CN115209504B - Mobile social network routing method based on preference communities and energy consumption factors - Google Patents

Mobile social network routing method based on preference communities and energy consumption factors Download PDF

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CN115209504B
CN115209504B CN202210850192.XA CN202210850192A CN115209504B CN 115209504 B CN115209504 B CN 115209504B CN 202210850192 A CN202210850192 A CN 202210850192A CN 115209504 B CN115209504 B CN 115209504B
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黄丽薇
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Southeast university chengxian college
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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
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/12—Messaging; Mailboxes; Announcements
    • 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

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Abstract

The invention relates to a mobile social network routing method based on preference communities and energy consumption factors, which comprises the following three parts: a. establishing a mobile model based on preference communities; b. proposing a community-based probability-assisted routing protocol CBPAR; c. providing an energy balance routing strategy CBEBR based on communities; regarding each person of the handheld wireless communication equipment as a node, introducing the concept of communities, moving the node among communities, moving the node to communities with different probabilities, using probability information of the node entering the communities, calculating social contact probability of a source node, a relay node and a target node, establishing a mobile model based on the communities, and providing a probability auxiliary routing protocol based on the mobile model of the communities; the energy consumption rate is calculated by considering initial energy, message transmission consumption energy, maintenance equipment active consumption energy and node residual energy, a routing strategy considering the energy is formulated, an energy threshold is set, and excellent information transmission performance and energy consumption ratio are sought.

Description

Mobile social network routing method based on preference communities and energy consumption factors
Technical Field
The invention provides a routing method based on preference communities and energy consumption factors aiming at a mobile social network (Mobile Social Networks, MSNs for short), which is mainly used for solving the information transmission problem in the mobile social network and belongs to the technical field of computer wireless communication.
Background
With the rapid development of technologies such as chips, sensors, communication and the like, intelligent mobile devices are widely popularized. The mobile devices may form a network through short-range wireless communication technology, such as a mobile ad hoc network, a delay tolerant network, etc., which can provide convenient communication without a communication infrastructure. The relationship between the mobile equipment and the person is more and more intimate, and the mobile characteristics of the intelligent mobile equipment comprise social attributes and motion rules of the person to a certain extent. Thus, more and more researchers are focusing on combining mobile networks with social networks, and the mobile social networks are emerging.
Regarding each person of the handheld wireless communication device as a node, a number of protocols for MSNs have been proposed, including Epidemic, LABEL, bus, propset, simBet, and so on. The main idea of Epidemic protocol is a flooding mechanism that the nodes in the protocol copy when meeting, the nodes in the protocol indicate the information stored in the node buffer zone through summary vectors, when two mobile nodes meet, the mutual vector information is checked to obtain the information situation carried by each mobile node but not the other mobile node, and the information is transmitted to the other mobile node. The protocol has high information transmission success rate due to the fact that all the information of the encountered node buffer areas is copied, huge information average delay exists, and the buffer area/power requirements on bandwidth/nodes are large. The Prophet protocol is based on a single copy mechanism, obtains and updates the probability of transmitting information by utilizing the history meeting condition, and takes the probability of transmitting information as the basis of whether to transmit information. The signaling probability when two nodes first meet is set to some initial value, and when more nodes meet, the signaling probability value is updated. If two nodes no longer meet within a specified period of time, the signaling probability value will decrease depending on the time factor. The probability of passing information updated by directly encountering a node also results in the probability of signaling by other nodes being indirectly updated.
During the course of research on MSNs, researchers have found patterns in which the social behavior of people represented as nodes tends to be repetitive. The relationship of individuals to areas of daily life tends to stabilize over a period of time. The area of a person's daily life depends on his role, responsibility, preferences. Many factors such as personal friends, weather, local popularity affect the movement behavior of a person. Everyone has a community of preference, which refers to a home, workplace, store, park, entertainment venue, etc. Two strangers can know each other through the community.
Existing mobility models and routing methods of MSNs focus on some typical information transfer performance metrics, such as information transfer success rate and information average delay, with little consideration on node mobility preferences and energy consumption in routing. In mobile social networks, mobile devices are often battery powered and the energy, buffer space, of the devices is limited, so energy consumption is an important part of the routing protocol to consider. For mobile devices that rely on battery power, the energy is limited, and when a node is too active, it will take over more message delivery tasks, increasing the node's energy consumption, and shortening the node's lifetime, thus taking into account the rate of energy consumption is critical.
