CN110035429B - Anti-interference minimum redundancy method in WiFi and ZigBee coexistence mode - Google Patents
Anti-interference minimum redundancy method in WiFi and ZigBee coexistence mode Download PDFInfo
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- CN110035429B CN110035429B CN201910281495.2A CN201910281495A CN110035429B CN 110035429 B CN110035429 B CN 110035429B CN 201910281495 A CN201910281495 A CN 201910281495A CN 110035429 B CN110035429 B CN 110035429B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/12—Detection or prevention of fraud
- H04W12/121—Wireless intrusion detection systems [WIDS]; Wireless intrusion prevention systems [WIPS]
- H04W12/122—Counter-measures against attacks; Protection against rogue devices
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
- H04W12/60—Context-dependent security
- H04W12/66—Trust-dependent, e.g. using trust scores or trust relationships
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- H—ELECTRICITY
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Abstract
The invention discloses an anti-interference minimum redundancy method in a WiFi and ZigBee coexistence mode, and belongs to the technical field of cognitive wireless sensor networks. The honeypot technology and the trust mechanism are fused and applied to the cognitive wireless sensor network, the sink node deploys node activities in the honeypot monitoring network, data sensed by the node cooperation spectrum are fused, the trust value is redistributed according to the fused spectrum result, and an idle channel is selected to avoid interference of WiFi signals in the same frequency band. By using the honeypot technology and the trust mechanism to detect the suspicious node, the communication interference of the malicious node to the wireless sensor network is suppressed. And then, malicious nodes are removed from the wireless sensor network node set, and the nodes for transmitting redundant information are adjusted to a sleep state by using the information entropy and a related graph method for the rest nodes in the network, so that the ZigBee node set with the minimum redundant transmission is obtained.
Description
Technical Field
The invention relates to the technical field of cognitive wireless sensor networks, in particular to an anti-interference minimum redundancy method in a WiFi and ZigBee coexistence mode.
Background
The WSN is a wireless self-organizing network consisting of a large number of micro sensor nodes with computing and communication capabilities, and has been widely applied to the fields of industry, agriculture, environmental monitoring and the like due to the characteristics of low cost and low power consumption. The current WSN communication frequency band mainly includes ISM 2.4GHz frequency band, however, WiFi, BlueTooth and other short-distance wireless communication technologies are deployed in the same frequency band, which may cause channel congestion and mutual interference. The cognitive radio CR is used for solving interference between frequency spectrums, the frequency spectrum sensing capability of the cognitive radio is applied to the sensor network, the reliability of the WSN in ISM frequency band communication is improved, and the cognitive radio sensor network CWSN becomes a research hotspot in the crossing field of the WSN and the CR at present.
The security problem of the wireless sensor network also attracts people's attention, and the security and stability of the network are easily affected by the environment, and certain measures need to be taken to ensure the security of network communication. The wireless sensor network is limited by various conditions, the resource of sensor node equipment is limited, mainly the memory and the storage space are limited, the energy of the sensor equipment is limited, the communication among the nodes adopts wireless broadcast communication, and the densely deployed nodes can generate network congestion, so that the transmission delay is increased. In addition, the wireless sensor network is vulnerable to various types of attacks, the physical layer, the link layer, the network layer and the transmission layer may be attacked, which may cause significant impact on system performance, and measures need to be taken to prevent the attack of malicious nodes.
Honeypots are a secure resource that is valuable in being scanned, attacked, and compromised, and upon detecting access to honeypots, the attackers are tracked and analyzed for their aggressive behavior. The honeypot technology researches and learns the attack purpose and the attack means of the attacker by attracting and luring the attacker, thereby delaying or even preventing the attack destructive behavior and effectively protecting real service resources. At present, the honeypot technology is applied to a wireless sensor network for less research, and the honeypot technology and a trust mechanism are fused to improve the network anti-interference performance.
