CN107484207B - Combined topology control and channel distribution load balancing method in wireless sensor network - Google Patents
Combined topology control and channel distribution load balancing method in wireless sensor network Download PDFInfo
- Publication number
- CN107484207B CN107484207B CN201710571325.9A CN201710571325A CN107484207B CN 107484207 B CN107484207 B CN 107484207B CN 201710571325 A CN201710571325 A CN 201710571325A CN 107484207 B CN107484207 B CN 107484207B
- Authority
- CN
- China
- Prior art keywords
- node
- nodes
- network
- power
- channel
- 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.)
- Active
Links
Images
Classifications
-
- 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
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
-
- 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
- H04W40/023—Limited or focused flooding to selected areas of a network
-
- 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
- H04W40/08—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on transmission power
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a load balancing method combining topology control and channel distribution in a wireless sensor network, which is used for researching the influence of the residual energy of neighbors and the distance between nodes on node loads and reducing node interference and energy consumption by reasonably adjusting the node power through power control. The low-energy nodes and the high-energy nodes are adjusted by using the node load capacity assessment factors, the residual energy of the nodes is comprehensively considered, so that the node energy in the subareas can be consumed in a balanced manner, the shortest paths from the nodes to other nodes in the communication area are selected by using the node path factors according to the density degree of the nodes in the communication area, the purpose of balancing the node load is achieved, and the life cycle of the network is prolonged to the maximum extent. The invention adopts the distributed and optimal response strategy to select the optimal channel and power, thus realizing the reliability of data transmission and the balance of network load.
Description
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a combined topology control and channel distribution load balancing method in a wireless sensor network.
Background
The Wireless Sensor Networks (WSNs) are infrastructure-free Networks, which are formed by a group of Sensor nodes in a Wireless self-organizing manner, and are intended to cooperatively sense, collect and process information of sensing objects in a geographic area covered by the network, process the data, and finally transmit the data to a user in need.
The wireless sensor network consists of a plurality of sensor nodes with small volume and limited energy. The nodes are often deployed in areas with complex environments, the energy consumption of the nodes is accelerated by the severe natural environments, and the energy consumption of the nodes is directly influenced by the neighbor nodes. When the neighbor energy of a node is exhausted or damaged due to environment, the load of the node is directly increased, so that the node fails prematurely, the network connectivity is reduced, and even the network is crashed. Therefore, each low-energy node can adjust the self load according to the residual energy condition of the neighbor nodes, and the life cycle of the low-energy node is prolonged. On the other hand, in the process of network information transmission, nodes often need to transmit data information to a base station through multiple hops, and the distance between the nodes directly affects the path length between the start-stop nodes. If the information resources processed by the network are limited, if the distance between the nodes is large, in order to ensure the successful transmission of the information, the nodes need to increase the power of the nodes, thereby causing serious energy waste.
The load of the nodes is balanced by singly considering topology or channels, the energy consumption and the interference of the nodes cannot be optimized simultaneously, the power and the channel distribution directly influence the network interference and the load, the network interference and the load of the nodes can be increased by overlarge node transmitting power or a poor channel distribution state, the node transmitting power is small, the energy consumption and the load of the nodes can be reduced, the life cycle of the nodes is prolonged, network partitioning is easily caused, and the practical value of the network is reduced. It can be seen that power and channel have significant impact on network interference and loading. In addition, power interacts with the channel, and the channel is constrained, and the two have a complex interaction relationship: on one hand, channel allocation is influenced by power control, the channel allocation is completed on the basis of fixed and invariable power, the power of nodes in the topology is different, and the optimal channel is also different; on the other hand, the power control result is also closely related to the channel state, and the network node transmission power obtained by different channels is also different. In conclusion, the two influence each other to determine the multi-aspect performance of the network, so that the joint optimization of power and channel is necessary.
In summary, the existing optimization algorithm does not consider the relationship between the mutual influence and mutual restriction between power and channels, and cannot simultaneously meet the requirements for reducing network interference and balancing network energy consumption and load.
