AU2020100816A4 - Online rechargeable sensor network charging scheduling system - Google Patents
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Abstract
ADstracy
The present invention relates to an online rechargeable wireless sensor network charging
scheduling system, which includes a plurality of wireless charging vehicles and a maintenance
station. The maintenance station includes a service queue system and a charging service system.
The service queue system receives charging requests and residual energy information sent by
energy-deficient nodes in a network, stores the charging requests in a service queue form into
service queues, divides the service queues into a plurality of charging service sub-queues
according to minimum survival time of the energy-deficient nodes according to a queuing theory
and inputs the charging service sub-queues into the charging service system. The charging service
system performs the priority measurement according to the position information and residual
energy information of each energy-deficient node, plans a reasonable charging path in real time
and uses a plurality of wireless charging vehicles to charge the energy-deficient nodes in sequence
in each charging service sub-queue according to the charging priority. On the premise of ensuring
the long-term running of the network, the present invention improves the charging efficiency and
the energy utilization rate of the charging vehicles, and reduces the total charging waiting time of
the nodes.
1
Drawings of Description
2 12 11
0 0 0 0
0
00 0 00
0 0 00
Sericeueesyte I C~mse
0 0 i
0 Chreh
Senoenewordes-i queue demand Y9
Noe I noe
sen by L.--\-s-rvi-e-e-erg
Fig.32
1
Description
ADstracy
The present invention relates to an online rechargeable wireless sensor network charging
scheduling system, which includes a plurality of wireless charging vehicles and a maintenance
station. The maintenance station includes a service queue system and a charging service system.
The service queue system receives charging requests and residual energy information sent by
energy-deficient nodes in a network, stores the charging requests in a service queue form into
service queues, divides the service queues into a plurality of charging service sub-queues
according to minimum survival time of the energy-deficient nodes according to a queuing theory
and inputs the charging service sub-queues into the charging service system. The charging service
system performs the priority measurement according to the position information and residual
energy information of each energy-deficient node, plans a reasonable charging path in real time
and uses a plurality of wireless charging vehicles to charge the energy-deficient nodes in sequence
in each charging service sub-queue according to the charging priority. On the premise of ensuring
the long-term running of the network, the present invention improves the charging efficiency and
the energy utilization rate of the charging vehicles, and reduces the total charging waiting time of
the nodes.
Drawings of Description
2 12 11
0 0 0 0 0 00 0 00
0 0 00
Sericeueesyte I C~mse 0 0 i 0 Chreh
Senoenewordes-i queue demand Y9
Noe I noe
sen by L.--\-s-rvi-e-e-erg
Fig.32
Description
Technical Field
The present invention belongs to the technical field of wireless rechargeable sensor
networks, and relates to a wireless rechargeable sensor network charging scheduling system
based on a Maslows energy demand theory.
Background
A wireless sensor network consists of hundreds of homogeneous and heterogeneous small
sensor nodes and can monitor and capture physical parameters of a target. However, the
traditional power supply with limited energy becomes a development bottleneck of the wireless
sensor network. In order to solve the energy problem, a sensing network with an energy
collection technology, i.e. a wireless rechargeable sensor network (WRSN) emerges. A typical
WRSN shall include the following several parts: a maintenance station, a wireless charging
vehicle (WCV) and a plurality of sensor nodes. The maintenance station serves as a data
collection center and an energy source to supply energy to the wireless charging vehicle. The
charging vehicle departs from the maintenance station to replenish the energy to the nodes
according to a scheduling strategy and then returns to the maintenance station to be replenished
with the energy so as to start the next charging task.
A charging scheduling method of the charging vehicle is always a hot spot of the research.
A lot of scholars have proposed relevant solutions, but a majority of solutions adopt a periodic
charging method, i.e. each charging vehicle has a fixed charging schedule, so the residual energy
and a working state of the node need to be forecast. However, in the practical running of the
MRSN, events occur randomly, the energy consumption of the sensor nodes is also random, and
the residual energy and working state of the nodes are difficult to forecast, thereby leading to
high charging cost.
