CN102404723B - Agent-based self-adaptive collaboration sensory method for wireless sensor network - Google Patents
Agent-based self-adaptive collaboration sensory method for wireless sensor network Download PDFInfo
- Publication number
- CN102404723B CN102404723B CN201110306794.0A CN201110306794A CN102404723B CN 102404723 B CN102404723 B CN 102404723B CN 201110306794 A CN201110306794 A CN 201110306794A CN 102404723 B CN102404723 B CN 102404723B
- Authority
- CN
- China
- Prior art keywords
- node
- application
- sensor node
- data
- reconstruct
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Landscapes
- Mobile Radio Communication Systems (AREA)
Abstract
The basic idea of an agent-based self-adaptive collaboration sensory method for a wireless sensor network is that a plurality of mobile agents are distributed in the network at first. The mobile agents have an application code required by a sensor node; moreover, the mobile agents can share the application code in a way of P2P (Peer-to-Peer). The mobile agents sense change of the surrounding environment therein at any time; if the surrounding environment is changed, the mobile agents judge whether an application is reconstructed according to a reconstruction strategy; if so, a new application code is transmitted to the sensor node which is required to reconstruct the application. When successfully obtaining the required application code, the node enters an execution phase; newly-collected data are transmitted by the mobile agents to a convergent node to converge. The way based on the mobile agents can satisfy application requirements of users in an increasing number; developers not only always develop new application codes according to the requirement, but also release the new application codes on the internet. Simultaneously, owing to the data transmission way based on the mobile agents, the communication load in the network is decreased greatly.
Description
Technical field
The present invention relates to the adaptive cooperation cognitive method based on agency in radio sensing network, basic thought is: first distribute multiple mobile agent in wireless sensor network.Mobile agent has the application code required for sensor node, and can carry out sharing application code by the mode of P2P between mobile agent.The change of the mobile agent surrounding environment at its place of perception at any time, if there occurs change, according to reconstruction strategy, mobile agent judges whether that needs carry out application reconstruct; If needed, then carry out applying the sensor node reconstructed to needs and transmit new application code.After node successfully gets the application code of needs, enter the execution phase, the new data collected is transferred to aggregation node through mobile agent to be polymerized.Mode based on mobile agent can meet the growing application demand of user, and developer develops new application code as required at any time and they is published on mobile agent.Significantly reduce the traffic load in network based on the data transfer mode of mobile agent simultaneously.Belong to computer network field.
Background technology
Wireless sensor network (Wireless Sensor Network) is the wireless network be made up of in Ad Hoc mode one group of transducer, its objective is the information of perceptive object in the geographic area of perception collaboratively, the acquisition and processing network coverage, and be distributed to observer.Participate in collaborative node and " understand others' activity, for the activity of oneself provides relevant information " by collaborative sensing.Wireless sensor network is monitored collaboratively by multiple sensor node, the information of perception and the various environment of collection or monitoring target, then sends these information to aggregation node.In the middle of this process, sensor node needs the change of perception ambient condition at any time to adjust adaptively and changes the application on it.Such as, utilize sensor network to monitor fire in intelligent building, under normal circumstances, sensor network only needs to monitor temperature data; When finding that temperature has abnormal rising, sensor node needs to change its application, starts to gather humidity and smoke data; When certain breaking out of fire time, then sensor node starts the video image gathering scene of fire; After fire condition is removed, sensor network should return to again collecting temperature data mode.During whole monitoring, wireless sensor network needs the application reconstructed adaptively with the change of monitoring of environmental state on it, to adapt to constantly change and unpredictable external environment.
Have two kinds of settling modes at present, first kind of way is by the change of sensor node self perception ambient condition, then judges whether to change application, if needed, then adjusts and changes application code to adapt to new application.The prerequisite of this mode is before sensor network initialization, is just once loaded on transducer by all application codes.But being subject to that transducer self stores, the restriction of computing capability and stand-by time, the mode that this code is once loaded is greatly limited in the middle of the application of reality, also cannot meet the growing application demand of user simultaneously.The second way is also the change being carried out perception ambient condition by sensor node self, is that change is reported to aggregation node by sensor node with the first difference, then carries out application by aggregation node and reconstruct judgement.If needed, then new application code is sent to sensor node.This mode relies on aggregation node to carry out decision-making completely and configures new application, therefore has very high requirement to the performance of aggregation node itself, there is the problem of single point failure.Aggregation node sends in the process of new application code again and consumes more bandwidth simultaneously, adds the traffic load of network.In addition, the change due to environment directly affects the application reconstruct of sensor network, and the so following new problem introduced is exactly the change how wireless sensor network perceives (or study is arrived) environment in real time.