Disclosure of Invention
The invention overcomes the defects in the prior art, provides a load balancing method combining topology control and channel allocation, combines topology and channels on the basis of ensuring network connectivity, balances the energy consumption and load of nodes while reducing network interference, and effectively prolongs the life cycle of a network.
In order to solve the problems in the prior art, the invention is realized by the following technical scheme:
a method for combining topology control and channel distribution load balancing in a wireless sensor network specifically comprises the following steps:
⑴ when the source node needs to communicate with the destination node, the source node broadcasts the route request RREQ data packet by flooding to all the neighbor nodes, and waits for the route to reply the RREQ data packet;
⑵ the source node updates its own neighbor node list content according to the received RREQ data packet, the RREQ data packet includes node residual energy e, minimum transmitting power p, receiving channel rc, neighbor node identification id and network connection factor f;
⑶ the source node calculates its load capacity evaluation factor Lb by using the residual energy of each node as an energy benefit factor according to the updated neighbor node list content, selects the shortest path from the source node to other nodes in the communication area according to the density degree of the nodes in the communication area, and calculates the node path factor LD;
⑷ the source node plays games according to the updated neighbor node list content, evaluates the network communication condition and the node benefit when the node selects different transmitting power and different channel strategies, and selects the power and channel game strategy when the node benefit is maximum.
Further, in step (3), the source node calculates its load capacity evaluation factor Lb by using the residual energy of each node as an energy benefit factor according to the updated content of the neighbor node list, that is, calculates its load capacity evaluation factor Lb value according to formula (I) for each neighbor node in the updated content of the neighbor node list:
wherein, the load capacity assessment factor value takes the residual energy of the source node and the neighbor nodes as an energy benefit factor, Ecost·uRepresents the remainder of node uEnergy, EvRepresenting the residual energy of node v, E0(u)，E0(v) Representing the initial energies of node u and node v, respectively.
Judging whether a neighbor node with larger residual energy is selected according to the load capacity evaluation factor Lb, wherein the more loads can be borne by the corresponding node; when the power of the node is not changed, if the residual energy of the neighbor node is larger, the load change of the node is more stable, the more information resources the node can process are, the less the node is prone to fail, and the more the node is likely to be selected as a receiving node to bear more load.
Further, in step (3), the shortest path from the source node to each other node in the communication area is selected according to the density of the nodes in the communication area, and the node path factor LD is calculated, that is, according to the updated neighbor list content, the node calculates the value of the path factor LD according to formula (II):
wherein, the path factor of the nodes is characterized by the node efficiency,
b is constant, N represents total number of nodes in the whole network, A represents total area of node distribution region, a represents constant, puRepresents the transmit power of the nodes and d (u, v) represents the distance between the nodes.
Using LD at the beginning of the next round of predictionuSelecting a node with shorter distance, wherein the closer the node is to other neighbor nodes, the easier the information circulation is, the less resources are consumed by information transmission, the smaller the load born by the node is, and the LD isuTo a certain extent reflects the size of the load that the node is subjected to.
Further, in the step (4), the evaluation node selects the network connection status and the node benefit when selecting different transmission powers and different channel strategies, and selects the power and channel game strategy when the node benefit is the maximum, that is, the network connection status and the node benefit of the node in different power levels and different channel states are compared, and whether the game model reaches nash equilibrium is judged.
The invention considers the topology control and the channel allocation jointly, ensures the reliability of data transmission and balances the energy consumption and the load as much as possible.
Due to the adoption of the technical scheme, compared with the prior art, the combined topology control and channel distribution load balancing method in the wireless sensor network has the following beneficial effects:
the invention constructs topology based on local information, reduces the complexity of information and is suitable for nodes and networks with limited resources. The invention jointly optimizes the energy consumption and load of the network by cooperating power and channels, and comprehensively considers the residual energy of the nodes, thereby avoiding the condition of excessive use of a single node in other algorithms, balancing the load of the whole network, and improving the service life and the service quality of the wireless sensor network.