Description
Summary The technical problem to be solved by the present invention is to provide an online rechargeable sensor network charging scheduling system, which can optimize a charging path of wireless charging vehicles and reduce a charging waiting time of sensor nodes. To solve the above technical problem, an online rechargeable wireless sensor network charging scheduling system of the present invention includes a plurality of wireless charging vehicles and a maintenance station. The maintenance station includes a service queue system and a charging service system. The service queue system receives charging requests and residual energy information sent by energy-deficient nodes, i.e., the sensor nodes with the energy less than a set threshold value, in a network, stores the charging requests in a service queue form into service queues, then divides the service queues into a plurality of charging service sub-queues according to a minimum survival time of the energy-deficient nodes according to a queuing theory and inputs the plurality of charging service sub-queues into the charging service system. The charging service system performs the priority measurement according to the position information and residual energy information of each energy-deficient node, plans a reasonable charging path in real time and uses a plurality of wireless charging vehicles to charge the energy-deficient nodes in sequence in each charging service sub-queue according to the charging priority. The service queue system calculates a shortest distance from each energy-deficient node to one wireless charging vehicle according to the position information of the energy-deficient node. Then a shortest path Lmin when the wireless charging vehicle traverses all energy-deficient nodes
is calculated, and a forecast time Tc for completing the charging is calculated. The service
[ C+1 queue is divided into L charging service sub-queues. Each wireless charging vehicle charges a plurality of energy-deficient nodes corresponding to one charging service sub-queue: Tc=Lmin/vc
ss -10
sok "p(s -1)
p
Description
wherein Vc is a traveling speed of the wireless charging vehicle; Ls is an average queue
length of the charging service sub-queues; p is charging service intensity, <P 1. " is a service rate of the wireless charging vehicle. X is an reciprocal of an average time interval for the
energy-deficient nodes to send the charging request. f is a retention time of the
energy-deficient nodes in the charging service sub-queue. 0 in So represents an initial period of
the charging period, k indicates a number of the time intervals, and Sk represents a sum of the duration of each charging interval. The charging service system performs the priority measurement for each energy-deficient node, i.e. the node to be charged, in each charging service sub-queue according to the following method: Firstly, selecting an initial charging node, calculating a Maslows energy demand moment of other nodes to be charged surrounding the initial charging node, and adding other nodes to be charged into a charging priority queue of the wireless charging vehicle according to the Maslows energy demand moment; after the charging task for the initial charging node is completed, deleting the initial charging node from the charging priority queue, using other nodes to be charged with the maximum energy demand moment as secondary nodes to be charged, and while charging the secondary nodes to be charged, selecting the nodes to be charged with the maximum Maslows energy demand moment surrounding the secondary nodes to be charged into the charging priority queue, wherein a calculation method of the Maslows energy demand moment is as follows:
Step I, setting an energy consumption rate of the node i to be charged as , and
calculating energy need 01 of the node i to be charged within the time t according to the following formula:
0; =1-13(1-_ t)
Step II, calculating a Maslows energy demand moment of each node to be charged in the
charging service sub-queue, wherein with respect to each node X to be charged, the Maslows energy demand moment is calculated according to the following formula:
cos(O) = cos(arctan( '-0/)) LkI
Description
wherein assuming that X4 is a target charging node to which the wireless charging vehicle
1 is driving, X, is one node to be charged surrounding the target charging node X4, L* indicates
a distance between the target charging node X* and the node X1 to be charged, 0 is energy
need of the target charging node X4, and 01 is the energy need of the node to be charged. A length of the charging priority queue is constant and is 3. A selection method of the initial charging node is as follows:
Selecting three nodes X , X2 and X3 to be charged with maximum Maslows energy demand moment on the periphery of the wireless charging vehicle, adding the three nodes to be charged into the charging priority queue with the length of 3, and selecting the initial charging node from the three nodes to be charged, wherein the selection of the initial charging nodes shall satisfy the following three constraint conditions: Constraint condition I: the selected initial charging node shall not die within the time that the wireless charging vehicle traverses the three nodes to be charged; Constraint condition II: the variance of the distance from the three nodes to be charged to the wireless charging vehicle shall be as small as possible; Constraint condition III: when the distance from the target charging node to the other two nodes to be charged is greater than the distance between the two nodes to be charged, the wireless charging vehicle updates the target charging node to the node to be charged and selects one of the two nodes to be charged as the target charging node, and the target charging node is the initial charging node. When the two nodes to be charged have same energy need, the node to be charged which is further from the current target charging node has higher charging priority. When the two nodes to be charged are equally distanced to the current target charging node, the node to be charged with higher energy need has higher charging priority.
The present invention proposes a charging priority strategy based on EM/1/3(FCFS)
Firstly, the initial charging node is selected, the Maslows energy demand moment of other nodes to be charged surrounding the initial charging node is calculated, and the nodes to be charged surrounding the initial charging node are sequentially added into the charging priority queue of the WCV according to the Maslows energy demand moment. After completing the charging task of the initial charging node, the WCV deletes the node from the charging priority queue and then uses the node with the maximum energy demand moment as a secondary node to be charged. While charging the secondary node to be charged, the WCV selects peripheral nodes with large
Description
energy demand moment into the queue and successively completes the charging work of the nodes. The present invention has the beneficial effects: (1) The service queue is divided into a plurality of charging service sub-queues and one wireless charging vehicle is allocated to each charging service sub-queue, so that the charging path of the wireless charging vehicle can be optimized, and the charging waiting time of the energy-deficient node can be reduced. (2) The strategy adopted by the present invention works on line, and the WCV can continuously receive the charging requests sent by the sensor nodes in the working process. The length of the charging priority queue of the charging strategy proposed by the present invention is constant and is 3, so the average response time fluctuation is not great, thereby having great advantage compared with other strategies. (3) An algorithm used in the present invention is to first select the energy-deficient node farther away as the priority charging node from the energy-deficient nodes with the same energy, so that all nodes in the charging service can be ensured not to die under the situation of high energy utilization rate. (4) With respect to the index, i.e. the number of sleep nodes in the network, since the nodes farther away are first selected to be charged when the nodes have the same residual energy, the sleep time of the nodes is long. However, since the nodes are low in energy consumption in the sleep state, the nodes can be ensured to be charged before the energy is used up, and the nodes will still not die.
Description of Drawings The present invention is further described in detail below in combination with the accompanying drawings and embodiments: Fig. 1 is a topological diagram of a rechargeable wireless sensor network. Fig. 2 is a structural block diagram of the present invention. Fig. 3 is a phase flow diagram of a queuing system. Fig. 4 is a schematic diagram of a Maslows hierarchical energy need model. Fig. 5 is a Maslows energy demand moment model among nodes. Fig. 6 is a selection schematic diagram of an initial target of a WCV. Fig. 7 is a selection strategy model diagram of nodes with same Maslows energy need. Fig. 8 is a selection strategy model diagram of nodes of the same distance.
Description
Fig. 9 shows comparison of an average response time. Fig. 10 shows comparison of an energy utilization rate. Fig. 11 shows comparison of a number of dead nodes. Fig. 12 shows comparison of an average sleep time.
