With the dramatically increasing deployment of the Internet of Things (IoT), remote monitoring of health data to achieve intelligent healthcare has received great attention recently. However, due to the limited computing power and storage capacity of IoT devices, users’ health data are generally stored in a centralized third party, such as the hospital database or cloud, and make users lose control of their health data, which can easily result in privacy leakage and single-point bottleneck. In this paper, we propose Healthchain, a large-scale health data privacy preserving scheme based on blockchain technology, where health data are encrypted to conduct fine-grained access control. Specifically, users can effectively revoke or add authorized doctors by leveraging user transactions for key management. Furthermore, by introducing Healthchain, both IoT data and doctor diagnosis cannot be deleted or tampered with so as to avoid medical disputes. Security analysis and experimental results show that the proposed Healthchain is applicable for smart healthcare system.

Privacy-preserving data aggregation has been extensively studied in smart grid. However, almost all existing schemes aggregate the total electricity consumption data of the whole user set, which sometimes cannot meet the fine-grained demands from control center in smart grid. In this paper, we propose a privacy-preserving multisubset data aggregation scheme, named PPMA, in smart grid. PPMA can aggregate users' electricity consumption data of different ranges, while guaranteeing the privacy of individual users. Detailed security analysis shows that PPMA can protect individual user's electricity consumption privacy against a strong adversary. In addition, extensive experiments results demonstrate that PPMA has less computation overhead and no more extra communication and storage costs.

Network Function Virtualization (NFV) and Software-Defined Networks (SDN) enable Internet Service Providers (ISPs) to place Virtual Network Functions (VNFs) to achieve the performance and security benefit without incurring high Operating Expenses (OPEX) and Capital Expenses (CAPEX). In NFV environment, Service Function Chains (SFCs) always need to steer the traffic through a series of VNF instances in predefined orders. Moreover, the required number and placement of VNF instances should be optimized to adapt to dynamic network load. Therefore, it is considerable for ISPs to conduct an optimal SFC embedding strategy to improve the network performance and revenue. In the paper, we study the SFC embedding problem (SFC-EP) with dynamic VNF placement in geo-distributed cloud system. We formulate this problem as a Binary Integer Programming (BIP) model aiming to embed SFC requests with the minimum embedding cost. Furthermore, the novel SFC eMbedding APproach (SFC-MAP) and VNF Dynamic Release Algorithm (VNF-DRA) have been proposed to efficiently embed SFC requests and optimize the number of placed VNF instances. Performance evaluation results show that the proposed algorithms can provide higher performance in terms of SFC request acceptance rate, network throughput and mean VNF utilization rate and efficiently reduce the total VNF running time compared with the algorithms in existing literatures.

In recent years, Small Satellite Networks (SSNs) are attracting increasing attention due to its economical prospects and advantages in high bandwidth and low latency. More and more companies and organizations are planning to construct large-scale SSNs to provide global services. In this way, the traffic pattern is more complicated because of random transmission requirements and stochastic packet generations/arrivals, and routing faces more challenges due to network scale and limited resource in small satellites. Traditional satellite routing algorithms, which attempt to exploit the predictable satellite's trajectory with time-discrete graph models, cannot handle these challenges. In this paper, we propose a novel Temporal Netgrid Model (TNM) to portray the time-varying topology of large-scale SSNs. In TNM, the whole space is divided into small cubes (i.e., netgrids) and then, satellites can be located by netgrids instead of coordinates. By doing so, we can construct a network topology for random traffic routing. Furthermore, an efficient Netgrid-based Shortest Path Routing (NSR) algorithm is proposed based on TNM; NSR attempts to find the optimal path from source netgrid to any other reachable netgrids. In this way, the routing complexity is significantly reduced. We also develop a Large-scale Satellite Network Simulator (LSNS) to validate our study. The results show that NSR achieves a significant reduction in computational complexity as well as near-optimal routing performance in terms of end-to-end delay and packet drop rate in scenarios of large-scale SSNs compared with existing satellite routing algorithms.

Entanglement routing establishes remote entanglement connection between two arbitrary nodes, which is one of the most important functions in quantum networks. The existing routing mechanisms mainly improve the robustness and throughput facing the failure of entanglement generations, which, however, rarely include the considerations on the most important metric to evaluate the quality of connection, entanglement fidelity. To solve this problem, we propose purification-enabled entanglement routing designs to provide fidelity guarantee for multiple Source-Destination (S-D) pairs in quantum networks. In our proposal, we first consider the single S-D pair scenario and design an iterative routing algorithm, Q-PATH, to find the optimal purification decisions along the routing path with minimum entangled pair cost. Further, a low-complexity routing algorithm using an extended Dijkstra algorithm, Q-LEAP, is designed to reduce the computational complexity by using a simple but effective purification decision method. Finally, we consider the common scenario with multiple S-D pairs and design a greedy-based algorithm considering resource allocation and re-routing process for multiple routing requests. Simulation results show that the proposed algorithms not only can provide fidelity-guaranteed routing solutions, but also has superior performance in terms of throughput, fidelity of end-to-end entanglement connection, and resource utilization ratio, compared with the existing routing scheme.

Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users' service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly.