ID multiannual funding through the PIDDAC program funds and National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2011 and PTDC/EEA-TEL/099074/2008 (MPSat) Projects.Dias, JAFF.; Rodrigues, JJPC.; Isento, JN.; Pereira, PRBA.; Lloret, J. (2011). Performance Assessment of Fragmentation Mechanisms for Vehicular Delay-Tolerant Networks. EURASIP Journal on Wireless Communications and Networking. 2011(195):1-14. https://doi.org/10.1186/1687-1499-2011-195S1142011195Tatchikou R, Biswas S, Dion F: Cooperative vehicle collision avoidance using inter-vehicle packet forwarding. In Presented at the IEEE Global Telecommunications Conference (IEEE GLOBECOM 2005). St. Louis, MO, USA; 2005.Park JS, Lee U, Oh SY, Gerla M, Lun DS: Emergency related video streaming in VANET using network coding. In The Third ACM International Workshop on Vehicular Ad Hoc Networks. (VANET 2006), Los Angeles, CA, USA; 2006:102-103.Buchenscheit A, Schaub F, Kargl F, Weber M: A VANET-based emergency vehicle warning system. Presented at the First IEEE Vehicular Networking Conference (IEEE VNC 2009), Tokyo, Japan 2009.Nekovee M: Sensor networks on the road: the promises and challenges of vehicular ad hoc networks and vehicular grids. In Proceedings of the Workshop on Ubiquitous Computing and e-Research. Edinburgh, UK; 2005.Blum J, Eskandarian A, Hoffmman L: Challenges of intervehicle ad hoc networks. IEEE Trans. Intell. Transport. Syst 2004, 5(4):347-351. 10.1109/TITS.2004.838218Yousefi S, Mousavi MS, Fathy M: Vehicular ad hoc networks (VANETs): challenges and perspectives. 6th International Conference on ITS Telecommunications (ITST 2006) 2006, 761-766.Füßler H, Torrent-Moreno M, Transier M, Festag A, Hartenstein H: Thoughts on a protocol architecture for vehicular ad-hoc networks. In Presented at the 2nd International Workshop on Intelligent Transportation (WIT 2005). Hamburg, Germany; 2005.Cerf V, Burleigh S, Hooke A, Torgerson L, Durst R, Scott K, Fall K, Weiss H: Delay-tolerant networking architecture. RFC 4838 2007. [Online] [ http://www.rfc-editor.org/rfc/rfc4838.txt ]Soares VNGJ, Farahmand F, Rodrigues JJPC: A layered architecture for vehicular delay-tolerant networks. In The Fourteenth IEEE Symposium on Computers and Communications (ISCC 2009). Sousse, Tunisia; 2009:122-127.Rodrigues JJPC, Soares VNGJ, Farahmand F: Stationary relay nodes deployment on vehicular opportunistic networks. In Mobile Opportunistic Networks: Architectures, Protocols and Applications. Edited by: Denko M. CRC Press, Taylor & Francis Group (hardcover); 2011:227-243.Postel J: Internet Protocol. RFC 791 1981. [Online] [ http://www.ietf.org/rfc/rfc791.txt ]Kent CA, Moguk JC: Fragmentation considered harmful. SIGCOMM Comput Commun Rev 1995, 25(1):75-87. 10.1145/205447.205456Kim B-S, Fang Y, Wong TF, Kwon Y: Throughput enhancement through dynamic fragmentation in wireless LANs. IEEE Trans Veh Technol 2005, 54(4):1415-1425. 10.1109/TVT.2005.851361Ginzboorg P, Niemi V, Ott J: Message Fragmentation in Disruptive Networks. Nokia Research Center, Technical Report; 2009.Legner M: Map-Based Geographic Forwarding in Vehicular Networks. Department of Informatic, University of Stuttgart; 2002.Li Q, Rus D: Sending messages to mobile users in disconnected ad-hoc wireless networks. 6th Annual International Conference on Mobile Computing and Networking, New York, USA 2000, 44-55.Vahdat A, Becker B: Epidemic Routing for Partially-Connected Ad-Hoc Networks. Duke University, Technical Report; 2000.Briesemeister L, Hommel G: Overcoming fragmentation in mobile ad-hoc networks. J Commun Netw 2000, 2(3):182-187.Liu H, Sheng H, Lv Z, Li L, Ma C: A cross layer design of fragmentation and priority scheduling in vehicular ad hoc networks. 7th World Congress on Intelligent Control and Automation (WCICA 2008) 2008, 6157-6160.Joshi HP: Distributed robust geocast: a multicast protocol for inter-vehicle communication. Master Thesis, North Carolina State University; 2006.Bachir A, Benslimane A: A multicast protocol in ad hoc networks: Inter-vehicles geocast. Proceedings of the 57th IEEE Vehicular Technology Conference, Korea 2003, 2456-2460.Mikko P, Ari K, Ott J: Message fragmentation in opportunistic DTNs. In 9th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM 2008). Newport Beach, CA, USA; 2008.Farrell S, Symington S, Weiss H: Delay-tolerant networking security overview. Internet Draft 2009. [Online] [ http://tools.ietf.org/html/draft-irtf-dtnrg-sec-overview-06 ]Magaia N, Pereira PR, Casaca A, Rodrigues J, Dias JA, Isento JN, Cervelló-Pastor C, Gallego J: Bundles fragmentation in vehicular delay-tolerant networks. 7th Euro-nf conference on next generation internet, Kaiserslautern, Germany 2011.Soares V, Rodrigues J, Farahmand F, Denko M: Exploiting node localization for performance improvement of vehicular delay-tolerant networks. In IEEE International Conference on Communications (ICC 2010). Cape Town, South Africa; 2010.Rubinstein MG, Abdesselm FB, Cavalcanti SR, Campista MEM, Alves RSA, Costa LHMK, Amorim MD, Duarte OCMB: Measuring the capacity of in-car to in-car vehicular networks. IEEE Commun Mag 2009, 47(11):128-136.Spyropoulos T, Psounis K, Raghavendra C S: Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In ACM SIGCOMM 2005--Workshop on Delay Tolerant Networking and Related Networks (WDTN-05). Philadelphia, PA, USA; 2005:252-259.Lindgren A, Doria A, Davies E, Grasic S: Probabilistic routing protocol for intermittently connected networks (2010). Internet Draft 2010. [Online] [ http://tools.ietf.org/html/draft-irtf-dtnrg-prophet-06 ]Teshima S, Ohta T, Kohno E, Kakuda Y: A data transfer scheme using autonomous clustering in VANETs environment. In 10th International Symposium on Autonomous Decentralized Systems (ISADS 2011). Tokyo, Japan; 2011:477-482.Psounis K: Efficient Routing for Safety Applications in Vehicular Networks. METRANS Project DTRS98-G0019, Electrical Engineering. University of Southern California, Los Angeles, USA; 2009.Li X, Shu W, Li M, Huang H, Min-You Wu: DTN routing in vehicular sensor networks. In IEEE Global Telecommunications Conference (IEEE GLOBECOM 2008). New Orleans, USA; 2008:1-5

