CN113613304A - AODV routing method based on fuzzy logic in wireless self-organizing network - Google Patents

AODV routing method based on fuzzy logic in wireless self-organizing network Download PDF

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CN113613304A
CN113613304A CN202110879596.7A CN202110879596A CN113613304A CN 113613304 A CN113613304 A CN 113613304A CN 202110879596 A CN202110879596 A CN 202110879596A CN 113613304 A CN113613304 A CN 113613304A
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CN113613304B (en
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孙畅
李佳珉
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H04W40/14—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality based on stability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an AODV routing method based on fuzzy logic in a wireless self-organizing network, which comprises the following steps: calculating a normalized time delay, a normalized stability and a normalized residual energy value by a node in the wireless self-organizing network; calculating the reliability value of the node based on fuzzy logic, accumulating the REL value of the source node, updating and broadcasting the routing request, and establishing a reverse route; the intermediate node selects the node with the maximum REL value in the RREQ message forwarded by the previous hop node as a relay node, accumulates the REL value, updates the RREQ and establishes a reverse route; the intermediate node sends RREQ information to the neighbor nodes except the previous hop node; if the node is not the destination node or no route to the destination node exists in the routing table of the node, the previous two steps are repeatedly executed, otherwise, a route response is generated and unicast is carried out to the source node. The optimal path determined by the method can effectively ensure the stability of the link, the service life of the route and the service quality of the network.

Description

AODV routing method based on fuzzy logic in wireless self-organizing network
Technical Field
The invention belongs to the field of mobile communication, and relates to an AODV routing method based on fuzzy logic in a wireless self-organizing network.
Background
A wireless Ad Hoc Network (MANET) consists of a group of wireless nodes, is a multi-hop Network system which does not depend on the existing fixed communication Network infrastructure and can be rapidly expanded and used, and is a Network without any central entity, self-organization and self-healing. The MANET can be applied to environments such as vehicles, unmanned planes and the like to form vehicle-mounted mobile communication Networks (Vehicular Ad Hoc Networks, VANET) and unmanned plane mobile communication Networks (Flying Ad Hoc Networks, FANET) respectively. Because the application environment has the characteristic of real-time change, the research of a routing protocol which can still effectively ensure the high reliability and low delay performance of the network under the premise of node movement and dynamic change of a topological structure is very important.
Routing protocols can be divided into table-driven routing protocols and on-demand routing protocols, depending on the route discovery process. The table driving routing protocol is suitable for networks with slow topological structure change, all nodes in the network need to maintain a plurality of routing information tables leading to other nodes, the protocol has the advantages of small delay, great improvement on the packet loss rate of the system, and the defects of adaptation to small-scale networks and high control cost of the networks.
In contrast, to cope with the rapidly changing topology of ad hoc networks and to reduce the overhead of the networks, on-demand routing protocols are a good choice. However, the conventional Routing protocol, such as AODV (Ad hoc On-Demand Vector Routing) protocol, lacks comprehensive consideration of multiple influencing factors such as link delay, stability, Routing life, and the like, and an intermediate node transmits an RREQ in a broadcast manner, resulting in large control overhead. Therefore, it is a technical problem to be solved to research a routing protocol that can reduce the link delay, improve the stability, prolong the routing life, and reduce the control overhead.
Disclosure of Invention
The invention provides an AODV routing method based on fuzzy logic in a wireless self-organizing network, which adopts a fuzzy logic mode in the process of establishing a reverse route by an intermediate node, selects a node with the largest REL as a relay node, and selects a path with the largest REL as an optimal path by a destination node. The routing method comprehensively considers the factors of time delay, stability and node residual energy, improves the reliability of a link, balances the energy consumption of the node, effectively prolongs the service life of the route and improves the service quality of the network.
