Patent ID: 11858627
Assignee: DALIAN UNIVERSITY OF TECHNOLOGY
Field: Transport (Mechanical engineering)
Classification: CPC B  G  Y | IPC B  G

Claim 0:
1. A design method of high energy efficiency unmanned aerial vehicle (UAV) green data acquisition system, comprising the following steps:
step 1, constructing a system optimization objective:
(1) serving a set of I ground sensors which are randomly distributed through time division multiple access (TDMA) by an unmanned aerial vehicle (UAV);
(2) flying, by the UAV, at a fixed altitude H with a maximum flight speed of Vm and a total cycle of T, and discretizing the cycle T into N time slots by a time discretization method, with the length of each time slot of, t
   =
   
    T
    N
   
  
  ;, the coordinate of the UAV is w[n]=[x(n),y(n)]T ∈R2×1 in time slot n, wherein x(n), y(n) are the x-coordinate and y-coordinate of the UAV respectively, and R2×1 is a two-dimensional vector space; for a sensor set SI={1, 2, . . . , I} of random distribution, the coordinate of sensor i is fixed as Li=[xi,yi]T∈R2×1, i∈SI, each sensor supports the energy of Ei,i∈SI and the data amount to be transmitted is Bi,i∈SI; if communication between the UAV and ground is line of sight (LoS) link communication, channel quality only depends on a distance between the UAV and the sensor, and power gain in unit reference distance is expressed as ρ0, then the channel power gain hi[n] of the sensor i in the time slot n conforms to a free space path loss model, i.e.,, h
     i
    
    [
    n
    ]
   
   =
   
    
     
      ρ
      0
     
     ⁢
     
      
       
        d
        i
       
       [
       n
       ]
      
      
       -
       2
      
     
    
    =
    
     
      ρ
      0
     
     
      
       H
       2
      
      +
      
       
        
        
         
          w
          [
          n
          ]
         
         -
         
          L
          i
         
        
        
       
       2
      
     
    
   
  
  ,
  
   ∀
   
    i
    ∈
    SI
   
  
  ,, di[n] is the distance between the UAV and the sensor i in the three-dimensional space;
(3) assuming that the UAV serves only one sensor in one time slot, and defining a binary variable Si[n]∈{0,1} to represent wake-up scheduling of the sensor; Si[n]=1 indicates that the UAV establishes communication with the sensor i in the time slot n; Si[n]=0 indicates that the UAV does not establish communication with the sensor i in the time slot n; then, the information transmission rate Rui[n] between the UAV and the sensor i in the time slot n is expressed as:, R
        u
        i
       
       [
       n
       ]
      
      =
      
       
        
         S
         i
        
        [
        n
        ]
       
       ⁢
       
        
         log
         2
        
        (
        
         1
         +
         
          
           
            P
            A
           
           ⁢
           
            ρ
            0
           
          
          
           
            (
            
             
              H
              2
             
             +
             
              
               
               
                
                 w
                 [
                 n
                 ]
                
                -
                
                 L
                 i
                
               
               
              
              2
             
            
            )
           
           ⁢
           
            σ
            2
           
          
         
        
        )
       
      
     
     ,
     
      ∀
      
       i
        
       ∈
       
        S
        ⁢
        I
       
      
     
    
   
   
    
     (
     1
     )
    
   
  
 

wherein σ2 is additive white gaussian noise (AWGN) at a receiving end of the UAV, and PA is the transmission power of a ground sensor during communication; the total information amount R(bits/Hz) transmitted in one cycle (N time slots) of serving of the UAV is expressed as:, R
       ¯
      
      (
      
       
        {
        W
        }
       
       ,
       
        {
        t
        }
       
       ,
       
        {
        S
        }
       
      
      )
     
     =
     
      
       ∑
       
        n
        =
        1
       
       N
      
      
       
        ∑
        
         i
         =
         1
        
        I
       
       
        
         
          R
          u
          i
         
         [
         n
         ]
        
        ⁢
        t
       
      
     
    
   
   
    
     (
     2
     )
    
   
  
 

for a rotary-wing UAV, when parameters are constant, the propulsion power P(V) of the UAV is mainly related to flight speed V; the propulsion power is composed of three parts: blade profile power, parasite power and induced power, expressed as:, P
      ⁡
      (
      V
      )
     
     =
     
      
       
        
         P
         0
        
        (
        
         1
         +
         
          
           3
           ⁢
           
            V
            2
           
          
          
