CN105357745B - Wireless sensor network self-organizing dormancy method based on cellular Automation Model - Google Patents
Wireless sensor network self-organizing dormancy method based on cellular Automation Model Download PDFInfo
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- 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
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a kind of wireless sensor network self-organizing dormancy method based on cellular Automation Model, using cellular automata mechanism simulation wireless sensor network, has the characteristics that structure is simple, is easy to realize on computers, can be with simple rule-revealing wireless sensor network complexity global property.The sensor node of random placement in wireless sensor network is mapped to the cellular in cellular automata by the present invention, based on designed transformation rule, conversion of each sensing node according to active or the dormant state, neighbor node and the energy residual of itself of its neighbor node relatively to control oneself state.In the case where sensor node distribution is more intensive, the present invention can reduce system capacity consumption under the premise of guaranteeing network topology connectivity and spreadability.
Description
Technical field
The invention belongs to technical field of communication network, and in particular to a kind of wireless sensor based on cellular Automation Model
The design of self-organization of network dormancy method.
Background technique
Wireless sensor network (wireless sensor networks, WSNs) is a large amount of within a certain area by disposing
Microsensor node composition, mode forms the ad hoc network system of a multi-hop to node by wireless communication.Its mesh
Be cooperation perception, acquisition and processing network's coverage area in monitored target be sent to observer.Since it is with low function
Consumption, low cost, the ability of self-organizing, and be widely used.
It is normally operated in due to sensor node in the severe or even dangerous remote environment that people can not approach, is supplied by battery
Electricity should not supplement energy using the mode of replacement battery, and the energy can not substitute, and in order to extend the life cycle of whole network, need
A large amount of redundant node is used, and them is enabled successively to work.It can be seen that energy consumption problem is wireless sensor network research
Therefore how key problem uses rational mechanism so that sensing node is entered suspend mode shape in turn while guaranteeing transmission reliability
State reduces its energy consumption, so that the life span for extending whole network is of great significance to wireless sensor network energy conservation.
Reducing wireless sensor network energy consumption can be from the node number and reduction communication network for reducing participation information transmitting
Two angles of communication link carry out.Based on this two o'clock, by running the self-organized algorithm of wireless sensor network, network will be produced
A raw subtopology, wireless sensor network self-organized algorithm can be divided into two kinds of removal redundant link and removal redundant node
Self-organized algorithm.The self-organized algorithm of removal redundant link only needs the opposite communication link simplified to carry out communication data transfer.
The node that the self-organized algorithm of removal redundant node only needs a part of performance parameter to be more conform with carries out information exchanging process.Its
In second of self-organized algorithm the node being removed can be made to be in sleep pattern to save energy, while remaining child node collection
A backbone network is formed to carry out data transmission.Wireless sensor network proposed by the present invention based on cellular Automation Model is certainly
Tissue dormancy method belongs to the self-organized algorithm of removal redundant node.
Cellular automata (cellular automata, CA) is defined in one with discrete, finite state by cellular
Composition spatially, according to certain local rule synchronizes the dynamic system to develop in discrete time dimension.Cellular is in microstructure layer
Face is interacted by simple rule, shows a kind of emergent colonization macroscopically.As collection mathematics, physics, biology and
The frontier area of the multi-crossed disciplines such as systematic science, in recent years, cellular automata are also applied to the research of network behavior, and
Because its structure is simple, it is easy to realize on computers, it can be with the global property of simple rule-revealing complexity, it has also become research
The important tool of self-organizing system temporal and spatial evolution.
Cellular automata is by four-tuple A={ Ld, S, N, f } and it constitutes, wherein LdFor cellular space, (d is the dimension in cellular space
Number), S be node state set, N is neighbor node set, f is state transition rules.Conventional based on cellular automata mould
In the WSN self-organization nodes suspend mode algorithm of type, network node generallys use " life according to distributed in grid, state transition rules
Game " rule, is in work/dormant state node number according to neighbor node to determine the state of itself subsequent time, or
Person only considered the energy residual situation of node and neighbor node, and both of which is incomplete.And it is usually tied in each period
Beam will carry out the judgement of state conversion, however in real network, in terms of the off/on operation of node is each energy consumption
More expensive a part, so continually state conversion increases the consumption of energy instead.