In the environment of wireless sensor network deployment, the sensor node equipment provides accurate and timely information about the environment, and the transmission operating efficiency of the WSN can be improved. However, given the large number of node devices, activating all sensors simultaneously in a given time would incur significant costs, especially in terms of energy consumption. In addition, measurements of sensors are often correlated based on distance, and densely deployed sensors transmit a large amount of redundant information. The ZigBee node for transmitting the redundant information in the wireless sensor network can cause network congestion and transmission delay, and the overall energy consumption of the network is increased. Therefore, it is necessary to design an efficient sensor transmission scheme to determine an optimal sensor set, achieving minimum redundancy of ZigBee nodes.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an anti-interference minimum redundancy method in a WiFi and ZigBee coexistence mode, a honeypot technology and a trust mechanism are fused and applied to a cognitive wireless sensor network, a sink node is deployed to monitor node activity in the network, data of node cooperative spectrum sensing are fused, a trust value is redistributed according to a fused spectrum result, and an idle channel is selected to avoid interference of WiFi signals in the same frequency band. By using the honeypot technology and the trust mechanism to detect the suspicious node, the communication interference of the malicious node to the wireless sensor network is suppressed. In addition, by deleting the malicious nodes in the suspicious node list, the ZigBee node set with minimum redundant transmission is obtained by using the information entropy and the method of the relevant graph for the rest nodes in the sensor network.
The anti-interference minimum redundancy method in the WiFi and ZigBee coexistence mode comprises the following steps:
s1: the method comprises the steps that nodes in a cognitive wireless sensor network perform cooperative spectrum sensing, sensed data are subjected to data fusion in a convergence center, and a spectrum state of the current environment is obtained in a hard combination mode;
s2: the sink node deploys honeypots, monitors the activity behaviors of nodes in the network by using a honeypot technology, lures malicious nodes, and delays or even prevents attack destruction behaviors;
s3: starting a trust mechanism, updating the trust value of the CR user according to whether the sensing data of the CR user is consistent with the final judgment result, and selecting an idle channel to avoid the interference of the signals in the same frequency band;
s4: synthesizing the activity records of the nodes in the honeypot pair network and a trust mechanism to generate a suspicious node list, setting the threshold value to be 90%, and determining whether the node is a malicious node by judging the packet delivery rate of the node;
s5: aiming at the malicious node, the source node is adopted to send an alarm packet, so that other nodes in the network refuse to respond to the request of the malicious node, and the malicious node is prevented from damaging the safe communication of the network;
s6: the suspicious nodes are removed from the sensor network node set by combining the suspicious node list obtained by detection, the nodes transmitting redundant information are adjusted to a sleep state by using the information entropy and the related graph method for the remaining nodes, the ZigBee node set with the minimum redundant transmission is obtained, and the overall energy consumption of the network is reduced;
the minimum redundancy design specifically includes:
expressing the topological structure of the sensor network as a correlation graph G (V, E), wherein spatial data X of sensor nodes obeys a Gaussian random process and has n-dimensional multi-element normal distribution Gn(μ,K)
Wherein K is a covariance matrix of X, μ is a mean vector, K describes a dependency between data, the dependency decreasing exponentially with distance between nodes;
in the information entropy method, let W ═ { X ═ Xi1,Xi2,…,XikIs a subset of the spatial data set X, W is a k-dimensional normal distribution Gk(μ,KW)，KWIs a covariance matrix of W, the joint entropy of the normal distribution is approximated as:
given W, the data rate r (u) of node u is the conditional entropy H (u | W), whose value is calculated as follows:
R(u)＝H(u|W)＝H(u,W)-H(u)
in order to describe the correlation of the spatial data, a correlation graph is constructed based on information entropy estimation and distributed node communication; the correlation diagram is a hypergraph
Wherein R represents a sensor set of the network, P (R) is a power set of R, and E represents a super edge;
the side (W, u) in the correlation graph G indicates that the sensor data of the node u is highly correlated with the spatial data subset W, i.e. within a certain error range, the data of u can be calculated from the data of the sensors in W, and the node u is a redundant node; w is defined as a relevant subset, u is defined as a relevant vertex set, and the condition that the condition entropy H (u | W) is smaller than a specified threshold exists in the super edge;