The invention adjusts the low-energy nodes and the high-energy nodes by using the node load capacity evaluation factor, comprehensively considers the residual energy of the nodes, thereby leading the node energy in the subarea to be consumed in a balanced manner, selects the shortest path of the node to other nodes in the communication area according to the density degree of the nodes in the communication area by using the node path factor, combines topology and channels on the basis of ensuring the network connectivity, reduces the network interference, balances the energy consumption and the load of the nodes, achieves the purpose of balancing the node load, and prolongs the life cycle of the network to the maximum extent.
The invention adopts the distributed and optimal response strategy to select the optimal channel and power, thus realizing the reliability of data transmission and the balance of network load.
Drawings
The invention method-the load balancing method (Topology Control Algorithm and Channel Allocation Algorithm Based on loadBalanzing in Wireless sensor network) of combining Topology Control and Channel Allocation in the wireless sensor network is called JACLB method for short hereinafter;
a comparison method- (1) a wireless sensor network Topology Control method (INATE) Based on a Load Balancing Evaluation model, which is hereinafter referred to as INATE method; the method takes the node power as an adjusting factor, improves the node fitness and the node efficiency, effectively balances the node load and reduces the energy consumption of the network;
⑵ A Joint Power control and Channel Allocation Game method (A Joint Game Algorithm of Power control and Channel allocation Consituating Channel Interval and Relay Transmission Obstacle) giving consideration to Channel spacing and Relay Transmission Obstacle, abbreviated as JACIRT method, which is a design Algorithm Considering the influence of power and Channel on network performance simultaneously, achieving the purpose of reducing interference and energy consumption, and effectively improving network performance.
FIG. 1 is a flow chart of the method of the invention (JACLB method);
FIG. 2 is a graph comparing the average power consumption of network nodes by three methods, JACLB, INATE and JACIRT;
FIG. 3 is a comparison of node average interference graphs of the available number of different channels in the JACLB method and the JACIRT method;
FIG. 4 is a comparison of node mean interference graphs for different node numbers in three methods, JACLB, INATE and JACIRT;
FIG. 5 is a graph comparing the average energy consumption of network nodes by three methods, JACLB, INATE and JACIRT;
fig. 6 is a comparison of network node life cycle diagrams of three methods, JACLB, INATE and JACIRT.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
the above technical solutions of the present invention are further described with reference to specific embodiments. It should be understood that these examples are only for illustrating the technical solutions of the present invention, and are not intended to limit the scope of the present invention. Furthermore, it should be understood that various changes and modifications can be made by those skilled in the art after reading the technical scheme of the invention, and the equivalents also fall into the protection scope of the invention defined by the claims.
The embodiment of the invention relates to a combined topology control and channel distribution load balancing method in a wireless sensor network, the flow chart of which is shown in figure 1, firstly, the broadcast of routing information is carried out, and then the content of a neighbor list is updated according to RREQ data packets.
The specific implementation process is divided into two stages: an initialization stage and a game execution stage. The method comprises the following steps:
Step 4, the source node u conducts game according to the updated neighbor node list content, different transmitting powers and different channel strategies are selected for the node u to evaluate the network communication condition and the benefit influence condition of the node, and the optimal power and channel game strategy are selected; the specific game strategy comprises the following steps:
when the network is updated, one or only one node can update the transmitting power and the channel strategy of the node, namely when a certain node updates the strategy, the power and the channel state of other nodes in the network are kept unchanged; initializing channels and power for all network nodes, wherein the initialization power of each node is the maximum power of the communication between the node and a neighbor node, and when a game updating strategy is started, the node firstly reduces one power level and then judges the connectivity of the whole network; if the network is not connected, the node cannot ensure the basic connection performance of the network when using the power level, then the node returns to the previous power level and keeps the current state of the node unchanged; if the network is still connected, respectively calculating the influence of the node on the benefit function by selecting different available channels under the power level, selecting a channel strategy corresponding to the maximum value in the benefit values as the optimal channel selection corresponding to the node at the moment, and when the benefit calculation corresponding to all the power levels of the node is completed, selecting the power value corresponding to the maximum benefit as the optimal power of the node; and repeating the game on each node until the states of all the nodes in the network are not changed, namely, a Nash equilibrium state is achieved, and the game is ended.
