We develop a novel construction method for n-dimensional Hilbert space-filling curves. The construction method includes four steps: block allocation, Gray permutation, coordinate transformation and recursive construction. We use the tensor product theory to formulate the method. An n-dimensional Hilbert space-filling curve of 2r elements on each dimension is specified as a permutation which rearranges 2rn data elements stored in the row major order as in C language or the column major order as in FORTRAN language to the order of traversing an n-dimensional Hilbert space-filling curve. The tensor product formulation of n-dimensional Hilbert space-filling curves uses stride permutation, reverse permutation, and Gray permutation. We present both recursive and iterative tensor product formulas of n-dimensional Hilbert space-filling curves. The tensor product formulas are directly translated into computer programs which can be used in various applications. The process of program generation is explained in the paper.

For the first stage of the multi-sensitive bucketization (MSB) method, the l-diversity grouping for multiple sensitive attributes is incomplete, causing more information loss. To solve this problem, we give the definitions of the l-diversity avoidance set for multiple sensitive attributes and the avoiding of a multiple dimensional bucket, and propose a complete l-diversity grouping (CLDG) algorithm for multiple sensitive attributes. Then, we improve the first stages of the MSB algorithms by applying the CLDG algorithm to them. The experimental results show that the grouping ratio of the improved first stages of the MSB algorithms is significantly higher than that of the original first stages of the MSB algorithms, decreasing the information loss of the published microdata.

It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.

EPCglobal architecture framework is divided into identify, capture, and share layers and defines a collection of standards. It is not fully adequate to build IoT applications because the transducer capability is lacking. IEEE 1451 is a set of standards that defines data exchange format, communication protocols, and various connection interfaces between sensors/actuators and transducer interface modules. By appending IEEE 1451 transducer capability to EPCglobal architecture framework, a consistent EPC scheme expression for heterogeneous things can be achieved at identify layer. It is benefit to extend the upper layers of EPCglobal architecture framework seamlessly. In this paper, we put our emphasis on how to leverage the transducer capability at the capture layer. A device cycle, transducer cycle specification, and transducer cycle report are introduced to collect and process sensor/actuator data. The design and implementation of GS1 EPCglobal Application Level Events (ALE) modules extension are proposed for explaining the design philosophy and verifying the feasibility. It will interact with the capture and query services of EPC Information Services (EPCIS) for IoT applications at the share layer. By cooperating and interacting with these layers of EPCglobal architecture framework, the IoT architecture EPCglobal+ based on international standards is built.