CN104378797A - Collaborative awareness node scheduling method for Internet of Things for manufacturing - Google Patents

Collaborative awareness node scheduling method for Internet of Things for manufacturing Download PDF

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CN104378797A
CN104378797A CN201410605776.6A CN201410605776A CN104378797A CN 104378797 A CN104378797 A CN 104378797A CN 201410605776 A CN201410605776 A CN 201410605776A CN 104378797 A CN104378797 A CN 104378797A
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CN104378797B (en
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程良伦
刘军
王涛
王建华
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Guangdong University of Technology
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
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Abstract

本发明公开了一种制造物联网协同感知节点调度方法，针对制造业物联网不确定性事件的动态变化特征与感知节点可用性动态变化特点，设计一种协同感知节点调度方法。本发明主要方法包括有：概率感知模型下的多节点协同感知模型，基于权威节点的协同感知调度方法，基于性价比的节点选择策略。有效提高了复杂应用环境中的感知精度以及延长了网络生命周期。

The invention discloses a scheduling method for collaborative sensing nodes of the manufacturing Internet of Things. Aiming at the dynamic change characteristics of uncertain events of the manufacturing Internet of Things and the dynamic changing characteristics of the availability of sensing nodes, a collaborative sensing node scheduling method is designed. The main method of the present invention includes: a multi-node cooperative perception model under the probability perception model, a cooperative perception scheduling method based on authoritative nodes, and a node selection strategy based on cost performance. It effectively improves the perception accuracy in complex application environments and prolongs the network life cycle.

Description

一种制造物联网协同感知节点调度方法A method for scheduling cooperative sensing nodes of manufacturing Internet of Things

技术领域technical field

本发明涉及物联网领域，更具体地，涉及一种制造物联网协同感知节点调度方法。The present invention relates to the field of the Internet of Things, and more specifically, to a scheduling method for collaborative sensing nodes of the Manufacturing Internet of Things.

背景技术Background technique

针对制造物联网络部署场景中感知节点异构分布，不确定性事件的动态变化特征与感知节点可用性动态变化特点，研究面向不确定性事件的可靠感知的节点调度优化机制与快速收敛的高效寻优方法是很有必要的。Aiming at the heterogeneous distribution of sensing nodes in the deployment scenario of the manufacturing Internet of Things, the dynamic change characteristics of uncertain events and the dynamic change characteristics of sensing node availability, the node scheduling optimization mechanism for reliable sensing of uncertain events and the efficient search for fast convergence are studied. An optimal method is necessary.

发明内容Contents of the invention

本发明的目的是提出一种感知精度稿和网络生命周期长的制造物联网协同感知节点调度方法，是依据事件位置感知节点自适应调度完成对目标事件的协同感知。The purpose of the present invention is to propose a scheduling method for cooperative sensing nodes of the manufacturing Internet of Things with high perception precision and long network life cycle, which is based on the adaptive scheduling of event location sensing nodes to complete the cooperative sensing of target events.

本发明的技术方案为：Technical scheme of the present invention is:

一种制造物联网协同感知节点调度方法，是依据事件位置感知节点自适应调度完成对目标事件的协同感知，方法包括以下步骤：A method for scheduling collaborative sensing nodes of the manufacturing Internet of Things, which is based on adaptive scheduling of event location sensing nodes to complete the collaborative sensing of target events. The method includes the following steps:

S1.当存在节点检测到事件后，进行广播，各节点判断事件P是否为自身感知区域；S1. When the existing node detects the event, it broadcasts, and each node judges whether the event P is its own sensing area;

S2.基于泛洪方式，不断交换信息得知事件P周围有能力感知的节点集合SP；S2. Based on the flooding method, constantly exchange information to know the set of nodes S P around the event P that are capable of sensing;

S3.选择权威节点s0，负责调度事件P的协同感知；S3. Select the authoritative node s 0 to be responsible for the collaborative perception of scheduling event P;

S4.权威节点s0向事件节点集合中交换信息，各自报告与事件距离，剩余能量，自身当前任务，权威节点s0对事件的能力节点依据性价比进行排序；S4. The authoritative node s 0 exchanges information with the event node set, and each reports the distance from the event, remaining energy, and its own current tasks. The authoritative node s 0 sorts the ability nodes of the event according to the cost performance;

S5.依据性价比从高到底，选择其中n个节点的协同感知值大于门限值η，而n-1个节点则小于门限值η，将这些节点作为协同节点队列。S5. According to the cost performance from high to low, select n nodes whose cooperative perception value is greater than the threshold value η, and n-1 nodes are smaller than the threshold value η, and use these nodes as a cooperative node queue.

