Due to the exponential growth of blockchain ledger sizes, federated-storage which enables multiple devices to jointly store data, has emerged as a promising solution for secure data storage in edge computing. However, how to achieve verifiable queries in such decentralized storage remains underexplored. Existing broadcast-based query methods lack a verification mechanism for query results, making it impossible to ensure their correctness and completeness. Meanwhile, authenticated data structure based (ADS-based) query strategies are constrained by the full ledger data and cannot provide verifiable query services for users in a federated-storage environment. To this end, this paper takes the lead to propose EVQ, a verifiable blockchain keyword query scheme tailored for federated-storage edge computing. We propose a split keyword-based ADS as the core structure of our framework which ensures that users can verify the correctness and completeness of query results while alleviating storage pressure of edge devices. Specifically, the proposed ADS is constructed through a two-phase process: top-bottom keyword index tree construction and bottomtop RSA accumulator integration. Splitting the ADS based on keywords enables distributed data storage and the generation of corresponding ADS for the stored data. To reduce the query costs incurred by edge devices during query processing, we formulate the Keyword Allocation Optimization (KAO) problem and propose a gain-ratio-based keyword allocation mechanism to determine the splitting scheme of the ADS. The experiments are conducted based on the Foursquare dataset, which contains approximately 18 months of global check-in data collected from Foursquare. The experimental results show that, compared to the merkle tree strategy that also integrates the RSA accumulator, our EVQ improves query performance by 24.77 x.

Blockchain, with its immutability and decentralization, drives innovation in finance and supply chain, but the growing data volume makes storing complete ledger replicas impractical for users, especially in the resource-constrained Internet of Thing (IoT) scenarios. Existing solutions focus on nodes storing only a partial ledger to alleviate storage burdens. Nonetheless, these approaches prioritize storage optimization by minimizing the query cost and lack control over storage cost. Furthermore, these approaches overlook the relationships between network users, thus failing to fully measure the future query cost. Thus, this article proposes BSSN, a blockchain storage technology based on social networks. The combined use of storage cost and query cost is introduced for the first time to formulate the node allocation optimization (NAO) problem, and the multipopulation genetic ant colony (MGAC) algorithm will be employed to derive node allocation strategies. Specifically, we address three technical challenges: 1) to predict the transactions that nodes will participate in the future, we employ the social ties to obtain the access frequencies among users; 2) to strike a balance between the storage cost and query cost, we jointly model the two costs as a multiobjective optimization problem to formulate the NAO problem; and 3) to solve the NP-hard NAO problem, we use the MGAC algorithm, where the storage and query populations collaboratively search for solutions based on four operations. Extensive experiments indicate that compared with existing work, BSSN can reduce the average query cost to 67% with its adjustable storage cost, ensuring a balanced data storage among users.

In recent years, sharding technology has been frequently applied in blockchain systems to increase scalability. However, when new shards are added, the system may result in significant overhead in terms of computing and networking since the data allocation approach is incompatible with dynamic changes in shards. Currently, S-Store, the state-of-the-art sharding solution built on the account model, has a high re-computing latency when growing shard numbers and an unbalanced sharded data distribution after growth. To address these issues, this paper presents Tangram, an efficient and balanced dynamic storage extension approach for sharding blockchain systems. Tangram reduces system extension overhead and latency while ensuring a balanced shard distribution. In implementing Tangram, we tackle three main technical challenges as follows. (1) Designing a novel state tree structure for the storage and maintenance of sharding state data. We introduce the Jump Merkle Tree (JMT) based on the Merkle Tree, which integrates node migration and orderliness. (2) Presenting a protocol to be compatible with dynamic shard scenarios. We devise a shard addition protocol to improve system extension availability and decrease shard extension delay. (3) Proposing an approach to guarantee system longevity after extension. We first devise algorithms for the state tree to eradicate invalid states after system expansion. Furthermore, we introduce a shard reduction protocol to enhance system storage extension support in complex scenarios, such as cleaning up inactive states to avoid bloating the state tree. We conduct extensive experiments to evaluate the performance of Tangram. Experiment results demonstrate that Tangram outperforms existing solutions, showing reduced latency and superior data balance. When compared to the state-of-the-art sharding storage solution, Tangram decreases the transaction execute time by up to 87.84%, the state data migration by more than approximately 74%, and achieves up to 7.63x improvement in the standard deviation of sharding data balance.

