Agent-based Broadcast Protocols for Wireless Heterogeneous Node Networks - Archive ouverte HAL
Article Dans Une Revue Computer Communications Année : 2018

Agent-based Broadcast Protocols for Wireless Heterogeneous Node Networks

Résumé

Internet of Things (IoT) is a wireless network composed of a variety of heterogeneous objects such as Connected Wearable Devices (sensors, smartwatches, smartphones, PDAs ...), Connected Cars, Connected Homes,...etc. These things use generally wireless communication to interact and cooperate with each other to reach common goals. IoT(T, n) is a network of things composed of T things with n items (packets) distributed randomly on it. The aim of the permutation routing is to route to each thing, its items, so it can accomplish its task. In this paper, we propose two agent-based broadcast protocols for mobile IoT, using a limited number of communication channels. The main idea is to partition the things into groups where an agent in each group manages a group of things. This partitioning is based on the memory capacities for these heterogeneous nodes. The first protocol uses a few communication channels to perform a parallel broadcasting and requires memory space, where k is the number of communication channels. The second protocol uses an optimal complexity of memory space for each thing to achieve the permutation routing with a parallel broadcasting using less number of channels. We give an estimation of the upper and lower bounds of the number of broadcast rounds in the worst case and we discuss experimental results.
Fichier principal
Vignette du fichier
cc74abb2-e282-4ae3-9bff-54bdc8839ef8-author.pdf (691.64 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02129722 , version 1 (15-05-2019)

Identifiants

Citer

Hicham Lakhlef, Abdelmadjid Bouabdallah, Michel Raynal, Julien Bourgeois. Agent-based Broadcast Protocols for Wireless Heterogeneous Node Networks. Computer Communications, 2018, 115, pp.51 - 63. ⟨10.1016/j.comcom.2017.10.020⟩. ⟨hal-02129722⟩
334 Consultations
350 Téléchargements

Altmetric

Partager

More