CN112702688A - Mobile car planning method combining energy supplement and data collection - Google Patents

Mobile car planning method combining energy supplement and data collection Download PDF

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CN112702688A
CN112702688A CN202010643978.5A CN202010643978A CN112702688A CN 112702688 A CN112702688 A CN 112702688A CN 202010643978 A CN202010643978 A CN 202010643978A CN 112702688 A CN112702688 A CN 112702688A
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焦万果
田梦秋
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Nanjing Forestry University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
    • 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 discloses a mobile car planning method combining energy supplement and data collection, and relates to the technical field of communication. The mobile car planning method combining energy replenishment and data collection employs a base station responsible for processing and receiving network-aware data, and a data collector with an MV serving as an auxiliary base station collects as much data as possible. DS data and other data which are not available for collection by the trolley can be directly transmitted to the base station in a multi-hop mode. Therefore, the energy consumption of remote data transmission can be obviously reduced, the service life of the network is prolonged, and meanwhile, data overflow and larger delay of DS data are avoided.

Description

Mobile car planning method combining energy supplement and data collection
Technical Field
The invention relates to the technical field of communication, in particular to a mobile car planning method combining energy supplement and data collection.
Background
Wireless Sensor Networks (WSNs) comprising many tiny sensor nodes have been widely used in various scenarios. Due to the fact that the battery capacity of the sensor node is limited, the sensor node cannot maintain long-time work, and therefore the development of the WSN is greatly limited. To maintain the survival of the network, dead nodes should be replaced or energy replenishment should be performed. In fact, the sensor nodes are often distributed in rare places, and the implementation of the method is very difficult and even difficult to realize. Since the energy replenishment and data collection operate at different frequencies, the cart can also act as a WDV to collect the sensory data in the network without interference while replenishing the network energy. Many studies propose efficient cart dispatch schemes that combine energy replenishment and data collection. These work show that network performance can be effectively improved by combining energy replenishment and data collection. However, most of the existing work assumes that the mobile car is responsible for collecting all the data perceived from the network, and the base station only processes the data it collects after one work cycle. Although this method can greatly reduce power consumption, there are some problems such as data overflow and large delay of sensitive data. In practical applications, due to the limited size of each sensor node buffer, the WDV may not collect the buffered data of all sensor nodes in time, thereby causing data overflow. Some delay-sensitive sensing data need to be transmitted to a base station in a short time (defined as DS data), as sensed by sensor nodes deployed in a fire-prone area. However, the WDV needs a certain time to complete the periodic work, which means that if DS data is transmitted to the WDV, the data may not arrive at the base station in time, and the service quality of the DS data cannot be guaranteed.
In a wirelessly rechargeable sensor network, energy replenishment combined with data collection mobile carts provide a promising solution for energy shortage and low energy efficiency in data transmission. Most of the existing work assumes that the MV collects all the data perceived from the network, which may lead to data overflow and large delays of delay sensitive data (DS data).
Disclosure of Invention
The technical problem to be solved by the invention is to provide a mobile trolley planning method combining energy supplement and data collection, provide a high-efficiency Neighbor Data Collection Algorithm (NDCA), select an optimal neighbor cluster group to send cache data, and further provide an Optimized Neighbor Data Collection Algorithm (ONDCA), thereby improving the normalization of the surrounding cluster groups and saving energy. A roadside cluster group collection algorithm (RCCA) considering Doppler effect is provided, so that the optimal roadside cluster group is selected to send the cache data to the mobile data collector, and the normalization of the roadside cluster group is maximized, and the energy is saved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the mobile trolley planning method combining energy supplement and data collection comprises the steps that a wireless mobile charging trolley periodically charges sensor nodes by utilizing a wireless energy transmission technology, so that self-maintenance of a network is realized, the sensor nodes are divided into non-uniform clusters according to residual energy and the distance from the sensor nodes to a base station, a charging algorithm based on the structure is provided, so that the time of the network is optimized, the residual energy, the node density and the distance from the sensor nodes to the base station are comprehensively considered, the weight of each sensor node to be charged is determined by utilizing fuzzy logic, the performance of the network is ensured, the difference between two charging modes of single-node charging and multi-node charging is analyzed, and WCV adopts different charging modes for different sensor nodes, so that the charging efficiency is optimized; the method has the advantages that a wireless mobile vehicle is used as a mobile data collector, long-distance communication is converted into short-distance communication, the service time of WRSN is effectively prolonged, the actual condition that a network is deployed in an isolated area is considered, the size of a cluster is determined according to the distance between the wireless mobile vehicle and a motion track in each period, cluster head nodes are dynamically selected according to residual energy, then each cluster head node sequentially sends cache data to a WDV, and the combined design of mobility control and space division multiple access technology is provided, so that the optimal compromise between a travel path and data transmission is realized; the mobile vehicle will act as WCV and WDV simultaneously, using WCV (also collect data) and WDV two different mobile vehicles, a distributed algorithm is proposed to adjust the data transmission rate of each sensor node, the total utility of the network can be maximized by sending buffered data to the mobile data collector at the optimal data rate, several wireless mobile vehicles are used to charge and collect buffered data for cluster groups simultaneously, anchor points responsible for transmitting the buffered data to the respective cluster groups on the vehicle are selected according to a semi-Markov energy prediction model, in order to minimize the travel distance, the mobile vehicle is planned to travel along the shortest Hamiltonian path, the network is divided into a plurality of units, each unit is assumed as a cluster group, the position of each anchor point in the cluster group is determined by its energy consumption rate, the mobile vehicle periodically goes to the respective cluster groups to charge and collect buffered data for the sensor nodes, thereby optimizing the data volume collected in the unit energy of the mobile trolley; the system model of the mobile cart that combines energy replenishment and data collection includes a network model, a charging model, innovative data collection strategies, data routing, and energy consumption.