Detailed Description 1. Overall structure The present invention provides an online rechargeable sensor network charging scheduling system based on a Maslows energy demand theory. As shown in Fig. 1, the system is applied to a rechargeable wireless sensor network consisting of a plurality of sensor nodes (the nodes are classified into energy sufficient nodes 11 and energy-deficient nodes 12), a plurality of movable wireless charging vehicles 2 (WCV) and a maintenance station 3 on the edge of the network. With the continuous running of the network, the energy of the nodes is gradually consumed. When the residual energy of some nodes in the network is less than an energy threshold value, the nodes need to be replenished with energy, and the nodes send charging requests to the maintenance station. As shown in Fig. 2, an online rechargeable sensor network charging scheduling system of the present invention includes a plurality of wireless charging vehicles and a maintenance station. The maintenance station includes a service queue system and a charging service system. The service queue system stores the charging requests into the service queue system in a service queue form and classifies energy-deficient nodes in the service queue according to residual energy information of the energy-deficient nodes to form a plurality of charging service sub-queues and inputs the charging service sub-queues into the charging service system. The charging service system uses a Maslow energy demand moment to perform the priority measurement according to the residual energy information and position information of the energy-deficient nodes, defines an initial path of the wireless charging vehicle, plans a shortest path, and determines the final charging path to complete the charging. 2. Embodiments 2.1 Service queue demand model Assuming that the events occur randomly and are independent, the data generated by the sensor nodes by sensing the peripheral environment follows the Poisson distribution, that is, the energy consumption of the sensor nodes also follows the Poisson distribution. With the running of the network, and when the residual energy of the sensor nodes is less than a threshold value, the charging request is sent to the maintenance station. Therefore, the charging requests of the
Description
energy-deficient nodes in the network shall follow a sum of the Poisson distribution, i.e. follows k -order Irish distribution (k indicates the number of the energy-deficient nodes, i.e. the
number of nodes to be charged in each charging sub-queue). Assuming that the charging time of the wireless charging vehicle (WCV) follows the negative exponential distribution when serving
the energy-deficient nodes, the charging service sub-queue follows E /M/1/ (FCFS)queuing
model, wherein Ek is Irish distribution, k indicates an order, and M is the number of service units in the queue of the queuing theory, as proved below.
Assuming that {N(x),x>0} indicates the sensing events occurring in the network, and the events occur randomly and are independent and follow the Poisson distribution, the occurrence time interval of the sensing events is also independent and follows the negative exponential
distribution. If T, are used to indicate the occurrence time of the sensing events, a
random variable XT-1, (j=1,2,...,k; T =0) indicates the occurrence time interval of the
sensing events, and XJ follows the negative exponential distribution
Pt X > tj = e-", (j= 1, 2,..., k) S, X, +X2+-- +X, !S, !x4 and { N(x) > k} are equivalent,
therefore:
( - P{ Sk > x} = P{ N(x)> k} = -e-Z )
j-k J! (15) It can be seen from the formula (15) that the time interval for the nodes in the network to
send the charging requests follows the k -order Irish distribution. k indicates the number of the
energy-deficient nodes of the charging requests, X indicates the sum of the time intervals of k charging requests, X indicates a reciprocal of the average time interval of k charging requests, and a probability distribution function is expressed as follows:
F(x)= P{Sk < X -1-- x) x j-k .! (16) The probability density function is:
f(x= F(x) - . e-./I (A2>O,x>O) dx (k-1)! (17)
The model of the charging service sub-queues follow the E/M/1/oo (FCFS) queuing model. The number of the wireless charging vehicles is determined according to the need of the queuing model.
Description
2.2 Determine the number of wireless charging vehicles
Assuming that the charging requests sent by the sensor nodes follow the k -order Irish
distribution, the average interval time is'a and there is only one movable charging vehicle, time for the WCV serves the node to be charged follows the negative exponential distribution
with a parameter of . The time interval of the WCV reaching the system is divided into k
independent phasesti with same negative exponential distribution, and average time interval is 1 E(t) =- , (1 i ! k) mathematically expected as kU . Each sensor node can send the charging
request only after passing by k phases, that is, each sensor node can send the charging request only after sensing a plurality of events and the residual energy reaches a given threshold value. Assuming that there are already n energy-deficient nodes to be charged in the system, the n
energy-deficient nodes pass by nk phases in total. Before entering the service queue, the n+1th energy-deficient node already passes by i-1 phases and is just located on the i th phase, and
then the total phases of the system are =nk+i-1. That is, the whole system can be expressed
as a homogeneous Markov chain {Xk 0 } based on the time interval of the charging requests. Assuming that there are n energy-deficient nodes to be charged in the current charging service queue and the possible phase before the n+lth energy-deficient node does not send the
charging request is t tk ... ,tk, the stable distribution of the node number of the energy-deficient nodes to be charged in the charging service queue is: (n+1)k-1
P - p1 (n > 0) J=nk (18) When the instantaneous change of the state in the service queue adds a phase, the
corresponding arrival rate is kA . If one energy-deficient node is completely charged, which is
equivalent to that the state minus k phases, the service rate of the WCV is , and the average
service time is P . The phase flow diagram of the queuing system is shown in Fig. 3. According to the phase flow diagram, it can obtain:
klpo= ppk (19)
kp 1 =Pk,1+klpo (20) klpj=kpj_1+ ppjk 1 j:k-1 (21) (k11 +p)pj=klpjl+ppjk j k (22)
Description
P(s)=Zpisi The probability generating function J=° is used, both sides of the formula (21)
and the formula (22) are multiplied by Si, and summing is performed forI, thereby obtaining:
$(kA + p)p - J=1 , s-' = $kp,_ J=1 1 s' + pp,,,s =-1 J=1 (23)
'0 The charging service intensity P of the queuing system is defined (X is the reciprocal of the average time interval for the energy-deficient nodes to send the charging requests, and the
service rate of the WCV is P), and Pk =k-ppo. When P <, the queuing system is kept LL stable. An average queue length s of the service queue and an average waiting queue length Lq
are calculated. 0 in So represents that the charging period is the initial period, k indicates the
number of the time intervals,and S° represents a sum of the duration of each charging interval
in the charging period. Pi indicates an average value of the charging request time interval of the
jth node to be charged.