constructing a hypergraph through communication between existing hyperedges and neighbor nodes, and knowing information of m-hop neighbors of each sensor node through m-round hello message exchange, wherein the information comprises an ID (identity), residual energy and coordinates;
a dyeing method is adopted for searching redundant nodes based on a hypergraph, and the process is as follows:
s61: listing a list of suspicious nodes;
s62: removing suspicious nodes, and initializing the remaining nodes into grey;
s63: if the nodes simultaneously meet the following 3 conditions, marking the nodes as white nodes; if the node does not satisfy the following 3 conditions, jumping to S67;
(1) checking neighbor nodes of the node u, wherein gray nodes in the neighbor nodes form a connected subgraph;
(2) checking the relevant subset list for the presence of a super edge (W, u), any sensor node in W being black or gray;
(3) checking the list of relevant vertices, for any excess edges (U, v), the node v is white or black or grey;
s64: the node broadcasts a white message to a neighbor node;
s65: when the node in W receives the white message, the node is marked as black, and broadcasts the black message to the direct neighbor;
s66: updating the neighbor node table by the W outer gray node, and jumping to S63;
s67: finishing;
by executing steps S61 to S67, the nodes transmitting redundant information are marked as white, and the white nodes are turned to a sleep state, while all the black nodes and gray nodes remain active, so as to form a relatively sparse network, and maintain good connectivity on the premise of ensuring communication quality.
The invention has the beneficial effects that: according to the invention, a honeypot technology and a trust mechanism are fused and applied to a cognitive wireless sensor network, a sink node deploys node activities in a honeypot monitoring network, data of node cooperative spectrum sensing are fused, spectrum sensing of the nodes is compared with a fusion result, a trust value is redistributed according to the conformity degree, and an idle channel is selected to avoid interference of WiFi signals in the same frequency band. By utilizing the honeypot and trust mechanism fusion, the communication interference of malicious nodes on the network is more quickly prevented, and the anti-interference performance of the network is improved. On the basis of ensuring the network communication quality and connectivity, the sensor network node set eliminates suspicious nodes, and the nodes transmitting redundant information are adjusted to a sleep state by using an information entropy and related graph method, so that the minimum redundancy of the ZigBee nodes is realized. And finally, the anti-interference and node minimum redundancy of the cognitive wireless sensor network are achieved.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of interference resistance and minimum node redundancy design of a cognitive wireless sensor network;
FIG. 2 is a schematic diagram of a cognitive wireless sensor network node;
FIG. 3 is a working scheme of a sink node deploying honeypot in conjunction with a trust mechanism;
FIG. 4 is a node minimal redundancy design.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
In order to realize the anti-interference of the wireless sensor network and the minimum redundancy of the nodes, the minimum redundancy design scheme is realized on the basis of the anti-interference, and the overall flow diagram is shown in figure 1:
the method comprises the following main steps:
step 1: nodes in the cognitive wireless sensor network perform cooperative spectrum sensing, sensed data are subjected to data fusion in a convergence center, and the spectrum state of the current environment is obtained in a hard combination mode
Step 2: the sink node deploys the honeypot, monitors the activity behavior of the nodes in the network by using the honeypot technology, lures out malicious nodes, and delays or even prevents the attack destructive behavior
Step 3: starting a trust mechanism, updating the trust value of the CR user according to whether the sensing data of the CR user is consistent with the final judgment result, and selecting an idle channel to avoid the interference of the signals in the same frequency band;
step 4: synthesizing node activity records and trust mechanisms in a honeypot pair network to generate a suspicious node list, setting a threshold value to be 90%, and determining whether the node is a malicious node or not by judging the packet delivery rate of the node
Step 5: aiming at the malicious node, the source node is adopted to send the warning packet, so that other nodes in the network refuse to respond to the request of the malicious node, and the malicious node is prevented from damaging the safe communication of the network
Step 6: and removing suspicious nodes from the sensor network node set by combining the suspicious node list obtained by detection, and turning the nodes transmitting redundant information to a sleep state by using the information entropy and the related graph method for the remaining nodes to obtain the ZigBee node set with the minimum redundant transmission, and reducing the overall energy consumption of the network.