A multi-tenant cloud-based DC nano grid for self-sustained smart buildings in smart cities

Energy is one of the most valuable resources of the modern era and needs to be consumed in an optimized manner by an intelligent usage of various smart devices, which are major sources of energy consumption nowadays. With the popularity of low-voltage DC appliances such as-LEDs, computers, and laptops, there arises a need to design new solutions for self-sustainable smart energy buildings containing these appliances. These smart buildings constitute the next generation smart cities. Keeping focus on these points, this article proposes a cloud-assisted DC nanogrid for self-sustainable smart buildings in next generation smart cities. As there may be a large number of such smart buildings in different smart cities in the near future, a huge amount of data with respect to demand and generation of electricity is expected to be generated from all such buildings. This data would be of heterogeneous types as it would be generated from different types of appliances in these smart buildings. To handle this situation, we have used a cloudbased infrastructure to make intelligent decisions with respect to the energy usage of various appliances. This results in an uninterrupted DC power supply to all low-voltage DC appliances with minimal dependence on the grid. Hence, the extra burden on the main grid in peak hours is reduced as buildings in smart cities would be self-sustainable with respect to their energy demands. In the proposed solution, a collection of smart buildings in a smart city is taken for experimental study controlled by different data centers managed by different utilities. These data centers are used to generate regular alerts on the excessive usage of energy from the end users' appliances. All such data centers across different smart cities are connected to the cloud-based infrastructure, which is the overall manager for making all the decisions about energy automation in smart cities. The efficacy of the proposed scheme is evaluated with respect to various performance evaluation metrics such as satisfaction ratio, delay incurred, overhead generated, and demand-supply gap. With respect to these metrics, the performance of the proposed scheme is found to be good for implementation in a realworld scenario

Internet of Autonomous Vehicles Communications Security: Overview, Issues, and Directions

© 2002-2012 IEEE. The Internet of Things (IoT) is an emerging technology that has gained a huge user base by facilitating Internet-connected devices being used in numerous applications including smart vehicular infrastructure. In this context, we focus on the traditional vehicular ad hoc network that has evolved into a new perception called the Internet of Vehicles (IoV), and is expected to soon transform into the Internet of Autonomous Vehicles (IoAV). IoAV hopes to facilitate smart vehicular infrastructure and autonomous driving without the need for human involvement. However, as the number of connected vehicles keeps increasing, so does the need for autonomous decision making. Hence, the IoAV must provide robust, secure, seamless, and scalable communication among the vehicles as well as the roadside units. This article provides an overview of autonomous vehicle communication layers, its associated properties, and security threats. Further, this article also briefly discusses the current research trends and future research issues