ABSTRACT Constrained multi‐objective optimization problems widely exist in real‐world applications, yet remain challenging due to the coexistence of multiple conflicting objectives and constraints. This study proposes a two‐archive constrained multi‐objective optimization algorithm with two‐stage weak cooperation, named BWC‐TAA. First, based on the two‐archive framework, a weak cooperation interaction mechanism is designed, in which the CA and DA evolve independently. They only merge offspring during the update stage to select high‐quality individuals, thereby preventing solutions from being confined to the parent population range. Second, a two‐stage evolutionary strategy is introduced to dynamically adjust the optimization objectives, enabling CA and DA to adopt different strategies in different phases. Finally, BWC‐TAA is evaluated on benchmark constrained problems against eight state‐of‐the‐art algorithms. The experimental results demonstrate that BWC‐TAA significantly outperforms the compared algorithms in terms of convergence and diversity indicators.

Federated learning is a machine learning paradigm through training on locally private data and aggregating local models to generate a federated model. However, due to the heterogeneity problems (data heterogeneity and model heterogeneity), federated learning suffers from convergence difficulties and excessive aggregation overhead on decentralized participants. Additionally, federated learning faces privacy concerns during the aggregation of local models. To this end, in this work, we propose FEDINF, an efficient and secure inference with federated participants. Specifically, FEDINF features the following characteristics. FEDINF overcomes convergence challenges through federated inference instead of federated training, which reduces computation and communication overhead. Moreover, we design secure computation protocols and aggregation mechanisms to measure contributions, and handle both data and model heterogeneity without sacrificing privacy. Results of experimental evaluations on common datasets demonstrate that the proposed FEDINF outperforms the existing federated learning approaches in terms of efficiency and heterogeneity.

Assigning sensing tasks to appropriate task participants is critical for mobile crowdsensing (MCS) and is an essential optimization problem. However, existing task assignment (or say participant selection) solutions for MCS generally support a single-objective optimization (e.g., minimizing travel distance or maximizing social welfare). Additionally, task assignment for MCS usually requires task participants’ and a task requester's location information, which compromises their location privacy and hinders participation willingness. To achieve task assignment with bi-objective optimization and safeguard bilateral privacy, in this paper, we propose RATE, a privacy-preserving task assignment with bi-objective optimization for MCS. RATE features the following characteristics. First, RATE enables task assignment with bi-objective optimization including maximizing the social welfare and the requester's revenue, simultaneously. Second, RATE achieves bilateral privacy-preserving task assignment with bi-objective optimization by carefully designing underlyingly secure computing protocols. Third, RATE approximates optimal results of task assignments without sacrificing privacy. Theoretical analyses show that RATE protects the location privacy of both the task requester and the task participants. Meanwhile, experimental evaluations demonstrate that RATE outperforms traditional task assignment solutions and generates the task assignment result effectively and efficiently.

Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

Attribute-Based Encryption Scheme solves the security problems faced by cloud storage. In many practical applications, the files encrypted by Attribute-Based Encryption have the characteristics of multiple levels. However, CPABE cannot encrypt the data according to the level. This paper proposes a hierarchy attribute-based encryption algorithm to support the direct revocation. First, the encryption process of algorithm is introduced, second, the model of hierarchy attribute-based encryption algorithm is proposed to support the direct revocation, and the security proof of the algorithm is also given. Finally, we design two experiments to compare the proposed algorithm with existing algorithms from the perspective of time.

A noncoherent multiuser transmission scheme is proposed for massive multiple-input multiple-output (M-MIMO) systems without explicit channel estimation. In particular, each user uses differential PSK modulation and the receiver employs differential detection. First, we propose a simple user selection scheme to optimize the distributions of power space profile (PSP) of individual users which alleviates the overlap of PSPs. Then, each output stream of the weighing filter is fed into a noncoherent successive interference cancellation (N-SIC) processor, and finally into a soft-input soft-output (SISO) multiple-symbol differential detector(MSDD). Employing the autocorrelation receiver (AcR) and the belief propagation(BP) message passing algorithm, the proposed SISO-MSDD framework can be easily integrated with advanced channel coding. The proposed scheme bears the potential to solve the high channel estimation overhead for conventional coherent M-MIMO systems. Simulation results show that the BER performance can be significantly improved within a few iterations of the proposed scheme.

Hadoop distributed file system (HDFS) was originally designed for streaming access large files, but the access and storage efficiency is low for the mass small files. This paper presents an access optimization approach for HDFS small file based on MapFile: TLB-MapFile. TLB-MapFile merges massive small files into large files by MapFile mechanism to reduce NameNode memory consumption and add fast table structure (TLB) in DataNode, and to improve retrieval efficiency of small files. First, according to MapFile mechanism, small files are merged into large files and stored in HDFS. Second, the access frequency and the ordered queue of small files (per unit time) can be obtained through accessing system audit logs in HDFS, and the mapping information between block and small files are stored in the TLB table with regularly being updated. TLB-MapFile improves access efficiency of small files through the prefetching of priori strategies based on TLB table. Experiment results show that this method can effectively reduce NameNode memory consumption and improve the reading speed of small files.