Research On Opportunistic Network Mobility Models

The characteristic of opportunistic network is that there is no complete link between the source node and destination node. It is the self-organizing network which exchanges data by the opportunity to meet coursed by the moving nodes within the communication range of the neighbor nodes. The opportunistic network has better feasibility and applicability in several extreme conditions. With the popularity of such mobile equipment as telephones and tablets, the applicable scope of opportunistic network is becoming more and more widely, which gives a rapid development chance to opportunistic network. In the scenes of real human life, it needs to spend a lot of manpower, material and financial resources to obtain accurate data and statistics, which brings a certain degree of blocking effect to the research of opportunistic network. So the validation of opportunistic network is almost entirely dependent on the simulation. Simulation results are highly dependent on the mobility model and routing protocol selected and performance evaluation of routing protocol is based on mobility model. Therefore, whether the mobility model is reasonable relates to the research and development of opportunistic network. Over the years, many classic mobility models are random or based on some mathematical features without taking the impact of human social characteristics on mobility models into account. So they are in terms of authenticity lacking. To solve this problem, this paper proposes a campus mobility model based on periodic purposes(Periodical-Purpose Campus Mobility Model, PPCMM). On the basis of this model, a new campus mobility model based on Particle Swarm Optimization is proposed(PSO Campus Mobility Model, PSOCMM). This article’s main jobs are as follows:(1) This paper sums up the behavior of human movement and analyses the characteristics of human movement. What’s more, it describes the periodicity and purpose particularly. Then it discusses the periodicity and purpose of students’ mobility in campus as a reference of the students’ daily behavior. On this basis, a campus mobility model based on periodic purposes is presented. Simulation results show that the model of distribution with a high purpose and mobility and has a high compatibility with students’ daily activities.(2) This article describes the group characteristics of human beings and discusses students’ group characteristics. And analyzed and instructed the basic particle swarm optimization algorithm. Combined with campus mobility model, a campus mobility model based on Particle Swarm Optimization is presented. Compared with the simulation of the random waypoint mobility model, it is superior at the points of the average end-to-end delay, packet successfully sent rates and average network throughput.