OC-AHPPSO Case Study
The modules of the OC-AHPPSO cluster constructor are, criteria computation, relative weights estimator and cluster-head nodes' selector. In the criteria computation module, criteria such as the Energy, coverage Area, Mobility, QoS are considered for computation. Each node computes its criteria components, and then periodically broadcasts the MEMB messages to identify its neighbors. Each node sends the MEMB packets to receive the criteria components of its neighbors. Each node stores its own criteria components, and its neighbors' components in the neighbor table.
In the relative weights estimator module, the mobile nodes in the undecided state (the state, where the mobile nodes are not assigned any role as cluster head, cluster-member or cluster …show more content…
The node in the neighbor table which has the maximum weight, claims to be the cluster-head, then applied to conduct optimization using PSO and changes its state as the cluster-head node. The cluster-head nodes broadcast their announcement messages within their transmission range. The nodes that receive this message will change their state to 'cluster-member' and joins the cluster. The maintenance of clusters, using the OC-AHPPSO, is also …show more content…
SPN can periodically distribute their information with Member beacon (MEMB) to the service repository in the cluster. SRN sends any request (SREQ) to the discovery agent, and then the agent discovers the services which will satisfy the SRNs functional requirement. Discovery agent deals a finding technique with QoS range cited by which the SRN could fix QoS constraints. So the searched services would meet the SRN’s QoS constraints. Then the discovery agent does the extraction and pre-processing, and it will be passed to LDA- based service selection component to analyze the QoS of every requested service based on the QoS information and the service information on the service. Finally, it will select the optimal service class. Then the parameter is matched with matchmaker in order to check whether the requested services is related to its repository if it is found to be related to its directed to the service requester. The SD-LDA is useful for searching optimum services in the
In the relative weights estimator module, the mobile nodes in the undecided state (the state, where the mobile nodes are not assigned any role as cluster head, cluster-member or cluster …show more content…
The node in the neighbor table which has the maximum weight, claims to be the cluster-head, then applied to conduct optimization using PSO and changes its state as the cluster-head node. The cluster-head nodes broadcast their announcement messages within their transmission range. The nodes that receive this message will change their state to 'cluster-member' and joins the cluster. The maintenance of clusters, using the OC-AHPPSO, is also …show more content…
SPN can periodically distribute their information with Member beacon (MEMB) to the service repository in the cluster. SRN sends any request (SREQ) to the discovery agent, and then the agent discovers the services which will satisfy the SRNs functional requirement. Discovery agent deals a finding technique with QoS range cited by which the SRN could fix QoS constraints. So the searched services would meet the SRN’s QoS constraints. Then the discovery agent does the extraction and pre-processing, and it will be passed to LDA- based service selection component to analyze the QoS of every requested service based on the QoS information and the service information on the service. Finally, it will select the optimal service class. Then the parameter is matched with matchmaker in order to check whether the requested services is related to its repository if it is found to be related to its directed to the service requester. The SD-LDA is useful for searching optimum services in the