This paper presents a ZUC-256 stream cipher algorithm hardware system in order to prevent the advanced security threats for 5G wireless network. The main innovation of the hardware system is that a six-stage pipeline scheme comprised of initialization and work stage is employed to enhance the solving speed of the critical logical paths. Moreover, the pipeline scheme adopts a novel optimized hardware structure to fast complete the Mod($2^{31}$-1) calculation. The function of the hardware system has been validated experimentally in detail. The hardware system shows great superiorities. Compared with the same type system in recent literatures, the logic delay reduces by 47% with an additional hardware resources of only 4 multiplexers, the throughput rate reaches 5.26 Gbps and yields at least 45% better performance, the throughput rate per unit area increases 14.8%. The hardware system provides a faster and safer encryption module for the 5G wireless network.
Health monitoring data or the data about infectious diseases such as COVID-19 may need to be constantly updated and dynamically released, but they may contain user’s sensitive information. Thus, how to preserve the user’s privacy before their release is critically important yet challenging. Differential Privacy (DP) is well-known to provide effective privacy protection, and thus the dynamic DP preserving data release was designed to publish a histogram to meet DP guarantee. Unfortunately, this scheme may result in high cumulative errors and lower the data availability. To address this problem, in this paper, we apply Jensen-Shannon (JS) divergence to design the OPTICS (Ordering Points To Identify The Clustering Structure) scheme. It uses JS divergence to measure the difference between the updated data set at the current release time and private data set at the previous release time. By comparing the difference with a threshold, only when the difference is greater than the threshold, can we apply OPTICS to publish DP protected data sets. Our experimental results show that the absolute errors and average relative errors are significantly lower than those existing works.
The traditional centralized data sharing systems have potential risks such as single point of failures and excessive working load on the central node. As a distributed and collaborative alternative, approaches based upon blockchain have been explored recently for Internet of Things (IoTs). However, the access from a legitimate user may be denied without the pre-defined policy and data update on the blockchain could be costly to the owners. In this paper, we first address these issues by incorporating the Accountable Subgroup Multi-Signature (ASM) algorithm into the Attribute-based Access Control (ABAC) method with Policy Smart Contract, to provide a fine-grained and flexible solution. Next, we propose a policy-based Chameleon Hash algorithm that allows the data to be updated in a reliable and convenient way by the authorized users. Finally, we evaluate our work by comparing its performance with the benchmarks. The results demonstrate significant improvement on the effectiveness and efficiency.
With the popularity of the internet, users hope to better protect their privacy while obtaining network services. However, in the traditional centralized authentication scheme, identity information such as the user's private key is generated, stored, and managed by the network operator. Users can’t control their identity information, which will lead to a great threat to the privacy of users. Based on redactable blockchain, we propose a fine-grained and fair identity authentication scheme for mobile networks. In our proposed scheme, the user’s identity information is generated and controlled by the users. We first propose a notion of score chameleon hash (SCH), which can delete or update the information of illegal users so as to dynamically update the status of users and provide users with more fine-grained and fair services. We propose another notion of self-updating secret sharing (SUSS), which allows users to update the trapdoor and the corresponding hash key after redacting the blockchain without requiring trusted authority to redistribute the trapdoor. Experimental results show that, compared with the immutable blockchain Bitcoin, the redactable blockchain in our identity authentication scheme provides users with fine-grained and fair redacting functions, and can be adopted with a small additional overhead.
To cope with the low latency requirements and security issues of the emerging applications such as Internet of Vehicles (IoV) and Industrial Internet of Things (IIoT), the blockchain-enabled Mobile Edge Computing (MEC) system has received extensive attention. However, blockchain is a computing and communication intensive technology due to the complex consensus mechanisms. To facilitate the implementation of blockchain in the MEC system, this paper adopts the committee-based Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and focuses on the committee selection problem. Vehicles and IIoT devices generate the transactions which are records of the application tasks. Base Stations (BSs) with MEC servers, which serve the transactions according to the wireless channel quality and the available computing resources, are blockchain nodes and candidates for committee members. The income of transaction service fees, the penalty of service delay, the decentralization of the blockchain and the communication complexity of the consensus process constitute the performance index. The committee selection problem is modeled as a Markov decision process, and the Proximal Policy Optimization (PPO) algorithm is adopted in the solution. Simulation results show that the proposed PPO-based committee selection algorithm can adapt to the system design requirements with different emphases and outperforms other comparison methods.