Secondly, wireless sensor network is as the Task network of multi-node collaborative, and multiple node not only will carry out collection and the transmission of data, and will be polymerized.Although traditional security mechanism based on key can ensure the confidentiality of task and data itself, but effectively can not identify the malicious node in wireless sensor network, the data that the agreement of excitation node and help aggregation node select the high node of reliability to provide are polymerized.
Therefore can perception changes in environmental conditions in the urgent need to designing one on resource-constrained sensor network, carry out the adaptive cooperation cognitive method applied reconstruct and complete data aggregate safely and effectively.The method is intended to provide a kind of adaptive safe multi-node collaboration mechanism, the final collaborative sensing problem solved between each sensor node.
Summary of the invention
The object of the present invention is to provide the adaptive cooperation cognitive method based on agency in a kind of radio sensing network.First the method distributes multiple mobile agent in wireless sensor network.Mobile agent is the special ageng of a class, it is except having the fundamental characteristics of ageng---autonomy, response, initiative and inferential except, also there is mobility, namely it can independently move to another main frame from a main frame on network, and representative of consumer completes the task of specifying.Mobile agent has the application code required for sensor node, and can carry out sharing application code by the mode of P2P between mobile agent.The change of the mobile agent surrounding environment at its place of perception at any time, if there occurs change, according to reconstruction strategy, mobile agent judges whether that needs carry out application reconstruct; If needed, then carry out applying the sensor node reconstructed to needs and transmit new application code.After node successfully gets the application code of needs, enter the execution phase, the new data collected is transferred to aggregation node through mobile agent to be polymerized.Advantage of the present invention is that wireless sensor network can the change of perception ambient conditions at any time, carries out adaptively applying reconstructing and ensureing the fail safe of data aggregate.Mode based on mobile agent can meet the growing application demand of user, and developer develops new application code as required at any time and they is published on mobile agent.Significantly reduce the traffic load in network based on the data transfer mode of mobile agent simultaneously.
For achieving the above object, the present invention takes following technical scheme:
Environment sensing and reconstruct decision phase is comprised when the present invention specifically implements; Code performs and the data aggregate stage.First distribute multiple mobile agent in wireless sensor network.The change of mobile agent perception surrounding environment at any time, gather environmental data and according to the domain knowledge in knowledge base, reasoning carried out to the change of ambient condition, judge whether the application task performed by present node is applicable to the change of ambient condition, if inadaptable, then carry out applying the sensor node reconstructed to needs and transmit new application code.Otherwise sensor node continues to perform former application task.In this manner, user only need on mobile agent once property Deployment Knowledge, mobile agent just the change of environmentally state can be reconstructed decision-making adaptively.Wherein reconstructing decision-making is comprised by three of mobile agent functional modules forming knowledge base, knowledge manager and reconstruct trigger.Specific implementation is as Fig. 1.After sensor node successfully gets the application code of needs, enter implementation, gather new data and be transferred to aggregation node by mobile agent and be polymerized, as Fig. 2.Specific practice uses to trust to carry out the behavior of evaluation node, and with the polymerization of its guide data.Every minor node is after completing alternately, and node all can be assessed this mutual behavior each other, and the result obtained is called Three dimensinal LEGION.Three dimensinal LEGION, as initial data, inputs into trust model.The overall confidence level of sensor node, by other nodes of trading activity occurring with it to its Three dimensinal LEGION, and the overall confidence level of these nodes calculates.After every minor node completes transaction, all can upgrade its local confidence level table.The data that the sensor node only having global reputation to reach certain threshold value provides just can be selected to data aggregate, and this mode can be got rid of false data as much as possible to the impact of polymerizing value and encourage the agreement of node.
