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of certain generation. This change rate is evaluated by the following expression: C = t R(k) = C(i) C(i + 1) k i
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where we assume the cost of the best solution of that generation changes at ith step and ChangeRate(k) = R(k)is the average change rate of cost at kth step (k > i). Once this value is less than a certain lower-bound MinChangeRate, GA computation may be stopped. The updating process may be improved by assuming that the price of each link and the congestion of the network will change gradually. So when new traf c comes, the SP will recompute routes for its customers. During the dynamic operation of the system, in order to improve the ef ciency of the algorithm, the results of the last computation can be partly reused. One possibility is to mix certain training genes into the initial population of the new route computation. However, this may lead to premature discards and prevent GA from nding better solutions. Instead of mixing the past solution into the initial solution of GA, they may be mixed into population after, for example, 70 % of MinTrails of GA loops. In this way, we can still take advantage of the results of the last computation and prevent premature discards at the same time. If the network conditions change smoothly, we can take advantage of the past best solution of last computation. If the network conditions change dramatically at a certain time and the optimal route may totally differ from the past solution, GA will not take advantage of the past best solution by mixing the past solution into the population during the GA computation. The ow chart in Figure 18.4 summarizes the operation of the algorithm. The integration of the mobile agents into the routing algorithm is presented in Figure 18.5. As shown in the gure, BrkAs, MAs, BAs and DAs are used to migrate among different network elements to implement the proposed routing algorithm. Once the PC client needs a connection to the CP, an MA will be sent from PC client to SP containing information about the upper bound of setup time delay of the connection and the corresponding QoS
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No Number of Yes Mix trails past >= 70 % of solution MinTrails Evaluate the fitnesses of new generation Best solution reservation and duplication
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In Initialize the first generation of solutions
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Number of trails <= MaxTrails Yes Monte or best solution Carlo is illegal or selection change rate >= MinChangeRate gi Give out the best solution of this computation
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Figure 18.4 Flow chart of GA.
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n Node with computation Power DA DA INAB
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PC client
Figure 18.5 Agents used to implement routing algorithms based on GA.
No Yes Choose best route from routing candidates Sending messenger agent No back to PC client refusing the connection
Time out
Route exists
Sending messenger agents to NABs
Start timer for routing algorithms
Receive messenger Yes agent containing route solution
Route good enough No Saving this route as routing candidate
Sending messenger agent back to PC Client Ye accepting the connection
Figure 18.6 Algorithm for BrkAs in SP to choose a route for its client. requirements. After receiving the MAs from PC client, the SP creates a BrkA to deal with this connection requirement. This BrkA creates MAs containing source and destination information, as well as QoS requirements, and multicasts the agents to each NAB that it is connected with. Then the BrkA in SP waits for the agents from NABs to obtain the routing solution according to the scheme depicted in Figure 18.6.
As seen by the ow chart, in order to control the connection setup time, a timer is used to determine the deadline of the route searching procedure. If the BrkA receives an MA from NAB with satisfactory routing solution before the expiration of the timer, the route searching process stops and this solution is selected. Otherwise, when the timer expires the agent chooses the best route among the route candidates found until that time. Each NAB also creates a BrkAs to deal with the connection when it receives the MAs with the corresponding connection request from the SP. Then, three kinds of agents are used to implement the routing algorithm as follows. A browser agent will be created and sent to nodes inside the individual private network that the NAB belongs to. These agents will collect resource information such as available bandwidth, delay of the link, price of the link, etc. In a similar way, the BrkA in each NAB will also send out BAs to INABs to see if it can take advantage of network resources from other NPs. A daemon agent containing the GA code and resource-related information will be created after collecting the necessary resource information, to implement the routing algorithm described in detail in the previous section. Instead of executing the algorithm in each NAB, the BrkA sends DAs to the most suitable nodes inside its private network (e.g. nodes with enough computation resources such as CPU, memory, etc). In this way, we can balance the computation load among nodes in the private networks, if needed. A messenger agent will be used to bring results back to the BrkA from DAs after the genetic-based route computation. This agent will be forwarded to the BrkA in the SP. Performance results for the algorithm can be found in Papavassiliou et al. [35].
18.4 AD HOC NETWORK MANAGEMENT We start by identifying some of the properties of ad hoc networks that make them dif cult to manage. 18.4.1 Heterogeneous environments First of all, nodes of an ad hoc network can range in complexity from simple sensors located in the eld to fully functional computers such as laptops. An implication of this diversity is that not all nodes will be able to contribute equally to the management task. For instance, it is likely that sensors and small personal digital assistant (PDA)-type devices will contribute minimally to the task of management, while more powerful machines will need to take on responsibilities such as collecting data before forwarding it to the management station, tracking other mobiles in the neighborhood as they move, etc. Thus, the management protocol needs to function in very heterogeneous environments. 18.4.2 Time varying topology One mission of a network management protocol is to present the topology of the network to the network manager. In wireline networks, this is a very simple task because changes to the topology are very infrequent (e.g. a new node gets added, failure of a node, or addition/deletion of a subnetwork, etc.). In mobile networks, on the other hand, the topology changes very frequently because the nodes move about constantly. Thus, the management