Secure authentication between user equipment and 5G core network is a critical issue for 5G system. However, the traditional authentication protocol 5G-AKA and the centralized key database are at risk of several security problems, e.g. key leakage, impersonation attack, MitM attack and single point of failure. In this paper, a blockchain based asymmetric authentication and key agreement protocol (BC-AKA) is proposed for distributed 5G core network. In particular, the key used in the authentication process is replaced from a symmetric key to an asymmetric key, and the database used to store keys in conventional 5G core network is replaced with a blockchain network. A proof of concept system for distributed 5G core network is built based on Ethereum and ECC-Secp256k1, and the efficiency and effectiveness of the proposed scheme are verified by the experiment results.
User profile matching can establish social relationships between different users in the social network. If the user profile is matched in plaintext, the user's privacy might face a security challenge. Although there exist some schemes realizing privacy-preserving user profile matching, the resource-limited users or social service providers in these schemes need to take higher computational complexity to ensure the privacy or matching of the data. To overcome the problems, a novel privacy-preserving user profile matching protocol in social networks is proposed by using $t$-out-of $n$ servers and the bloom filter technique, in which the computational complexity of a user is reduced by applying the Chinese Remainder Theorem, the matching users can be found with the help of any $t$ matching servers, and the privacy of the user profile is not compromised. Furthermore, if at most $t-1$ servers are allowed to collude, our scheme can still fulfill user profile privacy and user query privacy. Finally, the performance of the proposed scheme is compared with the other two schemes, and the results show that our scheme is superior to them.
The Internet of Things (IoT) has profoundly impacted our lives and has greatly revolutionized our lifestyle. The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions. However, the high frequency of collecting user data will raise people concerns about personal privacy. In recent years, many privacy-preserving data aggregation schemes have been proposed. Unfortunately, most existing schemes cannot support either arbitrary aggregation functions, or dynamic user group management, or fault tolerance. In this paper, we propose an efficient and privacy-preserving data aggregation scheme. In the scheme, we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence. On this basis, the proposed scheme supports not only arbitrary aggregation functions, but also flexible dynamic user management. Furthermore, the scheme achieves fault-tolerant capabilities by utilizing a future data buffering mechanism. Security analysis reveals that the scheme can achieve the desired security properties, and %extensive experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead.
In order to support the future digital society, sixth generation (6G) network faces the challenge to work efficiently and flexibly in a wider range of scenarios. The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline, which obviously leads to a 6G network lacking of adaptation to dynamic environments. Recently, with the aid of sensing enhancement, more environment information can be obtained. Based on this, from radio wave propagation perspective, we propose a predictive 6G network with environment sensing enhancement, the electromagnetic wave propagation characteristics prediction enabled network (EWaveNet), to further release the potential of 6G. To this end, a prediction plane is created to sense, predict and utilize the physical environment information in EWaveNet to realize the electromagnetic wave propagation characteristics prediction timely. A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWaveNet. Several promising application cases of EWaveNet are analyzed and the open issues to achieve this goal are addressed finally.
The sixth generation (6G) mobile network is envisaged to be commercially deployed around 2030, which will profoundly change people's lifestyles and accelerate the digitalization of society. To ensure that the requirements of 6G can be achieved, it is essential to establish a set of key performance indicators (KPIs). This paper comprehensively assesses the KPIs not only from the service requirements but also from the technical feasibility points of view. Specifically, theoretical derivations of KPIs have been clarified, and numerical evaluations have been conducted with reasonable technical assumptions. Evaluation results show that some KPIs defined from the service requirements can be improved through advanced technologies while some are still challenging for practical implementations, such as Tbps-level peak data rate and 0.1ms user plane latency. In addition, it is also necessary to comply with multiple KPIs for some cases. Furthermore, based on the technical analysis, the potential enabling technologies are outlined and foreseeable implementation challenges as well as possible solutions are presented, which promotes a more reasonable design for 6G mobile network.