Nash equilibrium is an equilibrium state of the game that is optimal for all game participants. According to the nash equalization definition, no node can obtain higher benefit by continuously reducing its power or changing its channel strategy. If the power is continuously reduced, the benefit is only reduced, and even the network is not connected; and the channel strategy selected by the node is the optimal channel selection under the current power condition, and the change of the channel strategy can only reduce the benefit of the node.
The method of the invention can make the node energy consumption and the node load of the whole network more balanced, and improve the survival time of the whole network, and the following explanation specifically shows that the broadcast RREQ data packet information comprises: residual energy e of neighbor node vvMinimum transmit power p (u, v), receive channel rcvConnection of a neighbor node identification id (v) to a networkGeneral factor fu(pu,p-u). The header format of the broadcast message is shown in table 1.
Table 1 header format of broadcast message
According to the broadcast message, the source node u is assumed to have n-1 neighbor nodes, which are v in sequence1,v2·…·····vn-1Then the power class set p of the source node uu＝{p(u,v1),p(u,v2)，......p(u,vn－1)}。
The evaluation factor value of the load capacity of the nodes in the path is as follows:
wherein, the load capacity assessment factor value takes the residual energy of the source node and the neighbor nodes as an energy benefit factor, Ecost·uRepresenting the residual energy of the source node u, EvRepresenting the residual energy of node v, E0(u)，E0(v) Representing the initial energy of nodes u, v, respectively.
According to the method, the neighbor node v with larger residual energy is judged and selected according to the node load capacity evaluation factor Lb, and the load born by the corresponding source node u is more; when the power of the source node u is unchanged, if the residual energy of the neighbor node v is larger, the load change of the source node u is more stable, the more information resources the source node u can process are, the less the source node u is prone to fail, and the more the source node u is likely to be selected as a receiving node to bear more load.
Path factor value LD of a node in a pathuComprises the following steps:
wherein, the path factor of the nodes is characterized by the node efficiency,
b is constant, N represents total number of nodes in the whole network, A represents total area of node distribution region, a represents constant, puRepresents the transmit power of the source node, d (u, v) represents the distance between nodes; when the next round of prediction begins, the LD is useduSelecting nodes with shorter distance, when the source node u is closer to other neighbor nodes, the easier the information circulation is, the less the resource consumed by information transmission is, the smaller the load born by the source node u is, and the LDuTo a certain extent reflects the size of the load that the node is subjected to.
In the method, the node load capacity evaluation factor and the node path factor can balance the node load, and a model basis is provided for constructing the low-energy-consumption topology. The analysis shows that the two are mutually influenced and complement each other, and the game theory can well process the optimization problem in the mutual decision process, so that the related knowledge of the game is utilized to comprehensively consider the node load capacity evaluation factor and the path factor to construct a topology control and channel allocation game model based on load balancing.
The method of the invention aims to prolong the network life period as much as possible, balance the load of the nodes and fully utilize the relationship of mutual influence between channel allocation and power control to reduce network interference and energy consumption together; the difference of the residual energy of each transmitting node also affects the life cycle of the network, so the problem of energy consumption balance also needs to be considered; therefore, a game model G of topology control and channel allocation based on load balancing is established by taking power, interference, residual energy and network connectivity as parameters, and is recorded as { I, S, u ═ IuAnd the game three elements are expressed as follows:
(1) participant I: in the whole network with the total number of nodes N, all sensor nodes are used as participants I, I ═ 1,2.
