CN107809764B - Markov chain-based multi-event detection method - Google Patents

Markov chain-based multi-event detection method Download PDF

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CN107809764B
CN107809764B CN201710860491.0A CN201710860491A CN107809764B CN 107809764 B CN107809764 B CN 107809764B CN 201710860491 A CN201710860491 A CN 201710860491A CN 107809764 B CN107809764 B CN 107809764B
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胡亮
任祝
徐伟强
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
    • H04W52/0248—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal dependent on the time of the day, e.g. according to expected transmission activity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to the technical field of wireless sensor networks, and discloses a Markov chain-based multi-event detection method, which comprises the following steps: (1) obtaining parameters in a wireless sensor network; (2) respectively calculating the expectation of the continuous time slot lengths of the single event which continuously occurs and the continuous non-occurrence time slot lengths; determining the length expectation of each updating process and the energy consumed in each updating process; calculating a sleep period of the sensor expected to be obtained by detecting an event to consume electric quantity each time on average; (3) calculating the occurrence number of events detected by the whole system and the occurrence frequency in the total time according to the detection strategy obtained in the step (2); and finally, obtaining the detection efficiency of the whole system and finishing the optimization of the detection efficiency of the system. The invention utilizes Lagrange multiplier method to ensure that the sensor is effectively scheduled in the wireless sensor network so as to improve the detection capability of multiple events in the system and achieve the purpose of reducing the consumption of the sensor and energy as much as possible.