Preferably, the network model uses V (| V | ═ n) to represent a set (L × L) of isomorphic sensor nodes deployed in the network, each sensor node V | >, andithe coordinate of e.V is recorded as (x)i，yi) The base station S in the center of the network is responsible for receiving and processing sensed data from the network, the battery capacity of the base station is assumed to be infinite, the network is divided into cluster groups based on the structure, a Cluster Head Node (CHN) of each cluster group is dynamically selected in each period according to residual energy, other nodes in the same cluster group send the sensed data to the CHN, and the sensor nodes viHas a battery capacity of BmaxThe residual energy of each sensor node at the time t is Bi，tIt can be seen that Bi，0＝Bmax。ρiRepresenting a sensor node viEnergy consumption rate of, then sensor node viCan be described as
Figure BSA0000213155640000031
Will hold the sensor node viIs defined as BminIf the remaining energy of the sensor node is liρiIs less than BminThe sensor node will stop working.
Preferably, the charging model defines C1Is to be treatedA set of charging cluster groups, the MV upon receiving a charging request leaving the base station, accessing the cluster groups to be charged along a predetermined route. After charging is finished, the mobile terminal returns to the base station for self-charging, and the travel path of the MV forms a closed cycle { S, c }1，c2，...，cnumS, where num is the number of clusters to be charged,
definition of tauc，iIs a pair of ithCluster group ci∈C1Time taken for node in the system to charge, i.e.
Figure BSA0000213155640000032
Wherein cn isiIs the number of nodes within the cluster set,
Figure BSA0000213155640000033
represents the energy, U, required by the jth node in the cluster groupi，jFor the charging efficiency of the trolley, which is related to the distance between the trolley and the node, the CHN consumes more energy than other nodes in the cluster group, and in view of the charging efficiency, we arrange the MV to stop at the CHN, so that the charging time of each cluster group can be more uniform, using tauchargeRepresents the total charge time per cycle, noted,
Figure BSA0000213155640000034
representing two adjacent cluster groups c to be charged by using Euclidean distanceiAnd ci+1Is indicated as
Di，j+1＝||(xi+1-xi)，(yi+1-yi)||，(2)
Total travel time of MV can be written as
Figure BSA0000213155640000035
Wherein D0，1Distance of the first charging cluster group to the base station, Dnum，num+1For the distance of the last charging cluster group to the base station,
in each charging cycle, the total time τ spent by the MV is
τ＝τtravel+τcharge，(4)。
Preferably, the innovative data collection strategy defines sensor nodes sensing DS data as DS sensor nodes, cluster groups containing DS sensor nodes as DS cluster groups, and children to reduce data overflow and delayiThe sensing data rate of the sensor node vi is defined as Ri (bit/s), BD (BD/RS), and the sensing data rate of the sensor node vi is set by the sub-cluster group for transmitting sensing data to the ith cluster group, and the sensing is set by all DS cluster groups in the networki，tThe total amount of data buffered by the CHN in the ith cluster group at a certain time t may be represented as:
Figure BSA0000213155640000041
the data buffered by the CHN in the ith cluster group includes the sensing data of the CHN and all the sensing data of the sub-cluster group except the DS data in the DS cluster group. The DS data can be directly transmitted to the base station in a multi-hop mode, and the problem of data overflow can be avoided because the base station can receive the sensing data which cannot be collected by the MV in time,
and when the MV reaches the cluster group, charging the nodes in the cluster, and simultaneously collecting the cache data of the CHN. The time taken to collect the cached data of the current charging cluster group is:
Figure BSA0000213155640000042
where G is the data collection rate of MV, e.g. 500kb/s [20 ]]. Collection time σ of cluster group cic，iLess than its charging time tauc，iIts neighbor cluster group may also send buffered data to the MV to maximize the total energy saved by the surrounding cluster groups. We define the set of neighbor cluster groups of the ith cluster group as nbi；
After the MV reaches the current cluster group to charge the sensor node, we assume that it can collect the buffered data of the surrounding cluster groups until the remaining time of the next cluster group to be charged reaches the minimum allowable level. Therefore, the trolley has more time to collect the cache data, saves more energy, and simultaneously ensures the life cycle of the network (which will be proved later), when the MV moves from the current cluster group to the next adjacent cluster group to be charged, the roadside cluster group is supposed to transmit the cache data to the moving MV, so that under the condition of unchanged driving track and time, more energy can be saved, and i is definedthAnd i +1thThe set of roadside clusters between the charging cluster groups is rsi，i+1To indicate the time points of change in each cycle, c1, c2 and c3 are three clusters to be charged, and their remaining life time points on the time axis are l1，l2And l3When the MV charges the node or moves along the travel path, the partial CHNs can send the buffered data to the MV as much as possible, and in a charging period, the remaining time of the next cluster group to be charged, such as c3, is longer or the data collection time of the current cluster group c2 is shorter than the charging time, so that the neighboring cluster group of the current cluster group can send the buffered data to the MV, thereby increasing the energy saving, and defining ciIdle time ftiFor MV in the current cluster group ciMaximum extra time that can be spent on, i.e. cluster group ciThe time between the completion of charging and the time remaining for the next cluster group to be charged minus the travel time of the cart, 2thFree time of cluster group is ft2＝l3-t4-(l3-t6)＝t6-t4Wherein t is4Is MV in 2thTime point when cluster group completes charging work,/3-t6Travel time for the cart. When ftiWhen the value is less than or equal to 0, let ft i0. Thus, cluster group ciIdle time ftiCan be written as:
Figure BSA0000213155640000051
wherein li+1Is a cluster group ci+1The remaining life time point, cfiIs MV complete cluster group ciAt the time point of charging operation, in the idle time of each cluster group, the MV can move a certain distance and approach to the CHNs of the adjacent cluster group;
