CN101018235A - Radio sensor network data convergence path planning method based on the intelligent agent - Google Patents
Radio sensor network data convergence path planning method based on the intelligent agent Download PDFInfo
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Abstract
The intelligent agent based wireless sensor network data aggregation path optimization method comprises: in wireless sensor network environment, generating the primary agent initial route, and allocating executive agent; deciding the route by executive agent; reporting to system; collecting and updating system information by the primary agent; re-calculating an optimized route; collecting data and state message by the executive agent. This invention belongs to instructive strategy, saves energy consumption on the aggregation node, and improves network performance.
Description
Technical field
The present invention is a kind of being used in wireless sensor network environment, adopts the cooperative information treatment technology of intelligent agent, realizes the technical scheme of data aggregate path optimization in the net.Present technique belongs to network information Distributed Calculation application.
Background technology
The develop rapidly of low-consumption wireless electrical communication technology, embedded computing technique, microsensor technology and integrated circuit technique and increasingly mature, make a large amount of, microsensor organizes themselves into wireless sensor network by Radio Link and becomes a reality cheaply.Wireless sensor network has characteristics such as self-organization, adaptivity and fault-tolerance height, in many key areas such as military affairs, industrial or agricultural, biologic medical, environmental monitoring prospect that is widely used.Because sensor node quantity is big and random distribution, adjacent transducer is monitored the data that obtained to same incident and is had similitude, and sensor node is limited on energy, memory space and computing capability, therefore being transmitted in to a certain degree of redundant data will consume too much energy, shorten the life cycle of whole network.For avoiding the problems referred to above, sensor network needs to use data aggregation technique in collecting data procedures, soon carry out integrated treatment from the data of multisensor node, draws more accurate complete information.
Intelligent agent is the product that distributed computing technology and artificial intelligence technology combine.Be a program entity in essence, have certain intelligence and judgement.It can move between network node according to certain rules under the control of oneself, seeks and the processing adequate resources, and representative of consumer is finished specific task.After wherein mobile agent is created in an execution environment, can carries oneself state and code and in network, move to recovery execution in another environment.
In handling, wireless sensor network data introduces intelligent agent technology, utilize technical specification that data are retained on the local node, and integrated treatment process (code) is transferred on the back end, to reduce the live load of network, solve network delay, improve stability and fault-tolerant ability.Intelligent agent can reduce wireless sensor network perception data redundancy, reduces the data traffic of wireless sensor network, saves energy of wireless sensor network, effectively prolongs the life cycle of whole network system.
Summary of the invention
Technical problem: the purpose of this invention is to provide a kind of intelligence software inter-entity cooperative information of utilizing and handle, solve the radio sensor network data convergence method for optimizing route based on intelligent agent of wireless sensor network polymerization Path selection problem, different with the data aggregate correlation technique that has found out at present, this method is a kind of inspiration tactic method, the method that the application of the invention proposes can be controlled the energy consumption of aggregation to a certain extent, improves the overall performance of network.
Technical scheme: method of the present invention is a kind of method that inspires tactic, agency by introducing several specific functions also utilizes the evolutionary computation method to propose, and its target is to cooperate to the decomposition of the optimized choice of sensing data aggregation, polymerization task and by the communication between the agency by the agency with realizing minimizing of wireless sensor network converging operation institute's energy requirement.
Provide the definition of three special mobile agents below:
Master agent (MasterAgent, MA): built-in monitoring/parser, reside in sensor network data aggregation node (Sink), be used to collect the message that resides in information agency issue on the node, optimize assignment and the establishment and the distributed tasks execution agency of data aggregate task.
The execution agency (Perform Agent, PA): be distributed to destination node by the MA establishment, data dispatching polymerization task executions, and the polymerization forwarding of data is finished in the information agency on the collaborative destination node.
Information agency (Information Agent, IA): reside on the sensor node, be responsible for to observe the operation of node, and for carry out the agency provide about this node location, with adjacent node distance, information such as dump energy.