Disclosure of Invention
Technical problems: the invention aims to provide a mobile social network routing method based on a preference community and energy consumption factors, which solves the problem of information transmission in a mobile social network. By using the method proposed by the present invention, an optimal trade-off between information transfer performance and energy consumption can be obtained.
The technical scheme is as follows: the invention relates to a mobile social network routing method based on preference communities and energy consumption factors, which comprises the following three parts:
a. establishing a mobile model based on preference communities;
b. proposing a community-based probability-assisted routing protocol CBPAR;
c. providing an energy balance routing strategy CBEBR based on communities;
Regarding each person of the handheld wireless communication equipment as a node, introducing the concept of communities, moving the node among communities, moving the node to communities with different probabilities, using probability information of the node entering the communities, calculating social contact probability of a source node, a relay node and a target node, establishing a mobile model based on the communities, and providing a probability auxiliary routing protocol based on the mobile model of the communities; the energy consumption rate is calculated by considering initial energy, message transmission consumption energy, maintenance equipment active consumption energy and node residual energy, a routing strategy considering the energy is formulated, an energy threshold is set, and excellent information transmission performance and energy consumption ratio are sought.
The mobile model based on the preference community comprises probability information of all nodes entering the community, and the setting mode is as follows: the simulation area is provided with a plurality of communities, each node is provided with three preference communities, each node goes to the three preference communities with randomly generated probability at the beginning of simulation, and the probability that each node goes to the three preference communities is not changed; the social contact probability calculation mode between two nodes is as follows: multiplying the probability that a certain node goes to a certain community by the probability that another node goes to the community to obtain the meeting probability established by the node and the other node through the community, and adding the meeting probabilities of the node and the other node in all communities to obtain the social contact probability between the two nodes.
The community-based energy balance routing strategy comprises efficacy functions of initial energy, message transmission consumed energy, active consumed energy of maintenance equipment, node remaining energy, energy consumption rate calculation mode, energy threshold setting mode, social contact probability and energy consumption factors.
The establishing the mobile model based on the preference community comprises the following steps:
a1. Assuming that the moving area of all nodes is a rectangular area with length L and width W; in the rectangular area, 9 communities are randomly distributed, the radius of each community is R c, some communities possibly overlap each other, the total number of nodes participating in simulation is N nodes, all the communities are randomly distributed in the 9 communities, three frequent communities are assumed to exist for each node, when a model is initialized, three preference communities are randomly set for each node, the probability that the node goes to the three preference communities is P1, P2 and P3 in sequence, wherein P1 is larger than P2 and P3, all the nodes can select the next destination according to the probability of the preference communities, the motion speed v is randomly selected between a specified maximum speed v max and a minimum speed v min, the motion speed v goes to the destination at the speed v epsilon (v max,vmin), and the Angle n between the position of a certain node and the destination is calculated through a formula (1):
wherein the coordinates of the current position of the node are (x 1,y1), and the coordinates of the central position of the destination are (x 2,y2);
the distance des of the node from the destination is given by equation (2):
When the distance des between the node and the destination is smaller than the radius R C of the community, the node can be considered to enter the community, then stay for T pause seconds in the community, and repeat the above process;
Dividing the total time T into time intervals T interval with fixed intervals, detecting the positions of all nodes every time interval T interval, and judging whether the distance between the node and the destination is smaller than the radius of the community, wherein the position of a certain node at the moment is obtained by the following formulas (3) and (4):
xnew＝xold+v×Tinterval×cosAnglen (3)
ynew＝yold+v×Tinterval×sinAnglen (4)
Where x new and y new are the new position coordinates of the node, v is the speed of motion, tinterval is the time interval;
a2. Each node has its own preference community, and given that two nodes have the same preference community, they will be more likely to meet in a common preference community; assuming that the preference communities of the node i are C1, C4 and C5, and the preference communities of the node d are C2, C4 and C6, the probability that the preference communities meet in the community C4 is larger;
Wherein C4 is a community commonly preferred by node i and node d, if the probability that node i goes to community C4 is denoted as P (i,C4), the probability that node d goes to community C4 is denoted as P (d,C4), and the probability of meeting established by node i and node d through community C4 is denoted as P (i,C4,d), as measured by formula (5):
P(i,C4,d)＝P(i,C4)×P(d,C4) (5)
There may also be several communities of preference between the two nodes i and d, where they may meet, so the social relationship between node i and node d is represented by the case where node i meets node d in all communities, called social contact probability (Social Contact Probability, SCP), represented by P (i,d), and can be calculated by equation (6):
Where C k represents a certain common preference community of node i and node d, C (i,d) represents a set of all common preference communities of node i and node d, Representing the probability that node i goes to community C k,/>Representing the probability that node d goes to community C k,/>Representing the relationship established by node i and node d through community C k.