Description

Markov chain-based multi-event detection method
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a Markov chain-based multi-event detection method.
Background
Wireless Sensor Networks (WSNs) are an emerging concept and technology that opens up new application areas. In today's world, sensor technology and sensor networks are considered a very promising research. In order to ensure the accuracy of the whole network, smooth operation of the nodes of the WSN is required. The research of the wireless sensing module with low power consumption has very important learning and research values, and the realization of the function has extremely important theoretical and practical significance.
The main purpose of the WSN is to detect events, and assuming that the probability of occurrence of an event obeys markov chain, it is difficult to detect an event using an energy-limited sensor network due to the randomness of the occurrence of an event, so we should consider the energy level of the sensor and the dynamic events. Most previous studies have focused on the problem of single event detection, and few cases have involved multiple sensors and multiple events. In the present invention, the main task is to detect multiple event points of interest in a non-rechargeable sensor network, with the goal of maximizing the event detection rate. Due to the influence of the geographical environment, the detection range of each sensor is different, some interested points are in the detection range of a plurality of sensors, and other interested points are only covered by a single sensor. Therefore, the various sensors should coordinate their work, optimize the energy ratio of each point of interest, and maximize the efficiency of the network. In order to be able to detect more events and to save energy as much as possible, the sensors generally follow a scheduling method, switching between the active and the dormant state. There is therefore a need for a method for Markov chain based multiple event detection with limited sensor energy.
Disclosure of Invention
The invention provides a Markov chain-based multi-event detection method aiming at the defects in the prior art, and the method considers the Markov chain-based multi-event detection problem in a wireless sensor network, can ensure that a sensor is effectively scheduled in the wireless sensor network so as to improve the detection capability of the multi-event in the system, achieves the aim of reducing the consumption of the sensor and energy as far as possible, and completes the multi-event detection in the wireless sensor network.
In order to solve the above technical problems, the present invention is solved by the following technical solutions.
A Markov chain-based multi-event detection method comprises the following steps:
(1) obtaining system parameters in a wireless sensor network, comprising: sensor SiA battery capacity B; event IjProbability of occurrence Pj(ii) a Event IjProbability a of continuing to occur from t slot occurrence to t +1 slot1Probability a of continuing to not occur from t slot not to t +1 slot2(ii) a Order to
Figure BDA0001414956000000021
And
Figure BDA0001414956000000022
respectively representing the sensor event I when in j time slotskThe likelihood of occurrence to nonoccurrence and nonoccurrence to occurrence; during time T, all sensor pairs IkThe consumed energy is
Figure BDA0001414956000000023
(2) According to the formula
Figure BDA0001414956000000024
A is to1And a2Substituting expected E [ X ] of time slot duration length for calculating single event continuous occurrence respectively1]And expected E X of consecutive non-occurring slot durations2](ii) a According to the formula
Figure BDA0001414956000000025
Determining the length expectation T of each update proceduree(ii) a According to the formula
Figure BDA0001414956000000026
Determining each TeEnergy consumed
Figure BDA00014149560000000210
According to the formula
Figure BDA0001414956000000027
Calculating an expected power consumption a for each event detectedk(ii) a Finally order
Figure BDA0001414956000000028
Deriving a sleep period SI of the sensor, wherein
Figure BDA0001414956000000029
Represents the steady-state probability of an event occurring at T → ∞;
(3) obtaining the detection strategy according to the step (2), and calculating the wholeI detected by the systemkNumber of occurrences
Figure BDA0001414956000000031
IkNumber of occurrences in total time T
Figure BDA0001414956000000032
Finally, the detection efficiency of the whole system is obtained
Figure BDA0001414956000000033
And finally, optimizing the system detection efficiency U by using a Lagrange multiplier method.
Preferably, in step (1), event IjThe probability of occurrence obeys a Markov chain, and a1＞0.5，a2＞0.5。
Preferably, in step (2), Te=t2-t1Wherein t is1And t2Indicating the time slot in which two adjacent sensors enter the sleep state.
Preferably, in step (3), the detection strategy includes: (1) if an event I occurs when the sensor operates, the sensor detects the event; (2) if the event I does not occur in the previous time slot, the sensor in the running state enters a dormant state; (3) the sensor in the sleep state automatically operates at intervals SI to detect whether the event I occurs.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
the method comprises the steps of firstly, obtaining parameters in a wireless sensor network; respectively calculating the expectation of the continuous length of the time slot in which the single event continuously occurs and the continuous time slot does not occur; then respectively determining the length expectation of each updating process and the energy consumed in each updating process; calculating a sleep period of the sensor expected to be obtained by detecting an event to consume electric quantity each time on average; according to the obtained detection strategy, calculating the occurrence number of events detected by the whole system and the occurrence frequency in the total time; and finally, obtaining the detection efficiency of the whole system and finishing the optimization of the detection efficiency of the system. The invention utilizes Lagrange multiplier method to ensure that the sensor is effectively scheduled in the wireless sensor network so as to improve the detection capability of multiple events in the system and achieve the purpose of reducing the consumption of the sensor and energy as much as possible.
Drawings
FIG. 1 is a schematic diagram of a sensor network system model in a Markov chain-based multi-event detection method of the present invention;
FIG. 2 is a schematic diagram of a workflow in a Markov chain-based multiple event detection method according to the present invention;
FIG. 3 is a simulation model diagram of a system in a Markov chain-based multi-event detection method of the present invention;
FIG. 4 is a diagram of an exemplary update cycle in a Markov chain-based multiple event detection method of the present invention;
FIG. 5 is an illustration diagram of the occurrence probability of each event in the Markov chain-based multiple event detection method;
FIG. 6 is an illustration of the detection rate of each event before optimization in a Markov chain-based multiple event detection method of the present invention;
FIG. 7 is an illustration of energy allocation for sensors in a method of estimating performance in a Markov chain-based multiple event detection method of the present invention;
FIG. 8 is an explanatory diagram of the detection rate of each optimized event in the Markov chain-based multiple event detection method.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1 to 8, a Markov chain-based multiple event detection method includes the following steps:
