NEW ADAPTIVE REBROADCASTING USING NEIGHBOUR LEARNING FOR WIRELESS NANOSENSOR NETWORKS

Abstract

Wirelesse Nanosensor Networks (WNSNs) contain a large number of independent and mobile nanodevices assembled with nanotransceivers and nanoantennas to work in Terahertz frequency band (0.1-10THz). These nanodevices exploit the properties of modern nanomaterials to recognize new varieties of events at the nanoscale, such as the presence of harmful viruses or bacteria and the detection of low concentrations of chemical and harmful gas molecules. Communication between nanonodes can be established by using molecular or electromagnetic communication approaches. One of the major problems of wireless nanosensor networks is the limited resources of nanodevices (e.g., computation, memory and power). On the other hand, such limited capacity cannot simply ensure communication between nanonodes using the flooding mechanism, which affects network performance and increases resource utilization. This paper considers the electromagnetic-based wireless nanosensor networks, and proposes a New Adaptive Probabilistic Based Broadcast Using Neighborhood Information. Simulations have been conducted using Nanosim simulator in order to compare our new schemes with the fixed probabilistic based broadcast. The experiments show that the proposed approach gives good results in terms of Packet Delivery Ratio (PDR reached 95%.), the amount of energy consumed (significantly reduced) for all the categories of density. No startup setup is required: the nanonodes adjust by themselves the broadcasting probability based on neighborhood collected information.