           
            Ω
            2
           
           ⁢
           
            r
            2
           
          
         
        
        )
       
       
        ︸
        
         blade
         ⁢
           
         profile
         ⁢
           
         power
        
       
      
      +
      
       
        
         1
         2
        
        ⁢
        
         d
         0
        
        ⁢
        ρ
        ⁢
        s
        ⁢
        A
        ⁢
        
         V
         3
        
       
       
        ︸
        
         parasite
         ⁢
           
         power
        
       
      
      +
      
       
        
         
          P
          i
         
         (
         
          
           
            1
            +
            
             
              V
              4
             
             
              4
              ⁢
              
               v
               0
               4
              
             
            
           
          
          -
          
           
            V
            2
           
           
            2
            ⁢
            
             v
             0
             2
            
           
          
         
         )
        
        
         1
         /
         2
        
       
       
        ︸
        
                         
         
          induced
          ⁢
            
          power
                          
         
        
       
      
     
    
   
   
    
     (
     3
     )
    
   
  
 

the speed of the time slot n is approximately expressed as, v
    [
    n
     
    ]
   
   =
   
    
     
      
      
       
        w
        [
        
         n
         +
         1
        
        ]
       
       -
       
        w
        [
        n
        ]
       
      
      
     
     t
    
    
     =
     Δ
    
    
     
      Δ
      n
     
     t
    
   
  
  ,, and Δn is defined as the flight distance of the time slot n; then the propulsion power Pprop[n] of the time slot n is approximated expressed by the following formula:, P
       prop
      
      [
      n
      ]
     
     =
     
      
       
        P
        0
       
       (
       
        1
        +
        
         
          3
          ⁢
          
           Δ
           n
           2
          
         
         
          
           Ω
           2
          
          ⁢
          
           r
           2
          
          ⁢
          
           t
           2
          
         
        
       
       )
      
      +
      
       
        1
        2
       
       ⁢
       
        d
        0
       
       ⁢
       ρ
       ⁢
       sA
       ⁢
       
        
         Δ
         n
         3
        
        
         t
         3
        
       
      
      +
      
       
        P
        i
       
       (
       
        
         
          1
          +
          
           
            Δ
            n
            4
           
           
            4
            ⁢
            
             v
             0
             4
            
            ⁢
            
             t
             4
            
           
          
         
        
        -
        
         
          Δ
          n
          2
         
         
          2
          ⁢
          
           v
           0
           2
          
          ⁢
          t
         
        
       
      
     
    
   
   
    
     (
     4
     )
    
   
  
 

in the formula, P0 and Pi are the blade profile power and the induced power respectively in a hovering state; Ω is blade angular velocity; r is rotor radius; d0 represents fuselage drag ratio; ρ is air density; s is rotor solidity; A is rotor disc area; v0 is mean rotor induced velocity; the above parameters are constants; the total propulsion energy E consumed by the UAV in one cycle of serving is expressed as:, E
      ⁡
      (
      
       
        {
        W
        }
       
       ,
       
        {
        t
        }
       
      
      )
     
     =
     
      
       ∑
       
        n
        =
        1
       
       N
      
      
       
        
         P
         prop
        
        [
        n
        ]
       
       ⁢
       t
      
     
    
   
   
    
     (
     5
     )
    
   
  
 

according to the definition of energy efficiency, the system optimization objective is represented as:, EE
      ⁡
      (
      
       
        {
        W
        }
       
       ,
       
        {
        t
        }
       
       ,
       
        {
        S
        }
       
      
      )
     
     =
     
      
       
        
         R
         ¯
        
        (
        
         
          {
          W
          }
         
         ,
         
          {
          t
          }
         
         ,
         
          {
          S
          }
         
        
        )
       
       
        E
        ⁡
        (
        
         
          {
          W
          }
         
         ,
         
          {
          t
          }
         
        
        )
       
      
      =
      
       
        
         ∑
         
          n
          =
          1
         
         N
        
        
         
          ∑
          
           i
           =
           1
          
          I
         
         
          
           R
           u
           i
          
          ⁢
          
           n
           [
           t
           ]
          
         
        
       
       
        
         ∑
         
          n
          =
          1
         
         N
        
        
         
          P
          prop
         
         ⁢
         
          n
          [
          t
          ]
         
        
       
      
     
    
   
   
    