(2) A strategy space S: set S ═ S of participant ownership policies1,s2.....suIs the policy space. Wherein s isu＝(pu,cu) Is a policy for the node u that is,puis the transmission power, p, of node uu∈ P, P being the power class of the node, cu∈ C, C being an optional set of channels for the node, C ═ 1,2.
(3) Benefit function uu: the benefit of the node u under the strategy of power p and channel c is expressed as uu(p, c). In order to prolong the network life and balance the load of the nodes as far as possible, the mutual influence relationship between channel allocation and power control is fully utilized to reduce network interference and energy consumption; the difference of the residual energy of each transmitting node also affects the life cycle of the network, so the problem of energy consumption balance also needs to be considered; therefore, a game model based on load balancing power control and channel allocation is constructed by taking power, interference, residual energy and network connectivity as parameters, and a benefit function is designed as follows:
as further illustrated in equation (III), α, λ represents a trade-off tuning factor, and in experiments, α ═ 20, β ═ 0.002, and λ ═ 0.002 gave the best performance
Representing the interference level of node u.
The interference of the node u is defined as the sum of all the link one-way interference values which are connected with the node and are transmitted according to the direction departing from the node. The interference formula for node u is:
the formula (IV) illustrates:
is the judgment formula for the presence of uv on channel c: if and only if
uvcWhen the two-dimensional model is simultaneously established as 1,
i.e. a link uv exists in the network and transmits using the c channel, otherwise
Representing the value of the one-way interference for which node u transmits its value of the interference IR to other nodes1Interference value IR with receiving node v2The sum, expressed as:
equation (V) illustrates:
is a decision when node v is within the interference range of node k, i.e. when uc＝vcAnd is
At the same time, when the utility model is in use,
that is to say the signal transmitted by node k directly affects node v, otherwise
Wherein u isc＝vcIndicating that node u uses the c channel simultaneously with node v. The interference formula for the node may change to:
the benefit value of each node can be derived from formula (III). Determining power and channel for each node in a networkWhether the strategy achieves nash equilibrium. If Nash equilibrium is reached then the algorithm is complete; when node updates power policy pu＝p(as)And judging whether the network is connected or not. If the network is not connected, the power and channel strategy p of the node is not changedu＝p(as-1),cu＝c(neig-1)(ii) a If the network is connected, calculating the current power of each node as pk＝p(m)The time benefit. Method for changing selectable channel strategy of node to obtain maximum benefit of node under current power
Then, the final power grade of the node is determined as
This determines the optimal channel selection for the node as
And the game is repeatedly played until the states of all the nodes in the network are not changed any more, and the game is ended.
In summary, the present invention provides a load balancing method combining topology control and channel allocation in a wireless sensor network, which can give consideration to both reliability of data transmission and load balancing, prolong the service life of the network, and provide an effective load balancing method for a WSN.
The above examples are only for illustrating the technical solution conception and the characteristics thereof, and are intended to enable those skilled in the art to understand the contents and practice the method of the present invention, and not to limit the protection of the present invention.
Selecting the optimal node power and the optimal channel number in the 4 steps, fixing the channel number of the nodes by taking an INATE method and a JACIRT method as comparison methods, changing the number of the nodes, and recording the average power of the network nodes and the average interference of the network nodes when the number of the different nodes in the network is different; the number of the node channels influences the interference of the nodes, so that the available number of the channels in the network is changed, and the average interference of the nodes in the network is recorded; and analyzing the life cycle of the node by comparing the average residual energy of the node with the number of dead nodes when the node runs in different rounds, and comparing and selecting the method with the optimal performance.