As shown in fig. 2, the cognitive wireless sensor network includes a sink node, a cognitive sensor node, and a malicious node. Malicious nodes existing in the cognitive wireless sensor network initiate communication interference on the network by being disguised as 'normal' sensor nodes. The malicious node injects the analog waveform signal into the idle frequency band to confuse CR users in the interference range of the malicious node, and finally the number of the idle frequency bands accessible to the cognitive system is reduced. In addition, some malicious nodes can also obtain the trust of surrounding nodes through deception and acquire the forwarding of the data packet, so that transmission information is stolen and the data packet is discarded, thereby causing interference to the secure communication of the network.
Aiming at the communication interference of the malicious nodes to the cognitive wireless sensor network, honeypot technology is adopted to combine with a trust mechanism to prevent the attack of the malicious nodes, honeypots are deployed at aggregation nodes, and the scheme design combining with the trust mechanism is shown in fig. 3.
The sink node is a coordinator and a fusion center of the network and is responsible for the establishment, organization and management of the whole network. The method comprises the steps of collecting, processing and storing collected data of sensing nodes, fusing local sensing results reported by the nodes, judging idle frequency bands to avoid interference of WiFi signals, comparing sensing frequency spectrums of the nodes with the fusion results, reallocating trust values according to the conformity degree, and marking the nodes as suspicious nodes when the trust values of the nodes are lower than a set value. Meanwhile, honeypots are deployed on the sink nodes, the destructive behavior of malicious nodes is delayed or even suppressed by attracting and luring, the network activity is monitored by using the honeypot technology, and suspicious nodes in the network are recorded.
The honeypot is essentially a trap, for normal nodes passing through the honeypot, the honeypot looks like a common network node, for malicious nodes, an attacker can be attracted to the honeypot to transfer the sight to avoid the attack behavior of the attacker on a real network, and the core value of the honeypot lies in monitoring, detecting and analyzing the attack activities, recording real network environment data and combining a trust mechanism to prevent the malicious nodes from interfering the communication of the real wireless sensor network.
The wireless sensor network is usually densely deployed, redundant information is collected and transmitted by sensors very commonly, unnecessary energy consumption is caused, a suspicious node list generated by a honeypot technology and a trust mechanism is used, the information entropy and a related graph are combined to adjust nodes transmitting the redundant information to a sleep state, and the minimum redundancy design of the nodes is realized on the premise of ensuring connectivity and communication quality.
The node minimum redundancy design scheme is that a topological structure of a sensor network is represented as a correlation graph G (V, E), spatial data X of sensor nodes obeys a Gaussian random process, and n-dimensional multi-element normal distribution G existsn(μ,K)
Wherein K is a covariance matrix of X, μ is a mean vector, K describes a dependency between data, the dependency decreasing exponentially with distance between nodes;
in the information entropy method, let W ═ { X ═ Xi1,Xi2,…,XikIs a subset of the spatial data set X, W is a k-dimensional normal distribution Gk(μ,KW)，KWIs a covariance matrix of W, the joint entropy of the normal distribution is approximated as:
given W, the data rate r (u) of node u is the conditional entropy H (u | W), whose value is calculated as follows:
R(u)＝H(u|W)＝H(u,W)-H(u)
in order to describe the correlation of the spatial data, a correlation graph is constructed based on information entropy estimation and distributed node communication; the correlation diagram is a hypergraph
Wherein R represents a sensor set of the network, P (R) is a power set of R, and E represents a super edge;
correlation graph G*The edge (W, u) in (a) indicates that the sensor data of the node u is highly correlated with the spatial data subset W, i.e. within a certain error range, the data of u can be calculated from the data of the sensors in W, and the node u is a redundant node; w is defined as a relevant subset, u is defined as a relevant vertex set, and the condition that the condition entropy H (u | W) is smaller than a specified threshold exists in the super edge;
constructing a hypergraph through communication between existing hyperedges and neighbor nodes, and enabling each sensor node to know information of m-hop neighbors of the sensor node through m-round hello message exchange, wherein the information comprises an ID, residual energy and coordinates.
