Intelligent transportation systems (ITSs) are essential for the development of future smart cities. They can improve traffic management, mitigate urban congestion, and provide extensive social services by disseminating traffic data collected from vehicles. To facilitate the exchange of sensory data with other vehicles and alleviate the local storage load, sensory data from vehicles is frequently uploaded to cloud or edge servers. However, current ITS data sharing frameworks exhibit certain security vulnerabilities. In particular, they are unable to support secure multikeyword searches or ensure verifiable retrieval of encrypted information. Moreover, some of the schemes cannot ensure the integrity of the retrieved data. Therefore, we propose an efficient multikeyword searchable and verifiable data sharing (EMKV-ABSE) for cloud-edge collaboration ITS. EMKV-ABSE integrates a multikeyword attribute-based searchable encryption (ABSE) scheme and a tamper-proof consortium blockchain (BC). In EMKV-ABSE, we store the keyword indexes and ciphertext hashes of the shared data on the BC, which can ensure the integrity and authenticity of the data. Meanwhile, to avoid security and trust issues associated with central search cloud servers, smart contracts are utilized to perform multikeyword searches and verify retrieval results. Furthermore, the EMKV-ABSE scheme outsources the complex computational tasks in encryption and decryption to edge servers, which realizes lightweight computation for resource-constrained users. The security analysis proves that EMKV-ABSE satisfies the indistinguishability under the chosen plaintext attack (IND-CPA) and the chosen keyword attack (IND-CKA) in the standard model. Performance evaluation shows the efficiency and practicability of our scheme.

Wireless Sensor Networks (WSNs) face critical security challenges due to their distributed architecture, dynamic topologies, and the complexity introduced by multisensor nodes. These factors result in highly variable traffic patterns, making stealthy and long-duration attacks difficult to detect. Recent Mamba-based sequence models have shown promise in capturing long-range dependencies with high efficiency, making them attractive for such scenarios. However, Mamba’s inherent causal modeling limits its ability to fully exploit hierarchical patterns that are crucial for detecting subtle or dispersed attacks. In this work, we propose MRMamba, a multi-resolution state-space architecture that equips Mamba with non-causal and cross-scale modeling capabilities. Specifically, MRMamba introduces a Multi-Resolution State Space Module that performs cross-scale attention fusion. This mechanism dynamically integrates context at multiple temporal resolutions and helps alleviate long-range dependency decay. Furthermore, a dual-branch architecture is constructed around MRMamba to model attacks from complementary perspectives, enabling more accurate and robust detection. Extensive experiments on five real-world public datasets show that MRMamba consistently outperforms existing attack detection methods. It improves the F1 score by up to 4.53% on CIC2023 and 6.6% on IoM2024 over the best of the compared methods.

For the efficient analysis of geo-distributed datasets, cloud providers implement data-parallel jobs across geo-distributed sites (e.g., datacenters and edge clusters), which are generally interconnected by wide-area network links. However, current state-of-the-art geo-distributed data analytic methods fail to make full use of the available network and computing resources. The main reason is that such geo-distributed methods must wait for bottleneck sites to complete the corresponding transmission and computation in each phase. Furthermore, such geo-distributed methods may be impractical to the network bandwidth dynamicity and diverse job parallelism. To this end, we propose a Simultaneous Data Transfer and Processing (SDTP) mechanism to accelerate wide-area data analytics, with the joint consideration of network bandwidth dynamics and job parallelism. In the SDTP, a site can execute the computation, provided that it obtains the required input data. As a result, the input data loading, map, shuffle, and reduce phases at each site need not wait for the completion of the previous phases of other sites. We further improve the SDTP method by offering more accurate time estimation and generalizing the mechanism to dynamic situations. The trace-driven results demonstrate that SDTP can improve the wide-area analytic job response time by 19% to 72% compared to other methods.

Owing to the advancement of the Internet of Things (IoT) and 5G mobile technologies, various IoT devices produce massive data, which is usually transferred to nearby sites, such as edge nodes or datacenters. Many large-scale IoT applications need to analyze the data distributed across multiple sites to obtain final results. A dominant challenge of this type of data analytics is the heterogeneities of resource capacities across geo-distributed sites. In this article, we find that the resource capacity as well as the resource price differ among sites, and the price heterogeneity has a significant impact on geo-distributed IoT data analytics. Thus, each geo-distributed IoT data analytics job prefers to minimize the job execution cost while guaranteeing its deadline requirement under the resource constraints of involved sites. Specifically, we propose to jointly consider the resource heterogeneities of both capacity and price, and minimize the cost of each job before its deadline. We characterize this optimization problem as a quadratically constrained quadratic programming problem. To tackle such an NP-hard problem, we propose the minimize the job completion cost before a given deadline (MCGL) method, which calculates a task placement solution by the gradient adjustment strategy according to the remarkable negative correlation relationship between job completion time and job completion cost of geo-distributed IoT data analytics job. The task placement strategy can optimize resource cost with respect to the deadline requirement of any geo-distributed data analytics job. The trace-driven evaluations indicate that MCGL significantly reduces the total cost compared with existing methods; moreover, they satisfy the deadline constraints simultaneously.