Fig. 2 is a graph comparing the average power consumption of the nodes in the three methods of JACLB, inite, and JACIRT, and it can be seen from fig. 2 that the average transmission power of the nodes in the three methods in the network decreases with the increase of the number of the nodes in the network because the nodes can ensure the network connection by using less power for transmission with the increase of the network scale. The topological average power of the JACLB method is smaller than the average power of networks adopting JACIRT and INATE methods, because the JACLB method directly adjusts the node transmitting power to adjust the path factor and the node load balancing factor on the basis of ensuring the network communication, and the node load is effectively balanced.
Fig. 3 shows a comparison of node mean interference graphs of JACLB and JACIRT in the case of different available channel numbers, as can be seen from fig. 3: in both methods, as the number of available channels increases, the number of node-selectable strategies increases, and the average interference in the network decreases. The node interference average value of the JACLB method is always lower than the network node interference average value in the JACIRT method, which shows that the JACLB method can effectively reduce the interference of the network node; fig. 4 shows a comparison of node mean interference graphs when the number of nodes is different in the three methods of JACLB, ate and JACIRT, and it can be seen in fig. 4 that when the nodes are gradually increased, the network node mean interference of the ate method is significantly greater than that of the other two algorithms, because the ate method only considers power to reduce energy consumption singly, and does not consider the influence of node interference caused by channels.
Fig. 5 is a comparison of average energy consumption graphs of network nodes by three methods, namely JACLB, inade and JACIRT, and it can be seen from fig. 5 that when the number of nodes in the network is constant, the average residual energy of the network nodes after the JACLB method is operated is always higher than that of the JACIRT method and the inade method, because the JACLB method can make the nodes obtain a larger load balancing evaluation factor and a smaller path factor by using a smaller power compared with the transmission power of the JACIRT method and the inade method, and the load of the nodes is balanced, so that the energy consumed by the nodes in the JACLB method is the minimum, and the average residual energy of the network nodes is the maximum. The JACLB method not only improves the energy benefit factor and the path factor of the node, but also balances the residual energy of the node and prolongs the network life cycle by adjusting the node transmitting power. It can be further seen from fig. 5 that during the operation of the network, the JACLB method has a smaller gradient relative to the other two algorithms, which indicates that the energy consumption speed of the node is smaller, and the more information resources are processed by the node in the limited node energy.
Fig. 6 shows a comparison of network node life cycle diagrams of three methods, JACLB, INATE and JACIRT, and it can be seen from fig. 6 that: with the increase of the number of running turns of the network, the number of nodes with energy exhausted in the network is increased continuously, and the number of dead nodes of the JACLB method is always smaller than that of dead nodes of the JACIRT method and the INATE method. This is because the JACLB method obtains power that can ensure that each performance of the network is optimal, and the optimal power is smaller than the average power of the network nodes in the JACIRT method. The JACLB method can achieve a longer life span than the JACIRT method and the inacte method.
Claims (4)
1. A method for combining topology control and channel distribution load balancing in a wireless sensor network is characterized in that: the method specifically comprises the following steps:
⑴ when the source node needs to communicate with the destination node, the source node broadcasts the route request RREQ data packet by flooding to all the neighbor nodes, and waits for the route to reply the RREQ data packet;
⑵ the source node updates its own neighbor node list content according to the received RREQ data packet, the RREQ data packet includes node residual energy e, minimum transmitting power p, receiving channel rc, neighbor node identification id and network connection factor f;
⑶ the source node calculates its load capacity evaluation factor Lb by using the residual energy of each node as an energy benefit factor according to the updated neighbor node list content, selects the shortest path from the source node to other nodes in the communication area according to the density degree of the nodes in the communication area, and calculates the node path factor LD;
⑷ the source node plays games according to the updated neighbor node list content, evaluates the network communication condition and the node benefit when the node selects different transmitting power and different channel strategies, and selects the power and channel game strategy when the node benefit is maximum.