One, architecture
The method of distribution of the distributed nature of combining with wireless sensor network data and data aggregate task mainly comprises two parts based on the network data convergence architecture of intelligent agent: intelligent agent entity and intelligent agent service environment, as shown in Figure 1.
(1) intelligent agent service environment is the core of system, a location transparency is provided, is convenient to control, safe and reliable data aggregate execution environment.It mainly is responsible for establishment, operation, hang-up, the termination of intelligent agent, the work of transmission and reception etc.This service environment carries into execution a plan according to the initiating task that presets, and determines the migration and strategy of cooperating and transport communication mechanism of intelligent agent.
(2) intelligent agent entity comprises that service broker t and courier act on behalf of two classes, is the assignment and the executor of network data convergence task.The service broker resides at the intelligent agent service environment, and the visit support and the assignment decisions of resource and basic environment is provided for the courier agency.The service broker sends Agent according to mobile agent control protocol (MACP, mobileagent control protocol), and controls its behavior according to the network data state information.The courier agency moves between the network data source, utilizes the purpose that the control of resource is satisfied the remote data source visit.
Two, method flow
The perception data of each node of sensor network has distributed nature because node is in image data amount, the difference of transmission on the data payload, so each node in the data aggregate process dump energy and the deal with data ability on have dynamic differential.The introducing of agency mechanism, realization, are generated and carry out the agency according to sensing node and the network state information known by master agent, prefabricated execution agency's of while migration path.Mobile agent by with the information agency collaborative work that resides at sensor node, according to certain criterion (energy consumption, polymerization time delay), select the data aggregate route scheme of a cover optimum (or suboptimum) to move at working space.Carry partial status information and aggregated data in the transition process.The planning problem in data aggregate path can be modeled as a constrained optimization problem.
The groundwork flow process is as shown in Figure 2:
1) after the master agent initialization route, generates and carry out the agency, and, assign and carry out the agency according to the data aggregate task.
2) carry out after the agency arrives destination node, the state information that provides according to the information agency of resident this node, and the routing policy information of sending out behind the master agent are carried out the decision-making of polymerization task.
3) master agent is according to the event message that is generated by the data aggregate incident, updating system information.Recomputate select route after, the agency is carried out in distribution, and the execution agency that the policy information notice has been sent out.
4) after the execution agency works in coordination with and finishes the data aggregate task, carry partial data result and state code and return aggregation node.
The selection in data aggregate path, is called " population (population) " beginning search procedure promptly from one group of initial solution that produces at random based on evolutionary programming algorithm.Each individuality of population is that one of problem is separated, and is called " chromosome (chromosome) ", and chromosome is a string symbol.These chromosomes are constantly evolved in successive iterations, are called heredity.For in forming, according to selecting the part offspring just when (Fitness) size, eliminating the part offspring at each, is constant thereby keep the population size.Just when the selected probability height of high chromosome.Through after some generations, algorithmic statement becomes problem optimal solution or suboptimal solution in best chromosome like this.
1. coding method
When the sensor node i that is in certain monitored area monitors target, when obtaining perception data, oneself ID is sent to aggregation node.After master agent is known ID, upgrade built-in vector table S.Disposed the perception data situation of sensor node among the S in each element representation zone, S
i(i=1 ... N) be defined as:
The data aggregate cost of node is σ
i(i=1 ... N), expected data polymerization cost is .If N
i tExpression i node data quantity transmitted after polymerization, N
r tThe data volume of receiving before the polymerization of expression node, E
t aThe power consumption values that the expression node data is handled, then the data aggregate cost is defined as the data volume of polymerization and the ratio of consumed energy, following formula:
Consider from network global optimization performance, require the integrated data polymerization cost of node must be not more than .