k
_ j-p= p-_ (sok -1)- j- - 0-s }=0 j so 1 (24) k
LI =Y(j-1)-p,3 L -(1-P0)= so js1 - so (25)
The retention time of the energy-deficient node in the service queue and the queuing w waiting time q are:
W ==sk0 = A p(s -1) (26) L 1 q 0p(se-1) (27) When the number of the energy-deficient nodes in the network is large, the number of the energy-deficient nodes in the service queue is large, the service queue of the energy-deficient nodes need to be divided into a plurality of charging service sub-queues, and each charging service sub-queue is allocated with one WCV.
Description
Firstly, an energy-deficient node table of the service queue is defined as A , a residual
energy table of each energy-deficient node is defined as N(A), and the position information of one WCV and each energy-deficient node is already known. The service queue calculates a shortest distance from each energy-deficient node to the WCV according to the position information of the WCV and each energy-deficient node. By calculation, the charging time
corresponding to the shortest path Lmin when WCV traverses all energy-deficient nodes is Te
and Tc is equal to the shortest path Lmi divided by the traveling speed vc of the WCV ( C is the
time on the charging path, because the charging time interval is considered when L, and Y
[ C+1 are calculated). The service queue may be divided into - ] charging service sub-queues and one wireless charging vehicle is allocated to each charging service sub-queue, so that the
TC+1 number of the wireless charging vehicles is !L]. A pseudo code for specific implementation is as follows: Charging service sub-queue division based on a WCV service capability algorithm
Input: A, N(A), positions of the WCV and the energy-deficient nodes
Output: Charging service sub-queuesIA 1 A2 ... A.} 1. Calculate a distance between each energy-deficient node and the WCV 2. Find the shortest path for the WCV to traverse each energy-deficient node
in the network A, and calculate the time Tc for completing the charging.
m=[ +1 3. Calculate the number -Lfi of the charging service sub-queues 4. While the energy-deficient nodes in A are not traversed 5. While the energy-deficient nodes can be charged by the k th WCV A,
Tsi > YWs 6. If j=1
7. Add the energy-deficient nodes i into A charging service sub-queues 8. Else if 9. k=k+1 10. End if
Description
11. End while 12. End while
13. Return, After one WCV is arranged for each charging service sub-queue, an appropriate charging sequence needs to be established for the charging service sub-queues, thereby proposing a concept of energy demand moment. 2.3 Energy demand moment 2.3.1 Maslows energy demand theory Firstly, the Maslows hierarchical demand theory is mainly used to analyze and describe the human demand problem, which can vividly divide the human demand into five stages from bottom to top: physiological demand, safety demand, emotional demand, respect demand, and self-realization demand. According to the Maslows hierarchical demand theory, the demand and the total resources do not present a linear increase relationship, that is, when the total resources are small, the increase speed of the demand is high; and when the total resources are vast, the increase speed of the demand is low. According to the Maslows demand theory, the Maslows energy need model is established for the nodes in the WRSN herein. The smaller the residual energy of the nodes in the network is, the greater the energy supplementing priority is, and the greater the timely supplementing need of the energy is. 2.3.2 Maslows energy need Fig. 4 shows the Maslows hierarchical energy need model proposed by the present invention. The model is a cone. A total volume of the cone indicates the total energy of the sensor nodes. When the energy consumed by the nodes increases continuously, the energy need increases continuously, which is consistent with the relationship between the residual energy of the nodes and the energy need. The volume of the cone in the model is set as V, and the height is set as H, so that a bottom area of the cone is S and is expressed as:
S=rcH2 tan2(-) 2 (28) P Assuming that the energy consumption rate of the node i is ' (since the energy
consumption rate of each node is different, 'is an average value of the energy consumption
rate of each node), and according to the formula (29), the energy need in the time t is calculated as follows:
Description
H- 3VE(1-FP ') 3 rctan2(a
H 1 ( ) (29) Through the derivation calculation of the energy need 0 inthetime t,thegradient P of theenergyneed 0 is obtained as follows: ao a(1- 3(1-P. t)) P at at 33 (1-P.t)2 (30)
It can be seen from the formula (30) that the gradient of the energy need increases gradually with the time, which presents a nonlinear relation. Therefore, when the node priority is established, the energy need of the nodes, the gradient of the need and the distance between the node and the wireless charging vehicle are all factors that need to be considered. The present invention proposes the concept of the energy demand moment and uses the energy demand moment to measure the charging priority of the energy nodes. 2.3.3 Maslows energy demand moment Fig. 5 is a schematic diagram of the Maslows energy demand moment proposed by the present invention. In the present invention, the Maslows energy demand moment is defined as a cosine value of a ratio of the Maslows energy need of one node to be charged to the distance
from the node to be charged to the WCV. Assuming that the two nodes to be charged are Xk
and X1 respectively, Xk is a target charging node to which the wireless charging vehicle is
driving, X1 is one node to be charged surrounding the target charging node Xk, Lkl indicates
a distance between the target charging node Xk and the node to be charged 1, 0 k is energy
need of the target charging node Xk, and 01 is the energy need of the node to be charged X1.