In order to evaluate the quality of wireless sensor network self-organized algorithm, the two-dimentional cellular of Wireless Sensor Networks is defined
The performance indicator of automaton model are as follows:
(1) network gross energy: in particular point in time, the summation of dump energy contained by all nodes；
(2) degree of communication: the node of all connections accounts for the ratio of total node, and (active node has active neighbor node, then
Think that the node has connectivity)；
(3) coverage: in particular point in time, all live-vertexs monitor percentage shared by region；
(4) the sensor node sum survived: in particular point in time, there are also the sensor node of dump energy sums；
(5) network lifetime: elapsed time is exhausted to all node energies since emulation；
(6) system Cost Index: in particular point in time, all node total numbers in active state and entire wireless sensing
The ratio of the number of nodes of device network, reflection system consumption energy number.
Summary of the invention
The purpose of the present invention is to solve the self-organizing suspend mode based on cellular Automation Model conventional in the prior art
Method proposes a kind of wireless biography based on cellular Automation Model in terms of reducing system capacity consumption and incomplete problem
Sensor self-organization of network dormancy method.
The technical solution of the present invention is as follows: a kind of wireless sensor network self-organizing suspend mode side based on cellular Automation Model
Method, comprising the following steps:
S1, sensing region is built；
S2, building cellular Automation Model；
S3, initialization node state；
S4, initialization Node Timer；
S5, each node obtain self attributes；
S6, judge whether otherwise the node there is also survival terminates if then entering step S7 to wireless sensor network
Adjusting；
S7, judge whether Node Timer is 0, if then sensor node judges the state at itself next moment, weight
Node Timer and return step S5 are set, the state that otherwise sensor node keeps itself original and return step S5.
Further, step S1 includes: setting sensing region size L, obtains existing sensor node sum
Communication distance Rc between NodeAmount and each sensor node of setting.
Further, step S2 include it is following step by step:
S21, cellular space is determined；
S22, neighbor node set is defined；
S23, definition node state set；
S24, definition status transformation rule.
Further, step S21 specifically:
The sensing region built in step S1 is mapped as cellular space first, is divided into the grid of L × L, each
Communication distance Rc of the distance of grid between sensor node, node random placement inside, if in grid include sensor section
Point, then map the two, and initialization cellular energy is the primary power of node, if not including sensor section in grid
This grid is then corresponded to the cellular for being converted to that an initialization energy is 0 by point；Thus cellular space is obtained are as follows:
L={ Ci,j|i,j∈Z,0≤i≤L,0≤j≤L}。
Further, neighbor node set N in step S22i,jIs defined as:
Further, step S23 interior joint state set is defined as:
S={ Si,j∈{0,1}|Ci,j∈ L }, wherein Si,jIndicate node Ci,jState, 0 and 1 respectively indicates at the node
In suspend mode or active state.
Further, state transition rules include: in step S24
Transformation rule 1: all the sensors node is constantly in active state, until depleted of energy；
Transformation rule 2: if certain moment sensor node has more than one neighbours to be in active state, it by oneself
State is set to dormant state；
Transformation rule 3: " Life of Game's " rule；
Transformation rule 4: judging whether sensor node has neighbor node to be in active state, if then the node is next
A period enters suspend mode；Otherwise the node enters active state with certain Probability p, enters dormant state with the probability of (1-p).
Further, step S4 specifically:
Node Timer is arranged to the arbitrary value in 1 to 5 time quantums, Node Timer with each period knot
Beam successively decreases 1, until being kept to 0.
Further, the self attributes of step S5 interior joint include node ID, node location, the state of node, residue
The value of energy and Node Timer.
Further, in step S7 when Node Timer is decremented to 0, sensor node judges according to transformation rule 4
The state at oneself next moment.
The beneficial effects of the present invention are: the wireless sensor node of random placement is mapped to mole type of rule by the present invention
Shape CA, but be special CA, so that node can be with random distribution；Frequent switching state and judgement are avoided using timer
On conflict；It joined energy state on the basis of wireless sensor network self-organized algorithm based on cellular automata in the past
Parameter avoids the unbalanced of dump energy between node, the segmentation of network caused by respective nodes premature death occurs, using up network can
It can uniformly consume energy, to reach better network performance.