The invention discloses an AODV routing method based on fuzzy logic in a wireless self-organizing network, which comprises the following steps:
step 1, calculating normalized time delay ND, normalized stability NS and normalized residual energy value NRE of nodes in a wireless self-organizing network;
step 2, calculating a reliability value REL of the node based on fuzzy logic according to the normalized time delay ND, the normalized stability NS and the normalized residual energy value NRE of the node;
step 3, the source node accumulates the reliability value REL, updates the route request RREQ, broadcasts the RREQ message to the neighbor nodes, and establishes a reverse route;
step 4, the intermediate node selects the node with the maximum REL value in the RREQ message forwarded by the previous hop node as a relay node, accumulates the REL value, updates the RREQ and establishes a reverse route;
step 5, the intermediate node sends RREQ information to the neighbor nodes except the previous hop node;
step 6, if the node is not the destination node or the routing table of the node does not have the route to the destination node, the step 4-5 is executed repeatedly; if the node is a destination node or a route to the destination node exists in the route table, a RREP message is generated and unicast is carried out to the source node according to the reverse route, and the optimal path is established at the moment.
Further, the calculation formula of the normalized time delay ND of the node in step 1 is as follows:
Figure BDA0003191613910000021
wherein
Figure BDA0003191613910000022
Wherein N isxA neighbor node, y, representing node xiRepresents the ith node among the neighbor nodes of node x, NDx (y) represents the nodeThe normalized time delay between x and the node y, n represents the number of neighbor nodes, d (x, y) represents the distance between the node x and the node y, and R represents the maximum distance which can be transmitted by the node;
the calculation formula of the normalized stability degree NS of the node is as follows:
Figure BDA0003191613910000023
wherein v (x) represents the velocity of node x, vminRepresenting the minimum value of all node velocities, vmaxRepresents the maximum value of all node speeds;
the calculation formula of the normalized residual energy value NRE of the node is as follows:
Figure BDA0003191613910000024
wherein
Ex＝E0-vx*t (5)
Wherein ExRepresenting the residual energy of node x, EminRepresents the minimum value of the remaining energy in all nodes, EmaxRepresenting the maximum value of the remaining energy in all nodes, E0Representing the initial energy, v, of node xxRepresenting the energy consumption rate of node x.
Further, in the step 2, according to the normalized time delay ND, the normalized stability NS and the normalized residual energy value NRE of the node, the reliability value REL of the node is calculated based on fuzzy logic; the method specifically comprises the following steps:
step 2.1, input fuzzification;
setting language variables of normalized time delay as LOW, MEDIUM and HIGH, and obtaining a membership function distribution diagram by adopting a triangular membership function;
the linguistic variables of the normalized stability are 'LOW', 'MEDIUM' and 'HIGH', and triangular membership functions are adopted to obtain a membership function distribution diagram;
the linguistic variables of the normalized residual energy value are 'LOW', 'MEDIUM', 'HIGH', 'LOW' adopting a trapezoidal membership function, and 'MEDIUM' and 'HIGH' adopting a triangular membership function to obtain a membership function distribution diagram;
step 2.2, the language variable of the node REL is set as: excelent, Good, Acceptable, Not Acceptable, Bad, Terrible; the six linguistic variables adopt triangular membership functions to obtain a REL membership function distribution diagram; taking different combinations of linguistic variables of normalized time delay, linguistic variables of normalized stability and linguistic variables of normalized residual energy value as judgment conditions, and respectively combining an inference formula and 27 if-then rules to carry out fuzzy logic inference to obtain linguistic variables and membership function values of the node REL; the reasoning formula is as follows:
μ＝min{1-μND,μNS,μNRE} (6)
wherein, mu represents the membership function value corresponding to the linguistic variable of the node REL, muNDMembership function value, mu, corresponding to linguistic variable representing NDNSMembership function value, mu, corresponding to linguistic variable representing NSNREAnd representing membership function values corresponding to linguistic variables of the NRE.
And 2.3, defuzzification is realized by using a gravity Center (COG) method to obtain a clear value of the node REL. The calculation formula is as follows:
Figure BDA0003191613910000031
wherein v is0Node REL clear value, μ, representing defuzzified outputv(v) Denotes the membership function of REL and v denotes the horizontal axis of the distribution diagram of the membership function of REL, i.e. the REL fuzzy value.