The proposed community-based probabilistic assisted routing protocol CBPAR includes:
b1. When the node i meets the node d, message transmission is carried out according to the following routing strategy, and if the node d is a destination node, the node i directly sends information to the node d; otherwise, the node d is called as a relay node, the social contact probability SCP of the relay node d and the destination node is calculated by the formula (6), if the SCP of the relay node d and the destination node is larger than the SCP of the node i and the destination node, the message is sent to the encountered relay node, otherwise, the message is not forwarded, and the node i carries the message to continue moving;
b2. Comparing CBPAR protocol with Epidemic protocol and propset protocol, three performance indexes are mainly referred to: message delivery rate, message delivery average delay, routing overhead rate;
Message delivery rate: the ratio representing the successful delivery of a message from a sending node i to a destination node d can be found by equation (7):
Wherein SD represents the number of messages successfully delivered by node i to destination node d, and SI represents the total number of messages generated by node i destined for node d;
Messaging average delay: the average time of a message from a sending node i to a destination node d is obtained by the formula (8):
Where T d(mn) represents the time at which message m n was successfully delivered to destination node d,
T i(mn) represents the time when node i generated message m n;
Routing overhead rate: the ratio of the total number of messages unsuccessfully delivered to the total number of messages successfully delivered to the destination node is calculated by equation (9):
the proposed community-based energy balance routing strategy CBEBR includes:
c1. All nodes consume energy when transmitting messages, and all nodes consume energy at all moments as long as the nodes are in an active state, and before the messages are transmitted, all nodes detect the energy; the node moves from the beginning to the current time t, and the initial value of the energy of the node i is recorded as The energy consumed for messaging is noted as/>The energy consumed to keep the device active is noted/>The energy remaining at node i is noted/>The node i residual energy is represented by equation (10):
To facilitate calculation of the rate of energy consumption of node i, a variable ρ i (t) is set, which represents the specific gravity of the energy consumed by the messaging of node i from the beginning to the current time t, to the total energy consumed by node i, calculated by equation (11):
Here, T' represents a short period of time nearest to the current time T; the energy consumption rate of the node i in the T 'period is represented by S i (T'), and the number of transmitted messages in the T 'period is represented by Q i (T'), and the energy consumption rate of the node i can be calculated by (12):
Where α is the amount of energy the node needs to consume each time it forwards the message, where α×q i (T ') in the formula can reflect the amount of energy the node i consumes for forwarding the message in the period T', where The size of the energy value available for forwarding the message by the node i at the current moment can be reflected;
Specifically, when node i meets node d, the two nodes calculate respective energy consumption rates according to equation (12), S D (T ') and S i (T'), respectively; when the energy consumption rate S D (T') of the d node is smaller than the energy consumption rate of the node i, the d node can receive the message from the node i; if the energy consumption rate of the node i is too small, the energy consumption rate of the relay node encountered by the node i is larger than that of the relay node, so that a plurality of messages carried by the node i are not sent out, the energy consumption rate of the node i is continuously reduced, and more messages are retained in the buffer space of the node i, so that the network performance is greatly influenced; thus, a constant θ greater than 1 is defined for setting the condition for message forwarding, as shown in equation (13):
Sd(T')<Si(T’)×θ (13)
when the energy consumption rate S d (T ') of the node d is smaller than the product of the energy consumption rate S i (T') of the node i and the constant θ, it means that the node d can accept the message; therefore, the energy consumption in the network can be balanced and the life cycle of the network can be improved while the message passing rate is not influenced;
c2. In addition to the energy consumption rate, a node remaining energy threshold E end is set, representing the energy required by the node to maintain the current state, taking into account whether the node has sufficient remaining energy to transfer messages; furthermore, the identifier X indicates whether the node can take on more forwarding tasks, X being calculated by equation (14):
