دانلود رایگان مقاله درخت پوشا مبتنی بر کنترل توپولوژی برای جمع آوری داده ها

abstract

In predictable delay tolerant networks (PDTNs), the network topology is known a priori or can be predicted over time such as vehicular networks based on public buses or trains and space planet network. Previous DTN research mainly focuses on routing and data access. However, data collecting are used widely in PDTNs and it is very important in practical application, how to maintain the dynamic topology of this type of PDTNs becomes crucial. In this paper, a spanning tree (ST) based topology control method for data collecting in PDTNs is proposed. The PDTN is modeled as layered space-time directed graph which includes spatial, temporal and energy cost information, which can be simplified as reduced aggregated directed graph. The topology control problem is defined as constructing a ST that the total energy cost of the ST is minimized and the time delay threshold is satisfied. We propose three heuristic algorithms based on layered space-time directed graph and reduced aggregated directed graph to solve the defined problem, and compare them in terms of energy cost and time delay. Extensive simulation experiments demonstrate that the proposed algorithms can guarantee data transmission effectively, reduce the network energy consumption significantly, and shorten the time delay of data transmission

6. Conclusion

In this paper, we model PDTN as a layered space-time weighted directed graph and reduced aggregated directed graph and define the topology control problem as finding a ST from the graph model to minimize energy cost. We propose three heuristic topology control algorithm, VKASPG, VPASPG and MSTRAG to solve this problem. The next step is to consider the following problems: (1) Improving the efficiency of the proposed heuristic topology control algorithms; (2) Solving the topology control problem for unpredictable DTNs;(3) Devising a method that can get topology information accurately, which is difficult to obtain in a real environment; (4) Dealing with topology control of PDTNs when some node may fail in the movement; (5) Balancing energy cost of the network.