Has the advantages that: the method is suitable for the wireless self-organizing network with frequent topology change, uneven node distribution and higher real-time requirement, and comprehensively considers the factors of time delay, stability and node residual energy when determining the optimal path, thereby improving the reliability of the link, balancing the energy consumption of the node, effectively prolonging the service life of the route and improving the service quality of the network.
Drawings
FIG. 1 is a flow chart of a routing method of the present invention;
FIG. 2(a) is a graph of a distribution of membership functions for ND;
FIG. 2(b) is a distribution plot of membership function for NS;
FIG. 2(c) is a graph of membership function distribution for NRE;
FIG. 2(d) is a graph of membership function distribution for REL;
fig. 3 is a schematic process diagram of a routing method according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described in the following by combining the drawings and the detailed description.
A fuzzy logic-based AODV routing method in a wireless self-organizing network comprises the following steps:
as shown in fig. 3, node 1 is a source node, node 25 is a target node, the positions, rates and consumption rates of the 25 nodes are initialized by using random numbers, and the maximum distance R that the node can transmit is defined as 200 meters. The generation process of one path will be specifically described below.
As shown in fig. 1, an AODV routing method based on fuzzy logic in a wireless ad hoc network specifically includes the following steps:
step 1, calculating Normalized Delay ND (ND), Normalized Stability NS (NS) and Normalized Remaining Energy NRE (NRE) of nodes in the wireless ad hoc network:
node x normalized delay nd (x) is calculated as follows:
Figure BDA0003191613910000041
wherein
Figure BDA0003191613910000042
Wherein N isxA neighbor node, y, representing node xiThe ith node in the neighbor nodes of the node x is represented, NDx (y) represents the normalized time delay between the node x and the node y, n represents the number of the neighbor nodes, d (x, y) represents the distance between the node x and the node y, and R represents the maximum distance which can be transmitted by the node. The smaller the distance between two nodes, the smaller the delay on the link. The normalized delay ND for node x is expressed as the average link delay with the neighbor node.
The node x normalized stability NS calculation formula is as follows:
Figure BDA0003191613910000051
where v (x) represents the velocity of node x, vminRepresenting the minimum value of all node velocities, vmaxRepresents the maximum value of all node speeds;
the calculation formula of the node x normalized residual energy value NRE is as follows:
Figure BDA0003191613910000052
wherein
Ex＝E0-vx*t (5)
Wherein ExRepresenting the residual energy of node x, EminRepresents the minimum value of the remaining energy in all nodes, EmaxRepresenting the maximum value of the remaining energy in all nodes, E0Representing the initial energy, v, of node xxRepresenting the energy consumption rate of node x.
Step 2, calculating the reliability value REL of the node based on fuzzy logic;
the fuzzy logic calculation process comprises input fuzzification, fuzzy logic reasoning and defuzzification. In the input fuzzification process:
step 2.1, input fuzzification;
the linguistic variables with "normalized delay" are "LOW", "MEDIUM", and "HIGH", and all adopt triangular membership function, to obtain a membership function distribution diagram of ND, as shown in fig. 2(a), if the input ND is 0.4, then the membership function value corresponding to each linguistic variable is { LOW: 0.2, MEDIUM: 0.8, HIGH: 0 };
the linguistic variables of the normalized stability are "LOW", "MEDIUM", and "HIGH", and all adopt triangular membership functions to obtain a membership function distribution diagram of the NS, as shown in fig. 2(b), if the input NS is 0.7, the membership function value corresponding to each linguistic variable is { LOW: 0, MEDIUM: 0.6, HIGH: 0.4 };
as shown in fig. 2(c), if the input NRE is 0.7, the membership function value corresponding to each linguistic variable is { LOW: 0, MEDIUM: 0.33, HIGH: 0.5 };
step 2.2, fuzzy logic reasoning;
the language variable of the node REL is set as: excelent, Good, Acceptable, Not Acceptable, Bad, Terrible. The six linguistic variables all adopt triangular membership functions to obtain a distribution diagram of REL membership functions, as shown in FIG. 2 (d).
Taking different combinations of linguistic variables of normalized time delay, normalized stability and normalized residual energy value as judgment conditions, and respectively combining an inference formula and 27 if-then rules to carry out fuzzy logic inference to obtain a membership function value and linguistic variables of the node REL; the reasoning formula is as follows:
μ＝min{1-μND,μNS,μNRE} (6)
wherein, mu represents the membership function value corresponding to the linguistic variable of the node REL, muNDMembership function value, mu, corresponding to linguistic variable representing NDNSMembership function corresponding to linguistic variable representing NSValue, muNREAnd representing membership function values corresponding to linguistic variables of the NRE.
The 27 if-then rules are shown in table 1, for example, in rule 1, if the value of the membership function of LOW in ND is 0.2, the value of the membership function of HIGH in NS is 0.4, and the value of the membership function of HIGH in NRE is 0.5, then REL corresponds to the linguistic variable Excellent and the value of the corresponding membership function is 0.4. The REL linguistic variables and the membership function values thereof output by the 27 if-then rules correspond to a subdomain in a REL membership function distribution diagram, and the subdomain is a closed graph surrounded by an excelent curve and a horizontal axis when the membership function value is 0.4; similarly, the REL linguistic variables, membership function values and corresponding sub-domains corresponding to the 27 rules can be obtained. And taking the union of all the subdomains to obtain the total domain.
Table 127 if-then rule tables
Figure BDA0003191613910000061
Figure BDA0003191613910000071
Step 2.3, defuzzification
And (3) defuzzification is realized by using a gravity Center (COG) method to obtain a clear value of the node REL, namely, the centroid of the total domain is solved. The calculation formula is as follows:
Figure BDA0003191613910000072
wherein v is0Node REL clear value, μ, representing defuzzified outputv(v) Denotes the membership function of REL and v denotes the horizontal axis of the distribution diagram of the membership function of REL, i.e. the REL fuzzy value.
Step 3, the source node accumulates the reliability value REL, updates the Route Request RREQ (Route Request, RREQ), broadcasts the RREQ message to the neighbor nodes, and establishes the reverse Route: accumulating the REL value corresponding to the source node 1, updating the RREQ, then broadcasting the RREQ message to the node 3, the node 4 and the node 2 by the source node 1, and setting the source node 1 as the destination node of the reverse routing path;
and 4, the intermediate node selects the node with the maximum REL value in the RREQ message forwarded by the previous hop node as a relay node, accumulates the REL value, updates the RREQ and establishes a reverse route: for example, when the node arrives at the intermediate node 6, the node 6 receives the RREQ messages from the nodes 3 and 4 within a specified time, the node 6 selects the node 4 as a relay node because the REL value of the node 4 is large, adds the REL value in the received RREQ and the REL value of the node 6, updates the RREQ, and establishes a reverse route to the node 4;
and 5, the intermediate node sends RREQ information to the neighbor nodes except the previous hop node: for example, node 6 sends an RREQ message to nodes 7, 12, 13, 11;
step 6, if the node is not the destination node or the routing table of the node does not have the route to the destination node, the step 4-5 is executed repeatedly; if the node is a destination node or a route to the destination node exists in the route table, a route response RREP message is generated and unicast is carried out to the source node according to a reverse route: when the destination node 25 is reached, the node 25 sends the RREP message along the path 25 → 19 → 13 → 6 → 4 → 1, at which point the optimal path has been established: 1 → 4 → 6 → 13 → 19 → 25.

Claims (3)

1. An AODV routing method based on fuzzy logic in a wireless self-organizing network is characterized by comprising the following steps:
step 1, calculating normalized time delay ND, normalized stability NS and normalized residual energy value NRE of nodes in a wireless self-organizing network;
step 2, calculating a reliability value REL of the node based on fuzzy logic according to the normalized time delay ND, the normalized stability NS and the normalized residual energy value NRE of the node;
step 3, the source node accumulates the reliability value REL, updates the route request RREQ, broadcasts the RREQ message to the neighbor nodes, and establishes a reverse route;
step 4, the intermediate node selects the node with the maximum REL value in the RREQ message forwarded by the previous hop node as a relay node, accumulates the REL value, updates the RREQ and establishes a reverse route;
step 5, the intermediate node sends RREQ information to the neighbor nodes except the previous hop node;
step 6, if the node is not the destination node or the routing table of the node does not have the route to the destination node, the step 4-5 is executed repeatedly; if the node is a destination node or a route to the destination node exists in the route table, a RREP message is generated and unicast is carried out to the source node according to the reverse route, and the optimal path is established at the moment.
2. The AODV routing method based on fuzzy logic in the wireless ad hoc network according to claim 1, wherein the normalized time delay ND of the node in step 1 is calculated as follows:
Figure FDA0003191613900000011
wherein
Figure FDA0003191613900000012
Wherein N isxA neighbor node, y, representing node xiRepresenting the ith node in the neighbor nodes of the node x, NDx (y) representing the normalized time delay between the node x and the node y, n representing the number of the neighbor nodes, d (x, y) representing the distance between the node x and the node y, and R representing the maximum distance which can be transmitted by the node;
the calculation formula of the normalized stability degree NS of the node is as follows:
Figure FDA0003191613900000013
wherein v (x) represents the velocity of node x, vminRepresenting the minimum value of all node velocities, vmaxRepresents the maximum value of all node speeds;
the calculation formula of the normalized residual energy value NRE of the node is as follows:
Figure FDA0003191613900000021
wherein
Ex＝E0-vx*t (5)
Wherein ExRepresenting the residual energy of node x, EminRepresents the minimum value of the remaining energy in all nodes, EmaxRepresenting the maximum value of the remaining energy in all nodes, E0Representing the initial energy, v, of node xxRepresenting the energy consumption rate of node x.
3. The AODV routing method based on fuzzy logic in the wireless self-organizing network according to claim 1, wherein in step 2, according to the normalized time delay ND, the normalized stability NS and the normalized residual energy value NRE of the node, the reliability value REL of the node is calculated based on fuzzy logic; the method specifically comprises the following steps:
step 2.1, input fuzzification;
setting language variables of normalized time delay as LOW, MEDIUM and HIGH, and obtaining a membership function distribution diagram by adopting a triangular membership function;
the linguistic variables of the normalized stability are 'LOW', 'MEDIUM' and 'HIGH', and triangular membership functions are adopted to obtain a membership function distribution diagram;
the linguistic variables of the normalized residual energy value are 'LOW', 'MEDIUM', 'HIGH', 'LOW', a trapezoidal membership function is adopted, and a triangular membership function is adopted for the 'MEDIUM' and 'HIGH', so that a membership function distribution diagram is obtained;
step 2.2, the language variable of the node reliability value REL is set as: excelent, Good, Acceptable, Not Acceptable, Bad, Terrible; the six linguistic variables adopt triangular membership functions to obtain a REL membership function distribution diagram; taking different combinations of linguistic variables of normalized time delay, normalized stability and normalized residual energy value as judgment conditions, and respectively combining an inference formula and 27 if-then rules to carry out fuzzy logic inference to obtain a membership function value and linguistic variables of the node REL;
the reasoning formula is as follows:
μ＝min{1-μND,μNS,μNRE} (6)
wherein, mu represents the membership function value corresponding to the linguistic variable of the node REL, muNDMembership function value, mu, corresponding to linguistic variable representing NDNSMembership function value, mu, corresponding to linguistic variable representing NSNREA membership function value corresponding to a linguistic variable representing NRE;
step 2.3, defuzzification is realized by using a gravity Center (COG) method to obtain a clear value of the node REL; the calculation formula is as follows:
Figure FDA0003191613900000031
wherein v is0Node REL clear value, μ, representing defuzzified outputv(v) Denotes the membership function of REL and v denotes the horizontal axis of the distribution diagram of the membership function of REL, i.e. the REL fuzzy value.
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