defining the sum of the charging time and the idle time as a cluster group ciAvailable collection time Ac ofiCan be written as Aci＝τc，i+fti. The collection time may be less than the available time, considering the amount of buffered data, for cluster group c2Data collection at time t5End, instead of t6. The defined time is greater than the charge completion time point but less than the actual data collection completion time point as an additional time σec，i，2thExtra time of cluster group σec，2Can be expressed as sigmaec，2＝t5-t4. Wherein t is5Represents the time point, σ, at which the MV completes the collection of the cached data for the current cluster group and the neighbor cluster groupec，i≤ftiConsidering the extra time for each cluster group to be charged, the total time spent by the cart in each cycle should be updated as:
Figure BSA0000213155640000052
under the proposed data collection strategy, the cluster groups can be divided into three types, namely, a cluster group (to-be-charged cluster group) which needs to be charged and transmits the cache data, a cluster group (neighbor and roadside cluster groups) which only transmits the cache data, and other cluster groups (such as DS cluster group), when the current charging cycle is finished, the remaining energy B of the CHN in different cluster groups is considered in consideration of the saved energyi，tCan be expressed as:
Figure BSA0000213155640000053
in the above equation, let us assume that the remaining time of the charged cluster group is Bmax，ρiτ isEnergy consumed in a cycle, ei，saveThe energy saved by sending data to the MV. Set D is a cluster group set for sending the buffered data to MVs, C1E.g., D, it can be seen that by transmitting the buffered data to the MV, the energy consumption of the network can be significantly reduced, thereby extending the time of the network.
Preferably, all data sensed by the data routing and energy consumption from the surrounding environment will be transmitted to the base station through multiple hops, assuming that the CHN transmits data to the base station through the shortest path, using f respectivelyijAnd fiSTo represent slave sensor node viTo vjAnd traffic rate to base station S, the traffic balance of the data route can be written as:
Figure BSA0000213155640000061
only the energy consumed in the processes of sensing, receiving and transmitting is considered, and a medium energy consumption model, gamma R, is adoptediIs a sensor node viA power consumption rate for sensing data, wherein gamma represents power consumed for sensing one unit data,
Figure BSA0000213155640000062
representing a sensor node viEnergy consumption rate for receiving data of other nodes, ε is energy consumed for receiving a unit data, CijRepresenting a sensor node viTo sensor node vjThe energy consumption rate for transmitting a unit of data is expressed as:
Figure BSA0000213155640000063
wherein DijFor two sensor nodes viAnd vjDistance between, β1Is a constant term independent of distance, beta2Is a coefficient relating to the distance that,
Figure BSA0000213155640000064
for watchesShowing path loss, where the exponent a is typically 2 or 4, and similarly, from the sensor node viThe energy consumption rate for transmitting data to the base station S can be expressed as
Figure BSA0000213155640000065
The sensor node v can be obtainediThe energy consumption of (a) is:
Figure BSA0000213155640000066
the above formula shows that the energy consumed by data transmission accounts for the most energy consumption, the sensing data is transmitted to the MV, the long-distance data transmission is converted into the short-distance data transmission, the energy consumption can be reduced remarkably, meanwhile, the data transmitted by the CHN is much more than the data transmitted by other nodes in the cluster group, so the energy consumption is faster, and in order to maintain the energy balance of the network, the CHN of each cluster group is dynamically selected according to the corresponding remaining survival time in each period.
The beneficial effect of adopting above technical scheme is: the mobile car planning method combining energy replenishment and data collection employs a base station responsible for processing and receiving network-aware data, and a data collector with an MV serving as an auxiliary base station collects as much data as possible. DS data and other data which are not available for collection by the trolley can be directly transmitted to the base station in a multi-hop mode. Therefore, the energy consumption of remote data transmission can be obviously reduced, the service life of the network is prolonged, and meanwhile, data overflow and larger delay of DS data are avoided. An efficient Neighbor Data Collection Algorithm (NDCA) is provided, an optimal neighbor cluster group is selected to send cache data, and an Optimized Neighbor Data Collection Algorithm (ONDCA) is further provided, so that the normalization of surrounding cluster groups is improved, and energy is saved. A roadside cluster group collection algorithm (RCCA) considering Doppler effect is provided, so that the optimal roadside cluster group is selected to send the cache data to the mobile data collector, and the normalization of the roadside cluster group is maximized, and the energy is saved.
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The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is an exemplary diagram of a network model of the present invention;
fig. 2 is an explanatory view of the time axis of the present invention.
Detailed Description
The following describes in detail a preferred embodiment of the method for mobile car planning in combination with energy replenishment and data collection according to the present invention with reference to the accompanying drawings.
Fig. 1 and 2 show an embodiment of a mobile cart planning method of the present invention that combines energy replenishment and data collection:
the wireless mobile charging trolley periodically charges the sensor nodes by utilizing a wireless energy transmission technology, so that the self-maintenance of the network is realized. And dividing the sensor nodes into non-uniform clusters according to the residual energy and the distance from the sensor nodes to the base station, and providing a charging algorithm based on the structure, so as to optimize the time of the network. The residual energy, the node density and the distance from the base station are comprehensively considered, the weight of each sensor node to be charged is determined by using fuzzy logic, and the performance of the network is guaranteed. The difference between the two charging modes of single-node charging and multi-node charging is analyzed. WCV use different charging methods for different sensor nodes to optimize charging efficiency. The above work as WCV provides an effective charging strategy, thus ensuring WCV performance to some extent. But does not take into account the energy efficiency of the data transmission. The efficiency of using a wireless mobile cart as an energy supplier is not high enough, and the functions thereof need to be expanded.