The node needs to satisfy the following 3 pieces at the same time to be marked as a white node
(1) Checking neighbor nodes of the node u, wherein the gray nodes in the neighbor nodes form a connected subgraph
(2) Checking the list of relevant subsets for the presence of a super edge (W, u), any sensor node in W being black or gray
(3) Checking the relevant vertex list, and for any superedge (U, v), the node v is white or black or gray, and adopting a dyeing method based on the supergraph to find redundant nodes, wherein the flow chart is shown in figure 4.
By executing the steps, the nodes for transmitting the redundant information are marked to be white, the white nodes are adjusted to be in a sleep state, all the black nodes and the gray nodes are kept in an active state, a relatively sparse network is formed, and good connectivity is still kept on the premise of ensuring the communication quality.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (1)
- The minimum anti-interference redundancy method in the WiFi and ZigBee coexistence mode is characterized by comprising the following steps: the method comprises the following steps:s1: the method comprises the steps that nodes in a cognitive wireless sensor network perform cooperative spectrum sensing, sensed data are subjected to data fusion in a convergence center, and a spectrum state of the current environment is obtained in a hard combination mode;s2: the sink node deploys honeypots, monitors the activity behaviors of nodes in the network by using a honeypot technology, lures malicious nodes, and delays or even prevents attack destruction behaviors;s3: starting a trust mechanism, updating the trust value of the CR user according to whether the sensing data of the CR user is consistent with the final judgment result, and selecting an idle channel to avoid the interference of the signals in the same frequency band;s4: synthesizing the activity records of the nodes in the honeypot pair network and a trust mechanism to generate a suspicious node list, setting the threshold value to be 90%, and determining whether the node is a malicious node by judging the packet delivery rate of the node;s5: aiming at the malicious node, the source node is adopted to send an alarm packet, so that other nodes in the network refuse to respond to the request of the malicious node, and the malicious node is prevented from damaging the safe communication of the network;s6: the suspicious nodes are removed from the sensor network node set by combining the suspicious node list obtained by detection, the nodes transmitting redundant information are adjusted to a sleep state by using the information entropy and the related graph method for the remaining nodes, the ZigBee node set with the minimum redundant transmission is obtained, and the overall energy consumption of the network is reduced;the minimum redundancy design specifically includes:expressing the topological structure of the sensor network as a correlation graph G (V, E), wherein spatial data X of sensor nodes obeys a Gaussian random process and has n-dimensional multi-element normal distribution Gn(μ,K)
Wherein K is a covariance matrix of X, μ is a mean vector, K describes a dependency between data, the dependency decreasing exponentially with distance between nodes;in the information entropy method, let W ═ { X ═ Xi1,Xi2,…,XikIs a subset of the spatial data set X, W is a k-dimensional normal distribution Gk(μ,KW)，KWIs the covariance matrix of WThe joint entropy of a normal distribution is approximated as:
given W, the data rate r (u) of node u is the conditional entropy H (u | W), whose value is calculated as follows:R(u)＝H(u|W)＝H(u,W)-H(u)in order to describe the correlation of the spatial data, a correlation graph is constructed based on information entropy estimation and distributed node communication; the correlation diagram is a hypergraphWherein R represents a sensor set of the network, P (R) is a power set of R, and E represents a super edge;
