# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 1 Introduction  
Low-power and lossy networks (LLNs) [1; 2], composed from a few dozen to thousands of nodes, are a kind of communication network which can support the following three kinds of communication modes: (1) point-to-point (traffic flow between two nodes); (2) point-to-multipoint (traffic flow from a node to multiple nodes); (3) multipoint-to-point (traffic flow from multiple nodes to a central node). The nodes and their interconnected links in LLN are constrained. That is the nodes are limited in memory capacity, processing power, and energy. Moreover, the interconnected links are characterized by lower data transmission rate, lower bandwidth, higher packet loss ratio, and worse network stability compared with other kind of wireless connected links. Furthermore, an IPv6 routing protocol for low-power and lossy networks (RPL) [3; 4; 5; 6], proposed by Internet Engineering Task Force (IETF), is known as a dedicated routing algorithm being utilized for LLNs.  
Despite the fact that RPL can support widespread applications for LLNs, it is still under improvement. In consequence, a novel algorithm of RPL based on a chaotic genetic algorithm (RPL-CGA) is proposed. RPL-CGA can significantly ameliorate the network performance of LLNs. The following 6 points are the major contributions of this paper.  
1. A composition metric is proposed, which simultaneously considers packet queue length in the buffer, end-to-end delay, residual energy ratio of nodes, number of hops, and expected transmission count (ETX) when selecting preferred parents (the next hop). These five routingmetrics have a significant effect on routing decisions. Hence, to choose the best paths, all of the routing metrics mentioned above should be evaluated comprehensively.
2. The routing algebra theoretical framework which ensures consistency, optimality, and no-loop for the newly-proposed routing metrics is analyzed.
3. RPL-CGA uses a chaotic genetic algorithm to determine weighting factors of routing metrics in composition metrics to comprehensively evaluate candidate parents (neighbors) when selecting preferred parents. In this way, the best weighting factors allocation scheme can be obtained. Then, among many candidate parents (neighbors), the optimal preferred parent (the next hop) can be selected.
4. A new holistic objective function is proposed. This objective function can provide a better description of the optimal routes to destination nodes, and can also provide a more comprehensive evaluation of candidate parents when selecting preferred parents.
5. A new method for calculating the rank values of nodes is proposed. Here, rank values are used to construct the network topologies and select the preferred parent (the next hop).
6. Simulation studies of RPL-CGA and several typical existing relevant routing algorithms are carried out in this paper. Simulation results demonstrate that RPL-CGA is superior to these typical existing relevant routing algorithms, and can obtain considerable enhancement on network performance of LLNs in the aspects of average end-to-end delay, average success rate, etc.  
The rest of this paper is structured as follows: Section 2 explains RPL and the recent research works related to our problems. Section 3 introduces the new created RPL-CGA algorithm and analyzes network performances in detail. Section 4 gives the experiment consequences and analysis of RPL-CGA and another popular related algorithm. Finally, this paper is discussed and concluded with future research directions in Sections 5 and 6.