The Maslows energy demand moment of the node to be charged X1 can be solved through the formula (31).
cos(Ok,)= cos(arc tan( Ok )) Lk1 (31) The charging priority is determined by the energy demand moment, thereby determining the charging sequence of the nodes to be charged in the charging service sub-queues. 2.4 Determine the charging sequence of the nodes to be charged 2.4.1 Select an initial charging node
Description
Three nodes XI, XI and x3 to be charged with the maximum Maslows energy demand moment on the periphery of the WCV are selected and added into the charging priority queue
7Tj with a length of 3, wherein T is a selection periodic number of the WCV charging target nodes, and i indicates the serial number of the nodes to be charged in the charging priority queue. Since the selection of the initial charging node cannot completely depend on the energy demand moment of the nodes to be charged, and a large space between the nodes to be charged can be prevented from increasing the length of the charging path, the selection of the initial charging node should satisfy the following three constraint conditions: (1) The selected charging node shall not die within the time that the wireless charging vehicle traverses the three nodes; (2) The variance of the distance from the three nodes to be charged to the WCV shall be as small as possible; (3) When the distance from the target charging node to the two nodes to be charged is greater than the distance between the two nodes to be charged, the WCV shall update the target charging node to the node to be charged and selects one of the two nodes to be charged as the target charging node. The first constraint condition shows that the three nodes to be charged selected for initialization shall be within the charging capability range of WCV. Since the online demand-based charging strategy is adopted by the present invention, all nodes to be charged have large energy need. After one of the selected three nodes to be charged is charged by the WCV, it shall ensure that the other two nodes to be charged will not die in the process of waiting for charging. The second constraint condition limits the position discreteness of the initial selected three nodes to be charged. The third constraint condition indicates that the selected target charging node shall not be too far away from other nodes to be charged. After the WCV charges the target charging node, the traveling distance from the WCV to the subsequent nodes to be charged can be reduced. When the three nodes to be charged selected initially by the WCV satisfy the first constraint condition and the second constraint condition, min S 2 (Dci,Dc 2 ,D 3 )
s.t. Dc1 + +L 23 + t, < min(tDitD 2 tD 3 VC n-1 (32)
Description
S2 (Dl,,D2,D 3) indicates the variance of the distance between the WCV and the three
nodes to be charged; Dc, indicates the distance from the WCV to the first node to be charged; L12 and L 23 indicate the distance from the first node to be charged to the second node to be
charged and the distance from the second node to be charged to the third node to be charged; v,
indicates the traveling speed of the WCV; ti indicates the time for the WCV to charge the ith node to be charged (the energy consumption percentage is divided by the charging efficiency);
and tDl tD2 and D3 indicate the survival time of the three nodes to be charged respectively. In order to realize the constraint condition 3, the present invention proposes a selection solution of a gravity core deviation angle. As shown in Fig. 6, the selected three nodes to be
charged are X , X2 andX 3 , and 0 is a gravity-core position of the three nodes to be charged. 1, #2 and '3 are gravity-core deviation angles of the nodes x, , X 2 and x3 to be charged, i.e. angles between each node to be charged and a joint line between the gravity core 0 and the WCV.
In the triangles formed by X1 , X2 and x , the longer the distance between the node to be 3
charged x, and X 2 is, the greater the discreteness is. Meanwhile, the discreteness between X 3
and x, and the discreteness between x3 and X2 are small. Considering from the three nodes to
be charged, when the target charging node at an initial moment is selected, Zx2< Zx<Zx 3 by comparing the angles of the three nodes to be charged. The WCV may make selection from the
two nodes to be charged x, and x3with large angles. Then by comparing the corresponding
gravity-core deviation angles,' ''3 is satisfied, so that the node to be charged X 3 has high energy demand priority and is selected as the initial target charging node of the WCV. 2.4.2 Select secondary charging nodes The conventional charging service system sequences the charging priority of the nodes to be
charged by comparing the energy demand moment Cos(0k,) the large energy demand moment of the node to be charged indicates the high charging priority, the node to be charged has large energy need or is farther away from the WCV, and in order to ensure the survival of the nodes to
be charged, the node shall be charged first; and the small energy demand momentcos(I) indicates the low charging priority, which indicates that the node to be charged has small energy
Description
need or is near to the WCV and is unnecessary to charge first. When the WCV selects the secondary charging nodes, the conditions as shown in formula (33) shall be satisfied.