在一种优选的方案中，步骤S3是依据事件区域的远近选择最近的节点作为权威节点s0负责感知任务的协同调度。In a preferred solution, step S3 is to select the nearest node as the authoritative node s0 according to the distance of the event area to be responsible for the cooperative scheduling of the sensing task.

在一种优选的方案中，在步骤S4中，对事件有感知能力的节点依据距离，剩余能量，当前自身任务来定义性价比，其性价比定义公式为φ代表节点被选择为事件感知节点的性价比；Γ(d,e)表示节点对于事件e，距离为d的感知精度；Er为节点剩余能量；T为自身其他感知任务；C为感知节点通信成本；α,β依据需求设置的权值。In a preferred solution, in step S4, the node with the ability to perceive the event defines the price-performance ratio according to the distance, remaining energy, and current self-task, and the price-performance ratio definition formula is φ represents the price/performance ratio of a node selected as an event-aware node; Γ(d,e) represents the sensory accuracy of a node for an event e at a distance of d; E r is the remaining energy of the node; T is its own other sensing tasks; C is the sensory node communication Cost; α, β are weights set according to requirements.

在一种优选的方案中，在步骤S5中对于协同节点的选择，选取其中性价比最高的节点进行协同。In a preferred solution, in step S5, for the selection of cooperative nodes, the node with the highest cost performance is selected for coordination.

本发明的有益效果是：所提出的一种制造物联网节点协同感知调度方法，依据任务当前位置，剩余能量，当前任务以及感知能力选择节点协同。依据概率感知模型，研究多节点协同感知模型。结合节点感知距离，剩余能量以及当前任务进行性价比排序选择最高的性价比使得网络任务能耗均衡，延长网络寿命。另外通过感知概率门限值的设定，确保多节点协同感知的感知精度，满足对不确定事件的监测需求。与已有的可靠感知调度方法相比，其感知精度和网络生命周期都有明显优势。The beneficial effects of the present invention are: the proposed method for cooperative perception scheduling of manufacturing Internet of Things nodes selects node coordination according to the current position of the task, the remaining energy, the current task and the sensing ability. Based on the probabilistic perception model, the multi-node cooperative perception model is studied. Combining the perceived distance of nodes, remaining energy, and current tasks for cost-effective sorting, the highest cost-effectiveness is selected to balance the energy consumption of network tasks and prolong network life. In addition, through the setting of the sensing probability threshold, the sensing accuracy of multi-node cooperative sensing is ensured to meet the monitoring requirements for uncertain events. Compared with the existing reliable perceptual scheduling methods, its perceptual accuracy and network life cycle have obvious advantages.

附图说明Description of drawings

图1是本发明的随机节点分布示意图。Fig. 1 is a schematic diagram of random node distribution in the present invention.

图2是本发明的随机节点协同感知精度比较图。Fig. 2 is a comparison diagram of random node collaborative sensing accuracy in the present invention.

图3网络协同感知时生命周期比较图。Fig. 3 Life cycle comparison diagram of network collaborative sensing.

具体实施方式Detailed ways

下面结合附图对本发明做进一步的描述，但本发明的实施方式并不限于此。实施例1The present invention will be further described below in conjunction with the accompanying drawings, but the embodiments of the present invention are not limited thereto. Example 1

针对制造业物联网应用特点，感知节点存在不可靠性，重点研究多节点对不确定事件的协同感知调度方法。本发明主要包括有：概率感知模型下的多节点协同感知模型，基于权威节点的协同感知调度方法，基于性价比的节点选择策略。According to the application characteristics of the Internet of Things in the manufacturing industry, the perception nodes are unreliable, and the research focuses on the multi-node cooperative perception scheduling method for uncertain events. The invention mainly includes: a multi-node cooperative perception model under the probability perception model, a cooperative perception scheduling method based on authoritative nodes, and a node selection strategy based on cost performance.

