Pheromone based Mobile Agent Migration Mechanisms
1) Random Approach
4.6 Results and Discussions
4.6.2 Dynamic Scenario
number of clones to be generated that can hog the bandwidth of the network and subsequently degrade the performance in a real system.
In such systems, the agents populating the network could be heterogeneous and thus carrying different services as payloads. EVAP and CLInG seem to inherently support only homogeneous agents. In a heterogeneous agent scenario, the use of EVAP and CLInG would mean that each node in the network would have to maintain the idleness and propagated idleness values of each type of agent. More the number of distinct agents carrying different services more will be the overheads of maintaining these values in each node and propagating them to their immediate neighbours in every time step. An inherent assumption made in the simulation of EVAP and CLInG is that the exchange of the idleness values between all nodes and their respective neighbours is a single step parallel operation. As pointed out earlier this is not true in the real world.
The process of exchanging the values also calls for more bandwidth, thus affecting performance. Further the use of EVAP and CLInG would pose difficulties in scaling the system when agents carrying new services are introduced into the network on the run.
Fig. 4-9
Graph depicting Number of Step-Counts Versus Runs for Dynamic Scenario: Case (I) - Single Agent and Four RRSs obtained for five individual runs
Fig. 4-10 Graph depicting Number of Intra-Node Computations Versus Runs for
Dynamic Scenario: Case (I) - Single Agent and Four RRSs obtained for five
individual runs
740 1322 1295 1308 368
279055 69799 384616 222205 89313310406 527298 708650 657464 390102
67640 83282 72332 66596 104544
246807136
1412930065
21149758832
4213520900 1836618079
29548 21724 19719 43423 2729615516 18012 16849 14301 14664
1 10 1000100 10000 100000 1000000 10000000 1000000001E+091E+101E+11
1 2 3 4 5
Number of Intra-Node Computation (Y-axis in Logarithmic Scale)
Runs
Dynamic: Case (I) - Single Agent and Four RRSs Intra-Node Computations Vs Runs
Random Conscientious EVAP CLInG G-B PherCon PherCon-C
740 1322 1295 1308 368299 123 354 243 151185 314 419 394 237
40 47 43 40 62
8 9 10 9 915
25 37 48 37
15 24 37 34 34
1 10 100 1000 10000
1 2 3 4 5
Number of Step-Counts (Y-axis in Logarithmic Scale)
Runs
Dynamic: Case (I) - Single Agent and Four RRSs Step-Counts Vs Runs
Random Conscientious EVAP CLInG G-B PherCon PherCon-C
Fig. 4-11 Graph depicting Number of Inter-Node Communications Versus Runs
for Dynamic Scenario: Case (I) - Single Agent and Four RRSs obtained for five
individual runs
Case (I) Single Mobile Agent and Four RRSs:
The graph in Figure 4-9 depicts the step-counts times taken by an agent to reach the four RRSs respectively by each of the seven migration strategies while those in Figure 4-10 and 4-11 show the energy consumed in terms of intra-node computations and inter-node communications for one agent to service four RRSs.
Comparing the graphs for the static scenario (Figure 4-3 to Figure 4-5) with those of the dynamic scenario (Figure 4-9 to Figure 4-11), it can be observed that there is an increase in the number of step-counts taken by all strategies in the latter scenario. This can be attributed to the dynamic nature of the robotic nodes which force the randomness into the agent migration strategies. Agents may
740 1322 1295 1308 368299 123 354 243 151
310406 527298 708650 657464 390102
135280 166564 144664 133192 209088
246810968
1412935480
21149765305
4213525793
1836623266
17955 12656 13404 25146 1774512736 17173 16202 11329 13993
1 10 100 1000 10000 100000 1000000 10000000 100000000 1E+09 1E+10 1E+11
1 2 3 4 5
Number of Inter-Node Computation (Y-axis in Logarithmic Scale)
Runs
Dynamic: Case (I) - Single Agent and Four RRSs Inter-Node Communications Vs Runs
Random Conscientious EVAP CLInG G-B PherCon PherCon-C
have been lead astray due to the movement of the nodes. However the relative trends in the performances of all the migration strategies seem to remain the same as in case of the static scenario. The random and conscientious migration strategies consume lesser energy in terms of intra-node computations and inter- node communications but take more step-counts to reach the RRS. The G-B migration strategy takes the minimum number of step-counts but is expensive in terms of energy. PherCon and PherCon-C provide a consistent performance in terms of both time and energy.
Case (II) Multiple Mobile Agents and Four RRSs:
The graphs in Figure 4-12 to 4-14 are similar to the graphs in Figure 4-9 to 4-11 but for the fact that the robotic nodes are dynamic. Here too the relative trends seem to be the same as observed in the static case. As in the static scenario, the difference between the performances of the migration strategies diminishes as the initial number of agents is increased. This is because of the increase in the number of agents carrying the same resource.
Fig. 4-12 Graph depicting Number of Step-Counts Versus Runs for
Dynamic Scenario: Case (II) - Multiple Mobile Agents and Four RRSs obtained for
five individual runs with 2, 3, 6, 9 and 12 Agents
495 303 151 40 37
173 137 86 26 12
340 86 29 32 3931 12 8 13 5
9 5 4 4 3
15 14 9 6 2
12 9 7 5 2
1 10 100 1000
2 3 6 9 12
Number of Step-Counts (Y-axis in Logarithmic Scale)
Number of Agents
Dynamic: Case (II) - Multiple Mobile Agents and Four RRSs Step-Counts Vs Number of Agents
Random Conscientious EVAP CLInG
495 303 151 40 37
173 137 86 26 12
556934 141598 48366 53970 66480105992 40524 27284 45080 16620
1488167133
240818 54683 91856 10217
10394 7614 5318 2957 400
6184 4113 3532 2128 404
1 10 100 1000 10000 100000 1000000 10000000 100000000 1E+09 1E+10
2 3 6 9 12
Number of Inter-Node Computation (Y-axis in Logarithmic Scale)
Number of Agents
Dynamic: Case (II) - Multiple Mobile Agents and Four RRSs Inter-Node Communications Vs Number of Agents
Random Conscientious EVAP CLInG
Fig. 4-13 Graph depicting Number of Intra-Node Computations Versus
Runs for Dynamic Scenario: Case (II) - Multiple Mobile Agents and Four RRSs
obtained for five individual runs with 2, 3, 6, 9 and 12 Agents
Fig. 4-14 Graph depicting Number of Inter-Node Communications Versus
Runs for Dynamic Scenario: Case (II) - Multiple Mobile Agents and Four RRSs
obtained for five individual runs with 2, 3, 6, 9 and 12 Agents
495 303 151 40 37
25859 26203 21718 3129 927
556934 41598 48366 53970 6648052996 20262 13642 22540 8310
1488160198
237741 52037 88842 8168
17193 14162 8026 3284 315
4744 2420 1605 798 118
1 10 100 1000 10000 100000 1000000 10000000 100000000 1E+09 1E+10
2 3 6 9 12
Number of Intra-Node Computation (Y-axis in Logarithmic Scale)
Number of Agents
Dynamic: Case (II) - Multiple Mobile Agents and Four RRSs Intra-Node Computations Vs Number of Agents
Random Conscientious EVAP CLInG