     (
     6
     )
    
   
  
 

step 2, constructing an optimization problem according to the energy efficiency formula in step 1, wherein an optimization objective is maximization of EE({W},{t},{S}), and constraints comprise UAV trajectory constraints, sensor wake-up scheduling constraints, sensor energy constraint and data amount constraint to construct the following optimization problem:, max
      
       S
       ,
       W
       ,
       t
      
     
     
      EE
      ⁡
      (
      
       
        {
        W
        }
       
       ,
       
        {
        t
        }
       
       ,
       
        {
        S
        }
       
      
      )
     
    
   
   
    
     (
     
      7
      ⁢
      a
     
     )
    
   
  
 

 
  
   
    
     
      s
      .
      t
      .
         
      
       w
       [
       1
       ]
      
     
     =
     
      w
      [
      N
      ]
     
    
   
   
    
     (
     
      7
      ⁢
      b
     
     )
    
   
  
 

 
  
   
    
     
      
       
        
        
         
          w
          [
          
           n
           +
           1
          
          ]
         
         -
         
          w
          [
          n
          ]
         
        
        
       
       2
      
      ≤
      
       γ
       ⁢
       
        H
        2
       
      
     
     ,
     
      n
      =
      1
     
     ,
     
      
       …
       ⁢
          
       N
      
      -
      1
     
    
   
   
    
     (
     
      7
      ⁢
      c
     
     )
    
   
  
 

 
  
   
    
     
      
       
       
        
         w
         [
         
          n
          +
          1
         
         ]
        
        -
        
         w
         [
         n
         ]
        
       
       
      
      ≤
      
       
        V
        m
       
       ⁢
       t
      
     
     ,
     
      n
      =
      1
     
     ,
     
      
       …
       ⁢
          
       N
      
      -
      1
     
    
   
   
    
     
      (
      
       7
       ⁢
       d
      
      )
     
    
   
  
 

 
  
   
    
     
      
       ∑
       
        i
        =
        1
       
       I
      
      
       
        S
        i
       
       [
       n
       ]
      
     
     =
     1
    
   
   
    
     (
     
      7
      ⁢
      e
     
     )
    
   
  
 

 
  
   
    
     
      
       
        S
        i
       
       [
       n
       ]
      
      ∈
      
       {
       
        0
        ,
        1
       
       }
      
     
     ,
     
      ∀
      
       i
       ∈
       SI
      
     
     ,
     
      ∀
      n
     
    
   
   
    
     
      (
      
       7
       ⁢
       f
      
      )
     
    
   
  
 

 
  
   
    
     
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        (
        
         
          
           R
           u
           i
          
          [
          n
          ]
         
         ⁢
         t
        
        )
       
      
      ≥
      
       B
       i
      
     
     ,
     
      ∀
      
       i
       ∈
       SI
      
     
    
   
   
    
     (
     
      7
      ⁢
      g
     
     )
    
   
  
 

 
  
   
    
     
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        (
        
         
          
           S
           i
          
          [
          n
          ]
         
         ⁢
         
          P
          A
         
         ⁢
         t
        
        )
       
      
      ≥
      
       E
       i
      
     
     ,
     
      ∀
      
       i
       ∈
       SI
      
     
    
   
   
    
     (
     
      7
      ⁢
      h
     
     )
    
   
  
 

in the above optimization problem, formulas (7b)-7(d) are trajectory constraints, Vm is the maximum speed of the UAV and the UAV returns to an initial position after flying by a cycle; formulas (7e) and (7f) are the sensor wake-up scheduling constraints; formula (7g) is the data amount constraint of the sensor and Bi is the data amount to be transmitted by the sensor i; formula (7h) is the sensor energy constraint and Ei is maximum energy supported by the sensor i in each cycle;
step 3, decomposing an original problem (7) into two sub-problems according to a block coordinate descent method; for the two sub-problems, approximately converting two non-convex problems into two convex optimization problems and calculating the problems by a successive convex approximation technique, as follows:
(1) optimization sub-problem of wake-up scheduling S and time slot t
fixing UAV trajectory W so that the sub-problem is the non-convex optimization problem of wake-up scheduling S and time slot t; firstly, for a binary variable S, slacking S to a continuous variable within a range [0,1]; then, introducing an auxiliary variable z[n] to satisfy, z
     [
     n
     ]
    
    2
   
   =
   
    
     
      
       t
       4
      
      +
      
       
        Δ
        n
        4
       
       
        4
        ⁢
        
         v
         0
         4
        
       
      
     
    
    -
    
     
      Δ
      n
      2
     
     
      2
      ⁢
      
       v
       0
       2
      
     
    
   
  
  ,
  
   ∀
   n
  
  ,
  
   i
   .
   e
   .
  
