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Joint Path Selection and Power Allocation Method for Energy Harvesting Nodes in Wireless Sensor Networks

A wireless sensor network, energy harvesting technology, applied in power management, wireless communication, energy reduction and other directions, can solve the problem of insufficient consideration of energy harvesting factors, performance improvement, joint resource allocation performance optimization, etc.

Inactive Publication Date: 2019-07-26
唐山市汉维科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] Aiming at the problems that the existing wireless sensor network node and gateway resource allocation methods do not fully consider the performance improvement brought about by energy harvesting factors, the performance optimization of joint resource allocation, and the practical application of low-complexity algorithms, the present invention proposes a wireless sensor The joint path selection and power allocation method of energy harvesting nodes in the network, considering the joint resource allocation of joint sensor nodes and gateways, combined with the energy saving scheme brought by energy harvesting technology, assists low-complexity iterative algorithms, and maximizes the network between communication nodes. performance

Method used

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  • Joint Path Selection and Power Allocation Method for Energy Harvesting Nodes in Wireless Sensor Networks

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Embodiment 1

[0073] The present invention is aimed at a special application scene, comes from practical application, and the scene setting is meticulous and reasonable, and has practical guiding significance. Specifically, a joint path selection and power allocation method for energy harvesting nodes in a wireless sensor network, comprising the following steps:

[0074] Step 1: System scenario analysis, problem description;

[0075] Consider a non-real-time data transmission scenario of a wireless sensor network based on energy harvesting. In the scenario, there is a sensor node A with stable power supply, a relay node R for energy harvesting and a target communication sensor node B. Consider sensor node A with stable power supply There is a direct path between the sensor node B and the target communication, and an energy harvesting relay node R can cooperate to forward information. The energy harvesting relay node R adopts the decoding and forwarding working mode. A transmission process i...

Embodiment 2

[0112] In order to make the algorithm closer to the actual application, it can better reflect the channel situation and be closer to the real channel. Further improvement on the basis of the foregoing embodiment one. Specifically, the use of the Bernoulli model is to simplify the calculation, which can better reflect the situation of the channel and is closer to the actual channel.

[0113] Said step 2 includes:

[0114] Step 2.1: Determine whether the decoding is successful

[0115]

[0116] In order to reduce the computational complexity, the Bernoulli process is used to describe whether the decoding is successful or not, where p is the parameter of the Bernoulli process, and D i Indicates whether the i-th transmission is successfully decoded, D i =1 means successful decoding, D i =0 means decoding failure, and the Bernoulli parameter p is expressed as:

[0117]

[0118] Step 2.2: We again define the link selection result

[0119] where r i = 1 means that the ...

Embodiment 3

[0128] In order to make the algorithm closer to practical application, we adopt a simplified energy harvesting curve, which can better reflect the real model of energy harvesting, simple and effective. It can be used on the basis of the foregoing embodiment one. The energy harvesting model uses a Bernoulli process, specifically,

[0129]

[0130] Among them: E represents the basic energy unit, Represents the energy collected in the i-th time slot, then the average energy collected in each time slot can be expressed as:

[0131]

[0132] Among them: ρ represents the parameters of the Bernoulli process, 2T p Indicates the length of a time slot; correspondingly, the energy causal constraint can be expressed as:

[0133]

[0134] For simplicity, let's assume that the battery capacity of the relay is large enough:

[0135]

[0136]

[0137] Among them, ε i+1 Indicates the remaining energy in the battery after the i+1th transmission slot, (x) + Indicates the fu...

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Abstract

The invention discloses a joint path selection and power distribution method for energy collection nodes in a wireless sensor network, and belongs to the field of cooperative communication technologies. The method comprises the steps of analyzing a system scene, describing problems; establishing a system mathematic model; and then finding the optimal solution by using an optimization method. The method aims at the special application scene and is derived from the actual application, and being different from the traditional independent sensor node or gateway resource distribution, the method comprehensively considers the joint power distribution and path selection of sensor nodes and gateways, and maximizes the handling capacity performance of the communication nodes by using the gateways as relay stations. According to the method of the invention, the solution of an optimization problem is processed by using convex optimization to convert a target function of the optimization problem without approximate calculation, so that computation complexity is greatly reduced while accuracy of the problem is not influenced, and delay generated by system overheads is reduced; a Lagrangian multiplier method is used in an optimizing process, and thus an optimizing speed is rapid; a subgradient method and an incremental step length are used in an iterative process, so that optimizing is more accurate.

Description

technical field [0001] The invention belongs to the technical field of cooperative communication, and more specifically relates to a joint path selection and power distribution method for energy collection nodes in a wireless sensor network. Background technique [0002] The sensor network integrates sensor technology, embedded computing technology, computer and wireless communication technology, and distributed information processing technology, and can monitor, sense, and collect information about various environments or monitoring objects in real-time in a collaborative manner through various integrated micro-sensors , process the information through the embedded system, and transmit the perceived information to the user terminal through the self-organizing wireless communication network, so as to truly realize the concept of "ubiquitous computing". A wireless sensor network is a network composed of multiple nodes with sensors, data processing units and communication modu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W40/22H04W52/24H04W52/26H04W52/46
CPCH04W40/22H04W52/241H04W52/267H04W52/46Y02D30/70
Inventor 梁广俊
Owner 唐山市汉维科技有限公司
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