No abstract

As a new advanced service‐oriented networked manufacturing mode, cloud manufacturing (CMfg) is currently one of the main directions of development in the manufacturing industry. In this study, a resource modeling method oriented to CMfg is proposed to solve manufacturing resource data consistency problems, which are often caused by heterogeneity, diversity, and complexity of manufacturing resource. Motivated by the manufacturing resource modeling literature, we construct a classification model of manufacturing resource using a line indexing method and a manufacturing resource information model for CMfg from the perspective of resource information. The concept hierarchy structure and ontology logical reasoning technology are employed to create this semantic modeling. Based on Ontology Web Language, we propose a new manufacturing resource ontology model that encompasses a resource multilayer model and includes a physical, virtual, and cloud resource data layers, as well as a cloud end layer. We propose a searching and matching mechanism based on semantic similarity degree to locate the manufacturing resource and achieve accurate matching of resource service messages. Such mechanism is an efficient procedure for the manufacturing resource service in the CMfg platform. We present a case study of the proposed framework and model to illustrate validity and practicability of the proposed model.

No abstract

In the utility-based routing protocol of delay-tolerant networks (DTNs), nodes calculate routing utility value by encounter time, frequency, and so on, and then forward messages according to the utility. The privacy information of encounter time and frequency will be leaked when nodes communicate with real IDs. Node ID anonymity can protect the privacy information, but it also prevents nodes from collecting encounter information to calculate the real utility value. To solve the above problem, this paper proposes a privacy-preserving protocol for utility-based routing (PPUR) in DTNs. When node encounter occurs in PPUR, they anonymously generate and collect the encounter record information by pseudo-IDs. Then, nodes forward the information to a trusted authority (TA), which calculates the routing utility value and returns it to the nodes, so that nodes can protect the privacy information and obtain the real utility value at the same time. PPUR also protects the confidentiality and integrity of messages through hashing and digital signature. The experimental results show that PPUR can not only protect nodes’ privacy information, but also effectively forward messages with real utility value.

No abstract