5G is envisioned to guarantee high transmission rate, ultra-low latency, high reliability and massive connections. To satisfy the above requirements, the 5G architecture is designed with the properties of using service-based architecture, cloud-native oriented, adopting IT-based API interfaces and introduction of the Network Repository Function. However, with the wide commercialization of 5G network and the exploration towards 6G, the 5G architecture exposes the disadvantages of high architecture complexity, difficult inter-interface communication, low cognitive capability, bad instantaneity, and deficient intelligence. To overcome these limitations, this paper investigates 6G network architecture, and proposes a cognitive intelligence based distributed 6G network architecture. This architecture consists of a physical network layer and an intelligent decision layer. The two layers coordinate through flexible service interfaces, supporting function decoupling and joint evolution of intelligence services and network services. With the above design, the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.
Visible light communications (VLC) is considered as an effective supplement technology for next-generation (6G) communications due to its abundant spectrum, high power efficiency and easy deployment. Optical orthogonal frequency division multiplexing (O-OFDM) is a common technology to obtain further promotion. In this paper, two typical O-OFDM schemes direct current biased O-OFDM (DCO-OFDM) and asymmetrically clipped O-OFDM (ACO-OFDM) are analyzed in terms of signal clipping at both transmitter and receiver under the constraints of maximum optical power and non-negative optical power. And effective electrical SNR models after signal clipping are proposed and verified by link simulation. Then a noise cancellation scheme is proposed based on received signal clipping and is proved to bring a significant gain for ACO-OFDM. By system simulation, we find that under a certain optical power limitation, most users can achieve above 4Gbps in indoor scenario when modulation bandwidth of the light emit diode (LED) or laser diode (LD) is 1GHz. Therefore, it can be expected that the throughput could reach tens Gbps when the LED/LD modulation bandwidth is increased and multiple LEDs/LDs are deployed.
Artificial intelligence (AI) has made a profound impact on our daily life. The 6th generation mobile networks (6G) should be designed to enable AI services. The native intelligence is introduced as an important feature in 6G. 6G native AI network is realized by the philosophy of federated learning (FL) to ensure data security and privacy. Federated learning over wireless communication networks is treated as a potential solution to realize native AI. However, introducing FL in the 6G will lead to expansive communication cost and unstable FL convergence with unreliable air interface. In this paper, we propose a solution for FL over wireless networks and analyze the training efficiency. To make full use of the advantages of the proposed network, we introduce a communication-FL joint optimization (CFJO) algorithm by jointly considering the effects of uplink resource, energy consumption and latency constraints. CFJO derives a transmission strategy with resource allocation and re-transmissions to reduce the wireless transmission interruption probability and model upload latency. The simulation results show that CFJO significantly improves the model training efficiency and convergence performance with lower interruption probability under the latency constraint.
Multi-beam satellite communication systems can improve the resource utilization and system capacity effectively. However, the inter-beam interference, especially for the satellite system with full frequency reuse, will degrade the system performance greatly due to the characteristics of multi-beam satellite antennas. In this article, the user scheduling and resource allocation of a multi-beam satellite system with full frequency reuse are jointly studied, in which all beams can use the full bandwidth. With the strong inter-beam interference, we aim to minimize the system latency experienced by the users during the process of data downloading. To solve this problem, deep reinforcement learning is used to schedule users and allocate bandwidth and power resources to mitigate the inter-beam interference. The simulation results are compared with other reference algorithms to verify the effectiveness of the proposed algorithm.
Reconfigurable intelligent surface (RIS) is more likely to develop into extremely large-scale RIS (XL-RIS) to efficiently boost the system capacity for future 6G communications. Beam training is an effective way to acquire channel state information (CSI) for XL-RIS. Existing beam training schemes rely on the far-field codebook. However, due to the large aperture of XL-RIS, the scatters are more likely to be in the near-field region of XL-RIS. The far-field codebook mismatches the near-field channel model. Thus, the existing far-field beam training scheme will cause severe performance loss in the XL-RIS assisted near-field communications. To solve this problem, we propose the efficient near-field beam training schemes by designing the near-field codebook to match the near-field channel model. Specifically, we firstly design the near-field codebook by considering the near-field cascaded array steering vector of XL-RIS. Then, the optimal codeword for XL-RIS is obtained by the exhausted training procedure. To reduce the beam training overhead, we further design a hierarchical near-field codebook and propose the corresponding hierarchical near-field beam training scheme, where different levels of sub-codebooks are searched in turn with reduced codebook size. Simulation results show the proposed near-field beam training schemes outperform the existing far-field beam training scheme.