The data aggregate path planning is mainly considered each node energy consumption harmony of network, so definition cost
iRepresent that i node is used for the energy consumption of transfer of data, and with this nodal distance aggregation node apart from d
i(representing with jumping figure hop usually) is directly proportional cost
i=kd
i, wherein k is a constant factor.
The optimum organization of node is target with the energy of consumes least.Optimization problem can be described as following data model:
Constraints:
Neuroid and genetic algorithm (HNN-GA) strategy is used to solve above-mentioned model.Neuroid definition constraint (4) belongs to a binary coding mode, is expressed as 0 or 1.Network configuration can be described as a figure.Vertex representation neuron, limit are defined as the connection between the neuron.Each neuron is corresponding to certain element S among the vector table S
iBehind each neuron 0 and 1 of random initializtion, neuroid adopts continuous operation modes, gene S
i(t) be illustrated in t neuron S constantly
iState.The correction criterion is as follows:
Chromosome is defined as: V
k=(S
K1S
K2S
KN), wherein k is the chromosome label.
2. fitness function
Fitness function is defined as:
E in the following formula
iBe the power consumption values of i node, be the difference of start node energy and dump energy.
3. selection genetic operator
1. select operator
Selecting operator is the basic operator of genetic algorithm, and its function is to the operation of selecting the superior and eliminating the inferior of the individuality in the colony, and defect individual is bred in the new colony of the next generation.Here adopt fitness than case selection method (also being the wheel disc method), select defect individual.
2. crossover operator
Two chromosome switching part genes that match mutually, thus new individuality formed.Here adopt the single-point interleaved mode, a crosspoint promptly only is set in the individual chromosome string at random, on this position, exchange two chromosome dyad genes that pairing is individual then mutually.During practical operation, by producing the number between 0～1 at random, if this number is less than crossover probability P
c, then this chromosome that matches is mutually intersected on the crosspoint that produces at random.
3. mutation operator
Selection that variation is individual and variation position are established a capital employing method at random really.Usually probability P makes a variation
mBe 10
-4～10
-1In actual mechanical process, produce the number between 0～1 at random, if this number is less than P
m, then to chromosome enterprising row variation operation in the variation position of determining at random.
4. select
Adopt deterministic selection strategy.That is to say,, meet the optimized individual of optimizing criterion and form population of new generation according to all parents of descending and offspring's individuality.
Excellent stroke of method in radio sensor network data convergence path based on intelligent agent of the present invention adopts evolution genetic algorithm and combining of neural network algorithm to optimize the selection of route, and be specific as follows:
The initial route of master agent generates, assigns and carry out the agency:
Step 1). the sensor node that is in certain monitored area monitors target, when obtaining perception data, oneself state information is sent to aggregation node,
Step 2). master agent upgrades built-in vector table according to after the sensor network nodes perception state information of knowing, realizes chromosome coding, produces initial population at random,
Step 3). calculate the ideal adaptation degree, and judge whether to meet the optimization criterion, if meet, export optimized individual and represent optimum Route Selection, and finish to calculate, otherwise turn to step 4,
Step 4). select regeneration individual according to fitness, the individual selected probability height that fitness is high, the individuality that fitness is low is eliminated,
Step 5). according to certain selection, intersection and variation probability and method, generate new individuality,
Step 6). by selecting, intersect the population of new generation that generates, return step 3,
Step 7). master agent is according to selected population rank results, and correspondence and sensing node, these nodes be as the trunk node in data aggregate path,
Step 8). master agent generates carries out the agency, and is distributed to data aggregate trunk node;
Carry out agency's decision-making route:
Step 9). after carrying out agency's arrival destination node, collect the state information that the resident information agency that changes node provides, comprise data volume, the neighbor node state of dump energy, perception and the forwarding of node itself,
Step 10). carry out the agency in follow-up process, collect the routing policy information of sending out behind the master agent,
Step 11). carry out the agency according to above-mentioned information, carry out the decision-making of polymerization task, comprise the data aggregate time slot of the resident aggregation of Collaborative Control, the migration of aggregated data and code,
Step 12). the execution agency carries partial data result and state code and returns aggregation node after working in coordination with and finishing the data aggregate task;
System Reports:
Step 13). the decision-making of carrying out the agency forms the topological structure that system event influences network, and notifies master agent with the form of event notice;
Master agent is collected and updating system information:
Step 14). master agent is collected in the data aggregate process, comes from sensor network nodes and the event message of carrying out the agency, updating system information; Master agent recomputates the route of an optimization:
Step 15). master agent is according to the identical method of above-mentioned initial route, recomputate select to optimize the path after, the agency is carried out in distribution, or the execution agency that routing policy has been distributed with the form of message notice;
Carry out the collection that the agency finishes data and state information:
Step 16). carry out agency's all the other nodes according to route planning strategy continuation access sensors network,
Step 17). the execution agency carries partial data and state information is returned master agent.