(1) obtaining parameters in a wireless sensor network: sensor SiBattery capacity B of (a); event IjProbability of occurrence Pj(ii) a Event IjProbability a of occurrence of t slot to continued occurrence of t +1 slot1(ii) a Event IjProbability a of not occurring from t slot to t +1 slot2；
(2) Respectively calculateExpected E X of time slot duration length of single event continuous occurrence and continuous non-occurrence1]And E [ X ]2](ii) a Determining the length expectation T of each update proceduree(ii) a Each TeThe consumed energy is
Figure BDA0001414956000000041
Calculating average power consumption expectation a for each detected eventk(ii) a Finally, obtaining a sleep period SI of the sensor;
(3) calculating I detected by the whole system according to the detection strategy obtained in the step (2)kNumber of occurrences Sd；IkNumber of occurrences S within total time T0(ii) a Finally, the detection efficiency of the whole system is obtained
Figure BDA0001414956000000051
And (4) optimizing the system detection efficiency U by utilizing a Lagrange multiplier method.
Detecting events requiring consumption of energy, having Bij{ b sensor i → event j }. According to the step (2), the consumed electric quantity per time slot of each actually detected event is a when the sensor workskTherefore, in the whole area, the detection efficiency of the whole sensor network for the event can be calculated, and for the event IkThe study was carried out:
during time T, all sensor pairs IkThe consumed energy is
Figure BDA0001414956000000052
Event IkThe probability of occurrence is
Figure BDA0001414956000000053
Detected by the whole system IkThe number of occurrences is:
Figure BDA0001414956000000054
Ikthe number of occurrences over the total time T is:
Figure BDA0001414956000000055
πonis the steady state probability of occurrence of a T → ∞ event;
for event IkThe detection efficiency of the system is as follows:
Figure BDA0001414956000000056
the detection efficiency of the whole system is as follows:
Figure BDA0001414956000000057
the invention requires optimization of the whole system with the goal of obtaining the maximum value of U.
Fig. 1 shows a sensor network system model. As shown, there are 6 sensors and 12 event point regions. Event I1Can pass through S1，S2And S4And (6) detecting. That is to say that
Figure BDA0001414956000000058
Therefore, S1，S2And S4Coordination should be coordinated. Our goal is that the energy of all sensors can be made to function to the maximum.
Fig. 2 is a schematic diagram of the workflow in the Markov chain-based multi-event detection method. Specifically, the following can be described:
a Markov chain-based multiple event detection method, comprising the steps of:
(1) obtaining parameters in a wireless sensor network: sensor SiBattery capacity B of (a); event IjProbability of occurrence Pj(ii) a Event IjProbability a of occurrence of t slot to continued occurrence of t +1 slot1(ii) a Event IjProbability a of not occurring from t slot to t +1 slot2；
(2) Calculating expected E [ X ] of continuous occurrence and non-occurrence time slot duration of single event respectively1]And E [ X ]2](ii) a Each time of determinationLength expectation of update procedure Te(ii) a Each TeThe consumed energy is
Figure BDA0001414956000000066
Calculating average power consumption expectation a for each detected eventk(ii) a Finally, obtaining a sleep period SI of the sensor;
(3) calculating I detected by the whole system according to the detection strategy obtained in the step (2)kNumber of occurrences Sd；IkNumber of occurrences S within total time T0(ii) a Finally, the detection efficiency of the whole system is obtained
Figure BDA0001414956000000061
And (4) optimizing the system detection efficiency U by utilizing a Lagrange multiplier method.
The system detection efficiency U obtained in the step (3) can ensure that the sensors are effectively scheduled in the wireless sensor network and the detection capability of multiple events in the system is improved, so that the aim of reducing the consumption of the sensors and energy as much as possible is fulfilled.
The technical solution of the present invention is further illustrated by the following specific examples. In the experiment, the hypothesis is
Figure BDA0001414956000000062
Figure BDA0001414956000000063
Specifically, the following experimental parameters were used:
wherein, the total time T is 10000 slots, the battery capacity B is 10000, and the energy consumption B is 2/slot when the sensor operates. The probability of occurrence of 4 events is:
Figure BDA0001414956000000064
Figure BDA0001414956000000065
by lagrange multiplier method:
Figure BDA0001414956000000071
Figure BDA0001414956000000072
can be solved to obtain: a is1＝2.25，a2＝2.30，a3＝2.30，a4＝3.20
The Markov chain event has a steady state probability of occurrence of
Figure BDA0001414956000000073
Can be solved to obtain:
Figure BDA0001414956000000074
the maximum detection rate can be obtained from the above equations.
FIG. 3 shows a simulation model diagram of a system in a Markov chain-based multi-event detection method.
FIG. 4 is a diagram showing a typical update cycle of a Markov chain-based multiple event detection method, where A denotes sensor operation, I denotes sensor sleep, Y denotes event occurrence, N denotes event non-occurrence, and t denotes1，t2Time slot, t, representing an event from occurrence to non-occurrence1～t2A typical update cycle is shown.
Fig. 5, 6, 7, and 8 are graphs of simulation verification results of the designed method by the Matlab system.
Fig. 5 shows a steady-state occurrence probability chart of events with 4 occurrence probabilities obeying the markov chain, wherein it can be seen that the occurrence probability of event 1 is the highest, so in order to improve the detection rate of the system, more energy is used for detecting event 1. Event 4 occurs with the lowest probability and therefore less energy is used for the detection of event 4.
Fig. 6 shows a plot of the detection rate for each event before system optimization, fig. 7 shows a plot of the redistribution of energy to sensors for system optimization, and fig. 8 shows a plot of the detection rate for each event after system optimization.
The method comprises the steps of firstly, obtaining parameters in a wireless sensor network; respectively calculating the expectation of the continuous length of the time slot in which the single event continuously occurs and the continuous time slot does not occur; then respectively determining the length expectation of each updating process and the energy consumed in each updating process; calculating a sleep period of the sensor expected to be obtained by detecting an event to consume electric quantity each time on average; according to the obtained detection strategy, calculating the occurrence number of events detected by the whole system and the occurrence frequency in the total time; and finally, obtaining the detection efficiency of the whole system and finishing the optimization of the detection efficiency of the system. The invention utilizes Lagrange multiplier method to ensure that the sensor is effectively scheduled in the wireless sensor network so as to improve the detection capability of multiple events in the system and achieve the purpose of reducing the consumption of the sensor and energy as much as possible.
In summary, the above-mentioned embodiments are only preferred embodiments of the present invention, and all equivalent changes and modifications made in the claims of the present invention should be covered by the claims of the present invention.

