Abstract
Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.
Highlights
Mobile autonomous vehicles based on wireless multi-sensor network have been extensively applied in precision agriculture [1,2], e.g., in order for vehicles to have the capability of weeding, fertilization, yielding analysis and conducting other agriculture activities, wireless multi-sensor networks [3,4] can monitor the vehicle condition, real-time position and implement feature detection on a crop field which can be used for autonomous vehicle navigation [5]
The main contributions of this paper are as follows:. This new architecture implements a connection state service to detect in real-time the state of vehicle sensor networks, achieves smooth stateful matching according to different scenario states and extends self-learning as a new OpenFlow action to enhance packet-processing capability in the network data plane
We primarily focus on the performance of failure recovery and handoff from Software Defined Networking (SDN) controller connection loss
Summary
Mobile autonomous vehicles based on wireless multi-sensor network have been extensively applied in precision agriculture [1,2], e.g., in order for vehicles to have the capability of weeding, fertilization, yielding analysis and conducting other agriculture activities, wireless multi-sensor networks [3,4] can monitor the vehicle condition, real-time position and implement feature detection on a crop field which can be used for autonomous vehicle navigation [5]. The main contributions of this paper are as follows: This new architecture implements a connection state service to detect in real-time the state of vehicle sensor networks, achieves smooth stateful matching according to different scenario states and extends self-learning as a new OpenFlow action to enhance packet-processing capability in the network data plane. This new architecture maintains the compatibility with OpenFlow, and the flexibility and scalability of SDN.
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