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Dynamic self-adaptive compressed sensing data collection method for network topology structure

A network topology and dynamic self-adaptive technology, applied in the field of data collection, can solve problems such as uneven spatial distribution, bottlenecks, and fast energy consumption, and achieve the effects of good compression sensing recovery performance, improved performance, and avoiding network bottlenecks

Active Publication Date: 2020-03-27
湖南工商大学
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Problems solved by technology

However, there are two main problems in this scheme: first, it assumes that each node has the global location information of all nodes
However, this scheme does not fundamentally solve the convergence problem of directed random walks
Its random walk path nodes are still unevenly distributed in space, and there is still the problem that the energy consumption near the SINK node is too fast, which is easy to form a network bottleneck

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  • Dynamic self-adaptive compressed sensing data collection method for network topology structure
  • Dynamic self-adaptive compressed sensing data collection method for network topology structure
  • Dynamic self-adaptive compressed sensing data collection method for network topology structure

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Embodiment Construction

[0049] Concrete realization process of the present invention is as follows:

[0050] 1. Network initialization, layer number generation and creation of two types of neighbor node sets

[0051] In the present invention, the traditional solution is firstly used to deploy sensor nodes. In the initialization phase, a node hierarchy number layer ID and two neighbor node sets are generated for each node, and these two neighbor node sets are the upper layer neighbor node set S u and the set of neighbor nodes at the same level S c . The two neighbor node sets of each node respectively reflect the relative positional relationship between the node and the neighbor nodes in the two types of node sets.

[0052] According to the hierarchical numbers of all nodes and the relative positional relationship reflected by the two types of neighbor node sets, we can obtain a ring-level hierarchical network topology. The hierarchical network topology is centered on SINK, and each node is locate...

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Abstract

The invention discloses a dynamic self-adaptive compressed sensing data collection method for a network topology structure. The method comprises the following steps: (1) nodes are deployed and initialized; (2) double random walk is realized through the combination of same-layer random walk and cross-layer random walk; (3) based on dual random walk, a compressed sensing measurement process and a measurement result synchronous uploading process are completed; and (4) the SINK node performs compressed sensing data recovery. The method gets rid of dependence on global position coordinates; directed random walk and routing node spatial distribution non-uniformity compensation are realized at the same time through dual random walk; the spatial distribution uniformity of random walk path nodes isgood, the bottleneck problem caused by the convergence effect can be avoided, and meanwhile, the data recovery precision is higher; unification of compressed sensing measurement and measurement result transmission is realized, dependence on a static transmission path is eliminated, and network topology dynamic adaptive performance is good.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, and relates to a method for realizing data collection by combining compressed sensing technology and dynamic routing technology. Background technique [0002] A typical wireless sensor network (WSN) usually includes a SINK node and a large number of wireless sensor nodes. Sensor nodes are usually powered by batteries and have limited energy reserves. Improving energy utilization efficiency is an important research content. Compressive sensing has been widely used in wireless sensor networks for efficient data acquisition. It combines data acquisition and data compression to go beyond the traditional Nyquist-Shannon boundary by mining sparse structures in sensor data. [0003] The topology of wireless sensor networks usually has certain dynamic characteristics. WSNs are usually deployed in unattended environments, even in harsh environments. The wireless network signal environment is us...

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Application Information

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IPC IPC(8): H04B17/382H04W40/04H04W40/24H04W84/18
CPCH04W40/04H04W40/24H04W84/18H04B17/382
Inventor 张平唐艳艳陈荣元
Owner 湖南工商大学
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