When X is 1, the description node i can also bear the forwarding task; when X is 0, the rest energy of the node i is only enough to maintain the current state and cannot bear more forwarding tasks;
c3. In order to balance the delivery rate and the energy consumption of the nodes, defining an efficacy function, and combining the social contact probability of the nodes with the residual energy of the nodes; assuming i is the sending node of the message and d is the destination node of the message, the efficacy function of the i node is calculated by equation (15):
Delta is a constant and represents the social contact probability of the node and the weight coefficient of the residual energy of the node; p (i,d) is the social contact probability from node i to node d, which is obtained by the formula (6), The residual energy of the node i is represented and is obtained by a formula (10);
c4. when node i meets node d, message passing occurs according to the following routing strategy:
If the node d is the destination node, the node i directly sends information to the node d; if the node d is not the destination node, the node d is called a relay node; the method comprises the following steps:
Step1, firstly calculating the residual energy of the node d by a formula (10), then calculating an identifier X of the node d by a formula (14), and judging whether the relay node d can bear an energy forwarding task according to the identifier X; if the identifier X is 1, executing Step2, otherwise, the message is not forwarded, and the node i carries the message to continue moving;
Step2, calculating the energy consumption rates of the node i and the relay node d by the formula (12), if the energy consumption rates of the node i and the relay node d meet the formula (13), executing Step3, otherwise, the message is not forwarded, and the node i carries the message to continue moving;
Step3, firstly, respectively obtaining social contact probability from the node i and the relay node d to the target node according to a formula (6), and then calculating efficacy functions of the node i and the relay node d according to a formula (15); if the efficacy function of the relay node d is larger than that of the node i, the message is copied and sent to the encountered relay node d, otherwise, the message is not forwarded, and the node i carries the message to continue moving.
Compared with the prior art, the method provided by the invention has the following advantages:
(1) According to the method, community preference of the nodes is utilized, the nodes move to communities with different probabilities to which the nodes prefer, social contact probability of the source node, the relay node and the target node is calculated by utilizing probability information of the nodes entering the communities, a community-based mobile model is established, a probability auxiliary routing protocol based on the community mobile model is provided, and information sending efficiency is improved;
(2) In the method, typical information transmission performance indexes such as information transmission success rate and information average delay are focused in a routing strategy, meanwhile, the energy consumption problem is considered, initial energy, information transmission consumption energy, active consumption energy of maintenance equipment and node residual energy are considered, the energy consumption rate is calculated, the routing strategy considering the energy is formulated, an energy threshold is set, and a better relation between transmission performance and energy consumption is sought.
Drawings
Figure 1 is a schematic diagram of a mobile social network routing method based on preferred communities and energy consumption factors nodes i and d going to the preferred communities,
Figure 2 is a community-based probabilistic assisted routing protocol CBPAR messaging flow diagram,
Figure 3 CBPAR is a diagram of the basic performance scenario of the protocol,
Figure 4 CBPAR/propset/Epidemic routing protocol routing overhead rate versus graph,
Figure 5 is a community-based energy-balanced routing policy CBEBR messaging flow diagram,
Figure 6 CBPAR/CBEBR is a delivery rate comparison graph of the protocol,
Fig. 7 CBPAR/CBEBR protocol is a graph of routing overhead rate versus time.
Detailed Description
There are three main stages in the implementation. The first stage mainly completes the establishment of a mobile model and the calculation of social contact probability. And in the second stage, information is transmitted mainly according to the social contact probability. And the third stage mainly completes the information transmission according to the social contact probability and the efficacy function of the energy consumption factors.
In the first stage 0-T time, the nodes move or stand still in a mode described by a, enter a preference community, meet other nodes in the preference community or in a communication radius, generate inter-node contact probability, and calculate social contact probability.