  ,
  
   
    t
    4
   
   =
   
    
     
      z
      [
      n
      ]
     
     4
    
    +
    
     
      
       Δ
       n
       2
      
      
       v
       0
       2
      
     
     ⁢
     
      
       z
       [
       n
       ]
      
      2
     
    
   
  
  ,
  
   
    ∀
    n
   
   ;, using z[n] to replace the third term of the propulsion power Pprop[n] in formula (4) to obtain the UAV propulsion power PpropA[n] under the sub-problem; introducing an auxiliary variable R_t[i] to satisfy, R_t
     [
     i
     ]
    
    2
   
   =
   
    
     ∑
     
      n
      =
      1
     
     N
    
    
     
      
       R
       u
       i
      
      [
      n
      ]
     
     ⁢
     t
    
   
  
  ,
  
   
    ∀
    i
   
   ;, after introducing the auxiliary variables, applying the successive convex approximation technique for non-convex constraints, converting hyperbolic constraints into SOCP and approximating the original non-convex sub-problem as a convex problem, expressed as:, max
      
       
        S
        ,
        W
        ,
        t
       
       
        
         
          z
          [
          n
          ]
         
         ,
        
        
         
          R_t
          [
          i
          ]
         
         .
        
       
      
     
     
      
       
        ∑
        
         i
         =
         1
        
        I
       
       
        
         R_t
         lb
        
        [
        i
        ]
       
      
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        
         
          P
          prop
          A
         
         [
         n
         ]
        
        ⁢
        t
       
      
     
    
   
   
    
     (
     
      8
      ⁢
      a
     
     )
    
   
  
 

 
  
   
    
     
      
       s
       .
       t
       .
          
       
        
        
         
          w
          [
          
           n
           +
           1
          
          ]
         
         -
         
          w
          [
          n
          ]
         
        
        
       
      
      ≤
      
       
        V
        m
       
       ⁢
       t
      
     
     ,
     
      n
      =
      1
     
     ,
     
      
       …
       ⁢
          
       N
      
      -
      1
     
    
   
   
    
     (
     
      8
      ⁢
      b
     
     )
    
   
  
 

 
  
   
    
     
      
       
        ∑
        
         i
         =
         1
        
        I
       
       
        
         S
         i
        
        [
        n
        ]
       
      
      ≤
      1
     
     ,
     
      ∀
      n
     
    
   
   
    
     
      (
      
       8
       ⁢
       c
      
      )
     
    
   
  
 

 
  
   
    
     
      0
      ≤
      
       
        S
        i
       
       [
       n
       ]
      
      ≤
      1
     
     ,
     
      ∀
      i
     
     ,
     
      ∀
      n
     
    
   
   
    
     
      (
      
       8
       ⁢
       d
      
      )
     
    
   
  
 

 
  
   
    
     
      
       
        R_t
        lb
       
       [
       i
       ]
      
      ≥
      
       B
       i
      
     
     ,
     
      ∀
      i
     
    
   
   
    
     (
     
      8
      ⁢
      e
     
     )
    
   
  
 

 
  
   
    
     
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        (
        
         
          
           S
           i
          
          [
          n
          ]
         
         ⁢
         
          P
          A
         
        
        )
       
      
      ≤
      
       
        
         E
         i
        
        (
        
         1
         t
        
        )
       
       lb
      
     
     ,
     
      ∀
      i
     
    
   
   
    
     (
     
      8
      ⁢
      f
     
     )
    
   
  
 

 
  
   
    
     
      
       t
       4
      
      ≤
      
       
        
         
          z
          
           (
           r
           )
          
         
         [
         n
         ]
        
        4
       
       +
       
        4
        ⁢
        
         
          
           z
           
            (
            r
            )
           
          
          [
          n
          ]
         
         3
        
        ⁢
        
         (
         
          
           z
           [
           n
           ]
          
          -
          
           
            z
            
             (
             r
             )
            
           
           [
           n
           ]
          
         
         )
        
       
       +
       
        
         