With the advent of network function virtualization (NFV) and mobile edge computing (MEC), outsourcing network functions (NFs, i.e., firewall) to the MEC is becoming popular among network service providers. Notably, NF outsourcing raises an essential security concern about whether these outsourced NFs and associated service function chains (SFCs) are correctly implemented according to enterprises’ specifications. In particular, SFC with parallel VNFs, which take advantage of parallelism, have been conducted to reduce the traffic delay of traditional sequential SFC, called the hybrid SFC in this paper. Nevertheless, how to ensure correct behaviors and discover runtime mistakes for hybrid SFC remains an open problem.In this paper, we propose vHSFC, a verification scheme for hybrid SFC, enabling enterprises to verify the correctness of SFC enforcement in real-time. vHSFC achieves its goal with a lightweight verified routing protocol, which detects various hybrid SFC violations and attacks, i.e., packet modification, incompliant forwarding path, etc. To demonstrate the feasibility and performance of vHSFC, we have implemented the prototype on top of several containers and conducted extensive experiments with real traffic. The experimental results show that our vHSFC can continuously ensure proper enforcement and discover unexpected violations while incurring sensible overhead.

Sharding technology has become crucial for enhancing the scalability of blockchains owing to the rapid extension of blockchain data. However, data migration and state reconstruction may cause a high overhead when a new shard is added. Existing solutions have high latency when expanding, and the balance of the state data between shards is poor after extension. To this end, this paper proposes an Effective and Balanced Storage Extension (EBSE) approach for sharding blockchain systems. EBSE can reduce the overhead and latency of the system extension, while ensuring a balance between shards after extension. When implementing the EBSE, we address the following three challenges. 1) To design a data structure that incorporates allocation principles, we designed a Jump Merkle Tree (JMT) based on the Merkle Tree prototype, incorporating node migration and orderliness. 2) To design additional rules that ensure the integrity of the state tree during shard addition, we designed a shard addition protocol to coordinate and standardize the behavior of each shard during the extension process. 3) To ensure the sustainability of the system after extension, we designed state tree addition and cleaning algorithms to remove the invalid information after the system extension. Extensive experiments are conducted to evaluate the performance of the proposed approach. The experimental results show that the EBSE outperforms the existing solutions in terms of balance and latency. Compared with the state-of-the-art sharding storage, EBSE effectively reduces the shard addition latency by 60% and achieves 4× superior shard data balance.

Mobile Edge Computing (MEC) is a promising cloud-network convergence paradigm which provides computational resources close to end devices at the network edge. There exist multiple Edge Infrastructure Providers (EIPs) in MEC which independently manage edges and provide services to customers. Due to the exponentially increasing data generated by end devices, it is almost impossible for a single EIP to accommodate offloaded data. Moreover, when considering that multiple EIPs provide services through federation, an urgent challenge is how to ensure the sustainability of federation. Most of the existing work improves the service provision capabilities of MEC by optimizing service placement without considering the existence of multiple EIPs. In this paper, we design the horizontal collaboration of edge federation, which integrates all edges of all EIPs. First, we model the service placement problem as a programming problem, towards the goal of maximizing social welfare. Then, we propose two dynamic pricing methods for EIPs to determine typical price for customers and insourcing price for other EIPs. The evaluation results based on two real-world data sets demonstrate that our proposed service placement model can increase the total gain of EIPs by up to 24.5% with a decrease of 35.5% in total delay.

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%.

An industrial mobile network is crucial for industrial production in the Internet of Things. It guarantees the normal function of machines and the normalization of industrial production. However, this characteristic can be utilized by spammers to attack others and influence industrial production. Users who only share spams, such as links to viruses and advertisements, are called spammers. With the growth of mobile network membership, spammers have organized into groups for the purpose of benefit maximization, which has caused confusion and heavy losses to industrial production. It is difficult to distinguish spammers from normal users owing to the characteristics of multidimensional data. To address this problem, this paper proposes a spammer identification scheme based on Gaussian mixture model (SIGMM) that utilizes machine learning for industrial mobile networks. It provides intelligent identification of spammers without relying on flexible and unreliable relationships. SIGMM combines the presentation of data, where each user node is classified into one class in the construction process of the model. We validate the SIGMM by comparing it with the reality mining algorithm and hybrid fuzzy c-means (FCM) clustering algorithm using a mobile network dataset from a cloud server. Simulation results show that SIGMM outperforms these previous schemes in terms of recall, precision, and time complexity.