2. The method for joint topology control and channel allocation load balancing in a wireless sensor network according to claim 1, wherein: in step (3), the source node calculates a load capacity evaluation factor Lb of each node by using the residual energy of each node as an energy benefit factor according to the updated content of the neighbor node list, that is, calculates a value of the load capacity evaluation factor Lb of each neighbor node according to formula (I):
wherein, the load capacity assessment factor value takes the residual energy of the source node and the neighbor nodes as an energy benefit factor, Ecost·uRepresenting the residual energy of node u, EvRepresenting the residual energy of node v, E0(u)，E0(v) Representing the initial energies of node u and node v, respectively.
3. The method for joint topology control and channel allocation load balancing in a wireless sensor network according to claim 1, wherein: in step (3), the shortest path from the source node to each other node in the communication area is selected according to the density of the nodes in the communication area, and the node path factor LD is calculated, that is, according to the updated neighbor list content, the node calculates the value of the path factor LD according to formula (II):
wherein the path factor of the nodes is characterized by the node efficiency
N represents the total number of nodes in the whole network, A represents the total area of the node distribution area, a represents a constant, and puRepresents the transmit power of the nodes and d (u, v) represents the distance between the nodes.
4. The method for joint topology control and channel allocation load balancing in a wireless sensor network according to claim 1, wherein: in the step (4), the evaluation node selects the network communication status and the node benefit when selecting different transmitting powers and different channel strategies, selects the power and channel game strategy when the node benefit is the maximum, namely compares the network communication status and the node benefit of the node under different power grades and different channel states, and judges whether the game model reaches nash balance;
the power and channel game strategy for selecting the node with the maximum benefit comprises the following steps:
when the network is updated, one or only one node can update the transmitting power and the channel strategy of the node, namely when a certain node updates the strategy, the power and the channel state of other nodes in the network are kept unchanged; initializing channels and power for all network nodes, wherein the initialization power of each node is the maximum power of the communication between the node and a neighbor node, and when a game updating strategy is started, the node firstly reduces one power level and then judges the connectivity of the whole network; if the network is not connected, the node cannot ensure the basic connection performance of the network when using the power level, then the node returns to the previous power level and keeps the current state of the node unchanged; if the network is still connected, respectively calculating the influence of the node on the benefit function by selecting different available channels under the power level, selecting a channel strategy corresponding to the maximum value in the benefit values as the optimal channel selection corresponding to the node at the moment, and when the benefit calculation corresponding to all the power levels of the node is completed, selecting the power value corresponding to the maximum benefit as the optimal power of the node; and repeating the game on each node until the states of all the nodes in the network are not changed, namely, a Nash equilibrium state is achieved, and the game is ended.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710571325.9A CN107484207B (en) | 2017-07-13 | 2017-07-13 | Combined topology control and channel distribution load balancing method in wireless sensor network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710571325.9A CN107484207B (en) | 2017-07-13 | 2017-07-13 | Combined topology control and channel distribution load balancing method in wireless sensor network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107484207A CN107484207A (en) | 2017-12-15 |
| CN107484207B true CN107484207B (en) | 2020-06-30 |
Family
ID=60596483
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710571325.9A Active CN107484207B (en) | 2017-07-13 | 2017-07-13 | Combined topology control and channel distribution load balancing method in wireless sensor network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107484207B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109600812B (en) * | 2019-02-21 | 2021-06-29 | 中国联合网络通信集团有限公司 | Routing method, routing device and sensor |