Detailed description of the invention
Fig. 1 is the wireless sensor network self-organizing dormancy method process provided by the invention based on cellular Automation Model
Figure.
Fig. 2 is the flow chart step by step of step S2 of the present invention.
Fig. 3 is four kinds of transformation rule lower network residue gross energy schematic diagrames.
Fig. 4 degree of communication schematic diagram between each sensor node under four kinds of transformation rules.
Fig. 5 is the coverage schematic diagram of four kinds of transformation rule lower sensor networks.
Fig. 6 is the sensor node sum schematic diagram survived in four kinds of transformation rule lower networks.
Fig. 7 is system Cost Index schematic diagram under four kinds of transformation rules.
Specific embodiment
The embodiment of the present invention is further described with reference to the accompanying drawing.
The present invention provides a kind of wireless sensor network self-organizing dormancy method based on cellular Automation Model is such as schemed
Shown in 1, comprising the following steps:
S1, build sensing region: setting sensing region size L, obtain existing sensor node sum NodeAmount with
And the communication distance Rc between each sensor node of setting.
In the embodiment of the present invention, sensing region size L=100, sensor node sum NodeAmount=5000 are each to pass
Communication distance Rc=1 between sensor node.
S2, building cellular Automation Model.
As shown in Fig. 2, the step include it is following step by step:
S21, it determines cellular space: the sensing region built in step S1 being mapped as cellular space first, is divided
For the grid of L × L, communication distance Rc of the distance of each grid between sensor node, node random placement inside, if side
Include sensor node in lattice, then map the two, initialization cellular energy is the primary power of node, if in grid
Not comprising sensor node, then this grid is corresponded to and be converted to an initialization energy as 0 cellular, and initialized energy and be not
0 cellular all includes a node number.Thus cellular space is obtained are as follows:
L={ Ci,j|i,j∈Z,0≤i≤L,0≤j≤L}。
Irregular cellular Automation Model can be converted to simple mole of type structure cell model using the method.
S22, neighbor node set is defined.
Node Ci,jNeighbor node set Ni,jIs defined as:
In the embodiment of the present invention, neighbor node is 8 adjacent cellulars, and different from traditional neighbor node, some is adjacent
Occupying the possible primary power of node is just 0.
S23, definition node state set: S={ Si,j∈{0,1}|Ci,j∈ L }, wherein Si,jIndicate node Ci,jState,
0 and 1, which respectively indicates the node, is in suspend mode or active state.
S24, definition status transformation rule.
State transition rules include:
Transformation rule 1: all the sensors node is constantly in active state, until depleted of energy.
Transformation rule 2: if certain moment sensor node has more than one neighbours to be in active state, it by oneself
State is set to dormant state.
Transformation rule 3: " Life of Game's " rule, i.e., according to neighbor node be in work/dormant state node number come
Determine the state of itself subsequent time.
Transformation rule 4: judging whether sensor node has neighbor node to be in active state, if then the node is next
A period enters suspend mode；Otherwise the node enters active state with certain Probability p, enters dormant state with the probability of (1-p).
S3, initialization node state.
In L × L cellular, only NodeAmount are actually to have corresponded to sensor node, by this
Randomly init state is active or suspend mode to NodeAmount node.
S4, initialization Node Timer.
Sensor node always sets the Node.timer timer of itself in init state or after having switched state
The arbitrary value being set in 1 to 5 time quantums, Node Timer successively decreases 1 with the end in each period, until being kept to 0；Each
In period, the node that timer is 0 executes a state transition rules to determine the state of subsequent time, and completes timing and think highly of
It sets, until this circulation can continue to that node energy exhausts.
S5, each node obtain self attributes.
Each node is obtained according to a mole type cellular automata network model, the node state of initialization and Node Timer
Self attributes, the value including node ID, node location, the state of node, dump energy and Node Timer.
S6, judge whether otherwise the node there is also survival terminates if then entering step S7 to wireless sensor network
Adjusting.
