Efficient forwarding strategy in a NDN-based internet of things

Aboud, Ahmed; Touati, Haifa; Hnich, Brahim

doi:10.1007/s10586-018-2859-7

Cited by 43 publications

(18 citation statements)

References 19 publications

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“…The authors in [ 19 ] propose a greedy Geographic Interest Forwarding (GIf) scheme for NDN-based IoT. To this end, a Neighbors Discovery phase is performed via HELLO messages, including the ID of the sender and its coordinates, and a Producers Discovery phase is initiated by content producers to announce their existence to the consumers before performing Data queries.…”

Section: Related Workmentioning

confidence: 99%

“…• We highlight the NDN-based forwarding peculiarities for wireless ad hoc networks; • We conduct a comparative analytical study of the existing forwarding solutions in NDN-based wireless ad hoc networks; • We devise a new Learning-based Adaptive Forwarding Strategy (LAFS) tailored for NDN-based wireless IoT networks; • We propose a smart healthcare system for COVID- 19 confinement area monitoring, as a relevant use case; and…”

Section: Introductionmentioning

confidence: 99%

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LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks

et al. 2021

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Named Data Networking (NDN) is a data-driven networking model that proposes to fetch data using names instead of source addresses. This new architecture is considered attractive for the Internet of Things (IoT) due to its salient features, such as naming, caching, and stateful forwarding, which allow it to support the major requirements of IoT environments natively. Nevertheless, some NDN mechanisms, such as forwarding, need to be optimized to accommodate the constraints of IoT devices and networks. This paper presents LAFS, a Learning-based Adaptive Forwarding Strategy for NDN-based IoT networks. LAFS enhances network performances while alleviating the use of its resources. The proposed strategy is based on a learning process that provides the necessary knowledge allowing network nodes to collaborate smartly and offer a lightweight and adaptive forwarding scheme, best suited for IoT environments. LAFS is implemented in ndnSIM and compared with state-of-the-art NDN forwarding schemes. As the obtained results demonstrate, LAFS outperforms the benchmarked solutions in terms of content retrieval time, request satisfactory rate, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks

et al. 2021

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“…The NDN is involved in wireless heterogeneous networks that use promising forwarding techniques to retrieve content. A geographic interest-forwarding scheme is adapted with a greedy forwarding strategy [9]. The naming schemes include information about the identity, location, and data prefixes.…”

Section: Introductionmentioning

confidence: 99%

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

et al. 2020

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Content retrieval is becoming an emerging application on an integrated network structure referred to as Software Defined Network and Vehicular Ad hoc Network (SDVN). The SDVN is expected to flexibly provide efficient packet routing without deploying road side units. In such the infrastructure-less environment, however, it is challenging to retrieve contents from dynamic networks clustered with vehicles on roadways. As a solution to the problem, we propose a new network design by conjoining Named Data Network (NDN) with SDVN (NDN-SDVN). The new NDN-SDVN is designed as to improve stableness of the cluster formation and hence enables to efficiently perform the content retrieval over Vehicle-to-Vehicle (V2V) communications. Note that one of the most important problems in the V2V is how to select a cluster head. The proposed NDN-SDN adaptively determines the cluster head by using the SDN controller under the maximum Quality of Service (QoS) criterion, while reserving an assistance head based on a Moth Flame Optimization (MFO) algorithm to immediately recompose a new cluster with the back-off time. Moreover, the content retrieval in the NDN-SDVN can be performed over both intra-and inter-cluster communications. To this end, a Kalman filter with Neural Network (KF-NN) is applied for selecting a certain number of preferable vehicles in 1-hop in order to mitigate the broadcast storm problem that often occurs in an NDN. Content retrieval is initiated by sending interest packets including two additional fields of the timestamp and the target MAC address. The head vehicle of the SDN controller maintains ontology regarding the content information cashed by other vehicles, which significantly improves the packet delivery ratio. This NDN-SDVN design is developed on the OMNeT++ simulator and their advantageous results are evaluated in terms of the head lifetime, the cluster lifetime, the satisfactory rate, the latency and the packet delivery ratio.

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“…Hence, since the launch of VANETs, several works on data routing have been conducted in order to optimize the packets dissemination in terms of end-to-end delay and packet delivery ratio. Some of these proposals [8], [9] are based on geographic position like GPSR, some are based on topology like AODV. But they are all linked only to the network layer without considering parameters of other layers.…”

Section: Introductionmentioning

confidence: 99%

Performance Impact Analysis of Security Attacks on Cross-Layer Routing Protocols in Vehicular Ad hoc Networks

Baccari¹,

Touati²,

Hadded

et al. 2020

2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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Recently, several cross-layer protocols have been designed for vehicular networks to optimize data dissemination by ensuring internal communications between routing and MAC layers. In this context, a cross-layer protocol, called TDMAaware Routing Protocol for Multi-hop communications (TRPM), was proposed in order to efficiently select a relay node based on time slot scheduling information obtained from the MAC layer. However, due to the constant evolution of cyber-attacks on the routing and MAC layers, data dissemination in vehicular networks is vulnerable to several types of attack. In this paper, we identify the different attack models that can disrupt the crosslayer operation of the TRPM protocol and assess their impact on performance through simulation. Several new vulnerabilities related to the MAC slot scheduling process are identified. Exploiting of these vulnerabilities would lead to severe channel capacity wastage where up to half of the free slots could not be reserved.

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Efficient forwarding strategy in a NDN-based internet of things

Cited by 43 publications

References 19 publications

LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks

LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks

Stabilized Clustering Enabled V2V Communication in an NDN-SDVN Environment for Content Retrieval

Performance Impact Analysis of Security Attacks on Cross-Layer Routing Protocols in Vehicular Ad hoc Networks

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