An Adaptive Trust Model Based On Time Series Analysis In Opportunity Networks

Opportunity networks is a kind of Ad Hoc network that use the chance encounterbrings mobile node to achieve self-organizing network communications, without acomplete link existing between the source node and the destination node. This kind ofnetwork use the "storage-carry-forwards" routing mechanism for message transmission,the traditional static routing mechanism network devolved into a network of opportunitiesfor single-hop routing decisions. Since the verity of node properties, leading to differentabilities of each node to transmit a message to the target node, besides there may be selfishnodes and malicious nodes affect the performance of the network transmission network.Therefore, how to determine the optimal forwarding node and choose the righttransmission time, become a key issue forwarded to design efficient routing protocols.In order to improve the chances of success rate of data transmission network,reducing network latency, This paper proposes an adaptive trust model based on the timeseries analysis in opportunity networks.First of all, the model according to the different characteristics of node attributes,parameter will evaluate nodes are divided into two kinds of process parameters andreal-time parameters. In the data transmission process, the node attribute nodes shown asprocess parameters, such as contact with the target node cycle frequency, and the targetnode distance; on the other hand, node instant visible attributes, such as power, speed asparameter in time, the final request service node selection of comprehensive utility valuethe neighbor node higher bonding process parameters and real-time parameters, andtransmits the data to the nodes in order to improve the success rate of data transmission.Secondly, the evaluation model to collect evidence using data transmission chain ofevidence, the evidence for process evaluation parameters, by collecting and processing thetarget nodes periodically, the weight coefficients of the target node based on the timeseries analysis theory of dynamic adjustment of the evaluation parameters, evaluation ofthe participating nodes forwarding data, in order to maximize the value of improving theevaluation accuracy and the effectiveness of the. The model prediction using dynamicCalman filter technology and adjustment in the future a period of time in the process of optimal evaluation value estimates, in order to ensure the accuracy and effectiveness of theevaluation in the network table.Then, model design will evaluate the table by covering the updating mechanism ofdiffusion into the distributed storage network, nodes meet compare their own storageevaluation timestamp table, retention and copy to the other party to the latest assessmenttable. When the node is met and request forwarding data, combining the processevaluation of immediate evaluation and storage met neighbor node table to choose the bestnext hop routing.Finally, based on the above research opportunity, I build an experimental platform fornetwork trust model to achieve a model of the structure of the model is centralizedcomputing, distributed storage, delivery rate in the network by simulation of the model,network overhead ratio and average delay successfully transmitted messages, etc. theresults of evaluating and analyzing shows that the model can improve the transmissionperformance of the network effectively.