The Road Ahead for Networking: A Survey on ICN-IP Coexistence Solutions

In recent years, the current Internet has experienced an unexpected paradigm shift in the usage model, which has pushed researchers towards the design of the Information-Centric Networking (ICN) paradigm as a possible replacement of the existing architecture. Even though both Academia and Industry have investigated the feasibility and effectiveness of ICN, achieving the complete replacement of the Internet Protocol (IP) is a challenging task. Some research groups have already addressed the coexistence by designing their own architectures, but none of those is the final solution to move towards the future Internet considering the unaltered state of the networking. To design such architecture, the research community needs now a comprehensive overview of the existing solutions that have so far addressed the coexistence. The purpose of this paper is to reach this goal by providing the first comprehensive survey and classification of the coexistence architectures according to their features (i.e., deployment approach, deployment scenarios, addressed coexistence requirements and architecture or technology used) and evaluation parameters (i.e., challenges emerging during the deployment and the runtime behaviour of an architecture). We believe that this paper will finally fill the gap required for moving towards the design of the final coexistence architecture.Comment: 23 pages, 16 figures, 3 table

Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

[EN] By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.This research was partially funded by the Italian Government under grant PON ARS01_00836 for the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) PON Project.Amadeo, M.; Ruggeri, G.; Campolo, C.; Molinaro, A.; Loscri, V.; Tavares De Araujo Cesariny Calafate, CM. (2019). Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms. Future Internet. 11(11):1-21. https://doi.org/10.3390/fi11110222S1211111Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. doi:10.1016/j.bushor.2015.03.008Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A., Briante, O., Iera, A., & Ruggeri, G. (2018). Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development. IEEE Internet of Things Journal, 5(4), 2557-2571. doi:10.1109/jiot.2017.2775739Chiang, M., & Zhang, T. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal, 3(6), 854-864. doi:10.1109/jiot.2016.2584538Openfog Consortiumhttp://www.openfogconsortium.org/Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., claffy, kc, Crowley, P., … Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44(3), 66-73. doi:10.1145/2656877.2656887Amadeo, M., Ruggeri, G., Campolo, C., & Molinaro, A. (2019). IoT Services Allocation at the Edge via Named Data Networking: From Optimal Bounds to Practical Design. IEEE Transactions on Network and Service Management, 16(2), 661-674. doi:10.1109/tnsm.2019.2900274ndnSIM 2.0: A New Version of the NDN Simulator for NS-3https://www.researchgate.net/profile/Spyridon_Mastorakis/publication/281652451_ndnSIM_20_A_new_version_of_the_NDN_simulator_for_NS-3/links/5b196020a6fdcca67b63660d/ndnSIM-20-A-new-version-of-the-NDN-simulator-for-NS-3.pdfAhlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D., & Ohlman, B. (2012). A survey of information-centric networking. IEEE Communications Magazine, 50(7), 26-36. doi:10.1109/mcom.2012.6231276NFD Developer’s Guidehttps://named-data.net/wp-content/uploads/2016/03/ndn-0021-diff-5..6-nfd-developer-guide.pdfPiro, G., Amadeo, M., Boggia, G., Campolo, C., Grieco, L. A., Molinaro, A., & Ruggeri, G. (2019). Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds. IEEE Transactions on Cloud Computing, 7(1), 210-223. doi:10.1109/tcc.2016.2573307Zhang, G., Li, Y., & Lin, T. (2013). Caching in information centric networking: A survey. Computer Networks, 57(16), 3128-3141. doi:10.1016/j.comnet.2013.07.007Yi, C., Afanasyev, A., Moiseenko, I., Wang, L., Zhang, B., & Zhang, L. (2013). A case for stateful forwarding plane. Computer Communications, 36(7), 779-791. doi:10.1016/j.comcom.2013.01.005Amadeo, M., Briante, O., Campolo, C., Molinaro, A., & Ruggeri, G. (2016). Information-centric networking for M2M communications: Design and deployment. Computer Communications, 89-90, 105-116. doi:10.1016/j.comcom.2016.03.009Tourani, R., Misra, S., Mick, T., & Panwar, G. (2018). Security, Privacy, and Access Control in Information-Centric Networking: A Survey. IEEE Communications Surveys & Tutorials, 20(1), 566-600. doi:10.1109/comst.2017.2749508Ndn-ace: Access Control for Constrained Environments over Named Data Networkinghttp://new.named-data.net/wp-content/uploads/2015/12/ndn-0036-1-ndn-ace.pdfZhang, Z., Yu, Y., Zhang, H., Newberry, E., Mastorakis, S., Li, Y., … Zhang, L. (2018). An Overview of Security Support in Named Data Networking. IEEE Communications Magazine, 56(11), 62-68. doi:10.1109/mcom.2018.1701147Cisco White Paperhttps://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdfAazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674-4682. doi:10.1109/tii.2018.2855198Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860-3873. doi:10.1109/tvt.2016.2532863Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., … Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289-330. doi:10.1016/j.sysarc.2019.02.009Baktir, A. C., Ozgovde, A., & Ersoy, C. (2017). How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions. IEEE Communications Surveys & Tutorials, 19(4), 2359-2391. doi:10.1109/comst.2017.2717482Duan, Q., Yan, Y., & Vasilakos, A. V. (2012). A Survey on Service-Oriented Network Virtualization Toward Convergence of Networking and Cloud Computing. IEEE Transactions on Network and Service Management, 9(4), 373-392. doi:10.1109/tnsm.2012.113012.120310Amadeo, M., Campolo, C., & Molinaro, A. (2016). NDNe: Enhancing Named Data Networking to Support Cloudification at the Edge. IEEE Communications Letters, 20(11), 2264-2267. doi:10.1109/lcomm.2016.2597850Krol, M., Marxer, C., Grewe, D., Psaras, I., & Tschudin, C. (2018). Open Security Issues for Edge Named Function Environments. IEEE Communications Magazine, 56(11), 69-75. doi:10.1109/mcom.2018.170111711801-2:2017 Information Technology—Generic Cabling for Customer Premiseshttps://www.iso.org/standard/66183.htm

