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Top1. Introduction
6LoWPAN-based communication platforms are extensively used and implemented for low powered devices throughout the world when mobile ad-hoc technology matures. There is a growing need for Internet of Things (IoT) devices to effortlessly traverse between different subnets yet still be able to utilize the service (internet). The 6LoWPAN is used in a wide range of low-powered wireless platform applications. The Internet Engineering Task Force (IETF) established the 6LoWPAN steering committee (Liu et al., 2005). In the year 2006, the 6LoWPAN technology was established. IoT systems may now enable network mobility by transmitting Ipv6 traffic across the low-powered wireless network by utilizing this new adaptation layer included in the regulation.
6LoWPAN's structure is depicted in Figure 1. Entities in a 6LoWPAN may sometimes act as hosts or routers, and there may be one or more interconnectors, namely “Edge Routers (ER)”. Neighbor node determination is a key concept in 6LoWPAN that makes it easier for peers to enroll with the ER for the smooth conduction of routing and improve network connectivity. Thus, such discovery forms a base strategy for any hosts and routers to communicate over the same connection. Hence the deployed nodes are enabled to route their data packetsamong ER of various LoWPAN. Optimized mechanisms and inclusion of adaptation layer allow optimal usage of IPv6 across low-power and low-rate connectionless networks for basic hardware platforms. A 6LoWPAN-based routing algorithm supports the multicast network structure.
This study utilized the extended 6LoWPAN, Mulligan, G. (2007), (Atzori et al., 2010), (Shelby & Bormann, 2009) which feature numerous ER and a backbone connection to integrate every other subnet. The ER acts as a channel for transporting required data (to and from the LoWPAN).Routing through the adaptation layer of 6Lowpan (Kim et al., 2012) has four necessities:
§ The router must be able to go into a dormant state for energy conservation;
§ Accumulated overhead on datagram must be minimal;
§ Control overhead during data routing must be minimal;
§ Relatively limited computational and storage constraints are needed to be balanced.
Nodes in 6LoWPAN may vary in size from just a hundred to thousands/millions, which necessitates a minimalistic approach to access the maximum range of available resources. Ideally, the routing strategy should handle at least ~250 wireless nodes, especially for any automated function. In contrast, the route optimization strategy for urbanized systems should be enabled to cluster a substantial percentage of 6LoWPAN nodes in the deployed areas.
Figure 1.
6LoWPAN Stack and its associated Packet Structure
Figure. 1 represents the typical 6LoWPAN stack and its associated packet structure. The routing analysis in mobile 6LoWPAN is still missing in basic research. Therefore, a great deal of effort has to be done in terms of the conceptual elements of the course. These difficulties encompass stability and dependability and stacked multi-layers (especially in hierarchical architecture). Such constraints severely limit the realistic implementation of the infrastructure. It is thus imperative that new routing strategies for the next level 6LoWPAN be proposed to address the present issues with 6LoWPAN and substantially maximize the efficiency of route optimization capabilities.
One approach to include an optimum routing method is to blend soft computing with complex learning methods. For ongoing issues that seem difficult to represent analytically, seeks to propose newer models and resolutions. As a result, via the use of uncertainties and quantified logic with perfect reasoning, soft computing approaches will provide an alternative that is resilient, feasible and reduces cost. Numerous systems now effectively employ such computational intelligence approaches to the betterment of optimizing the resultants.