min(cosOkI, cosOk 2 , cos0k3 ,...,cOk1) (33) is a serial number of the nodes to be charged surrounding the target charging node k
. When the two nodes to be charged have the same energy need, the solution of the energy
demand moment is shown in Fig. 7. In Fig. 7, ', is the target charging node of the current
WCV, '2 and X 3 are nodes to be charged around x, the target node to be charged, and X 2
and x3 have same Maslows energy need. Then x3 is farther from x, than X2 i.e. L- >L
Since the WCV can reach the node to be charged x 3 farther away after driving for a longer time, the node to be charged farther away shall have higher charging priority. According to the
definition of the Maslows energy demand moment, due to 03 '012, the energy demand moment
of the two nodes to be charged satisfiesCOSOL >cos, so that the WCVshall select the node to
be charged x3 with small Maslows energy demand moment as the secondary charging node. When the node to be charged have a same distance as the target charging node, the solution
of the energy demand moment is shown in Fig. 8. Assuming that X 2 and x3 are two nodes to be
charged that are equally distanced to the target charging node x, , L1 3 = L 2 . The energy of the
node X2 to be charged with high energy need is exhausted first, so that the node to be charged
has high charging priority. It can be seen from Fig. 8 that 013 >12, and energy demand moment
cos012 >cos013 so that the WCV selects the node to be charged X 2 as the secondary charging node. Therefore, the present invention proposes a charging priority strategy based on E,/M/1/3(FCFS>. Firstly, the initial charging node is selected, the Maslows energy demand
moment of other nodes to be charged surrounding the initial charging node is calculated, and the nodes to be charged surrounding the initial charging node are sequentially added into the charging priority queue of theWCV according to the Maslows energy demand moment. After completing the charging task of the initial charging node, the WCV deletes the node from the charging priority queue and then uses the node with the maximum energy demand moment as the secondary node to be charged. While charging the secondary node to be charged, the WCV selects the peripheral nodes with large energy demand moment into the queue to successively
Description
complete the charging work of the nodes. A pseudo code for specific implementation is as follows:
Charging priority strategy based on E, /M/1/3(FCFS) queuing model Input: A position coordinate of the node to be charged in the charging service sub-queue, a Maslows energy demand degree of the node to be charged and the gradient of the Mslows energy demand degree of the node 7 Output: J 1. Calculate a distance from the WCV to each node to be charged; 2. Calculate the energy demand moment of the nodes to be charged, sequence, and select
three nodes to be charged with maximum energy demand moment into the queue 3. While the charging service sub-queue is not empty; 4. The WCV charges the node to be charged with the highest priority;
ici + 5. Calculate the energy demand moment of the nodes to be charged after /1;
tel+ 6. Compare the energy demand moment of each node to be charged after P , and
select the nodes to be charged with the maximum energy demand moment into the queue 71; 7 7. Return J 8. End while.
3. Implementation effect: In order to measure the performance of the online charging algorithm based on the Maslows energy demand moment proposed by the present invention, the online charging algorithm is compared with the NJNP algorithm and ant colony algorithm (ACO). The performance indicators of the nodes such as the average sleep time, the energy utilization rate, the average response time of the network node and the number of dead nodes are mainly measured and analyzed so as to evaluate the feasibility of the charging strategy herein. 3.1 Implementation environment: A set simulation scene is a square area of 100mx1m, 100 network sensor nodes are deployed randomly, and a maximum energy value of the sensor nodes is 100kJ. The energy
consumption percentage -workof the sensor node at a normal working mode is 0.02/s, the
energy consumption percentage /""P at a sleep mode is 0.0040/s , and a maintenance station
Description
is arranged on the edge of the network area. According to the movable charging strategy proposed by the present invention, the number of the WCV is consistent with that of the charging service sub-queues, that is, there is one WCV in each charging service sub-queue to provide
charging service for the nodes to be charged. The traveling speed of the WCV is 4m/s and the
charging efficiency a, is 0.1 /S . The WCV leaves the node only after fully charging the node to be charged. In order to ensure the service quality of the network, the node being charged is regarded as the normal working node, that is, the node can work while being charged. The
energy consumption rate P of the WCV in the driving process is 0.04kJ . The charging
threshold value of the node is P=10%, that is, when the residual energy of the sensor node is less than 10%, the charging request is sent to the maintenance station. 3.2 Average response time The average response time of the sensor nodes in the network refers to the time when the node sends the charging request to when receives the confirmation feedback of the WCV. Fig. 9 shows that the average response time of the strategy proposed by the present invention is less than the average response time of NJNP and ACO. Because the length of the charging priority queue of the charging strategy proposed by the present invention is constant and is 3, the average response time fluctuation is not great and is relatively stable. With respect to the NJNP and ACO strategies, since the length of the service queue is not limited, the average response time is longer. In addition, since the NJNP and ACO strategies cannot ensure the survival of the nodes, the average response time will be shorter and shorter as more and more nodes die. 3.3 Energy utilization rate The energy utilization rate of the charging strategy refers to the percentage of energy supplemented by the WCV to the node in the total consumption of the energy and reflects the energy utilization condition of the WCV. For each WCV, if the time that the WCV establishes the charging link to the node is neglected, the state can be classified into a charging state and a driving state. The serial number of the WCV in the j th charging service sub-queue is defined as
, assuming that the driving distance of the WCV in the time t of the network is Li, the L./ duration of the WCV in the driving state is t c = , and the duration of the WCV in the
charging state istC t tti. Thus, it can be obtained that the energy utilization rate Lc of the WCV is shown in formula (34).