1) And constructing a movement model, generating a preference community and a forward probability of each node, and generating movement information such as direction/speed/stay condition and the like. The simulation area was set to 2500 x 2500 square meters, and 9 circular areas with a radius of 100 meters were set as communities within this area, some of which may overlap each other. The person of each handheld wireless communication device is regarded as a node, and the number of mobile nodes participating in simulation is 50, and the mobile nodes are randomly distributed in the 9 communities. Each node has three preference communities, and each node goes to its preference communities with a certain probability. The total probability that each node goes to three preference communities is 1, the probability that each node goes to the three preference communities is randomly generated in the initial stage of mobile model establishment, the probability is not changed any more after the generation, and the node selects a target community according to the probability. The movement direction of the node is the straight line direction from the starting point in the initial community to the destination in the destination community, and the movement speed is randomly selected from the specified maximum speed to the minimum speed.
2) Dividing the total time T into time periods with fixed intervals, recording the meeting condition of the nodes at the end of each time period, and determining the movement condition of the nodes at the moment. The angle between the node position and the destination can be calculated by the formula (1), the distance between the node and the destination is obtained by the formula (2), the position of the node at the moment can be obtained by the formulas (3) and (4), when the distance between the node and the destination is smaller than the radius of a community, the node is considered to enter the communication range of the community, then the node stays in the community for a plurality of seconds, and the above process is repeated. Judging whether the node reaches a target community, if not, moving the node according to the original speed/direction, and if so, checking the time label of the node staying in the community. If the residence time of the node in the community is less than the specified length, the node continues to stay in the community, and if the residence time is up to the specified length, the node proceeds to the next community in the linear motion direction and the newly generated speed according to the probability of proceeding to other communities generated during initial modeling.
3) And calculating the social contact probability. And at the time of the first period T, all nodes store data such as the number of other nodes encountered/time of each encounter/number of encounters and the like in the preference community, the contact probability of two nodes established through a single public preference community can be calculated according to a formula (5), and the social contact probability of two nodes established through a plurality of public preference communities and the like can be calculated according to a formula (6).
And in the second stage 0-T time, generating information, and transmitting the information according to the social contact probability.
1) The second phase total time T is divided into time segments of fixed interval, and each node generates a message at the end of each time segment in the second phase 0-T/4. The length of information generated by all nodes is set to be the same, and the life cycle of the information is set to be infinitely long.
2) And when each time period is finished, checking the positions of all nodes, if the distance between two nodes is smaller than the specified meeting distance or the two nodes are in the same community, namely meeting, checking whether the opposite party is a target node carrying information by the node, if so, directly transmitting the information to the opposite party, and if not, judging whether the opposite party node has higher social contact probability than the opposite party node. The node judges according to the social contact probability calculated in the formula (6), if the social contact probability of the node of the opposite side is higher, the information carried by the node is transmitted to the opposite side, otherwise, the node keeps the information.
3) And when each time period is finished, counting the data such as the number of messages successfully transmitted to the destination node/the number of messages successfully transmitted through the relay node/the number of messages in the relay node, counting the data such as the number of nodes required to be successfully transmitted by each message/the delay time required to be successfully transmitted by each message, and calculating the current information transmission success rate/the information average delay/the routing overhead rate.
4) The proposed CBPAR protocol is compared with Epidemic, prophet. After the set time is up, the time is taken as the horizontal axis, the signaling situation is taken as the vertical axis, and a CBPAR protocol basic performance situation diagram can be drawn. With time as the horizontal axis and routing overhead of CBPAR, epidemic, prophet as the vertical axis, a routing overhead graph of CBPAR, epidemic, prophet can be drawn. Fig. 3 is a diagram of the basic performance of CBPAR protocols. Fig. 4 compares the routing overhead rates of CBPAR, epidemic, prophet. Epidemic protocol delivers copies of messages in a flooded mode, thus having the lowest average delay of message transmission, but producing a large number of copies of messages, resulting in a very high routing overhead rate. The propset protocol performs message delivery by comparing delivery probabilities, and uses probabilities to assist in deciding whether to forward a message, so that the protocol has a higher time delay than the Epidemic protocol, but has a lower routing overhead rate. The CBPAR protocol makes message delivery decisions by comparing social contact probabilities, and when forwarding a message, only nodes with higher social contact probabilities are encountered to deliver the message, thus possessing higher message delivery delays, but the routing overhead rate is lowest.