          Δ
          n
          2
         
         
          v
          0
          2
         
        
        ⁢
        
         (
         
          
           
            
             z
             
              (
              r
              )
             
            
            [
            n
            ]
           
           2
          
          +
          
           2
           ⁢
           
            
             z
             
              (
              r
              )
             
            
            [
            n
            ]
           
           ⁢
           
            (
            
             
              z
              [
              n
              ]
             
             -
             
              
               z
               
                (
                r
                )
               
              
              [
              n
              ]
             
            
            )
           
          
         
         )
        
       
      
     
     ,
     
      ∀
      n
     
    
   
   
    
     (
     
      8
      ⁢
      g
     
     )
    
   
  
 

 
  
   
    
     
      
       
       
        
         [
         
          
           2
           ⁢
           
            R_t
            [
            i
            ]
           
          
          ,
          
           
            
             ∑
             
              n
              =
              1
             
             N
            
            
             
              R
              u
              i
             
             [
             n
             ]
            
           
           -
           t
          
         
         ]
        
        †
       
       
      
      ≤
      
       
        
         ∑
         
          n
          =
          1
         
         N
        
        
         
          R
          u
          i
         
         [
         n
         ]
        
       
       +
       t
      
     
     ,
     
      ∀
      i
     
    
   
   
    
     (
     
      8
      ⁢
      h
     
     )
    
   
  
 

in the sub-problem (8), PpropA[n] is the propulsion power after the auxiliary variable z[n] is introduced, and is a convex function of t and z[n]; R_tlb[i] is the lower bound of first-order taylor expansion of the auxiliary variable R_t[i]2, and is a linear function of, R_t
   [
   i
   ]
  
  ;
  
   
    (
    
     1
     t
    
    )
   
   lb, is the lower bound of first-order taylor expansion of, 1
   t
  
  ,, and has a linear relationship with t; the constraints of the sub-problem (8) are convex constraints; the optimization objective (8a) is a standard concave-convex fractional programming problem with concave numerator over convex denominator; because the constraint range is reduced by the successive convex approximation technique, the optimal solution of the convex problem after approximation is the lower bound of the optimal solution of an original sub-problem;
(2) optimization sub-problem of UAV trajectory W
fixing wake-up scheduling S and time slot t so that the sub-problem is a non-convex optimization problem of the UAV trajectory W; introducing an auxiliary variable y[n] to satisfy, y
     [
     n
     ]
    
    2
   
   =
   
    
     
      
       t
       4
      
      +
      
       
        Δ
        n
        4
       
       
        4
        ⁢
        
         v
         0
         4
        
       
      
     
    
    -
    
     
      Δ
      n
      2
     
     
      2
      ⁢
      
       v
       0
       2
      
     
    
   
  
  ,
  
   ∀
   n
  
  ,
  
   i
   .
   e
   .
  
  ,
  
   
    
     t
     4
    
    
     
      y
      [
      n
      ]
     
     2
    
   
   =
   
    
     
      y
      [
      n
      ]
     
     2
    
    +
    
     
      Δ
      n
      2
     
     
      v
      0
      2
     
    
   
  
  ,
  
   
    ∀
    n
   
   ;, using y[n] to replace the third term of the propulsion power Pprop[n] in formula (4) to obtain the UAV propulsion power PpropB[n] under the sub-problem; after introducing the auxiliary variables, applying the successive convex approximation technique for non-convex constraints, and approximating the original non-convex sub-problem as a convex problem, expressed as:, max
      
       W
       ,
       
        y
        [
        n
        ]
       
      
     
     
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        
         ∑
         
          i
          =
          1
         
         I
        
        
         
          
           R
           u
           
            i
            ,
            lb
           
          
          [
          n
          ]
         
         ⁢
         t
        
       
      
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        
         
          P
          prop
          B
         
         [
         n
         ]
        
        ⁢
        t
       
      
     
    
   
   
    
     (
     
      9
      ⁢
      a
     
     )
    
   
  
 

 
  
   
    
     
      s
      .
      t
      .
         
      
       w
       [
       1
       ]
      
     
     =
     
      w
      [
      N
      ]
     
    
   
   
    
     (
     
      9
      ⁢
      b
     
     )
    
   
  
 

 
  
   
    
     
      
       
        
        
         
          w
          [
          
           n
           +
           1
          
          ]
         
         -
         
          w
          [
          n
          ]
         
        
        