协同覆盖方法是从应用需求的角度出发，考虑节点感知概率的融合，通过综合多个节点的感知结果，给出被监测目标点的监测结果，使其满足应用的监测感知概率要求。这里我们相对节点的感知概率引入节点的漏检概率，定义为The collaborative coverage method starts from the perspective of application requirements, considers the fusion of node perception probabilities, and provides the monitoring results of the monitored target points by synthesizing the sensing results of multiple nodes, so that it can meet the monitoring and perception probability requirements of the application. Here we introduce the missed detection probability of the node relative to the perceived probability of the node, which is defined as

m(i,j)＝1-c(i,j)         (1)m(i,j)=1-c(i,j) (1)

这里的c(i,j)表示节点i对被监测目标j的感知概率。这样当多个节点同时对被监测区域内某一个目标点进行监测时，依据概率中的交集的思想，此时网络对于该目标点的协同漏检率可以被定义为Here c(i,j) represents the perception probability of node i to the monitored target j. In this way, when multiple nodes monitor a certain target point in the monitored area at the same time, according to the idea of intersection in probability, the cooperative missed detection rate of the network for this target point can be defined as

mm cc (( ii )) == ΠΠ jj ∈∈ SS mm (( ii ,, jj )) == ΠΠ jj ∈∈ SS (( 11 -- cc (( ii ,, jj )) )) -- -- -- (( 22 ))

其中S为被监测区域中所有节点的集合，同时假设节点对目标区域的感知是相互独立的。由此式可知，可以通过多个节点的协同降低对目标点的漏检率。Among them, S is the set of all nodes in the monitored area, and it is assumed that the nodes' perception of the target area is independent of each other. It can be seen from the formula that the missed detection rate of the target point can be reduced through the cooperation of multiple nodes.

在概率模型中，目标被节点检测到的概率与目标和节点之间的距离及方向有关，本实施例采用的数学模型为一般概率模型，监测点j被放置在点i处类型为k传感器节点检测到的概率为：In the probability model, the probability that the target is detected by the node is related to the distance and direction between the target and the node. The mathematical model used in this embodiment is a general probability model. The monitoring point j is placed at point i and the type is k sensor node The probability of detection is:

pp ijkijk == ee -- αα kk dd ijij -- -- -- (( 33 ))

其中αk为类型为k传感器的参数，其值取决于传感器的类型。dij为点i和点j之间的欧氏距离。点j未能被放置在i点处类型为k传感器节点检测到的概率为：Among them, α k is a parameter of type k sensor, and its value depends on the type of sensor. d ij is the Euclidean distance between point i and point j. The probability that point j cannot be detected by a sensor node of type k placed at point i is:

点不能被所有传感器节点检测的概率为：The probability that a point cannot be detected by all sensor nodes is:

pp ′′ == ΠΠ ii == 11 NN ΠΠ kk == 11 KK (( 11 -- xx ikik pp ijkijk )) ≤≤ LL -- -- -- (( 55 ))

L为概率上界0<L<1.将上式两边取对数进行变换可得L is the upper bound of the probability 0<L<1. Take the logarithm on both sides of the above formula and transform it to get

&Pi;&Pi; ii == 11 NN &Pi;&Pi; kk == 11 KK lnln (( 11 -- xx ikik pp ijkijk )) &le;&le; lnln LL -- -- -- (( 66 ))

由于所以可变为because so can be changed to

&Pi;&Pi; ii == 11 NN &Pi;&Pi; kk == 11 KK lnln (( 11 -- pp ijkijk )) xx ikik &le;&le; lnln LL -- -- -- (( 77 ))

则在概率模型下：Then under the probability model:

aijk＝-ln(1-pijk)，bj＝-ln L        (8)a ijk ＝-ln(1-p ijk ), b j ＝-ln L (8)

当i＝j时，pijk＝piik＝1，(1-pijk)＝0，对数无意义，令pijk＝0.999。When i=j, p ijk =p iik =1, (1-p ijk )=0, the logarithm is meaningless, let p ijk =0.999.

当检测到事件时，距离事件最近的节点定义为权威节点，它调度周围节点其他节点协同感知。When an event is detected, the node closest to the event is defined as the authoritative node, which schedules other nodes around the node to coordinate perception.

1.假如权威节点足够资源和能力能够对事件进行有效感知时，此时协同感知退化为单一感知，即仅权威节点对目标事件进行感知。1. If the authoritative node has sufficient resources and capabilities to effectively perceive the event, then the collaborative perception degenerates into a single perception, that is, only the authoritative node perceives the target event.

2.假如权威节点无法有效感知时，需要调度周围节点对目标时间进行协同感知，以及周围节点本身任务，能量，时间距离等因素综合考虑，使得协同感知效率最大化和网络生命周期最大化。2. If the authoritative node cannot effectively perceive, it is necessary to schedule the surrounding nodes to perform cooperative perception of the target time, and comprehensively consider the surrounding nodes' own tasks, energy, time distance and other factors to maximize the efficiency of cooperative perception and maximize the network life cycle.

