Binary Neural Networks (BNNs) quantize weights and activations to −1 and +1 to achieve significant memory reduction and computational acceleration, which have been extensively explored in image and video tasks. The development for training high-accuracy BNNs holds substantial commercial value, underscoring the critical importance of emphasizing risks related to intellectual property (IP) infringement. However, existing IP protection methods focus on float-point models, neglecting protection for low-bit models. To this end, we provide a study tailored for IP protection of BNNs. We adopt a passport-based watermarking method as our baseline, known for resisting both removal and ambiguity attacks. We observe that the discretization of weights and activations introduces instability in the joint training stage, leading to a significant accuracy decrease in the target model. To overcome this challenge, we present a novel passport-aware module for BNNs to improve gradient optimization stability and reduce sensitivity to binary weights flipping. Furthermore, we conduct a comprehensive evaluation of the robustness of BNNs against various attacks. Extensive evaluations show our method improves model performance and robustness against attacks. We hope that our research will contribute to the further advancement of IP protection for low-bit networks, especially BNNs.

Edge storage systems have drawn many efforts to extend the storage and service capabilities of cloud data centers. A pivotal aspect lies in the data-sharing mechanism, which integrates geographically dispersed weak edge servers into an efficient storage system. It enables users to launch data operations at any server and retrieve the desired data across the distributed system. However, it remains open to meeting the increasing demand for similarity retrieval across edge servers. The intrinsic reason is that the existing solutions can only return an exact data match for a query while more general edge applications require the data similar to a query input from any server. To fill this gap, this paper pioneers the similarity edge data sharing mechanism, a new paradigm to support high-dimensional similarity search at network edges. First, through deeply thinking about the nature of similarity data sharing, we propose the problem of Optimal Indexing and formulate it as the optimal transport problem from the data space to the network space. On this basis, we propose Prophet, the first known architecture for similarity data indexing at the edge. We first divide the feature space of data into plenty of subareas, then project both subareas and edge servers into a virtual space where the distance between any two points can reflect not only data similarity but also network latency. When any edge server submits a request for data insert, delete, or query, it computes the data feature and the virtual coordinate; and then iteratively forwards the request via greedy routing based on the forwarding tables and the virtual coordinates. By Prophet, similar high-dimensional features would be stored by a common server or several nearby servers. Compared with distributed hash tables in P2P networks, Prophet requires to visit logarithmic servers for a data request and reduces the network latency from the logarithmic to the constant level of the server number. Evaluation results indicate that Prophet achieves the comparable retrieval accuracy and significantly shortens the query latency compared with centralized schemes, while the load balancing performance is nearly optimal.

Edge storage promises to be crucial for edge computing infrastructure, which enables users to access data within a low delay from widespread storage nodes at the network edge. The key challenge is how to integrate massive geographically distributed weak edge nodes to form an efficient storage system, enabling users to launch data operations from any node or retrieve the desired data across the entire distributed system. To address this data-sharing problem, researchers from both the traditional peer-to-peer (P2P) overlay networking and emerging edge computing fields have proposed some decentralized indexing mechanisms. However, existing studies lack insightful descriptions and analyses about the nature of the data-sharing problem at the network edge. It motivates us to rethink the edge data-sharing framework and provide the problem reformulation for analyzing the limitations of existing schemes. We reveal that the existing data-sharing schemes fail in complex network topologies which can be regarded as high-dimensional network spaces beyond the representation of low-dimensional Euclidean spaces or other existing hash spaces. A better space abstraction is an urgent need to alleviate the performance degradation due to the dimensional mismatch between network spaces and virtual spaces. To fill this gap, this paper proposes the Kautz metric space, a novel space abstraction extended from Kautz graphs, where the coordinates and the metric are defined as Kautz strings and Kautz distances (i.e., the shortest distances in undirected Kautz graphs), respectively. We design a dynamic programming algorithm to directly compute the Kautz distances. Then, we propose KMSharing, an efficient edge data-sharing scheme: both nodes and data are represented in a Kautz metric space, where the Kautz distance of any two Kautz strings reflects the network delay of the corresponding nodes. The workflow of KMSharing consists of three core components: the virtual address allocation represents edge nodes in the Kautz metric space; the data-to-node mapping ensures the uniqueness of target nodes; and forwarding table construction ensures the data delivery. Theoretical analyses confirm that KMSharing ideally achieves$\mathcal {O}\left ({{ \tau }}\right)$network delays,$\mathcal {O}\left ({{ \log N }}\right)$overlay hops, and$\mathcal {O}\left ({{ 1 }}\right)$forwarding entries in an N-node edge system with the network radius$\tau $, while the successive ensuring data delivery. Its worst-case network delay$\mathcal {O}\left ({{ \tau \log N }}\right)$is also much better than${\mathcal {O}\left ({{ \tau N^{\alpha } }}\right)},\alpha \mathrm {\in }(0,1)$, the worst case of the baselines using Euclidean spaces. Evaluation on various network topologies also shows that our KMSharing effectively reduces network delays and indexing costs than existing data-sharing schemes.