The wireless mobile vehicle is used as a mobile data collector, long-distance communication is converted into short-distance communication, and the service time of the WRSN is effectively prolonged. Consider the fact that the network is deployed in an isolated area. In each period, the size of the cluster is determined according to the distance between the cluster and the motion trail, and the cluster head nodes are dynamically selected according to the residual energy. Then, each cluster head node sequentially sends the cached data to the WDV. A joint design of mobility control and spatial partitioning multiple access techniques is proposed in order to achieve an optimal trade-off between travel path and data transmission. According to the above work, it can be found that, by collecting all data sensed from the network by using the WDV, the energy consumed by data transmission can be significantly reduced, and at the same time, the life cycle of the network can be ensured.
To improve the energy efficiency of the data transfer, the moving vehicle will act as both WCV and WDV. With WCV (also collecting data) and WDV two different moving vehicles, a distributed algorithm is proposed to adjust the data transfer rate of each sensor node. By sending the buffered data to the mobile data collector at the optimal data rate, the overall utility of the network can be maximized. Several wireless mobile vehicles are used to simultaneously charge the cluster group and collect the buffered data. Anchor points responsible for transmitting the buffered data to the various cluster groups on the vehicle are selected according to a semi-markov energy prediction model. To minimize the travel distance, the moving vehicle is planned to travel along the shortest hamiltonian path. The network is divided into a number of units, each unit being assumed to be a cluster group. The position of each anchor point in the cluster group is determined by its energy consumption rate. The mobile trolley periodically goes to each cluster group to charge the sensor nodes and collect cache data, so that the data volume collected in the unit energy of the mobile trolley is optimized.
The system model of the mobile cart that combines energy replenishment and data collection includes a network model, a charging model, innovative data collection strategies, data routing, and energy consumption.
1. Network model
V (| V | ═ n) is used to represent a set of homogeneous sensor nodes (L × L) deployed in the network. Each sensor node viThe coordinate of e.V is recorded as (x)i，yi). The base station S, located at the hub, is responsible for receiving and processing the perceived data from the network. Assume that the battery capacity of the base station is infinite. We partition the network into cluster groups based on the structure [21 ]]A Cluster Head Node (CHN) of each cluster group is dynamically selected in each cycle according to the remaining energy. Other nodes in the same cluster group send sensing data to the CHN, an example of which is shown in fig. 1. Sensor node viHas a battery capacity of BmaxThe residual energy of each sensor node at the time t is Bi，tIt can be seen that Bi，0＝Bmax。ρiRepresenting a sensor node viEnergy consumption rate of, then sensor node viCan be described as
Figure BSA0000213155640000081
Will hold the sensor node viIs defined as Bmin,. Therefore, if the remaining energy of the sensor node is liρiIs less than BminThe sensor node will stop working.
2. Charging model
Definition C1Is a collection of groups of clusters to be charged. Upon receiving a charging request, the MV leaves the base station and accesses the cluster group to be charged along a predetermined route. And after the charging is finished, returning to the base station for self-charging. The travel path of the MV forms a closed loop { S, c }1，c2，...，cnumS, where num is the number of clusters to be charged.
Definition of tauc，iIs a pair of ithCluster group ci∈C1Time taken for node in the system to charge, i.e.
Figure BSA0000213155640000082
Wherein cn isiIs the number of nodes within the cluster set,
Figure BSA0000213155640000083
represents the energy, U, required by the jth node in the cluster groupi，jFor the charging efficiency of the trolley, this parameter is related to the distance between the trolley and the node. Obviously, the CHN consumes more energy than other nodes in the cluster group, and in view of charging efficiency, the MV is arranged to stop at the CHN, so that the charging time of each cluster group can be more uniform. At the same time, using τchargeRepresents the total charge time per cycle, noted,
Figure BSA0000213155640000091
representing two adjacent cluster groups c to be charged by using Euclidean distanceiAnd ci+1Is indicated as
Di，i+1＝||(xi+1-xi)，(yi+1-yi)||。(2)
Total travel time of MV can be written as
Figure BSA0000213155640000092
Wherein D0，1Distance of the first charging cluster group to the base station, Dnum，num+1The distance from the last charging cluster group to the base station.
In each charging cycle, the total time τ spent by the MV is
τ＝τtravel+τcharge (4)
3. Innovative data collection strategy
And defining the sensor nodes sensing the DS data as DS sensor nodes, and defining the cluster group containing the DS sensor nodes as a DS cluster group. To reduce data overflow and latency, we propose a new data collection strategy. Definitions childreniFor the set of sub-cluster groups transmitting sensing data to the ith cluster group, Sensitive is the set of all DS cluster groups in the network. Sensor node viIs defined as Ri (bit/s). BDi，tThe total amount of data buffered in the ith cluster group by the CHN at a certain time t can be expressed as
Figure BSA0000213155640000093
In the above equation, it can be found that the data buffered by the CHN in the ith cluster group includes the sensing data of the CHN and all the sensing data of its sub-cluster group, except the DS data in the DS cluster group. Indicating that the DS data can be directly transmitted to the base station through a multi-hop manner. Meanwhile, the base station can receive the sensing data which cannot be collected by the MV in time, and the problem of data overflow can be avoided.