Beneficial effect: the inventive method has proposed to utilize intelligent agent to realize the selection new method in radio sensor network data convergence path.Intelligent agent comes down to a kind of network calculations, and it can select place of operation and opportunity voluntarily, interrupts the execution of self as the case may be, moves on another equipment and resumes operation, and in time relevant result is returned.The purpose that moves is to make the as close as possible data source of program implementation, reduces the communication overhead of network, and balanced load improves finish the work ageing.Essence that we can say intelligent agent is the transmission of computing capability, is about to calculate import the data field into, rather than imports data into the calculating district as traditional approach.Intelligent agent technology has: network traffics are connected number of times and time, asynchronous process, dynamic fixation service are provided with network, dynamically assembly behavior, and characteristic and advantages such as seamless integrated, the outstanding mobile computing ability of distributed architecture, tunneling, good adaptability to changes, good steadfastness and fault-tolerance but reduce, and these characteristics make intelligent agent have application prospects in the application of wireless-sensor network distribution type data aggregate.Simultaneously, the present invention incorporates genetic algorithm and acts on behalf of in the optimal path planning.Genetic algorithm essence is a kind of method of efficient, parallel, global search, and it can obtain in search procedure and the knowledge that accumulates relevant search volume automatically, and controls search procedure adaptively in the hope of optimal solution.By the optimal control of evolution genetic algorithm, improved reasonability that aggregation selects to a certain extent with of overall importance, can effectively reach target with minimum energy consumption realization network data convergence.
Description of drawings
Fig. 1 is that intelligent agent is formed structural representation.Comprise among the figure: the communication control processor between mobile Agent service environment, sensor network data source, service agent, courier Agent and the agency.
Fig. 2 is the schematic flow sheet of the inventive method.
Embodiment
For convenience of description, our supposition has following application example:
One, primary data polymerization path generates
Primary data polymerization route planning flow process is as follows:
1. the sensor node that is in certain monitored area monitors target, when obtaining perception data, own state information (node ID, dump energy, with the jumping figure of aggregation apart from) is sent to aggregation node.
2. master agent upgrades built-in vector table S according to after the sensor network nodes perception state information of knowing (node ID).
3. master agent is set initial polymerization cost =1, calculates the actual polymerization cost value σ of given data sensing node
i, transfer of data energy consumption cost
i, K=1 wherein.
4. master agent initial population V at random
k, individual S
iNumber is all known perception data node numbers.Each individuality is expressed as the chromogene coding.
5. calculate the ideal adaptation degree, and judge whether to meet the optimization criterion.If meet, then export the optimal route selection of optimized individual and representative thereof, and finish to calculate.Otherwise change 6.
6. select regeneration individual according to fitness, the individual selected probability height that fitness is high, the individuality that fitness is low may be eliminated.
7. according to selecting probability=0.2, individuality directly copies to the next generation.
8. according to crossover probability=0.75, adopt the single-point cross method, generate new individual.
9. according to variation probability=0.05, adopt random device, generate new individual.