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.

The traditional migration methods are confronted with formidable challenges when data deduplication technologies are incorporated. First, the deduplication creates data-sharing dependencies in the stored files; breaking such dependencies in migration may attach extra space overhead. Second, the redundancy elimination makes the storage system reserves only one copy for each storage file, and heightens the risk of data unavailability. The existing methods fail to tackle them in one shot. To this end, we propose Jingwei, an efficient and adaptive data migration strategy for deduplicated storage systems. To be specific, Jingwei tries to minimize the extra space cost in migration for space efficiency. Meanwhile, Jingwei realizes the service adaptability by encouraging replicas of hot files to spread out their data access requirements. We first model such a problem as an integer linear programming (ILP) and solve it with a commercial solver when only one empty migration target server is allowed. We then extend this problem to a scenario wherein multiple non-empty target servers are available for migration. We solve it by effective heuristic algorithms based on the Bloom Filter-based data sketches. The Jingwei strategy can suffer from performance degradation when the heat degree varies significantly. Therefore, we further present incremental adjustment strategies for the two scenarios, which adjust the number of block replicas and their locations in an incremental manner. The mathematical analyses and trace-driven experiments show the effectiveness of our Jingwei strategy. To be specific, Jingwei fortifies the file replicas by 25% with only 5.7% of the extra storage space, compared with the latest “Goseed” method. With the small extra space cost, the file retrieval throughput of Jingwei can reach up to 333.5 Mbps, which is 12.3% higher than that of the Random method.

The traditional migration methods are confronted with formidable challenges when data deduplication technologies are incorporated. Firstly, the deduplication creates data-sharing dependencies in the stored files; breaking such dependencies in migration would attach extra space overhead. Secondly, the redundancy elimination heightens the risk of data unavailability during server crashes. The existing methods fail to tackle them at one shot. To this end, we propose Jingwei, an efficient and adaptable data migration strategy for deduplicated storage systems. To be specific, Jingwei tries to minimize the extra space cost in migration for space efficiency. Meanwhile, Jingwei realizes the service adaptability by encouraging replicas of hot data to spread out their data access requirements. We first model such a problem as an integer linear programming (ILP) and solve it with a commercial solver when only one empty migration target server is allowed. We then extend this problem to a scenario wherein multiple non-empty target servers are available for migration. We solve it by effective heuristic algorithms based on the Bloom Filter-based data sketches. Trace-driven experiments show that Jingwei fortifies the file replicas by 25%, while only 5.7% of the extra storage space is occupied compared with the latest "Goseed" method.

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.

Indoor landmarks, like corners, staircases and etc, play an important role in crowdsourcing-based indoor localization systems. This paper studies the problem of indoor corner detection and matching from crowdsourced movement trajectories. For corner detection, we adopt a machine learning approach by training a corner detector with both time and frequency features. For corner matching, we first apply the multidimensional scaling technique for matrix dimensionality reduction and then propose an improved K-means algorithm to obtain an intermediate matching result for each feature dimension. We also propose a voting algorithm to obtain the final matching result for each corner sample based on its all intermediate dimension matching results. Experiment results show that the machine learning-based corner detection can achieve much better detection performance, compared with the existing algorithms based on signal change detection. For corner matching, the proposed scheme can achieve high matching accuracy and the constructed corner fingerprints can achieve the nearest distance with their respective reference corner fingerprints.