| CN112565078B (en) * | 2020-11-20 | 2022-10-14 | 普联技术有限公司 | Network routing method, device, equipment and computer readable storage medium |
| CN112689282B (en) * | 2020-12-16 | 2022-07-05 | 曲阜师范大学 | Anonymous communication method for protecting position privacy in wireless sensor network |
| CN114070773B (en) * | 2021-10-13 | 2023-04-25 | 杭州电子科技大学 | Space network routing strategy based on shortest path length |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101808384A (en) * | 2010-03-25 | 2010-08-18 | 中国电信股份有限公司 | Wireless sensor network, routing method and node equipment |
| CN102209368A (en) * | 2011-06-16 | 2011-10-05 | 中国人民解放军理工大学 | Multi-service cooperation multiple access method based on cooperative game in distributed self-organized network |
| CN102969720A (en) * | 2012-11-01 | 2013-03-13 | 北京交通大学 | Load dynamic control and analysis method capable of being applied in smart power grids |
| CN105722173A (en) * | 2016-01-26 | 2016-06-29 | 华东交通大学 | Large-scale WSNs topology control method based on two-way attraction mechanism |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040199786A1 (en) * | 2002-12-02 | 2004-10-07 | Walmsley Simon Robert | Randomisation of the location of secret information on each of a series of integrated circuits |
-
2017
- 2017-07-13 CN CN201710571325.9A patent/CN107484207B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101808384A (en) * | 2010-03-25 | 2010-08-18 | 中国电信股份有限公司 | Wireless sensor network, routing method and node equipment |
| CN102209368A (en) * | 2011-06-16 | 2011-10-05 | 中国人民解放军理工大学 | Multi-service cooperation multiple access method based on cooperative game in distributed self-organized network |
| CN102969720A (en) * | 2012-11-01 | 2013-03-13 | 北京交通大学 | Load dynamic control and analysis method capable of being applied in smart power grids |
| CN105722173A (en) * | 2016-01-26 | 2016-06-29 | 华东交通大学 | Large-scale WSNs topology control method based on two-way attraction mechanism |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107484207A (en) | 2017-12-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107484207B (en) | Combined topology control and channel distribution load balancing method in wireless sensor network | |
| CN107182074B (en) | A kind of routing optimal path choosing method based on Zigbee | |
| Smail et al. | A multipath energy-conserving routing protocol for wireless ad hoc networks lifetime improvement | |
| JP4598000B2 (en) | Cost determination in multi-hop networks | |
| US20060176829A1 (en) | Mobile ad-hoc network | |
| Ali et al. | An on-demand power and load-aware multi-path node-disjoint source routing scheme implementation using NS-2 for mobile ad-hoc networks | |
| CN103220688A (en) | Moving-load balancing method based on efficacy function in LTE (long term evolution) self-organized network | |
| Dana et al. | A Reliable routing algorithm for Mobile Adhoc Networks based on fuzzy logic | |
| Prasad et al. | Performance comparison of multipath routing protocols for mobile ad hoc network | |
| Liao et al. | Adaptive situation-aware load balance scheme for mobile wireless mesh networks | |
| Gawas et al. | QoS based multipath routing in MANET: A cross layer approach | |
| CN106792744B (en) | The topology control method being connected to based on k point under two channel-connectivities of building | |
| Al Sharah et al. | Trade-off between Energy Consumption and Transmission Rate in Mobile Ad-Hoc Network | |
| Majdkhyavi et al. | A survey of existing mechanisms in energy-aware routing in Manets | |
| Chaudhari et al. | Multilayered distributed routing for power efficient MANET performance | |
| CN107801227A (en) | A kind of routing scheduling method towards wireless sensor network stratification analysis | |
| Rasheed et al. | Cluster-quality based hybrid routing for large scale mobile multi-hop networks | |
| Balan et al. | Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks | |
| Tabatabaei et al. | Routing and quality of service support for mobile ad hoc networks | |
| Mahimkar et al. | S-mecra: A secure energy-efficient routing protocol for wireless ad hoc networks | |
| Musyoka et al. | Mutation Based Hybrid Routing Algorithm for Mobile Ad-hoc Networks | |
| Alleema et al. | Cooperative and fresher encounter algorithm for reducing delay in MANET | |
| Visumathi et al. | Enhancing Reliability in Multi-Path Mobile Wireless Sensor Network | |
| CN110505669A (en) | Lose the chance method for routing based on emission rate in sensitive wireless sensor network | |
| Taranum | Load Aware Congestion Control and Optimized Multipath Routing Protocol with Link Repair Mechanism for Wireless Networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |