Load-Aware Congestion Adaptive Multipath Multicasting
Abstract
In recent years, all communication system becomes wireless due to vast development and advancements in wireless technology. Such network offers a platform to deploy a wide range of multimedia and other services with different data types. In mobile ad hoc networks one of the most prominent factors which affect the overall performance of the network is congestion. Congestion is one of the most misinterpreted concepts in the context of the wireless network. In general, excessive data traffic in the network referred to as congestion. However, in the wireless network, the congestion occurs due to unavailability of resources such as insufficient bandwidth, low battery power, and so on which leads to high packet losses, bandwidth reduction, and wastage of energy and time in recovering congestion. In addition to this, another factor which degrades the performance is improper load balancing due to certain routing metrics limitation. From the literature, it is observed that many of the existing solutions handle the above problems separately in a not- adaptive manner. However, addressing these issues together in an adaptive manner provides an effective solution to balance the load in the network as well as congestion. The proposed work addresses congestion and load balancing problems parallel with the aim to improve and enhance the overall network performance and lifetime. The proposed Load Aware Congestion Adaptive Multipath Multicast (LACAMM) routing approach adapt to current changes in the load and congestion level to find a suitable path even in the case of congestion scenario and node resource constraints. The proposed scheme measures the node resources such as residual bandwidth and the residual battery to predict the node stability. Redirects the data transmission under congested scenario from the congested node through the non-congested alternate path thereby improves the overall network performance. This work performs well under burst traffic with the harsh environment.