correlation graph G*The edge (W, u) in (a) indicates that the sensor data of the node u is highly correlated with the spatial data subset W, i.e. within a certain error range, the data of u can be calculated from the data of the sensors in W, and the node u is a redundant node; w is defined as a relevant subset, u is defined as a relevant vertex set, and the condition that the condition entropy H (u | W) is smaller than a specified threshold exists in the super edge;constructing a hypergraph through communication between existing hyperedges and neighbor nodes, and knowing information of m-hop neighbors of each sensor node through m-round hello message exchange, wherein the information comprises an ID (identity), residual energy and coordinates;a dyeing method is adopted for searching redundant nodes based on a hypergraph, and the process is as follows:s61: listing a list of suspicious nodes;s62: removing suspicious nodes, and initializing the remaining nodes into grey;s63: if the nodes simultaneously meet the following 3 conditions, marking the nodes as white nodes; if the node does not satisfy the following 3 conditions, jumping to S67;(1) checking neighbor nodes of the node u, wherein gray nodes in the neighbor nodes form a connected subgraph;(2) checking the relevant subset list for the presence of a super edge (W, u), any sensor node in W being black or gray;(3) checking the list of relevant vertices, for any excess edges (U, v), the node v is white or black or grey;s64: the node broadcasts a white message to a neighbor node;s65: when the node in W receives the white message, the node is marked as black, and broadcasts the black message to the direct neighbor;s66: updating the neighbor node table by the W outer gray node, and jumping to S63;s67: finishing;by executing steps S61 to S67, the nodes transmitting redundant information are marked as white, and the white nodes are turned to a sleep state, while all the black nodes and gray nodes remain active, so as to form a relatively sparse network, and maintain good connectivity on the premise of ensuring communication quality.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101529795A (en) * | 2006-11-02 | 2009-09-09 | 皇家飞利浦电子股份有限公司 | Distributed device revocation |
| CN103561004A (en) * | 2013-10-22 | 2014-02-05 | 西安交通大学 | Cooperative type active defense system based on honey nets |
| CN105103489A (en) * | 2013-03-28 | 2015-11-25 | 原子能和能源替代品委员会 | Method and device for forming secure wireless network with limited resources |
| CN107623554A (en) * | 2017-08-24 | 2018-01-23 | 西安电子科技大学 | A kind of cooperative frequency spectrum sensing method based on trust value, cognition wireless network |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8532008B2 (en) * | 2011-01-03 | 2013-09-10 | Arnab Das | Systems, devices, and methods of managing power consumption in wireless sensor networks |
| US10368222B2 (en) * | 2017-09-25 | 2019-07-30 | Intel Corporation | Self-directing node |
| US10778556B2 (en) * | 2017-12-28 | 2020-09-15 | Intel Corporation | Efficient mesh network data gathering |
-
2019
- 2019-04-09 CN CN201910281495.2A patent/CN110035429B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101529795A (en) * | 2006-11-02 | 2009-09-09 | 皇家飞利浦电子股份有限公司 | Distributed device revocation |
| CN105103489A (en) * | 2013-03-28 | 2015-11-25 | 原子能和能源替代品委员会 | Method and device for forming secure wireless network with limited resources |
| CN103561004A (en) * | 2013-10-22 | 2014-02-05 | 西安交通大学 | Cooperative type active defense system based on honey nets |
| CN107623554A (en) * | 2017-08-24 | 2018-01-23 | 西安电子科技大学 | A kind of cooperative frequency spectrum sensing method based on trust value, cognition wireless network |
Non-Patent Citations (3)
| Title |
|---|
| Analysis of key aspects to manage Wireless Sensor Networks in Ambient Assisted Living environments;Henar Martín ect.;《IEEE》;20100108;全文 * |
| 认知无线电网络安全综述;裴庆祺，李红宁，赵弘洋，李男，闵莹;《通信学报》;20130131;全文 * |
| 认知无线电频谱感知安全的威胁与防御;徐谖钦、徐以涛、罗康、王阵、马云峰;《军事通信技术》;20140630;全文 * |
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