And when the MV reaches the cluster group, charging the nodes in the cluster, and simultaneously collecting the cache data of the CHN. The time taken to collect the cache data of the current charging cluster group is
Figure BSA0000213155640000094
Where G is the data collection rate of MV, e.g. 500kb/s [20 ]]. When cluster group ciIs collected for a time σc，iLess than its charging time tauc，iIts neighbor cluster group may also send buffered data to the MV to maximize the total energy saved by the surrounding cluster groups. We define the set of neighbor cluster groups of the ith cluster group as nbi。
After the MV reaches the current cluster group to charge the sensor node, the MV is assumed to collect the cache data of the surrounding cluster groups until the remaining time of the next cluster group to be charged reaches the minimum allowable level. The cart therefore has more time to collect the cached data, saving more energy while guaranteeing the network life cycle (as will be demonstrated later). When an MV moves from the current cluster group to the next adjacent cluster group to be charged, it is assumed that the roadside cluster group can transmit the buffered data to the moved MV. Therefore, under the condition that the driving track and the time are not changed, more energy can be saved. Definition of ithAnd i +1thThe set of roadside clusters between the charging cluster groups is rsi，i+1. To represent the time points of change in each cycle, the concept of a time axis is introduced, as shown in fig. 2.
In fig. 2, c1, c2 and c3 are three clusters to be charged, and their corresponding remaining survival time points on the time axis are l1， l2And l3. From the above, when the MV charges the node or moves along the travel path, the partial CHNs can send buffered data to it as much as possible. In a charging cycle, the remaining time of the next cluster group to be charged, such as c3, is longer or the data collection time of the current cluster group c2 is shorter than the charging time of the next cluster group, so that the neighboring cluster group of the current cluster group can send the buffered data to the MV, thereby improving the savingEnergy. Definition ciIdle time ftiFor MV in the current cluster group ciMaximum extra time that can be spent on, i.e. cluster group ciThe time between the charge completion time and the time remaining for the next cluster group to be charged minus the travel time of the cart. For example, 2thFree time of cluster group is ft2＝l3-t4-(l3-t6)＝t6-t4Wherein t is4Is MV in 2thTime point when cluster group completes charging work,/3-t6Travel time for the cart. When ftiWhen the value is less than or equal to 0, let ft i0. Thus, cluster group ciIdle time ftiCan be written as
Figure BSA0000213155640000101
Wherein li+1Is a cluster group ci+1The remaining life time point, cfiIs MV complete cluster group ciTime point of charging operation. It can be seen that during the idle time of each cluster group, the MV can move a certain distance, approaching the CHNs of the neighboring cluster groups.
Defining the sum of the charging time and the idle time as a cluster group ciAvailable collection time Ac ofiCan be written as Aci＝τc，i+fti. The collection time may be less than the available time, taking into account the amount of buffered data. As shown in fig. 2, for cluster group c2Data collection at time t5End, instead of t6. The defined time is greater than the charge completion time point but less than the actual data collection completion time point as an additional time σec，i. For example, 2thExtra time of cluster group σec，2Can be expressed as sigmaec，2＝t5-t4. Wherein t is5Indicating the time point when the MV completes the collection of the cached data for the current cluster group and the neighbor cluster group. Note that σec，i≤fti. The total time spent by the cart in each cycle should be updated to account for the extra time of each cluster group to be charged
Figure BSA0000213155640000111
Under the proposed data collection strategy, the cluster groups can be divided into three types, namely, a cluster group (to-be-charged cluster group) which needs to be charged and transmits the buffered data, a cluster group (neighbor and roadside cluster groups) which transmits only the buffered data, and other cluster groups (such as DS cluster groups). When the current charge cycle is over, the remaining energy B of the CHN in the different cluster groups, considering the saved energyi，tCan be expressed as:
Figure BSA0000213155640000112
in the above equation, let us assume that the remaining time of the charged cluster group is Bmax。ρiτ is the energy consumed in the cycle, ei，saveThe energy saved by sending data to the MV. Set D is a cluster group set for sending the buffered data to MVs, C1E.g. D. It has been found that by transmitting the buffered data to the MV, the energy consumption of the network can be significantly reduced, thereby extending the time of the network.
4. Data routing and energy consumption
If no sensor node sends a charging request, i.e. the MV stays at the base station, all data perceived from the surroundings will be transmitted to the base station via multiple hops. It is assumed that the CHN transmits data to the base station through the shortest path. Respectively using fijAnd fiSTo represent slave sensor node viTo vjAnd the traffic rate to the base station S. Traffic balancing for data routing may be written as
Figure BSA0000213155640000113
Only the energy consumed in the sensing, receiving and transmitting process is considered. An energy consumption model is used. Gamma RiIs a sensor node viThe rate of energy consumption for sensing the data,where γ represents the energy consumed to sense one unit of data.
Figure BSA0000213155640000114
Representing a sensor node viThe energy consumption rate of receiving data of other nodes, epsilon, is the energy consumed by receiving one unit of data. CijRepresenting a sensor node viTo sensor node vjThe energy consumption rate for transmitting a unit of data is expressed as
Figure BSA0000213155640000115
Wherein DijFor two sensor nodes viAnd vjDistance between, β1Is a constant term independent of distance, beta2Is a distance dependent coefficient.
Figure BSA0000213155640000116
For indicating path loss, where the exponent a is typically 2 or 4. Similarly, the slave sensor node viThe energy consumption rate for transmitting data to the base station S can be expressed as
Figure BSA0000213155640000117
The sensor node v can be obtainedjThe energy consumption of
Figure BSA0000213155640000121
By the above formula, it can be found that the energy consumed for transmitting data accounts for the most energy consumption. By transmitting the perception data to the MV and converting the long-distance data transmission into the short-distance data transmission, the energy consumption can be significantly reduced. Meanwhile, the CHN transfers much more data than other nodes in the cluster group, and thus consumes much more power. To maintain the energy balance of the network, the CHNs of the respective cluster groups will be dynamically selected according to the corresponding remaining lifetime in each cycle.