认为节点都是自私的，即当无其他节点或机制调度事件周围节点协同时，节点仅感知离自身周最近事件信息，或者即使采集其他远离事件信息也不会传输或处理。因此需要协同调度机制完成对事件的可靠感知。It is considered that nodes are all selfish, that is, when there is no other node or mechanism to coordinate the coordination of nodes around the event, the node only perceives the nearest event information around itself, or even collects other event information that is far away from it, and will not transmit or process it. Therefore, a cooperative scheduling mechanism is required to complete reliable perception of events.

协同感知思想是在事件周围存在有若干个对事件具有感知能力的感知节点，选择其中一个作为权威节点，其作为协同感知的领导者负责调度周围节点对目标事件感知协同。在协同时需要考虑使得保证整体协同感知事件的感知精度大于门限值η，其次选择节点协同时还需要考虑节点距离事件距离d，距离与感知精度和感知能耗有关；需要考虑感知节点自身其他感知任务T；需要考虑感知节点剩余能量Er；以及感知节点通信成本C。The idea of collaborative sensing is that there are several sensing nodes around the event that have the ability to perceive the event, and one of them is selected as the authoritative node, which is responsible for scheduling the surrounding nodes to perceive the target event as the leader of the collaborative sensing. During collaboration, it is necessary to consider ensuring that the perception accuracy of the overall cooperative perception event is greater than the threshold value η. Secondly, when selecting nodes for coordination, it is also necessary to consider the distance d between the node and the event. The distance is related to the perception accuracy and perception energy consumption; Sensing task T; the remaining energy E r of the sensing node needs to be considered; and the communication cost C of the sensing node.

综合以上考虑节点性价比排序，然后按从高到底累加感知概率求和，当感知概率满足门限值时停止。Based on the above considerations, the cost-effective ranking of nodes is considered, and then the sum of the perception probabilities is accumulated from high to low, and stops when the perception probability meets the threshold value.

关于性价比φ公式定义：Regarding the definition of cost-effective φ formula:

&phi;&phi; == &Gamma;&Gamma; (( dd ,, ee )) ++ EE. rr (( EE. rr -- &alpha;T&alpha;T )) ++ &beta;C&beta;C -- -- -- (( 99 ))

φ代表节点被选择为事件感知节点的性价比；Γ(d,e)表示节点对于事件e，距离为d的感知精度；Er为节点剩余能量；T为自身其他感知任务；C为感知节点通信成本；α,β依据需求设置的权值。φ represents the price/performance ratio of a node selected as an event-aware node; Γ(d,e) represents the sensory accuracy of a node for an event e at a distance of d; E r is the remaining energy of the node; T is its own other sensing tasks; C is the sensory node communication Cost; α, β are weights set according to requirements.

协同感知节点调度方法：Collaborative sensing node scheduling method:

输入：事件坐标P(x,y)Input: event coordinates P(x,y)

输出：协同节点队列Sc Output: collaborative node queue S c

1.各节点判断事件P是否为自身感知区域；1. Each node judges whether the event P is its own perception area;

2.若是，则向周围节点广播；2. If so, broadcast to surrounding nodes;

3.不断交换信息得知事件P周围有能力感知的节点集合SP；3. Constantly exchange information to know the set S P of nodes capable of sensing around the event P;

4.选出其中事件区域内最近的节点作为权威节点s0，负责调度事件P的协同感知；4. Select the nearest node in the event area as the authoritative node s 0 , responsible for the collaborative perception of scheduling event P;

5.s0向事件节点集合中交换信息，各自报告与事件距离，剩余能量，自身当前任务；5. s 0 exchanges information with the event node set, and each reports the distance from the event, remaining energy, and its current task;

6.权威节点对事件的能力节点依据性价比进行排序；6. The authoritative node sorts the ability nodes of the event according to the cost performance;

7.依据性价比从高到底，选择其中n个节点的协同感知值大于门限值η，而n-1个节点则小于门限值η，将这些节点假如协同节点队列Sc；7. According to the cost performance from high to low, select n nodes whose collaborative perception value is greater than the threshold value η, and n-1 nodes are smaller than the threshold value η, and these nodes are assumed to be the cooperative node queue S c ;

8.返回协同节点队列Sc；8. Return to the collaborative node queue S c ;