S7, judge whether Node Timer is 0, if then sensor node judges the state at itself next moment, weight
Node Timer and return step S5 are set, the state that otherwise sensor node keeps itself original and return step S5.
In the embodiment of the present invention, when Node Timer is decremented to 0, sensor node has 4 kinds of transformation rules to judge certainly
The state at oneself next moment, i.e. transformation rule 1-4 defined in step S24.First three transformation rule in existing paper and
There is elaboration in patent, the state at oneself next moment is carried out now to be judged according to transformation rule 4 to sensor node
Detailed description:
Proper CA can only have a state parameter, still, in practical applications, can have multiple state ginsengs
Amount.The dump energy of node is also thought of as the state parameter of node by the present invention.In state transition rules, if average_
Energy is the average residual energy of certain node and its neighbor node, then has following formula:
Wherein, E is the dump energy of this node,For the gross energy of this node neighbor node；N is neighbor node
Number.In the embodiment of the present invention, consideration be each node neighbours be adjacent 8 grids in node, can be by formula (1)
Conversion are as follows:
Transformation rule 4 are as follows:
Wherein each variable meaning are as follows: energy_left indicates that this residue energy of node, sum_energy indicate neighbor node
Remaining gross energy, average_energy indicate that the average residual energy of this node and neighbor node, sum_state indicate neighbours
The sum of node state, state indicate node current period state, and next_state indicates the state of node next cycle.
The factor, i.e. A=are enlivened using the ratio of energy_left and average_energy as what state was converted
energy_left/average_energy.Value range (by Limit Analysis) be (0,9], a left side is opened the right side and is closed.Rand (1) is to obey
0 to 1 is uniformly distributed, and is uniformly distributed so rand (1) × A obeys 0 to A.A is bigger, and the probability of rand (1) A > 0.5 * is also got over
Greatly, when but ratio is smaller, that is, the dump energy of this node is few, would not directly turn as existing transformation rule
For active state, but more maximum probability in a dormant state.Enliven the influence that factors A converts state:
(1) as A < 0.5: rand (1) × A > 0.5 probability is that 0, rand (1) × A < 0.5 probability is 1.
(2) as A > 0.5: rand (1) × A > 0.5 probability is (A-0.5)/A=1-0.5/A, rand (1) × A <
0.5 probability is 0.5/A.
Further, operation is defined:
So in the case where sum_state=0, node is p=(1-0.5/ in the probability that next period is active state
A)+, it is 1-p=1- (1-0.5/A) in the probability that next period is dormant state+。
The advantages of to further illustrate transformation rule 4 proposed by the invention, now the simulation parameter according to shown in following table into
Row emulation:
Emulation obtain network gross energy under four kinds of transformation rules, degree of communication, coverage, survival sensor sum, be
Cost Index difference unite as shown in figure 3 to figure 7.
From the figure 3, it may be seen that network gross energy successively decreases at any time, this can be explained by the use of linear decrease function.
Only use different transformation rules, the time that energy is able to maintain is also different, in other words, nodes can survive when
Between it is different.
By Fig. 4 and Fig. 5 it is found that in transformation rule 1, all nodes are all constantly in working condition, so degree of communication and covering
Cover degree is all very high, and only the energy of node exhausts quickly, and network is dead.It is right using transformation rule 2 in other three transformation rules
The network connectivity answered is less than the degree of communication using network when transformation rule 4, and the degree of communication using network when transformation rule 4 is less than
Using the degree of communication of network when transformation rule 3.But when using transformation rule 3, node is also dead quickly；Using transformation rule 4
When network coverage curve it is the most smooth, with illustrating no node unexpected mortality causes network to divide.
It will be appreciated from fig. 6 that all nodes are in synchronization death, because of their initial cell energy in transformation rule 1
Equally, active state is at when the network operation again.And in other three transformation rules, in transformation rule 2, node is compared with latter two
It is dead most fastly；In transformation rule 3, sensor node number has bust at the last moment, the reason is that always because of these nodes
Suspend mode till now, may is that because Node distribution do not reach relative to communication distance it is intensive degree, to reach " life
The active state of game " becomes can not；And in transformation rule 4, node obtains death curve also the most smoothly, illustrates that node is all
It makes the best use of everything, and saves energy when guaranteeing coverage and connection degree ground as far as possible.