The third stage adjusts the signaling mechanism to obtain new signaling performance and energy consumption. And in the third stage 0-T time, generating information, and transmitting the information according to the social contact probability and the efficacy function of the energy consumption factors.
1) The total time T is divided into time periods of fixed interval, and each node generates a message at the end of each time period within 0-T/4. The information generated by all nodes is set to be the same in length, and the life cycle of the information is set to be infinitely long.
2) When each time period is finished, checking the positions of all nodes, if the distance between two nodes is smaller than the specified meeting distance or the two nodes are in the same community, namely meeting, checking whether the encountered node d is a destination node carrying information of the node i, and if the node d is the destination node, directly sending information to the node d by the node i; if node d is not the destination node, node d is referred to as a relay node. The method comprises the following steps:
Step1, firstly calculating the residual energy of the node d by the formula (10), then calculating the identifier X of the node d by the formula (14), and judging whether the relay node can bear the energy forwarding task according to the identifier X. If the identifier X is 1, step2 is executed, otherwise the message is not forwarded, and the node i carries the message to continue moving.
Step2, calculating the energy consumption rates of the node S and the relay node d by the formula (12), if the energy consumption rates of the node i and the relay node d meet the formula (13), executing Step3, otherwise, the message is not forwarded, and the node i carries the message to continue moving.
Step3, firstly, respectively obtaining the social contact probability from the node i and the relay node d to the target node according to the formula (6), and then calculating the efficacy functions of the node i and the relay node d according to the formula (15). If the efficacy function of the relay node d is larger than that of the node i, the message is copied and sent to the encountered relay node d, otherwise, the message is not forwarded, and the node i carries the message to continue moving.
3) And when each time period is finished, counting the data such as the number of messages successfully transmitted to the destination node/the number of messages successfully transmitted through the relay node/the number of messages in the relay node, counting the data such as the number of nodes required to be successfully transmitted by each message/the delay time required to be successfully transmitted by each message, and calculating the current information transmission success rate/the information average delay/the routing overhead rate.
4) The CBEBR protocol is an improved routing protocol based on the CBPAR protocol, considers the energy consumption rate and the residual energy of the nodes, combines the social contact probability and the residual energy of the nodes, provides an efficacy function, and carries out signaling decision according to the efficacy function. The CBEBR protocol is compared with the CBPAR protocol. After the set time is up, the time is taken as a horizontal axis, the information delivery rate condition is taken as a vertical axis, and a comparison chart of the two protocol delivery rate conditions can be drawn. By taking time as the horizontal axis and routing cost as the vertical axis, a comparison graph of the routing cost of two protocols can be drawn. As shown in fig. 6, CBEBR protocol has a higher delivery rate than CBPAR protocol. As shown in fig. 7, the routing overhead rate of both protocols is gradually decreasing with increasing time. The CBEBR protocol incorporates an efficacy function, and the social contact probability and the residual energy of the nodes have different weights when the message is forwarded. And CBPAR protocol only considers social contact probability, so that message forwarding selectivity is higher. The CBEBR protocol has a higher routing overhead rate than the CBPAR protocol.