The adaptive cooperation cognitive method based on agency that the present invention proposes compared with prior art, has following significantly advantage and beneficial effect:
1) can the change of perception ambient conditions at any time based on mobile agent wireless sensor network, carry out application reconstruct adaptively.
2) mode based on mobile agent can meet the growing application demand of user, and developer develops new application code as required at any time and they is published on mobile agent.
3) traffic load in network is significantly reduced based on the data transfer mode of mobile agent.
4) in the process of data aggregate, integrated trust management technology can reduce the node of malice by the chance selected by faith mechanism, is excluded in outside raw data acquisition and transmission.
The adaptive cooperation cognitive method based on agency that the present invention proposes, self adaptation perception changes in environmental conditions also effectively can identify malicious node, encourage the agreement of node and carry out data aggregate safely.The method be intended to provide a kind of can perception the multi-node collaboration mechanism of the safety of adaptive environment change, the final collaborative sensing problem solved between each sensor node.
Accompanying drawing explanation
Fig. 1 reconstructs the functional module of decision-making;
Fig. 2 data are by the transmitting procedure of mobile agent;
Fig. 3 specific implementation process;
Embodiment
Specific implementation process, as Fig. 3, has following characteristics:
Whole wireless sensor network comprises sensor node, mobile agent, environmental data, trust data and application data.
Environment sensing and reconstruct decision phase is comprised when the present invention specifically implements; Code performs and the data aggregate stage.
Environment sensing and reconstruct decision phase;
First distribute multiple mobile agent in wireless sensor network.The change of mobile agent perception surrounding environment at any time, gather environmental data and according to the domain knowledge in knowledge base, reasoning carried out to the change of ambient condition, judge whether the application task performed by present node is applicable to the change of ambient condition, if inadaptable, then carry out applying the sensor node reconstructed to needs and transmit new application code.Otherwise sensor node continues to perform former application task.Wherein, reconstruct decision-making to be comprised by three functional modules of mobile agent and form knowledge base, knowledge manager and reconstruct trigger.Knowledge base stores the predefined domain knowledge of user.It is formula below by the representation of knowledge for knowledge and by rule-based method that ambient condition and transducer need the relation between the application that performs abstract by we:
R→F(or?IF?R?THEN?F)
Wherein, R is the former piece of formula, and represent the environmental condition that this knowledge is relevant, F is the consequent of formula, represents reconstruct behavior, i.e. the environmental condition lower sensor node application that should provide.Knowledge manager is in charge of knowledge, user can define domain knowledge and be left in knowledge base, knowledge manager can use knowledge to carry out to the current environment data gathered the related conclusions that reasoning obtains current environment change, and sends this result to reconstruct trigger.Whether the conclusion that reconstruct trigger compares the environmental change that knowledge manager obtains mates with the application task that current sensor node runs, and then judges whether that needs carry out application reconstruct and how to be reconstructed.If need reconstruct, just carry out applying the sensor node reconstructed to needs and transmit new application code., otherwise continue to keep former application.
Code performs and the data aggregate stage:
After node successfully gets the application code of needs, enter implementation, gather new data and be transferred to aggregation node by mobile agent and be polymerized.Specific practice uses to trust to carry out the behavior of evaluation node, and with the polymerization of its guide data.Every minor node is after completing alternately, and node all can be assessed this mutual behavior each other, and the result obtained is called Three dimensinal LEGION.Three dimensinal LEGION, as initial data, inputs into trust model.The overall confidence level of sensor node, by other nodes of trading activity occurring with it to its Three dimensinal LEGION, and the overall confidence level of these nodes calculates.After every minor node completes transaction, all can upgrade its local confidence level table.The data that the sensor node only having global reputation to reach certain threshold value provides just can be selected to data aggregate, and this mode can be got rid of false data as much as possible to the impact of polymerizing value and encourage the agreement of node.The overall confidence level computing formula of node is as follows:
Wherein V
xthe overall confidence level of representation node x, S is the set that the node of concluding the business occurred with node x.F
yxthe local confidence value of node y to node x.W
ylocal confidence level f
yxweight.The computational process of whole trust adopts the method repeatedly iterated, until V
xconverge to a stable value.
Last it is noted that above embodiment only in order to illustrate the present invention and and unrestricted technical scheme described in the invention; Therefore, although this specification with reference to each above-mentioned embodiment to present invention has been detailed description, those of ordinary skill in the art should be appreciated that and still can modify to the present invention or equivalent to replace; And all do not depart from technical scheme and the improvement thereof of the spirit and scope of invention, it all should be encompassed in the middle of right of the present invention.
Claims (3)
1. in a radio sensing network based on agency adaptive cooperation cognitive method, adopt based on the mode acted on behalf of from the change adapting to perception ambient condition, judge whether that needs carry out application reconstruct, and transmission application code is to sensor node, and use to trust and carry out the behavior of evaluation sensor node, and with the polymerization of its guide data; It is characterized in that comprising the following steps:
Environment sensing and reconstruct decision phase:
The change of mobile agent perception surrounding environment, another sensor node is independently moved to from a sensor node, gather environmental data and according to the domain knowledge in himself knowledge base, reasoning carried out to the change of ambient condition, judge whether the application task performed by present node is applicable to the change of ambient condition, if inadaptable, then carry out applying the sensor node reconstructed to needs and transmit new application code; Otherwise sensor node continues to perform former application task;
Code performs and the data aggregate stage:
Implementation is entered after node successfully gets the application code of needs, gather new data and send to neighbouring mobile agent, and directly transfer data to aggregation node by mobile agent and be polymerized, use the behavior of trusting and carrying out evaluation node, and with the polymerization of its guide data, the data that the sensor node only having global reputation to reach certain threshold value provides just can be selected to data aggregate.
2. in radio sensing network according to claim 1 based on agency adaptive cooperation cognitive method, it is characterized in that: reconstruct decision-making is comprised by three functional modules of mobile agent and forms knowledge base, knowledge manager and reconstruct trigger, and following formula can be adopted to state:
R→F(or?IF?R?THEN?F)
Wherein, R is the former piece of formula, represent the environmental condition that this knowledge is relevant, F is the consequent of formula, represent reconstruct behavior, the i.e. environmental condition lower sensor node application that should provide, mobile agent acquisition environmental data also carries out reasoning according to the domain knowledge in knowledge base to the change of ambient condition, judges whether current sensor node carries out application reconstruct.
3. in radio sensing network according to claim 1 based on agency adaptive cooperation cognitive method, it is characterized in that: use the behavior of trusting and carrying out evaluation sensor node, and with the polymerization of its guide data, the overall confidence level of sensor node, by other nodes of trading activity occurring with it to its Three dimensinal LEGION, and the overall confidence level of these nodes calculates, following formula can be adopted to state:
Wherein V
xthe overall confidence level of representation node x, S is the set that the node of concluding the business occurred with node x, f
yxthe local confidence value of node y to node x, w
ylocal confidence level f
yxweight, the computational process of whole trust adopts the method repeatedly iterated, until V
xconverge to a stable value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110306794.0A CN102404723B (en) | 2011-10-11 | 2011-10-11 | Agent-based self-adaptive collaboration sensory method for wireless sensor network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110306794.0A CN102404723B (en) | 2011-10-11 | 2011-10-11 | Agent-based self-adaptive collaboration sensory method for wireless sensor network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102404723A CN102404723A (en) | 2012-04-04 |
| CN102404723B true CN102404723B (en) | 2015-01-21 |
Family
ID=45886408
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110306794.0A Expired - Fee Related CN102404723B (en) | 2011-10-11 | 2011-10-11 | Agent-based self-adaptive collaboration sensory method for wireless sensor network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102404723B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106792435B (en) * | 2016-11-23 | 2019-11-26 | 西安电子科技大学 | Compressed sensing based data aggregation method in a kind of wireless sensor network |
| WO2018228693A1 (en) | 2017-06-15 | 2018-12-20 | Telefonaktiebolaget Lm Ericsson (Publ) | Hardware platform based on fpga partial reconfiguration for wireless communication device |
| CN108303910A (en) * | 2018-01-15 | 2018-07-20 | 中山大学 | A kind of population parameter realtime four-dimensional reconstruct collaboration sensing control system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1809012A (en) * | 2006-02-14 | 2006-07-26 | 北京邮电大学 | Wireless sensor network system and method supporting reconstruction of environment adaptive application |
| CN101316279A (en) * | 2008-07-09 | 2008-12-03 | 南京邮电大学 | Subjective interest driven wireless multimedia sensor network design method |
| WO2009059727A1 (en) * | 2007-11-09 | 2009-05-14 | Panasonic Corporation | Route optimization continuity at handover from network-based to host-based mobility |
-
2011
- 2011-10-11 CN CN201110306794.0A patent/CN102404723B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1809012A (en) * | 2006-02-14 | 2006-07-26 | 北京邮电大学 | Wireless sensor network system and method supporting reconstruction of environment adaptive application |
| WO2009059727A1 (en) * | 2007-11-09 | 2009-05-14 | Panasonic Corporation | Route optimization continuity at handover from network-based to host-based mobility |
| CN101316279A (en) * | 2008-07-09 | 2008-12-03 | 南京邮电大学 | Subjective interest driven wireless multimedia sensor network design method |
Non-Patent Citations (1)
| Title |
|---|
| JXTA平台下基于信誉的对等网信任模型研究;金兰芳;《信息科技辑》;20061231(第12期);第3.3.5章 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102404723A (en) | 2012-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Batool et al. | Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models | |
| Chand et al. | Heterogeneous HEED protocol for wireless sensor networks | |
| Banerjee et al. | Effective fault detection and routing scheme for wireless sensor networks | |
| Nourildean et al. | Internet of things based wireless sensor network: a review | |
| Li et al. | Cast: Context-aware security and trust framework for mobile ad-hoc networks using policies | |
| Basu et al. | A post-disaster demand forecasting system using principal component regression analysis and case-based reasoning over smartphone-based DTN | |
| Usman et al. | Toward trust based protocols in a pervasive and mobile computing environment: A survey | |
| Moin et al. | Optimized design of a human intranet network | |
| Mendoza et al. | Defense for selective attacks in the IoT with a distributed trust management scheme | |
| Chithaluru et al. | Energy-balanced neuro-fuzzy dynamic clustering scheme for green & sustainable IoT based smart cities | |
| Wu et al. | A dynamic trust model exploiting the time slice in WSNs | |
| Goh et al. | CogSeNet: A concept of cognitive wireless sensor network | |
| CN102404723B (en) | Agent-based self-adaptive collaboration sensory method for wireless sensor network | |
| Kumar et al. | A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN) | |
| Mostafaei et al. | A distributed efficient algorithm for self-protection of wireless sensor networks | |
| Kar et al. | Connectivity reestablishment in self-organizing sensor networks with dumb nodes | |
| Rais et al. | Impact of loosely coupled data dissemination policies for resource challenged environments | |
| Aghaei et al. | LA-Trickle: A novel algorithm to reduce the convergence time of the wireless sensor networks | |
| Guizani et al. | Effects of social network structure on epidemic disease spread dynamics with application to ad hoc networks | |
| Rani et al. | A probabilistic routing-based secure approach for opportunistic IoT network using blockchain | |
| Wakili et al. | AI-enhanced context-aware optimization of RPL routing protocol for IoT environments | |
| Shahbazi et al. | Minimum power intelligent routing in wireless sensors networks using self organizing neural networks | |
| Qi et al. | A novel trust model based on node recovery technique for WSN | |
| Wang | Trust based cyber-physical systems network design | |
| Dima et al. | FLOGERA—A fuzzy logic event recognition algorithm in a WSN environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C53 | Correction of patent of invention or patent application | ||
| CB02 | Change of applicant information |
Address after: 710121 Changan District, Shaanxi, Wei Guolu, Applicant after: Xi'an University of Posts & Telecommunications Address before: 710121 Changan District, Shaanxi, Wei Guolu, Applicant before: Xi'an Post & Telecommunication College |
|
| COR | Change of bibliographic data |
Free format text: CORRECT: APPLICANT; FROM: XI'AN POST + TELECOMMUNICATION COLLEGE TO: XI'AN UNIVERSITY OF POSTS + TELECOMMUNICATIONS |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20150121 Termination date: 20151011 |
|
| EXPY | Termination of patent right or utility model |