Description
100s., tc 100s.,,ktC + PC [t (34)
work is the energy consumption rate of the node in the normal working state; tc is the
duration of the WCV in the charging state; c is the energy consumption rate of the WCV in
the driving process; and t' is the driving time of the WCV. The comparison of the energy utilization rate is as shown in Fig. 10. The utilization rate of the ACO strategy is apparently less than that of the other two strategies, which shows that the energy utilization rate of the WCV cannot be improved by simply selecting the shortest charging path for the WCV. With respect to the NJNP strategy and the strategy proposed by the present invention, the WCV charges the node based on the energy need of the node, thereby obtaining high energy utilization rate. At the early stage of running of the network, since the NJNP strategy selects the nearest nodes to be charged, the WCV does not need more driving time, and the energy utilization rate of the WCV is slightly high. With the continuous running of the network, the NJNP strategy may cause the death of some nodes, the WCV needs longer driving time, and the energy utilization rate of the WCV begins to decrease. Since the strategy proposed by the present invention can maximally ensure the survival of the nodes, the energy utilization rate is greater than that of the NJNP strategy. 3.4 Number of dead nodes The long-term effective work of the nodes in the network is very crucial, so that the present invention researches an indicator, i.e. the number of dead nodes in the network, and the simulation graph is shown in Fig. 11. It can be seen from Fig. 11 that the number of the network dead nodes of the NJNP strategy and the ACO strategy is far greater than the number of dead nodes of the strategy proposed by the present invention. The NJNP strategy and the ACO strategy do not ensure the survival of the nodes. With the continuous running of the network, the number of the dead nodes is apparently increased. The strategy proposed by the present invention ensures the survival of the nodes as far as possible, so that the number of the dead nodes is minimum. When the network runs for 4h, by adopting the strategy of the present invention, there is no dead node. The number of the dead nodes of the NJNP strategy and the ACO strategy are 8 and 12 respectively. After the network runs for 8h, the number of the dead nodes in WRSN adopting the ACO strategy reaches 39. The number of the dead nodes in WRSN adopting the NJNP strategy reaches 23. The number of the dead nodes by adopting the strategy proposed by
1R
Description
the present invention is five. When more nodes survive, the sensing function of the network can be better ensured. The strategy proposed by the present invention can ensure the maximum survival of the nodes, which sufficiently reflects the advantage of the present strategy. 3.5 Average sleep time The state of the nodes in the network can be classified into the normal working state and the dormancy state. When the node is in the normal working state, the information transmission and the energy consumption are normal; and when the node is in the dormancy state, the data communication work is not carried out, the node can be sensed, and the energy consumption speed is low. When the residual energy of the nodes in the network is less than the energy threshold value, the nodes enter the sleep state. Although the nodes in the sleep state can be sensed, the data communication cannot be carried out. Therefore, the number of the sleep nodes in the network and the sleep duration of the nodes may severely influence the service quality of the network. The average sleep duration of the network nodes is analyzed in the present invention. Assuming that the node enters the sleep state after sending the charging request to the maintenance station, the energy consumption can be reduced. Assuming that the time that the i th
node in the network sends the charging request is 1 .TkIrespectively, the time that the node establishes the charging link to the WCV is neglected. k indicates the times of the i th
node sending the charging requests. The sleep time ,ik of the i node after sending the
charging request at k th times is equal to the time interval of the k+1th charging request and the k th charging request minus the working time of the node in the normal working state. It can be
known from calculation that the time 7nof the node from the full-power state to the 10%
residual energy is 1000 10%/sw =k4500s o that the average sleep time of 100 nodes in the simulation environment can be solved as: k-i
100 1 - - T-) k s= ~i~1 k-I 100 (35) Through the comparison with the ACO strategy and the NJNP strategy, simulation results are as shown in Fig. 12. The average sleep time of the nodes adopting the ACO strategy and the NJNP strategy is almost same and less than the sleep time of the strategy adopted by the present invention. Since the strategy proposed by the present invention ensures the survival of the nodes, with respect to the charging request nodes with the same residual energy, the WCV may select
Description
the node to be charged farther away, so that the sleep time of the nodes is relatively long. However, since the energy consumption of the nodes during the sleep is relatively low, as long as the WCV reaches the node before the energy of the node is used up, the node can still be ensured not to die. 4. Innovation points: 1. In order to measure the priority of the charging task, the present invention proposes the concept of the Maslows energy demand moment based on the Maslows hierarchical demand theory. The energy demand moment comprehensively considers the position and energy need of the nodes and realizes the planning of the optimum path of the wireless charging vehicle.
2. In order to better process the charging requests sent by a plurality of sensor nodes in
batch, the present invention utilizes the E,/M/1/oo (FCFS) queuing model to establish the
charging service sub-queues, thereby ensuring that the wireless charging vehicles can charge the
nodes within the charging capability range.
Claims (4)
1. An online rechargeable wireless sensor network charging scheduling system, comprising a plurality of wireless charging vehicles and a maintenance station, wherein the maintenance station comprises a service queue system and a charging service system; the service queue system receives charging requests and residual energy information sent by energy-deficient nodes, i.e., sensor nodes with the energy less than a set threshold value, in a network, stores the charging requests in a service queue form into service queues, then divides the service queues into a plurality of charging service sub-queues according to a minimum survival time of the energy-deficient nodes according to a queuing theory and inputs the plurality of charging service sub-queues into the charging service system; the charging service system performs the priority measurement according to the position information and residual energy information of each energy-deficient node, plans a reasonable charging path in real time and uses a plurality of wireless charging vehicles to charge the energy-deficient nodes in sequence in each charging service sub-queue according to the charging priority.
2. The online rechargeable wireless sensor network charging scheduling system according to claim 1, wherein the service queue system calculates a shortest distance from each energy-deficient node to one wireless charging vehicle according to the position information of the energy-deficient node; then a shortest path Lmin when the wireless charging vehicle traverses
all energy-deficient nodes is calculated, and a forecast time Tc for completing the charging is
[ C+1 calculated; the service queue is divided into L charging service sub-queues; each wireless charging vehicle charges a plurality of energy-deficient nodes corresponding to one charging service sub-queue: Tc=Lmin/vc
Ls
k W = "p(s -1)
p
wherein Vis atraveling speed of the wireless charging vehicle; Lsis an average queue
length of the charging service sub-queues; pis charging service intensity, P41; - is aservice rate of the wireless charging vehicle;XAis an reciprocal ofan average time interval for the
energy-deficient nodes to send the charging request; is a retention time of the
Claims
energy-deficient nodes in the charging service sub-queue; 0 in s° represents an initial period of
the charging period, k indicates a number of the time intervals, and So represents a sum of the duration of each charging interval.