Claims (1)

1. A mobile social network routing method based on preference communities and energy consumption factors is characterized by comprising the following three parts:
a. establishing a mobile model based on preference communities;
b. proposing a community-based probability-assisted routing protocol CBPAR;
c. providing an energy balance routing strategy CBEBR based on communities;
Regarding each person of the handheld wireless communication equipment as a node, introducing the concept of communities, moving the node among communities, moving the node to communities with different probabilities, using probability information of the node entering the communities, calculating social contact probability of a source node, a relay node and a target node, establishing a mobile model based on the communities, and providing a probability auxiliary routing protocol based on the mobile model of the communities; the method for establishing the mobile model based on the preference community comprises the following steps of considering initial energy, message transmission consumption energy, maintenance equipment active consumption energy and node residual energy, calculating energy consumption rate, making a routing strategy considering energy, setting an energy threshold, searching excellent information transmission performance and energy consumption ratio, and establishing the mobile model based on the preference community:
a1. Assuming that the moving area of all nodes is a rectangular area with length L and width W; in the rectangular area, 9 communities are randomly distributed, the radius of each community is R c, some communities possibly overlap each other, the number of all nodes participating in simulation is N nodes, all the communities are randomly distributed in the 9 communities which are randomly distributed, each node is assumed to have three preferential communities, when the model is initialized, each node randomly sets three preferential communities, the probability that the node goes to the three preferential communities is sequentially P1, P2 and P3, wherein P1 is larger than P2 and P3, all the nodes can select the next destination according to the probability of the preferential communities, the motion speed v is randomly selected between the specified maximum speed v max and the minimum speed v min, the motion speed v is v epsilon (v max,vmin) to the destination, and the Angle n between the position of a certain node and the destination is calculated through a formula (1):
wherein the coordinates of the current position of the node are (x 1,y1), and the coordinates of the central position of the destination are (x 2,y2);
the distance des of the node from the destination is given by equation (2):
When the distance des between the node and the destination is smaller than the radius R C of the community, the node can be considered to enter the community, then stay for T pause seconds in the community, and repeat the above process;
Dividing the total time T into time intervals T interval with fixed intervals, detecting the positions of all nodes every time interval T interval, and judging whether the distance between the node and the destination is smaller than the radius of the community, wherein the position of a certain node at the moment is obtained by the following formulas (3) and (4):
xnew＝xold+v×Tinterval×cosAnglen (3)
ynew＝yold+v×Tinterval×sinAnglen (4)
Where x new and y new are the new position coordinates of the node, v is the speed of motion, and T interval is the time interval;
a2. Each node has its own preference community, and given that two nodes have the same preference community, they will be more likely to meet in a common preference community; assuming that the preference communities of the node i are C1, C4 and C5, and the preference communities of the node d are C2, C4 and C6, the probability that the preference communities meet in the community C4 is larger;
Wherein C4 is a community commonly preferred by node i and node d, if the probability that node i goes to community C4 is denoted as P (i,C4), the probability that node d goes to community C4 is denoted as P (d,C4), and the probability of meeting established by node i and node d through community C4 is denoted as P (i,C4,d), as measured by formula (5):
P(i,C4,d)＝P(i,C4)×P(d,C4) (5)
There may be several communities of preference between the two nodes i and d, and the communities may meet in the communities, so that the social relationship between the node i and the node d is represented by the condition that the node i meets the node d in all communities, called social contact probability, represented by P (i,d), and can be calculated by the formula (6):
Where C k represents a certain common preference community of node i and node d, C (i,d) represents a set of all common preference communities of node i and node d, Representing the probability that node i goes to community C k,/>Representing the probability that node d goes to community C k,Representing the distress probabilities established by node i and node d through community C k;
the proposed community-based probabilistic assisted routing protocol CBPAR includes:
b1. When the node i meets the node d, message transmission is carried out according to the following routing strategy, and if the node d is a destination node, the node i directly sends information to the node d; otherwise, the node d is called as a relay node, the social contact probability SCP of the relay node d and the destination node is calculated by the formula (6), if the SCP of the relay node d and the destination node is larger than the SCP of the node i and the destination node, the message is sent to the encountered relay node, otherwise, the message is not forwarded, and the node i carries the message to continue moving;
b2. Comparing CBPAR protocol with Epidemic protocol and propset protocol, three performance indexes are mainly referred to: message delivery rate, message delivery average delay, routing overhead rate;
Message delivery rate: the ratio representing the successful delivery of a message from a sending node i to a destination node d can be found by equation (7):