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

NFaaS: Named Function as a Service

In the past, the Information-centric networking (ICN) community has focused on issues mainly pertaining to traditional content delivery (e.g., routing and forwarding scalability, congestion control and in-network caching). However, to keep up with future Internet architectural trends the wider area of future Internet paradigms, there is a pressing need to support edge/fog computing environments, where cloud functionality is available more proximate to where the data is generated and needs processing. With this goal in mind, we propose Named Function as a Service (NFaaS), a framework that extends the Named Data Networking architecture to support in-network function execution. In contrast to existing works, NFaaSbuilds on very lightweight VMs and allows for dynamic execution of custom code. Functions can be downloaded and run by any node in the network. Functions can move between nodes according to user demand, making resolution of moving functions a first-class challenge. NFaaSincludes a Kernel Store component, which is responsible not only for storing functions, but also for making decisions on which functions to run locally. NFaaSincludes a routing protocol and a number of forwarding strategies to deploy and dynamically migrate functions within the network. We validate our design through extensive simulations, which show that delay-sensitive functions are deployed closer to the edge, while less delay-sensitive ones closer to the core

On Computing In the Network: Covid-19 Coughs Detection Case Study

Computing in the network (COIN) is a promising technology that allows processing to be carried out within network devices such as switches and network interface cards. Time sensitive application can achieve their quality of service (QoS) target by flexibly distributing the caching and computing tasks in the cloud-edge-mist continuum. This paper highlights the advantages of in-network computing, comparing to edge computing, in terms of latency and traffic filtering. We consider a critical use case related to Covid-19 alert application in an airport setting. Arriving travelers are monitored through cough analysis so that potentially infected cases can be detected and isolated for medical tests. A performance comparison has been done between an architecture using in-network computing and another one using edge computing. We show using simulations that in-network computing outperforms edge computing in terms of Round Trip Time (RTT) and traffic filtering