Claims (4)

1. A Markov chain-based multi-event detection method is characterized by comprising the following steps:
(1) obtaining system parameters in a wireless sensor network, comprising: sensor SiA battery capacity B; event IjProbability of occurrence Pj(ii) a Event IjProbability a of continuing to occur from t slot occurrence to t +1 slot1Probability a of continuing to not occur from t slot not to t +1 slot2(ii) a Order to
Figure FDA0002643764530000011
And
Figure FDA0002643764530000012
respectively representing the sensor event I when in j time slotskThe likelihood of occurrence to nonoccurrence and nonoccurrence to occurrence; during time T, all sensor pairs IkThe consumed energy is
Figure FDA0002643764530000013
(2) Let a be the probability of an event occurring, b be the probability of an event not occurring, and b be 1-a; according to the formula
Figure FDA0002643764530000014
A is to1And a2Substituting expected E [ X ] of time slot duration length for calculating single event continuous occurrence respectively1]And expected E X of consecutive non-occurring slot durations2](ii) a According to the formula
Figure FDA0002643764530000015
Determining the length expectation T of each update procedureeSI is a sleep period; according to the formula
Figure FDA0002643764530000016
Determining each TeEnergy consumed
Figure FDA00026437645300000113
According to the formula
Figure FDA0002643764530000017
Calculating an expected power consumption a for each event detectedkSlot is a time slot; finally order
Figure FDA0002643764530000018
Deriving a sleep period SI of the sensor, wherein
Figure FDA0002643764530000019
Represents the steady-state probability of an event occurring at T → ∞;
(3) obtaining a detection strategy according to the step (2), and calculating the I detected by the whole systemkNumber of occurrences
Figure FDA00026437645300000110
IkOccurring over a total time TNumber of times
Figure FDA00026437645300000111
Figure FDA00026437645300000112
Is the steady state occurrence probability of event k; system to event IkDetection efficiency U ofk＝Sd/S0(ii) a And finally, optimizing the system detection efficiency U by using a Lagrange multiplier method.
2. A Markov chain based multiple event detection method as claimed in claim 1, wherein: in step (1), event IjThe probability of occurrence obeys a Markov chain, and a1＞0.5，a2＞0.5。
3. A Markov chain based multiple event detection method as claimed in claim 1, wherein: t in step (2)e＝t2-t1Wherein t is1And t2Indicating the time slot in which two adjacent sensors enter the sleep state.
4. A Markov chain based multiple event detection method as claimed in claim 1, wherein: in the step (3), the detection strategy includes: (1) if an event I occurs when the sensor operates, the sensor detects the event; (2) if the event I does not occur in the previous time slot, the sensor in the running state enters a dormant state; (3) the sensor in the sleep state automatically operates at intervals SI to detect whether the event I occurs.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102665253A (en) * 2012-04-20 2012-09-12 山东大学 Event detection method on basis of wireless sensor network
CN105191257A (en) * 2013-03-29 2015-12-23 英国电讯有限公司 Method and apparatus for detecting a multi-stage event

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US9357472B2 (en) * 2010-04-27 2016-05-31 International Business Machines Corporation Adaptive wireless sensor network and method of routing data in a wireless sensor network

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102665253A (en) * 2012-04-20 2012-09-12 山东大学 Event detection method on basis of wireless sensor network
CN105191257A (en) * 2013-03-29 2015-12-23 英国电讯有限公司 Method and apparatus for detecting a multi-stage event

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
弱稀疏性下的无线传感器网络事件检测算法;赵秀兰;《计算机应用与软件》;20140506;全文 *
无线传感器网络中一种基于权重模型的高效事件检测技术;许嘉;《计算机研究与发展》;20080626;全文 *

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