As shown in Figure 7, in transformation rule 1, when Network Survivability, all nodes work always, so system Cost Index
It is maximum；Transformation rule 2 and transformation rule 4 compare, it can be seen that the corresponding Cost Index of transformation rule 4 is slightly smaller, that is, more
Save energy；It is minimum using system Cost Index when transformation rule 3, but be to sacrifice connection degree, coverage as cost.
The present invention combines two states parameter it can be seen from simulation result: active/dormant state of neighbours, itself
And the dump energy of neighbours, it is all shown on balancing energy, network coverage, node degree of communication, network lifetime excellent
Gesture, and special mole type structure is not confined between grid but also network model is more flexible.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field
Those of ordinary skill disclosed the technical disclosures can make according to the present invention and various not depart from the other each of essence of the invention
The specific variations and combinations of kind, these variations and combinations are still within the scope of the present invention.
Claims (4)
1. a kind of wireless sensor network self-organizing dormancy method based on cellular Automation Model, which is characterized in that including with
Lower step:
S1, sensing region is built；
Between size L in sensing region is set, obtains existing sensor node sum NodeAmount and each sensor node of setting
Communication distance Rc；
S2, building cellular Automation Model；
S21, cellular space is determined；
The sensing region built in step S1 is mapped as cellular space first, is divided into the grid of L × L, each grid
Communication distance Rc of the distance between sensor node, node random placement inside, if in grid including sensor node,
The two are mapped, initialization cellular energy is the primary power of node, will if not including sensor node in grid
This grid is corresponding to be converted to the cellular that an initialization energy is 0；Thus cellular space is obtained are as follows:
L={ Ci,j|i,j∈Z,0≤i≤L,0≤j≤L}；
S22, neighbor node set is defined；
Neighbor node set Ni,jIs defined as:
S23, definition node state set；
S24, definition status transformation rule；
S3, initialization node state；
S4, initialization Node Timer；
Node Timer is arranged to the arbitrary value in 1 to 5 time quantums, and Node Timer is passed with the end in each period
Subtract 1, until being kept to 0；
S5, each node obtain self attributes；
The self attributes of node include node ID, node location, the state of node, dump energy and Node Timer
Value；
S6, judge whether otherwise the node there is also survival terminates the tune to wireless sensor network if then entering step S7
Section；
S7, judge whether Node Timer is 0, if then sensor node judges the state at itself next moment, resetting section
Point timer and return step S5, the state and return step S5 that otherwise sensor node keeps itself original.
2. wireless sensor network self-organizing dormancy method according to claim 1, which is characterized in that the step S23
Interior joint state set is defined as:
S={ Si,j∈{0,1}|Ci,j∈ L }, wherein Si,jIndicate node Ci,jState, 0 and 1 respectively indicate the node be in stop
Dormancy or active state.
3. wireless sensor network self-organizing dormancy method according to claim 1, which is characterized in that the step S24
Middle state transition rules include:
Transformation rule 1: all the sensors node is constantly in active state, until depleted of energy；
Transformation rule 2: if certain moment sensor node has more than one neighbours to be in active state, it is by the state of oneself
It is set to dormant state；
Transformation rule 3: " Life of Game's " rule；
Transformation rule 4: judging whether sensor node has neighbor node to be in active state, if then the node is in next week
Phase enters suspend mode；Otherwise the node determines the state of next cycle by enlivening factors A；
Wherein, as A < 0.5: rand (1) × A > 0.5 probability is that 0, rand (1) × A < 0.5 probability is 1；
As A > 0.5: rand (1) × A > 0.5 probability is (A-0.5)/A=1-0.5/A, rand (1) × A < 0.5 probability
For 0.5/A；
Node is p=(1-0.5/A) in the probability that next period is active state+, it is in the probability that next period is dormant state
1-p=1- (1-0.5/A)+, in formula, define operation
4. wireless sensor network self-organizing dormancy method according to claim 3, which is characterized in that in the step S7
When Node Timer is decremented to 0, sensor node judges the state at oneself next moment according to transformation rule 4.
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