The MV is used to supplement the network energy. However, when the network is large in scale, some sensor nodes may be exhausted and cannot be charged in time, which may result in death of the sensor nodes and degradation of network performance. We use normalized death time ηiMetric sensor node viCan be expressed as
Figure BSA0000213155640000122
Wherein t ischarge，iIs ithThe point in time at which the cluster group is charged by the MV. Normalized death time eta for all sensor nodes in a networksumIs expressed as
Figure BSA0000213155640000123
The lifetime of a network maximization problem can be modeled as a normalized time-to-death minimization problem. As mentioned previously, the solution to this problem is to find the optimal charging sequence. Given a set of clusters to be charged, the plan MV charges them one by one. And determining the optimal charging sequence according to the remaining time of the cluster group to be charged by a classical charging algorithm. Assuming that the minimum normalized death time for WRSN has been achieved, note
Figure BSA0000213155640000124
To improve the energy efficiency of data transmission, the MV serves both as a provider of energy and as a collector of data. We propose a term normalized energy saving ei，saveMeasure of i, to measurethThe energy saved by the CHN in the cluster group can be written as
Figure BSA0000213155640000125
Wherein, the dataiIs the total amount of data transmitted by the ith CHN, and datai≤BDi。DiSDenotes the distance of the CHN from the base station, DiMIndicating the CHN to MV distance. It can be found that when D is usediM＜DiSBy sending the buffered data to the closer MV, the energy consumed by the transmission can be greatly reduced.
esumThe sum of the normalized saved energy for all cluster groups sending buffered data to the MV is recorded as
Figure BSA0000213155640000126
In order to improve the network performance, the total normalized energy saving should be maximized on the premise of ensuring the network life cycle. When the MV supplements the network energy, it can collect cache data from the cluster groups around the MV. As the MV follows the trajectory, it can collect buffered data from the cluster set around the travel path. Thus, the normalized energy-saving maximization problem can be divided into two sub-problems of the surrounding cluster group and the roadside cluster group.
First, the normalized energy saving maximization problem for the surrounding cluster group is defined as: given a set of cluster groups to be charged, when the available data collection time is relatively long, how to arrange MVs to collect buffered data from surrounding cluster groups as much as possible maximizes the energy saved by normalization. Meanwhile, since the MV can move a distance to be close to the neighboring cluster group at the idle time. We further define an optimized normalized energy-saving maximization problem, finding the optimal moving distance of MV close to the corresponding neighbor cluster group, thereby optimizing the normalized energy-saving sum of the surrounding cluster groups. It is also assumed that the set of roadside clusters around the trajectory can send the buffered data to the MV as it travels along the path. Also, since the MV is a moving data collector, the doppler effect needs to be considered. The problem of maximizing the normalized saved energy of the roadside cluster group is defined as maximizing the saved energy by arranging for the moving MVs to collect the buffered data from as many roadside cluster groups as possible in consideration of the doppler effect.
The overall normalized energy saving maximization problem with the minimization of the overall normalized death time is defined as follows:
max esum (17a)
s.t.
Figure BSA0000213155640000131
Figure BSA0000213155640000132
0≤dn，move≤dn，i (17d)
Figure BSA0000213155640000133
Figure BSA0000213155640000134
(17b) ensuring that the life cycle of the network is maximized. t is tn，avaIs the time to allocate a neighbor cluster group, 17(c) indicates that the total time spent in and allocated to the cluster group should be less than the available data collection time. This constraint may guarantee the lifetime of the next cluster group to be charged. During the idle time of each cluster group, the MVs may be close to the selected neighbor cluster group, thereby increasing the energy saved by the normalization of the surrounding cluster groups. (17d) Indicating that the moving distance of the MV is not greater than the distance between the CHN in the current cluster group and its neighbor cluster head. Since the travel time is not negligible, the roadside cluster group will send the buffered data to the car, since the MV is mobile, (17d) defines the doppler effect that affects the quality of the wireless data transmission. (17f) The total time distributed to the roadside cluster groups is smaller than the travel time between the two to-be-charged cluster groups, and the traveling path is guaranteed to be unchanged.
The above is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, many variations and modifications can be made without departing from the inventive concept of the present invention, which falls into the protection scope of the present invention.

Claims (5)

1. A mobile trolley planning method combining energy supplement and data collection is characterized in that: the mobile trolley planning method combining energy supplement and data collection comprises the steps that a wireless mobile charging trolley periodically charges sensor nodes by utilizing a wireless energy transmission technology, so that self-maintenance of a network is realized, the sensor nodes are divided into non-uniform clusters according to residual energy and the distance from the sensor nodes to a base station, a charging algorithm based on the structure is provided, so that the time of the network is optimized, the residual energy, the node density and the distance from the sensor nodes to the base station are comprehensively considered, the weight of each sensor node to be charged is determined by utilizing fuzzy logic, the performance of the network is ensured, the difference between two charging modes of single-node charging and multi-node charging is analyzed, and WCV adopts different charging modes for different sensor nodes, so that the charging efficiency is optimized; the method has the advantages that a wireless mobile vehicle is used as a mobile data collector, long-distance communication is converted into short-distance communication, the service time of WRSN is effectively prolonged, the actual condition that a network is deployed in an isolated area is considered, the size of a cluster is determined according to the distance between the wireless mobile vehicle and a motion track in each period, cluster head nodes are dynamically selected according to residual energy, then each cluster head node sequentially sends cache data to a WDV, and the combined design of mobility control and space division multiple access technology is provided, so that the optimal compromise between a travel path and data transmission is realized; the mobile vehicle will act as WCV and WDV simultaneously, using WCV (also collect data) and WDV two different mobile vehicles, a distributed algorithm is proposed to adjust the data transmission rate of each sensor node, the total utility of the network can be maximized by sending buffered data to the mobile data collector at the optimal data rate, several wireless mobile vehicles are used to charge and collect buffered data for cluster groups simultaneously, anchor points responsible for transmitting the buffered data to the respective cluster groups on the vehicle are selected according to a semi-Markov energy prediction model, in order to minimize the travel distance, the mobile vehicle is planned to travel along the shortest Hamiltonian path, the network is divided into a plurality of units, each unit is assumed as a cluster group, the position of each anchor point in the cluster group is determined by its energy consumption rate, the mobile vehicle periodically goes to the respective cluster groups to charge and collect buffered data for the sensor nodes, thereby optimizing the data volume collected in the unit energy of the mobile trolley; the system model of the mobile cart that combines energy replenishment and data collection includes a network model, a charging model, innovative data collection strategies, data routing, and energy consumption.