10. by selecting, intersect and variation generation population of new generation, return 5.
Two, the agency is carried out in generation and assignment
1. master agent is according to selected population rank results, the ID of corresponding and sensing node.These nodes are as the trunk node in data aggregate path.
2. master agent generates and carries out the agency.And be distributed to data aggregate trunk node.
Three, carry out the collaborative data aggregate of finishing of agency
1. carry out after the agency arrives destination node, collect the state information that the resident information agency that changes node provides, comprise the data volume, neighbor node state of dump energy, perception and the forwarding of node itself etc.
2. carry out the agency in follow-up process, collect the routing policy information of sending out behind the master agent.
3. carry out the agency according to above-mentioned information, carry out the decision-making of polymerization task, comprise the data aggregate time slot of the resident aggregation of Collaborative Control, the migration of aggregated data and code etc.
The execution agency carries partial data result and state code and returns aggregation node after working in coordination with and finishing the data aggregate task.
Four, master agent continues the selection of polymerization route
1. master agent is collected in the data aggregate process, from the event message of sensor network (node, carry out the agency), updating system information.
2. master agent is according to the identical method of above-mentioned initial route, recomputate select to optimize the path after, the agency is carried out in distribution, or the execution agency that routing policy has been distributed with the form of message notice.
Claims (1)
1. excellent stroke of method in radio sensor network data convergence path based on intelligent agent is characterized in that adopting evolution genetic algorithm and combining of neural network algorithm to optimize the selection of route, and be specific as follows:
The initial route of master agent generates, assigns and carry out the agency:
Step 1). the sensor node that is in certain monitored area monitors target, when obtaining perception data, oneself state information is sent to aggregation node,
Step 2). master agent upgrades built-in vector table according to after the sensor network nodes perception state information of knowing, realizes chromosome coding, produces initial population at random,
Step 3). calculate the ideal adaptation degree, and judge whether to meet the optimization criterion, if meet, export optimized individual and represent optimum Route Selection, and finish to calculate, otherwise turn to step 4,
Step 4). select regeneration individual according to fitness, the individual selected probability height that fitness is high, the individuality that fitness is low is eliminated,
Step 5). according to certain selection, intersection and variation probability and method, generate new individuality,
Step 6). by selecting, intersect the population of new generation that generates, return step 3,
Step 7). master agent is according to selected population rank results, and correspondence and sensing node, these nodes be as the trunk node in data aggregate path,
Step 8). master agent generates carries out the agency, and is distributed to data aggregate trunk node;
Carry out agency's decision-making route:
Step 9). after carrying out agency's arrival destination node, collect the state information that the resident information agency that changes node provides, comprise data volume, the neighbor node state of dump energy, perception and the forwarding of node itself,
Step 10). carry out the agency in follow-up process, collect the routing policy information of sending out behind the master agent,
Step 11). carry out the agency according to above-mentioned information, carry out the decision-making of polymerization task, comprise the data aggregate time slot of the resident aggregation of Collaborative Control, the migration of aggregated data and code,
Step 12). the execution agency carries partial data result and state code and returns aggregation node after working in coordination with and finishing the data aggregate task;
System Reports:
Step 13). the decision-making of carrying out the agency forms the topological structure that system event influences network, and notifies master agent with the form of event notice;
Master agent is collected and updating system information:
Step 14). master agent is collected in the data aggregate process, comes from sensor network nodes and the event message of carrying out the agency, updating system information;
Master agent recomputates the route of an optimization:
Step 15). master agent is according to the identical method of above-mentioned initial route, recomputate select to optimize the path after, the agency is carried out in distribution, or the execution agency that routing policy has been distributed with the form of message notice;
Carry out the collection that the agency finishes data and state information:
Step 16). carry out agency's all the other nodes according to route planning strategy continuation access sensors network,
Step 17). the execution agency carries partial data and state information is returned master agent.
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