| Assumptions: |
| (i) nodes are uniformly dispersed within a square field. |
| (ii) Each node has unique ID. Nodes located in the event area are grouped into one cluster. |
| (iii) Nodes are location-aware and quasi-stationary. |
| (iv) A sensor can compute the approximate distance based on the received signal strength (RSS), |
| and the radio power can be controlled |
| (v) A fixed base station can be located inside or outside the network sensor fields. |
| (vi) All nodes are capable of operating in cluster head mode and sensing mode. |
| (vii) Data fusion is used to reduce the total data message sent. |
| Variables: |
| (i) D is the dimension of the squared region. |
| (ii) Nodes are the number of nodes in the () region. |
| (iii) is the Initial energy of the nodes. |
| (iv) Alive_Nodes is the number of nodes left after the successful execution of previous round. |
| (v) is the distance between the nodes. |
| (vi) is the threshold distance value between the free space and multipath fading model. |
| (vii) is the amplification energy. |
| (viii) is the free space energy consumption. |
| (ix) N is the set of sensor nodes. |
| (x) Nodes_C is the nodes in sub-region which is under consideration for CH selection. |
| (xi) Energy is the residual energy of the nodes. |
| (xii) is list of possible nodes positions to become a sub-region CH computed using ABC algorithm. |
| (xiii) CH_s is the list of CHs in the round. |
| //Set up phase |
| Step 1. Take a region of (m2) with number of nodes. |
| Step 2. Initialize the Nodes () with initial Energy |
| Step 3. For each round Until Alive_Nodes > 0, then |
| Repeat Step to Step |
| function Alive_Nodes (Nodes) |
| Total_Alive := 0 |
| for each node := 1 to length (Nodes) do |
| if Energy [Nodes[]] > 0 then |
| NODE_STATUS := 1 |
| Total_Alive := Total_Alive + 1 |
| Else |
| NODE_STATUS := 0 |
| end if |
| end for |
| return Total_Alive |
| endfunction |
| Step 4. Compute the distance of each node from other nodes and the BS by using Euclidian distance formula. |
| |
| where and are -coordinates of nodes and and are -coordinates of the nodes. |
| Step 5. Compute for optimal number of sub_regions that can be formed in given region (m2) |
| and divide the given region (m2) into sub_regions based on value. |
| function Region Optimum Value {NODES} |
| := Null |
| if BS.location is “Outside” then |
| if then |
| |
| else |
| |
| endif |
| endif |
| return |
| endfunction |
| Step 6. For each sub_region repeat Steps 7 and 8. |
| Step 7. Select CH, for each sub_region using ABC algorithm using function ABC_Cluster_Head, |
| Which accepts two arguments, (1) Nodes residual Energy and (2) Nodes in that particular sub_region. |
| function ABC_Cluster_Head |
| Fitness_to_be_CH := Null |
| for each node := 1 to length(Nodes_C) do |
| Fitness_to_be_CH := Fitness(Nodes_C[] × ID, BS) |
| end for |
| Threshold_Energy := NULL |
| Residual_Energy := NULL |
| for each node := 1 to length(Nodes_C) do |
| Residual_Energy := Residual_Energy + |
| Energy(Nodes_C[]) |
| end for |
| |
| for each node := 1 to length(Nodes_C) do |
| |
| end for |
| Threshold_Probability := 1/length(Nodes_C) |
| // New Position Calculating |
| for each node := to length(Nodes_C) do |
| if Probability_Nodes_to_CH[] > |
| Threshold_Probability then |
| rand_ABC:= Upper_Bound_ABC + |
| (Lower_Bound_ABC-Upper_Bound_ABC) × rand |
| := Fitness_Node_CH(Prev_CH_Node) − rand_ABC × (Fitness_Node_CH(Prev_CH_Node) − |
| Fitness_Node_CH (Nodes_C[])) |
| end if |
| end for |
| Node CH Id:= Nodes C_max() |
| endfunction |
| Step 8. For each sub_region, repeat Steps and . |
| Step 9. Divide the sub_region into sub_region_parts based on value for this sub region. |
| function Sub_Region_Optimum_Value |
| := Null |
| if CH.location is at “Centre” then |
| if then |
| |
| else |
| |
| endif |
| else if CH.location is at “Corner” then |
| if then |
| |
| else |
| |
| endif |
| else if CH.location is at “mid-point of the bottom side” then |
| if then |
| |
| else |
| |
| endif |
| end if |
| Endfunction |
| Step 10. Select the sub_cluster_head (SCH) for each sub_region_parts based on given fitness formula |
| (i.e residual energy and distance to sub region CH). |
| Each SCH is responsible to communicate with CH of that sub region |
| Step 11. Select optimal path for CH of all sub regions for data transmit to BS using ACO algorithm. |
| function ACO_Optimal_Path |
| for each node to length(CH_s) do |
| Fitness Fitness(CH_s[], BS) |
| end for |
| for each node to length(CH_s) do |
| for each node to length(CH_s) do |
| if then |
| |
| end if |
| end for |
| end for |
| endfunction |
| // Steady state phase |
| Step 12. Nodes wake up and sensed data. |
| Step 13. Node forwards sensed data to SCH using TDMA with (2). |
| Step 14. SCH receives sensed data from nodes by using (3) and transmits aggregated data to CH using CDMA with (2). |
| Step 15. CH aggregates the data receivsed from all SCHs and send to BS using CDMA through a next hop receiver |
| (i.e CH of other sub region) with (2). |
| Step 16. For each region, |
| if Node_Energy then go to Step 3. |
