Resolution strategies for networking the IoT at the edge via named functions

Scherb, Christopher; Grewe, Dennis; Wagner, Marco; Tschudin, Christian

doi:10.1109/ccnc.2018.8319235

Cited by 22 publications

(12 citation statements)

References 16 publications

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“…Three resolution strategies are defined to support edge find or execute (Edge-FoX), Find-and-Execute (FaX) and Find-or-Pull-and-Execute (FoP)aX. These strategies can be applied to a smart home or smart building [160]. Further, roles and addition of added nodes to perform the in-network computation is needed to explore.…”

Section: F Edge Computing (In-network Computation) and Cloud Computingmentioning

confidence: 99%

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Arshad

Azam

Rehmani

et al. 2019

IEEE Internet Things J.

188

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Information-Centric Networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current IP-address based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, caching the content at intermediate nodes to provide reliable, efficient data delivery and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoTs) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoTs attracts both industry and research communities. IoTs is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoTs by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN based caching, naming, security and mobility approaches for IoTs with appropriate classification. Furthermore, we present operating systems (OS) and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoTs.

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Section: F Edge Computing (In-network Computation) and Cloud Computingmentioning

confidence: 99%

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Arshad

Azam

Rehmani

et al. 2019

IEEE Internet Things J.

188

View full text Add to dashboard Cite

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“…A pioneering work extending NDN to support distributed in-network computations, with a special focus on IoT at the edge, is Named Function Networking (NFN) [6,10,46]. In NFN, a name can represent a content, a function capable of processing the content, or an expression combining both.…”

Section: Distributed Mechanismsmentioning

confidence: 99%

“…If n is not able to perform the service, it can push the computation towards another on-path node or even towards an off-path node, e.g., a cloud facility. In Reference [10], FoX is extended for orchestrating IoT services at the edge. The resulting EdgeFoX strategy assumes that nodes at the network edge are NFN capable and that they may advertise the name of the functions they are able to execute.…”

Section: Distributed Mechanismsmentioning

confidence: 99%

“…For instance, an intuitive mechanism could be to execute the service as close as possible to the place where data are produced in order to reduce the data retrieval latency [6]. Another option could be to execute the service in the less-loaded node in order to reduce the processing latency [9,10]. An alternative strategy could be to select the executor according to the service requirements [7,8], e.g., compute-intensive services could be deployed in more powerful nodes.…”

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confidence: 99%

“…Service orchestration strategies to identify the best in-network service executors [6][7][8][9][10].…”

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confidence: 99%

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

et al. 2019

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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.Smart home, smart building, smart transportation, and smart energy are just a few examples of IoT application domains. In these contexts, the cloud and fog computing paradigms will play a crucial role in supporting the variety of expected services (e.g., data aggregation, filtering, manipulation for monitoring, and surveillance purposes) and in fulfilling their requirements in the presence of a huge number of heterogeneous connected devices. According to References [3,4], fog and cloud computing will complement each other to form a service continuum between the cloud and the IoT devices by providing computing, storage, control, and networking resources.On the one side, by leveraging remote data centers and centralized control, the cloud becomes the perfect candidate for managing long-term data storage and for executing complex analytics algorithms. On the other side, the fog brings computing and storage services close to the end-users, where data are generated and/or consumed. The advantages are clear: enabling interactive and delay-sensitive applications, reducing the amount of traffic reaching the core network segment, and guaranteeing high performance also in the presence of limited network bandwidth and intermittent connectivity. In the most ambitious scenario, network edge routers and hosts, ranging from access points, backhaul nodes, and Points of Presence (PoPs) to IoT gateways and user devices like smartphones and tablets, will turn into fog elements that execute services on demand.An ...

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