本实施例所提出的一种制造物联网节点协同感知调度方法，依据任务当前位置，剩余能量，当前任务以及感知能力选择节点协同。依据概率感知模型，研究多节点协同感知模型。结合节点感知距离，剩余能量以及当前任务进行性价比排序选择最高的性价比使得网络任务能耗均衡，延长网络寿命。另外通过感知概率门限值的设定，确保多节点协同感知的感知精度，满足对不确定事件的监测需求。与已有的可靠感知调度方法相比，其感知精度和网络生命周期都有明显优势。A method for cooperative perception and scheduling of nodes in the manufacturing Internet of Things proposed in this embodiment selects the coordination of nodes according to the current position of the task, the remaining energy, the current task and the perception capability. Based on the probabilistic perception model, the multi-node cooperative perception model is studied. Combining the perceived distance of nodes, remaining energy, and current tasks for cost-effective sorting, the highest cost-effectiveness is selected to balance the energy consumption of network tasks and prolong network life. In addition, through the setting of the sensing probability threshold, the sensing accuracy of multi-node cooperative sensing is ensured to meet the monitoring requirements for uncertain events. Compared with the existing reliable perceptual scheduling methods, its perceptual accuracy and network life cycle have obvious advantages.

以上所述的本发明的实施方式，并不构成对本发明保护范围的限定。任何在本发明的精神原则之内所作出的修改、等同替换和改进等，均应包含在本发明的权利要求保护范围之内。The embodiments of the present invention described above are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims (4)

1.一种制造物联网协同感知节点调度方法，是依据事件位置感知节点自适应调度完成对目标事件的协同感知，其特征在于，所述方法包括以下步骤：1. A manufacturing Internet of Things collaborative sensing node scheduling method is to complete the collaborative sensing of the target event according to the adaptive scheduling of the event position sensing node, and it is characterized in that the method comprises the following steps: S1.当存在节点检测到事件后，进行广播，各节点判断事件P是否为自身感知区域；S1. When the existing node detects the event, it broadcasts, and each node judges whether the event P is its own sensing area; S2.基于泛洪方式，不断交换信息得知事件P周围有能力感知的节点集合SP；S2. Based on the flooding method, constantly exchange information to know the set of nodes S P around the event P that are capable of sensing; S3.选择权威节点s0，负责调度事件P的协同感知；S3. Select the authoritative node s 0 to be responsible for the collaborative perception of scheduling event P; S4.权威节点s0向事件节点集合中交换信息，各自报告与事件距离，剩余能量，自身当前任务，权威节点s0对事件的能力节点依据性价比进行排序；S4. The authoritative node s 0 exchanges information with the event node set, and each reports the distance from the event, remaining energy, and its own current tasks. The authoritative node s 0 sorts the ability nodes of the event according to the cost performance; S5.依据性价比从高到底，选择其中n个节点的协同感知值大于门限值η，而n-1个节点则小于门限值η，将这些节点作为协同节点队列。S5. According to the cost performance from high to low, select n nodes whose cooperative perception value is greater than the threshold value η, and n-1 nodes are smaller than the threshold value η, and use these nodes as a cooperative node queue. 2.根据权利要求1所述的物联网协同感知节点调度方法，其特征在于，步骤S3是依据事件区域的远近选择最近的节点作为权威节点s0负责感知任务的协同调度。2. The Internet of Things cooperative sensing node scheduling method according to claim 1, wherein step S3 is to select the nearest node as the authoritative node s0 according to the distance of the event area to be responsible for the cooperative scheduling of sensing tasks. 3.根据权利要求1所述的物联网协同感知节点调度方法，其特征在于，在步骤S4中，对事件有感知能力的节点依据距离，剩余能量，当前自身任务来定义性价比，其性价比定义公式为φ代表节点被选择为事件感知节点的性价比；Γ(d,e)表示节点对于事件e，距离为d的感知精度；Er为节点剩余能量；T为自身其他感知任务；C为感知节点通信成本；α,β依据需求设置的权值。3. The Internet of Things collaborative sensing node scheduling method according to claim 1, characterized in that, in step S4, the nodes that have the ability to perceive the event define the cost performance according to the distance, the remaining energy, and the current self-task, and the cost performance definition formula for φ represents the price/performance ratio of a node selected as an event-aware node; Γ(d,e) represents the sensory accuracy of a node for an event e at a distance of d; E r is the remaining energy of the node; T is its own other sensing tasks; C is the sensory node communication Cost; α, β are weights set according to requirements. 4.根据权利要求1所述的物联网协同感知节点调度方法，其特征在于，在步骤S5中对于协同节点的选择，选取其中性价比最高的节点进行协同。4. The method for scheduling cooperative sensing nodes of the Internet of Things according to claim 1, characterized in that, for the selection of cooperative nodes in step S5, the node with the highest cost performance is selected for coordination.
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