Fault-Tolerant Mechanism for Multimedia Transmission in Wireless Sensor Networks

Bouatit, Mohamed Nacer; Boumerdassi, Selma; Minet, Pascale; Djama, Adel

doi:10.1109/vtcfall.2016.7881016

Cited by 6 publications

(1 citation statement)

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“…In fact, IP-based routing solutions have been widely explored in this design space [3], i.e., ad hoc environment, where the research focus is based on the support of its various inherent constraints, such as resource limitations (energy and processing), and the dynamic and unpredictable location of the nodes. In this scope, several solutions have been proposed, such as [4] to overcome dynamic holes through multipath forwarding in Wireless Multimedia Sensor Networks, or [5] for the support of the nodes' energy depletion and fault tolerance in WSNs.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%