The delay/disruption tolerant network (DTN) has been researched actively in the last years because of its high applicability to ubiquitous network services such as sensor networks and intelligent transport system (ITS) networks. An efficient data forwarding method for those network services is one of the key components in DTN due to the limitation of wireless network resources. This paper proposes a new DTN scheme for vehicle network systems by introducing the parameter, “approach ratio”, which represents node movement history. The proposal utilizes passive copy strategy, where nodes within one hop area of packet forwarders receive, copy and store packets (namely, passive copies) for future forwarding, in order to obtain higher delivery rate and lower delivery delay whilst suppressing the network resource consumption. Depending on its approach ratio, a node with passive copy decides whether it forwards the passive copy or not by referring to the approach ratio threshold. The approach ratio allows our proposal to adjust the property of both single-copy type scheme, that can lower network resource consumption, and multi-copy type scheme, that can enhance the performance of delivery rate and delay time. In simulation evaluation, the proposal is compared with three typical existing schemes with respect to network consumption, delivery rate and delivery delay. Our proposal shows the superior performance regarding the targeted purpose. It is shown that the approach ratio plays the significant role to obtain the higher delivery rate and lower delay time, while keeping network resource consumption lower.

Combinatorial optimization problems with a large solution space are difficult to solve just using von Neumann computers. Ising machines or annealing machines have been developed to tackle these problems as a promising Non-von Neumann computer. In order to use these annealing machines, every combinatorial optimization problem is mapped onto the physical Ising model, which consists of spins, interactions between them, and their external magnetic fields. Then the annealing machines operate so as to search the ground state of the physical Ising model, which corresponds to the optimal solution of the original combinatorial optimization problem. A combinatorial optimization problem can be firstly described by an ideal fully-connected Ising model but it is very hard to embed it onto the physical Ising model topology of a particular annealing machine, which causes one of the largest issues in annealing machines. In this paper, we propose a fully-connected Ising model embedding method targeting for CMOS annealing machine. The key idea is that the proposed method replicates every logical spin in a fully-connected Ising model and embeds each logical spin onto the physical spins with the same chain length. Experimental results through an actual combinatorial problem show that the proposed method obtains spin embeddings superior to the conventional de facto standard method, in terms of the embedding time and the probability of obtaining a feasible solution.