With the widespread use of Internet of Things (IoT) devices and the arrival of the 5G era, edge computing has become an attractive paradigm to serve end-users and provide better QoS. Many efforts have been paid to provision some merging public network services at the network edge. We reveal that it is very common that specific users call for private and isolated edge services to preserve data privacy and enable other security intentions. However, it still remains open to fulfill such kind of mixed requests in edge computing. In this article, we propose a cooperative edge computing framework, i.e., HyEdge, to offer both public and private edge services systematically. To fully exploit the benefits of this novel framework, we define the problem of optimal request scheduling over a given placement solution of hybrid edge servers to minimize the response delay. This problem is further modeled as a mixed integer non-linear programming problem (MINLP), which is typically NP-hard. Accordingly, we propose the partition-based optimization method, which can efficiently solve this NP-hard problem via the problem decomposition and the branch and bound strategies. We finally conduct extensive evaluations with a real-world dataset to measure the performance of our method. The results indicate that the proposed method achieves elegant performance with low computation complexity.

High-quality reconstruction of 3D geometry and texture plays a vital role in providing immersive perception of the real world. Additionally, online computation enables the practical usage of 3D reconstruction for interaction. We present an RGBD-based globally-consistent dense 3D reconstruction approach, where high-quality (i.e., the spatial resolution of the RGB image) texture patches are mapped on high-resolution ([Formula: see text]) geometric models online. The whole pipeline uses merely the CPU computing of a portable device. For real-time geometric reconstruction with online texturing, we propose to solve the texture optimization problem with a simplified incremental MRF solver in the context of geometric reconstruction pipeline using sparse voxel sampling strategy. An efficient reference-based color adjustment scheme is also proposed to achieve consistent texture patch colors under inconsistent luminance situations. Quantitative and qualitative experiments demonstrate that our online scheme achieves a realistic visualization of the environment with more abundant details, while taking fairly compact memory consumption and much lower computational complexity than existing solutions.

As the development of new generation mobile communication technology, the mobile core network also needs to be upgraded by new network technologies, e.g., software defined networking (SDN) and network function virtualization (NFV). With NFV, virtual network functions (VNFs) can be deployed on commodity devices to support various network function requirements and attain system's flexibility and elasticity in network edge. Meanwhile, a set of selected VNFs are usually chained as a service function chain (SFC) to serve a given flow in a specified order. Since the devices have heterogeneous execution environments and the VNFs have various requirements, one fundamental challenge is how to embed SFC for each flow on the shared NFV infrastructure (NFVI) with the goal of minimizing the flow completion time. Furthermore, multiple flows always compete for resources of those devices hosting SFCs. In this general setting, there is an urgent need to study efficient scheduling mechanism to minimize the total completion time of all flows. In this paper, by jointly considering VNF placement and flow scheduling, we first formulate this problem as an integer programming problem, and further prove that it is NP-hard in general case. We then design a PDG method to find the optimal solution in single flow case and an LRD method to achieve a high-quality feasible solution in multiple flows case. The extensive experiment results indicate that our LRD method can reduce the total completion time of all flows by 22.04, 60.99 and 39.95, percent against three compared methods, respectively.

Edge computing responds to users’ requests with low latency by storing the relevant files at the network edge. Various data deduplication technologies are currently employed at edge to eliminate redundant data chunks for space saving. However, the lookup for the global huge-volume fingerprint indexes imposed by detecting redundancies can significantly degrade the data processing performance. Besides, we envision a novel file storage strategy that realizes the following rationales simultaneously: 1) space efficiency, 2) access efficiency, and 3) load balance, while the existing methods fail to achieve them at one shot. To this end, we report LOFS, a Lightweight Online File Storage strategy, which aims at eliminating redundancies through maximizing the probability of successful data deduplication, while realizing the three design rationales simultaneously. LOFS leverages a lightweight three-layer hash mapping scheme to solve this problem with constant-time complexity. To be specific, LOFS employs the Bloom filter to generate a sketch for each file, and thereafter feeds the sketches to the Locality Sensitivity hash (LSH) such that similar files are likely to be projected nearby in LSH tablespace. At last, LOFS assigns the files to real-world edge servers with the joint consideration of the LSH load distribution and the edge server capacity. Trace-driven experiments show that LOFS closely tracks the global deduplication ratio and generates a relatively low load std compared with the comparison methods.

Blockchain-powered edge computing (BEC) is a promising extension to strengthen the security and the trustworthiness among collaborative edge clouds for delivering computation-intensive and delay-sensitive services in the environments of IoT and 5G. A fundamental challenge is how to respond to the maximum number of IoT requests at the network edge instead of the remote cloud. Although some work has been done to consider service provisioning and request scheduling in collaborative edge clouds, they assume that a single service is used to respond to each request. This assumption, however, is not practical to meet the demand of emerging IoT applications. In reality, the request needs to call a set of services with a chain-based structure. To tackle this challenge, in this article, we first propose a chain-based service request model for emerging IoT applications and further study the joint service provisioning and request scheduling problem for chain-based service requests at the network edge. We characterize this problem as an integer linear programming (ILP) model and prove the NP-hardness of this joint optimization problem. Furthermore, we prove that the related problem is of approximate submodularity with an approximation ratio guarantee. Finally, a novel two-stage optimization (TSO) scheme is proposed, and the results of extensive experiments show the efficiency and the effectiveness of the TSO scheme.

Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.

Edge computing has become the de facto method for delay-sensitive applications, in which the computation and storage resources are placed at the edge of network. The main responsibility of edge computing is to carry data from the Cloud downlinks and terminal uplinks, and organize these data well on the edge side. This is the basis for subsequent analysis and processing of data. Therefore, this brings about a question as to how those data should be organized on the edge and how to store and retrieve them. In response to this demand, some methods have been proposed to solve the related problems of how to build data storage and retrieval services on the edge side. Those methods propose three different solutions: structured, unstructured, and hybrid schemes. However, the data storage and retrieval services for the heterogeneous edge environment is still lack of research. It is still not considered an important design test load balancing when the data is stored on the edge side. In this paper, we design and implement w-strategy, a load balance approach that implements the appropriate load balance among the heterogeneous edge nodes by using the weighted Voronoi diagram. Our solution utilizes the software defined networking paradigm to support a virtual-space based distributed hash tables (DHTs) to distribute data. Evaluation results show that w-strategy achieves better load balancing among the heterogeneous edge nodes compared to the existing methods, GRED and Chord. And, the w-strategy improves the average underutilization of the resources by 20%.

The edge storage is a promising paradigm to support the Internet of Things (IoT) data storage, and is more efficient than the cloud storage in terms of the bandwidth overhead, the response latency, and so on. However, existing edge storage models cannot offer the security-aware data robustness and the adaptable data sharing among many uncertain users, due to the limitations of the utilized fault-tolerant storage technologies and the access control methods. In this article, we propose a secure and trust-oriented edge storage model, which would efficiently tackle the aforementioned two challenging issues in the IoT environment. More precisely, we first propose a robust and secure edge storage (RoSES) model using the totally local reconstruction code (TLRC) method presented in this article. It can achieve data robustness, high security, and lightweight computation at end devices. We further propose a trust-oriented data access (TODA) strategy for our RoSES model, which supports a wide and adaptable range of legitimate data accesses from uncertain requesters for IoT data sharing. We conduct extensive comparison and simulations to evaluate the performance of our new edge storage models. The results show that our model can efficiently realize the data storage, data recovery, and data sharing at the network edge, saving about 35% of storage cost and 76% degraded read latency. Besides, the data leakage probability is significantly reduced during the data storage and sharing processes.