| 摘要(英) |
In this era of the Internet of Things, a large number of devices need to be connected to the Internet, from smart home applications to smart city and industries 4.0. It seems to be an infinite business opportunity, but in reality, there is no uniform standard. The 6LoWPAN working group under the Internet Engineering Task Force (IETF) defines an interface between IPv6 and IEEE 802.15.4, enabling a large number of wireless devices to connect directly over IPv6. The ROLL Working Group defines the RPL protocol to make a low-power wireless network based on IPv6. Not only is its low cost, low power consumption, but it is also suitable for large deployments and is compatible with existing Internet protocols. Further, there are already standards to follow.
The RPL protocol uses the "objective function" to define networking behavior. Each node must select the parent node according to the path cost to form a network with the lowest cost. Traditional wireless networks use the simplest "accumulation" for path cost calculations. Adding the cost of each hop on the path is the total cost of reaching the root node. However, the accumulated metrics do not truly reflect the actual cost of the path. For example, the total cost is 6 paths. It can consist of a different set of paths, which can be {2, 2, 2}, {5, 1} or {3, 3}, etc. Assume that the path cost is proportional to the distance of the communication. If you include a higher path cost segment in the set, it will have an adverse effect on communication quality. The accumulated metric cannot distinguish whether the parent node′s path contains long hops. The parent node is frequently replaced with the child node, causing the control message to be transmitted in a large amount in the network, and the packet loss is more serious.
This article will determine the path cost of the parent node from a statistical point of view, using the concept of approximate mean and standard deviation to analyze the path set. Considering the high time complexity of calculating the standard deviation, the mean absolute deviation is used instead, and the cost metric is multicast to neighbor nodes so that the neighbor nodes have enough information to decide the parent node. After the simulation, it is proved that compared with the officially defined two objective functions, under the deployment of high-density nodes, there is a lower packet loss rate, a slightly lower delay, and a slightly increased throughput. It is a big step forward for overall wireless network reliability. |
| 參考文獻 |
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