All robot navigation needs to solve such a problem: how does the robot know the way to the destination. In wireless sensor networks, information communication routing between wireless nodes is also a problem to be solved first. As mentioned earlier, due to the fixed geographic information, spatial path planning and information communication routing in WiME can work in the same way. Therefore, the following path planning is used to explain such a route storage and query mode selection problem.
In wireless sensor networks, due to energy constraints, wireless nodes use low-power embedded processors, which have limited computing power and storage space. WiME is no exception. Generally, it is not possible to directly store path information or store map information on nodes to calculate the optimal path when needed. For this, first consider the following 4 methods.
Method 1: As a commonly used method, you can query the route information of the entire map. Due to the large number of rooms n (think n is not less than 1000), the route data is huge (there are n (n-1) / 2 routes), such a map can be provided by one or more main servers. Any wireless node or a limited number of neighboring nodes cannot satisfy such storage capacity. A natural method is to store the global map on the server, and the robot terminal downloads the route information from the server when necessary. This is similar to how GPS devices work.
Method 2: According to the broadcast wireless routing communication protocol used, establish a wireless communication link to the target point, and use the established communication line as a geographic navigation line.
Method 3: Using the idea of ​​dynamic path planning, each node stores geographic information related to itself within a certain range and generates optimal path information.
Method 4: Each node stores the geographical information and connection relationship of the global node distribution, and cooperates with the neighboring nodes to calculate the optimal path when necessary. This is based on the concept of distributed computing in computer networks.
Each method has its own advantages and disadvantages. The first method is easy to modify, adding or deleting nodes only needs to be updated on the main server side. The second method does not need to know the geographic location information of the node in advance, and the entire path information is dynamically established and modified. The third method can dynamically adjust the optimal path according to the road conditions. Since the nodes can observe the road information in real time, parameters can be introduced to reflect the current surrounding road conditions, such as the degree of road congestion, and thus dynamically maintain such an optimal path table that includes itself and neighboring areas. However, these three methods are all completed in the case of multi-hop communication. Returning complete path information requires more communication bandwidth and longer communication delay, which challenges the robustness of the communication protocol. The storage capacity of the fourth method is relatively small, the same order of magnitude as the number of nodes, but the real-time distributed calculation of the optimal path of multi-node collaboration is undoubtedly a difficult problem for wireless sensor nodes. After all, current distributed computing is still limited to the field of computer networks. How to apply the ideas of distributed computing and the latest grid computing to wireless sensor networks may become the next direction in the field of embedded systems.
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