A distributed reinforcement learning (RL) based resource management framework is proposed for a mobile edge computing (MEC) system with both latency-sensitive and latency-insensitive services. We investigate joint optimization of both computing and radio resources to achieve efficient on-demand matches of multi-dimensional resources and diverse requirements of users. A multi-objective integer programming problem is formulated by two sub-problems, i.e., access point (AP) selection and subcarrier allocation, which can be solved jointly by our proposed distributed RL-based approach with a heuristic iteration algorithm. The proposed algorithm allows for the reduction in complexity since each user needs to consider only its own selection of AP without knowing full global information. Simulation results show that our algorithm can achieve near-optimal performance while reducing computational complexity significantly. Compared with other algorithms that only optimize either of the two sub-problems, the proposed algorithm can serve more users with much less power consumption and content delivery latency.
Wireless Sensor Networks (WSNs) play an indispensable role in the lives of human beings in the fields of environment monitoring, manufacturing, education, agriculture etc., However, the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence. In this context, several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time. However, there still exists a room for improvement in Cluster Head (CH) selection based on the integration of critical parameters. The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant. In this paper, a hybrid Marine Predators Optimization and Improved Particle Swarm Optimization-based Optimal Cluster Routing (MPO-IPSO-OCR) is proposed for ensuring both efficient CH selection and data transmission. The robust characteristic of MPOA is used in optimized CH selection, while improved PSO is used for determining the optimized route to ensure sink mobility. In specific, a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA. The high-speed ratio, unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point. The simulation investigation and statistical results confirm that the proposed MPO-IPSO-OCR is capable of improving the energy stability by 21.28 %, prolonging network lifetime by 18.62 % and offering maximum throughput by 16.79 % when compared to the benchmarked cluster-based routing schemes.
Heterogeneous networks (HetNets) attracts a lot of attention due to its high capacity and large coverage for future communication networks. However, with the large-scale deployment of small cells, HetNets bears dramatically increasing backhaul, which leads to a decrease of the outage performance. To improve the outage performance of HetNets, we propose a wireless backhaul scheme for a two-layer HetNets, which automatically switches the three basic modes of orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) and cooperative non-orthogonal multiple access (CNOMA). First, we analyze the backhaul capacity and outage performance of these three basic modes. Then, we design the power allocation schemes based on minimizing outage probability for NOMA and CNOMA. Using the designed power allocation schemes, we propose a wireless backhaul scheme that switches the three modes according to the channel quality among different base stations (BSs). Moreover, the closed-form of the corresponding outage probability is derived. Compared with the three basic modes, the proposed wireless backhaul scheme can achieve the best outage performance and a higher backhaul capacity. Finally, all the analytical results are validated by simulations.
The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research.Since Bitcoin was created by Nakamoto in 2009, it has, to some extent, deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations.In this paper, the power-law distribution that the Bitcoin network obeys is given with mathematical proof, while traditional de-anonymous methods such as clustering fail to satisfy it.Therefore, considering the profit-oriented characteristics of Bitcoin traders in such occasion, we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT), with real-time exchange rate of Bitcoin involved.With heuristic approach used for de-anonymity, algorithm that deals with the adjacency matrix and transition probability matrix are also put forward, which then makes it possible to apply clustering to the IP matching method.Basing on the heuristic approach and additional algorithm for clustering, finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain. Experiments on IP matching method are applied to the actual data. It turns out that similar behavioral pattern between IP and transaction records are shown, which indicates the superiority of IP matching method.
The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests, however, the misdiagnosis rates have been relatively high.In this study, we applied brain-computer interface (BCI) to awareness detection with a passive auditory stimulation paradigm. 12 subjects with normal hearing were invited to collect electroencephalogram (EEG) based on a BCI communication system, in which EEG signals are transmitted wirelessly. After necessary pre-processing, RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject, RBF-SVM can distinguish his (her) name stimuli awareness with classification accuracies ranging from 60-95$\%$. EEGNet was used to learn all subjects’ data and improved accuracy to 78.04$\%$ for characteristics finding and model generalization. Moreover, we completed the supplementary analysis work from the time domain and time-frequency domain. This study applied BCI communication to human awareness detection, proposed a passive auditory paradigm, and proved the effectiveness, which could be an inspiration for brain, mental or physical diseases diagnosis and detection.