2. The mobile cart planning method in combination with energy replenishment and data collection of claim 1, wherein: the network model uses V (| V | ═ n) to represent a set (L × L) of homogeneous sensor nodes deployed in the network, each sensor node V | >, andithe coordinate of e.V is recorded as (x)i，yi) The base station S in the center of the network is responsible for receiving and processing sensed data from the network, the battery capacity of the base station is assumed to be infinite, the network is divided into cluster groups based on the structure, a Cluster Head Node (CHN) of each cluster group is dynamically selected in each period according to residual energy, other nodes in the same cluster group send the sensed data to the CHN, and the sensor nodes viHas a battery capacity of BmaxThe residual energy of each sensor node at the time t is Bi，tIt can be seen that Bi，0＝Bmax。ρiRepresenting a sensor node viEnergy consumption rate of, then sensor node viCan be described as
Figure FSA0000213155630000011
Will hold the sensor node viIs defined as BminIf the remaining energy of the sensor node is liρiIs less than BminThe sensor node will stop working.
3. The mobile cart planning method in combination with energy replenishment and data collection of claim 1, wherein: the charging model definition C1For a set of clusters to be charged, the MV, upon receiving a charging request, leaves the base station and visits the clusters to be charged along a predetermined routeThe cluster group of (1). After charging is finished, the mobile terminal returns to the base station for self-charging, and the travel path of the MV forms a closed cycle { S, c }1，c2，...，cnumS, where num is the number of clusters to be charged,
definition of tauc，iIs a pair of ithCluster group ci∈C1Time taken for node in the system to charge, i.e.
Figure FSA0000213155630000021
Wherein cn isiIs the number of nodes within the cluster set,
Figure FSA0000213155630000025
represents the energy, U, required by the jth node in the cluster groupi，jFor the charging efficiency of the trolley, which is related to the distance between the trolley and the node, the CHN consumes more energy than other nodes in the cluster group, and in view of the charging efficiency, we arrange the MV to stop at the CHN, so that the charging time of each cluster group can be more uniform, using tauchargeRepresents the total charge time per cycle, noted,
Figure FSA0000213155630000022
representing two adjacent cluster groups c to be charged by using Euclidean distanceiAnd ci+1Is indicated as
Di，i+1＝||(xi+1-xi)，(yi+1-yi)||，(2)
Total travel time of MV can be written as
Figure FSA0000213155630000023
Wherein D0，1Distance of the first charging cluster group to the base station, Dnum，num+1Distance from last charging cluster group to base stationAfter the separation, the water is separated from the water,
in each charging cycle, the total time τ spent by the MV is
τ＝τtravel+τcharge，(4)。
4. The mobile cart planning method in combination with energy replenishment and data collection of claim 1, wherein: the inventive data collection strategy defines sensor nodes sensing DS data as DS sensor nodes, cluster groups containing the DS sensor nodes as DS cluster groups, and children for reducing data overflow and delayiThe sensing data rate of the sensor node vi is defined as Ri (bit/s), BD (BD/RS), and the sensing data rate of the sensor node vi is set by the sub-cluster group for transmitting sensing data to the ith cluster group, and the sensing is set by all DS cluster groups in the networki，tThe total amount of data buffered by the CHN in the ith cluster group at a certain time t may be represented as:
Figure FSA0000213155630000024
the data buffered by the CHN in the ith cluster group includes the sensing data of the CHN and all the sensing data of the sub-cluster group except the DS data in the DS cluster group. The DS data can be directly transmitted to the base station in a multi-hop mode, and the problem of data overflow can be avoided because the base station can receive the sensing data which cannot be collected by the MV in time,
and when the MV reaches the cluster group, charging the nodes in the cluster, and simultaneously collecting the cache data of the CHN. The time taken to collect the cached data of the current charging cluster group is:
Figure FSA0000213155630000031
where G is the data collection rate of MV, e.g. 500kb/s [20 ]]. When cluster group ciIs collected for a time σc，iLess than its charging time tauc，iIts neighbor cluster group may also send buffered data to the MV to maximize the weekTotal energy saved by the cluster enclosure group. We define the set of neighbor cluster groups of the ith cluster group as nbi；
After the MV reaches the current cluster group to charge the sensor node, we assume that it can collect the buffered data of the surrounding cluster groups until the remaining time of the next cluster group to be charged reaches the minimum allowable level. Therefore, the trolley has more time to collect the cache data, saves more energy, and simultaneously ensures the life cycle of the network (which will be proved later), when the MV moves from the current cluster group to the next adjacent cluster group to be charged, the roadside cluster group is supposed to transmit the cache data to the moving MV, so that under the condition of unchanged driving track and time, more energy can be saved, and i is definedthAnd i +1thThe set of roadside clusters between the charging cluster groups is rsi，i+1To indicate the time points of change in each cycle, c1, c2 and c3 are three clusters to be charged, and their remaining life time points on the time axis are l1，l2And l3When the MV charges the node or moves along the travel path, the partial CHNs can send the buffered data to the MV as much as possible, and in a charging period, the remaining time of the next cluster group to be charged, such as c3, is longer or the data collection time of the current cluster group c2 is shorter than the charging time, so that the neighboring cluster group of the current cluster group can send the buffered data to the MV, thereby increasing the energy saving, and defining ciIdle time ftiFor MV in the current cluster group ciMaximum extra time that can be spent on, i.e. cluster group ciThe time between the completion of charging and the time remaining for the next cluster group to be charged minus the travel time of the cart, 2thFree time of cluster group is ft2＝l3-t4-(l3-t6)＝t6-t4Wherein t is4Is MV in 2thTime point when cluster group completes charging work,/3-t6Travel time for the cart. When ftiWhen the value is less than or equal to 0, let fti0. Thus, cluster group ciIdle time ftiCan be written as:
Figure FSA0000213155630000032
wherein li+1Is a cluster group ci+1The remaining life time point, cfiIs MV complete cluster group ciAt the time point of charging operation, in the idle time of each cluster group, the MV can move a certain distance and approach to the CHNs of the adjacent cluster group;
defining the sum of the charging time and the idle time as a cluster group ciAvailable collection time Ac ofiCan be written as Aci＝τc，i+fti. The collection time may be less than the available time, considering the amount of buffered data, for cluster group c2Data collection at time t5End, instead of t6. The defined time is greater than the charge completion time point but less than the actual data collection completion time point as an additional time σec，i，2thExtra time of cluster group σec，2Can be expressed as sigmaec，2＝t5-t4. Wherein t is5Represents the time point, σ, at which the MV completes the collection of the cached data for the current cluster group and the neighbor cluster groupec，i≤ftiConsidering the extra time for each cluster group to be charged, the total time spent by the cart in each cycle should be updated as:
Figure FSA0000213155630000041
under the proposed data collection strategy, the cluster groups can be divided into three types, namely, a cluster group (to-be-charged cluster group) which needs to be charged and transmits the cache data, a cluster group (neighbor and roadside cluster groups) which only transmits the cache data, and other cluster groups (such as DS cluster group), when the current charging cycle is finished, the remaining energy B of the CHN in different cluster groups is considered in consideration of the saved energyi，tCan be expressed as:
Figure FSA0000213155630000042
in the above equation, let us assume that the remaining time of the charged cluster group is Bmax，ρiτ is the energy consumed in the cycle, ei，saveThe energy saved by sending data to the MV. Set D is a cluster group set for sending the buffered data to MVs, C1E.g., D, it can be seen that by transmitting the buffered data to the MV, the energy consumption of the network can be significantly reduced, thereby extending the time of the network.
5. The mobile cart planning method in combination with energy replenishment and data collection of claim 1, wherein: all data sensed by the data routing and energy consumption from the surrounding environment are transmitted to the base station through multi-hop, and the CHN is assumed to transmit data to the base station through the shortest path, and f is used respectivelyijAnd fiSTo represent slave sensor node viTo vjAnd traffic rate to base station S, the traffic balance of the data route can be written as:
Figure FSA0000213155630000043
only the energy consumed in the processes of sensing, receiving and transmitting is considered, and a medium energy consumption model, gamma R, is adoptediIs a sensor node viA power consumption rate for sensing data, wherein gamma represents power consumed for sensing one unit data,
Figure FSA0000213155630000044
representing a sensor node viEnergy consumption rate for receiving data of other nodes, ε is energy consumed for receiving a unit data, CijRepresenting a sensor node viTo sensor node vjThe energy consumption rate for transmitting a unit of data is expressed as:
Figure FSA0000213155630000045
wherein DijFor two sensor nodes viAnd vjDistance between, β1Is a constant term independent of distance, beta2Is a coefficient relating to the distance that,
Figure FSA0000213155630000046
for indicating path loss, where the exponent a is typically 2 or 4, and similarly, from the sensor node viThe energy consumption rate for transmitting data to the base station S can be expressed as
Figure FSA0000213155630000047
The sensor node v can be obtainediThe energy consumption of (a) is:
Figure FSA0000213155630000051
the above formula shows that the energy consumed by data transmission accounts for the most energy consumption, the sensing data is transmitted to the MV, the long-distance data transmission is converted into the short-distance data transmission, the energy consumption can be reduced remarkably, meanwhile, the data transmitted by the CHN is much more than the data transmitted by other nodes in the cluster group, so the energy consumption is faster, and in order to maintain the energy balance of the network, the CHN of each cluster group is dynamically selected according to the corresponding remaining survival time in each period.
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CN113365222A (en) * 2021-08-11 2021-09-07 浙江师范大学 Mobile sensor intelligent track design method based on sustainable data acquisition
CN113630747A (en) * 2021-08-16 2021-11-09 中国联合网络通信集团有限公司 Traffic information processing method and device

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CN113365222A (en) * 2021-08-11 2021-09-07 浙江师范大学 Mobile sensor intelligent track design method based on sustainable data acquisition
CN113365222B (en) * 2021-08-11 2021-11-12 浙江师范大学 Mobile sensor intelligent track design method based on sustainable data acquisition
CN113630747A (en) * 2021-08-16 2021-11-09 中国联合网络通信集团有限公司 Traffic information processing method and device
CN113630747B (en) * 2021-08-16 2023-07-18 中国联合网络通信集团有限公司 Traffic information processing method and device

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