       
       2
      
      ≤
      
       γ
       ⁢
       
        H
        2
       
      
     
     ,
     
      n
      =
      1
     
     ,
     
      
       …
       ⁢
          
       N
      
      -
      1
     
    
   
   
    
     
      (
      
       9
       ⁢
       c
      
      )
     
    
   
  
 

 
  
   
    
     
      
       
       
        
         w
         [
         
          n
          +
          1
         
         ]
        
        -
        
         w
         [
         n
         ]
        
       
       
      
      ≤
      
       
        V
        m
       
       ⁢
       t
      
     
     ,
     
      n
      =
      1
     
     ,
     
      
       …
       ⁢
          
       N
      
      -
      1
     
    
   
   
    
     
      (
      
       9
       ⁢
       d
      
      )
     
    
   
  
 

 
  
   
    
     
      
       
        ∑
        
         n
         =
         1
        
        N
       
       
        (
        
         
          
           R
           u
           
            i
            ,
            lb
           
          
          [
          n
          ]
         
         ⁢
         t
        
        )
       
      
      ≥
      
       B
       i
      
     
     ,
     
      ∀
      i
     
    
   
   
    
     
      (
      
       9
       ⁢
       e
      
      )
     
    
   
  
 

 
  
   
    
     
      
       t
       4
      
      
       
        y
        [
        n
        ]
       
       2
      
     
     ≤
     
      
       (
       
        
         
          y
          
           (
           r
           )
          
         
         [
         n
         ]
        
        2
       
       )
      
      +
      
       2
       ⁢
       
        
         y
         
          (
          r
          )
         
        
        [
        n
        ]
       
       ⁢
       
        (
        
         
          y
          [
          n
          ]
         
         -
         
          
           y
           
            (
            r
            )
           
          
          [
          n
          ]
         
        
        )
       
      
      -
      
       
        
         
         
          
           
            w
            
             (
             r
             )
            
           
           [
           
            n
            +
            1
           
           ]
          
          -
          
           
            w
            
             (
             r
             )
            
           
           [
           n
           ]
          
         
         
        
        2
       
       
        v
        0
        2
       
      
      +
     
    
   
   
    
     (
     
      9
      ⁢
      f
     
     )
    
   
  
 

 
  
   
    
     
      2
      
       v
       0
       2
      
     
     ⁢
     
      
       (
       
        
         
          w
          
           (
           r
           )
          
         
         [
         
          n
          +
          1
         
         ]
        
        -
        
         
          w
          
           (
           r
           )
          
         
         [
         n
         ]
        
       
       )
      
      ·
      
       (
       
        
         w
         [
         
          n
          +
          1
         
         ]
        
        -
        
         w
         [
         n
         ]
        
       
       )
      
     
    
   
   
    
     
      (
      
       9
       ⁢
       g
      
      )
     
    
   
  
 

in the sub-problem (9), PpropB[n] is the propulsion power after the auxiliary variable y[n] is introduced, and is a convex function of w[n]; Rui,lb[n] is the lower bound of first-order taylor expansion of the information transmission rate Rui[n] on ∥w[n]−Li∥, and is a concave function of w[n]; the solving method of the sub-problem (9) is the same as that of the sub-problem (8); the optimal solution of the convex problem after approximation is the lower bound of the optimal solution of the original sub-problem;
(3) overall iterative algorithm design
in each iteration, by solving the sub-problem (8) and the sub-problem (9), alternately optimizing wake-up scheduling S, the time slot t and the UAV trajectory W; using the solution obtained in each iteration as the input of next iteration; the termination condition for iteration is that the increase of optimization values of one iteration and the previous iteration is less than a set threshold; the details are as follows:
3.1) setting an iteration termination threshold ε, an initial trajectory w0 and an iteration index r=0;
3.2) in the r+1 iteration, using the trajectory wr obtained from the r iteration to solve the sub-problem (8) to obtain the optimization result of the sub-problem (8) of the r+1 iteration, namely, wake-up scheduling Sr+1 and time slot tr+1;
3.3) solving the sub-problem (9) by the given wr, Sr+1 and tr+1, to obtain the optimization result of the sub-problem (9) of the r+1 iteration, namely trajectory wr+1;
3.4) if the increase of an optimization target value is greater than a threshold ε, then updating the iteration index r=r+1; skipping back to step 4.2) for the next iteration; and if the increase of the target value is less than the threshold ε, terminating the iteration.