3. The online rechargeable wireless sensor network charging scheduling system according to claim 2, wherein the charging service system performs the priority measurement for each energy-deficient node, i.e. the node to be charged, in each charging service sub-queue according to the following method, and a length of the charging priority queue is constant and is 3; firstly, selecting an initial charging node, calculating a Maslows energy demand moment of other nodes to be charged surrounding the initial charging node, and adding other nodes to be charged into a charging priority queue of the wireless charging vehicle according to the Maslows energy demand moment; after the charging task for the initial charging node is completed, deleting the initial charging node from the charging priority queue, using other nodes to be charged with the maximum energy demand moment as secondary nodes to be charged, and while charging the secondary nodes to be charged, selecting the nodes to be charged with the maximum Maslows energy demand moment surrounding the secondary nodes to be charged into the charging priority queue, and when the two nodes to be charged have same energy demand moment, selecting a node to be charged which is further from a target charging node, wherein a calculation method of the Maslows energy demand moment is as follows:
Step I, setting an energy consumption rate of the node i to be charged as , and
calculating energy need 0 of the node i to be charged within the time t according to the following formula:
; =1- (1- t)
Step II, calculating a Maslows energy demand moment of each node to be charged in the
charging service sub-queue, wherein with respect to each node XI to be charged, the Maslows energy demand moment is calculated according to the following formula:
cos(O) = cos(arctan( '-0/)) LIU
wherein assuming that Xk is a target charging node to which the wireless charging vehicle
is driving, XI is one node to be charged surrounding the target charging node Xk, L indicates
Claims
a distance between the target charging node X4 and the node to be charged X1, 04 is energy
need of the target charging node X4, and 01 is the energy need of the node to be charged.
4. The online rechargeable wireless sensor network charging scheduling system according to claim 3, wherein a selection method of the initial charging node is as follows:
selecting three nodes X , X2 and 3 to be charged with maximum Maslows energy demand moment on the periphery of the wireless charging vehicle, adding the three nodes to be charged into the charging priority queue with the length of 3, and selecting the initial charging node from the three nodes to be charged, wherein the selection of the initial charging nodes shall satisfy the following three constraint conditions: constraint condition I: the selected initial charging node shall not die within the time that the wireless charging vehicle traverses the three nodes to be charged; constraint condition II: the variance of the distance from the three nodes to be charged to the wireless charging vehicle shall be as small as possible; constraint condition III: when the distance from the target charging node to the other two nodes to be charged is greater than the distance between the two nodes to be charged, the wireless charging vehicle updates the target charging node to the node to be charged and selects one of the two nodes to be charged as the target charging node, and the target charging node is the initial charging node.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112578813A (en) * | 2020-12-10 | 2021-03-30 | 东南大学 | Unmanned aerial vehicle auxiliary charging method in wireless sensor network |
| CN113179457A (en) * | 2021-03-09 | 2021-07-27 | 杭州电子科技大学 | Method for charging space-time part during road passing in wireless chargeable sensing network |
| CN113659670A (en) * | 2021-08-11 | 2021-11-16 | 南京邮电大学 | Wireless sensor network charging method based on region division |
| CN114915049A (en) * | 2022-03-31 | 2022-08-16 | 南京邮电大学 | Robust multi-coverage placement method for directional wireless charger |
| CN115276765A (en) * | 2022-07-18 | 2022-11-01 | 南京信息工程大学 | Service priority oriented ATDM satellite communication scheduling method |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112519626B (en) * | 2020-11-24 | 2023-11-21 | 西安西科阳光电子有限公司 | Charging method for mobile energy storage charging vehicle |
| CN112803550B (en) * | 2021-03-02 | 2022-08-30 | 南京邮电大学 | Method and system for maximizing utilization rate of wireless charging vehicle based on multiple thresholds |
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| US10211662B2 (en) * | 2014-08-15 | 2019-02-19 | Analog Devices Global | Wireless charging platform using environment based beamforming for wireless sensor network |
| CN104953643B (en) * | 2015-05-12 | 2017-03-08 | 合肥工业大学 | A kind of wireless sensor network charging method of many charge node |
| CN107657374B (en) * | 2017-09-25 | 2021-01-08 | 中南大学 | On-demand charging scheduling method based on dynamic changes of energy consumption and distance |
| CN109982452B (en) * | 2019-03-18 | 2020-09-22 | 华南理工大学 | Quasi-array-based wireless chargeable sensor network charging scheduling method |
| CN110300418B (en) * | 2019-06-05 | 2022-04-01 | 云南电网有限责任公司丽江供电局 | Space-time scheduling algorithm for charging according to needs in wireless chargeable sensor network |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112578813A (en) * | 2020-12-10 | 2021-03-30 | 东南大学 | Unmanned aerial vehicle auxiliary charging method in wireless sensor network |
| CN113179457A (en) * | 2021-03-09 | 2021-07-27 | 杭州电子科技大学 | Method for charging space-time part during road passing in wireless chargeable sensing network |
| CN113179457B (en) * | 2021-03-09 | 2022-06-14 | 杭州电子科技大学 | Method for charging space-time part during road passing in wireless chargeable sensing network |
| CN113659670A (en) * | 2021-08-11 | 2021-11-16 | 南京邮电大学 | Wireless sensor network charging method based on region division |
| CN113659670B (en) * | 2021-08-11 | 2023-07-07 | 南京邮电大学 | Wireless sensor network charging method based on region division |
| CN114915049A (en) * | 2022-03-31 | 2022-08-16 | 南京邮电大学 | Robust multi-coverage placement method for directional wireless charger |
| CN115276765A (en) * | 2022-07-18 | 2022-11-01 | 南京信息工程大学 | Service priority oriented ATDM satellite communication scheduling method |
| CN115276765B (en) * | 2022-07-18 | 2023-06-06 | 南京信息工程大学 | ATDM satellite communication scheduling method for service priority |
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