Wherein SD represents the number of messages successfully delivered by node i to destination node d, and SI represents the total number of messages generated by node i destined for node d;
Messaging average delay: the average time of a message from a sending node i to a destination node d is obtained by the formula (8):
Where T d(mn) represents the time when message m n was successfully delivered to destination node d, T i(mn) represents the time when node i generated message m n;
Routing overhead rate: the ratio of the total number of messages unsuccessfully delivered to the total number of messages successfully delivered to the destination node is calculated by equation (9):
the proposed community-based energy balance routing strategy CBEBR includes:
c1. All nodes consume energy when transmitting messages, and all nodes consume energy at all moments as long as the nodes are in an active state, and before the messages are transmitted, all nodes detect the energy; the node moves from the beginning to the current time t, and the initial value of the energy of the node i is recorded as The energy consumed for messaging is noted as/>The energy consumed to keep the device active is noted/>The energy remaining at node i is noted/>The node i residual energy is represented by equation (10):
To facilitate calculation of the rate of energy consumption of node i, a variable ρ i (t) is set, which represents the specific gravity of the energy consumed by the messaging of node i from the beginning to the current time t, to the total energy consumed by node i, calculated by equation (11):
T' represents a short period of time nearest to the current time T; the energy consumption rate of the node i in the T 'period is represented by S i (T'), and the number of transmitted messages in the T 'period is represented by Q i (T'), and the energy consumption rate of the node i can be calculated by (12):
Where α is the amount of energy the node needs to consume each time it forwards the message, where α×q i (T ') in the formula can reflect the amount of energy the node i consumes for forwarding the message in the period T', where The size of the energy value available for forwarding the message by the node i at the current moment can be reflected;
Specifically, when node i meets node d, the two nodes calculate respective energy consumption rates according to equation (12), S D (T ') and S i (T'), respectively; when the energy consumption rate S D (T') of the node d is smaller than the energy consumption rate of the node i, the node d can receive the message from the node i; if the energy consumption rate of the node i is too small, the energy consumption rate of the relay node encountered by the node i is larger than that of the relay node, so that a plurality of messages carried by the node i are not sent out, the energy consumption rate of the node i is continuously reduced, and more messages are detained in a buffer space of the node i to influence the network performance; thus, a constant θ greater than 1 is defined for setting the condition for message forwarding, as shown in equation (13):
Sd(T')<Si(T’)×θ (13)
When the energy consumption rate S d (T ') of the node d is smaller than the product of the energy consumption rate S i (T') of the node i and the constant theta, the node d can accept the message, so that the energy consumption in the network can be balanced and the life cycle of the network can be improved while the message transfer rate is not influenced;
c2. In addition to the energy consumption rate, a node remaining energy threshold E end is set, representing the energy required by the node to maintain the current state, taking into account whether the node has sufficient remaining energy to transfer messages; furthermore, the identifier X indicates whether the node can take on more forwarding tasks, X being calculated by equation (14):
When X is 1, the description node i can also bear the forwarding task; when X is 0, the rest energy of the node i is only enough to maintain the current state and cannot bear more forwarding tasks;
c3. In order to balance the delivery rate and the energy consumption of the nodes, defining an efficacy function, and combining the social contact probability of the nodes with the residual energy of the nodes; assuming i is the sending node of the message and d is the destination node of the message, the efficacy function of the i node is calculated by equation (15):
Delta is a constant and represents the social contact probability of the node and the weight coefficient of the residual energy of the node; p (i,d) is the social contact probability from node i to node d, which is obtained by the formula (6), The residual energy of the node i is represented and is obtained by a formula (10);
c4. when node i meets node d, message passing occurs according to the following routing strategy:
If the node d is the destination node, the node i directly sends information to the node d; if the node d is not the destination node, the node d is called a relay node; the method comprises the following steps:
Step1, firstly calculating the residual energy of the node d by a formula (10), then calculating an identifier X of the node d by a formula (14), and judging whether the relay node d can bear an energy forwarding task according to the identifier X; if the identifier X is 1, executing Step2, otherwise, the message is not forwarded, and the node i carries the message to continue moving;
Step2, calculating the energy consumption rates of the node i and the relay node d by the formula (12), if the energy consumption rates of the node i and the relay node d meet the formula (13), executing Step3, otherwise, the message is not forwarded, and the node i carries the message to continue moving;
Step3, firstly, respectively obtaining social contact probability from the node i and the relay node d to the target node according to a formula (6), and then calculating efficacy functions of the node i and the relay node d according to a formula (15); if the efficacy function of the relay node d is larger than that of the node i, the message is copied and sent to the encountered relay node d, otherwise, the message is not forwarded, and the node i carries the message to continue moving.
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