DAWN: A Density Adaptive Routing for Deadline-Based Data Collection in Vehicular Delay Tolerant Networks

Vehicular Delay Tolerant Networks （DTN） use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness （DAWN）. DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes＇ transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.

Efficient optimization method for variable-specific-impulse low-thrust trajectories with shutdown constraint

This paper develops a sequential convex programming(SCP)-based method to solve the minimum-fuel variable-specific-impulse low-thrust transfer problem considering shutdown constraint,with emphasize on improving the computational efficiency.The variable parameter engine is more applicable for many low-thrust scenarios,therefore,both a continuously variable model and a ladder variable model are adopted.First,the original problem is convexified by processing the constraint feasible domain,which is composed of the nonlinear dynamic equations and second-order equality constraint,into convex sets.Then,the approximation is generated to close the optimal solution of the low-thrust problem by iteratively solving the convexified subproblem.Moreover,the switching self-detection and adaptive node refinement methods are presented,which can improve the accuracy of the solution and accelerate the convergence during the approximation process and is especially necessary and effective in the scenarios with shutdown constraint.In numerical simulations,the comparison with the homotopic approach shows that the proposed method only needs 4%computational time as that of the homotopic approach,and two variable-specificimpulse examples further demonstrate the effectiveness and efficiency of the proposed method.