In the upcoming era of 6G, the accelerated development of the Internet of Everything and high-speed communication is poised to provide people with an efficient and intelligent life experience. ...However, the exponential growth in data traffic is expected to pose substantial task processing challenges. Relying solely on the computational resources of individual devices may struggle to meet the demand for low latency. Additionally, the lack of trust between different devices poses a limitation to the development of 6G networks. In response to this issue, this study proposes a blockchain-based 6G task offloading and collaborative computational resource allocation (CERMTOB) algorithm. The proposed first designs a blockchain-based 6G cloud-network-edge collaborative task offloading model. It incorporates a blockchain network on the edge layer to improve trust between terminals and blockchain nodes. Subsequently, the optimization objective is established to minimize the total latency of offloading, computation, and blockchain consensus. The optimal offloading scheme is determined using the wolf fish collaborative search algorithm(WF-CSA) to minimize the total delay. Simulation results show that the WF-CSA algorithm significantly reduces the total delay by up to 42.58% compared to the fish swarm algorithm, wolf pack algorithm and binary particle swarm optimisation algorithm. Furthermore, the introduction of blockchain to the cloud-side-end offloading system improves the communication success rate by a maximum of 14.93% compared to the blockchain-free system.
Three-dimensional (3D) imaging of space targets can provide crucial information about the target shape and size, which are significant supports for the application of automatic target classification ...and recognition. In this paper, a new 3D imaging of space spinning targets via a factorization method is proposed. Firstly, after the translational compensation, the scattering centers two-dimensional (2D) range and range-rate sequence induced by the target spinning is extracted using a high resolution spectral estimation technique. Secondly, measurement data association is implemented to obtain the scattering center trajectory matrix by using a range-Doppler tracker. Then, we use an initial coarse angular velocity to generate the projection matrix, which consists of the scattering centers range and cross-range, and a factorization method is applied iteratively to the projection matrix to estimate the accurate angular velocity. Finally, we use the accurate estimate spinning angular velocity to rescale the projection matrix and the well-scaled target 3D geometry is reconstructed. Compared to the previous literature methods, ambiguity in the spatial axes can be removed by this method. Simulation results have demonstrated the effectiveness and robustness of the proposed method.
Three-dimensional (3D) target reconstruction from inverse synthetic aperture radar (ISAR) data has a wide application in target scattering modelling, detection, and identification. In ISAR imaging of ...targets with complex motions such as the non-cooperative manoeuvring targets, the scattering centres on the target may rotate slowly in 3D space during the observation time. In this study, the authors have developed a new formulation for 3D target geometry reconstruction from the scattering centres high-resolution range (HRR) measurements, based on target motion features. First, after the translation compensation, the multi-view HRR of the scattering centres is extracted by HR spectral estimation technique. Then, the multi-view measurements data without correspondence information are associated using the multiple hypotheses tracking algorithm. Finally, the 3D target geometry and motion are reconstructed from the singular value decomposition of the correlated HRR data matrix. The effectiveness of the proposed algorithm is demonstrated by both simulated and real data experiment results.
Knowledge of the clutter rate is of critical importance in multi-target Bayesian tracking. However, estimating the clutter rate is a difficult problem in practice. In this paper, an improved ...multi-Bernoulli filter based on random finite sets for multi-target Bayesian tracking accommodating non-linear dynamic and measurement models, as well as unknown clutter rate, is proposed for radar sensors. The proposed filter incorporates the amplitude information into the state and measurement spaces to improve discrimination between actual targets and clutters, while adaptively generating the new-born object random finite sets using the measurements to eliminate reliance on prior random finite sets. A sequential Monte-Carlo implementation of the proposed filter is presented, and simulations are used to demonstrate the proposed filter's improvements in estimation accuracy of the target number and corresponding multi-target states, as well as the clutter rate.
This study investigates the target localisation problem by using the hybrid bistatic range (BR), time difference of arrival, and angle of arrival (AOA) measurements in multistatic passive radar. An ...explicit algebraic solution is developed by linearising the non-linear measurement equations through introducing multiple nuisance parameters in the first stage and estimating the localisation error of the first stage solution to refine the final target position estimate in the second stage. The closed-form resulting estimator has attractive computational complexity. Theoretical analysis of the proposed method is presented, and it is shown to be able to reach the Cramer–Rao lower bound at low noise levels. Simulations are included to validate the performance of the proposed estimator.
Next generation optical metro networks need to serve heterogeneous access traffic with guaranteed quality of service (QoS) and lower CAPEX and OPEX. In this context, an integrated network ...infrastructure enabling multiple services access and network slicing is necessary. In this paper, we investigate recent research efforts on network slicing in optical metro networks, the MTN and M-OTN. These technologies have limitations in flexible network resource slicing and efficient bandwidth utilization. Therefore, we propose a new all optical metro network SiMON for achieving flexibility in network slicing and efficiency in bandwidth utilization. In addition, we theoretically investigate the SiMON in achieving end-to-end deterministic latency. A jitter reduction method for SiMON network slices is proposed by formulating the latency components of its communication paths. Theoretical analysis and numerical studies support that the SiMON outperforms the MTN in flexible network resource slicing, and achieves higher bandwidth efficiency than the M-OTN. Moreover, an experimental setup of the SiMON system has been implemented by FPGA. Experimental results show that the SiMON achieves 96.5% of bandwidth utilization with dynamic adjustable network slicing. Below 2<inline-formula><tex-math notation="LaTeX">\mu s</tex-math></inline-formula> jitter is achieved in the SiMON network slices under burst traffic, and below 0.2<inline-formula><tex-math notation="LaTeX">\mu s</tex-math></inline-formula> jitter has been achieved for constant frame rate traffic with short frame lengths.
The recognition of human movements based on radar m-D (micro-Doppler) signatures attracts great interest in the field of radar research on automatic target recognition. Because there are multiple ...frequency components overlapping seriously in the radar echoes from walking humans, it is a very difficult work to recognize walking humans based on radar echoes. In this paper, a recognition method of walking humans based on radar m-D signatures is proposed. In this method, the m-D spectrum is generated by generalized S transform first, and then the entropy segmentation is used to segment the interesting region from the original spectrum. Next, the m-D features are extracted from the m-D region. Lastly, the support vector machine is used to recognize different walking human targets. The simulation experiments considering two factors of height and velocity are also conducted to test the performance of this proposed method.
In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to ...the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate.
Traditional multi-target tracking algorithms assume that each target can generate at most one detection per scan. However, a target may produce multiple detections (MDs) in many practical ...applications, e.g. over-the-horizon radar (OTHR), tracking for extended target and tracking with multiple sensors. In this study, the authors propose a new algorithm for tracking multiple targets with MD observation. The proposed technique is based on the labelled random finite set (RFS), which estimates the number of targets and the trajectories of their states. Furthermore, they propose two methods, pre-partition and joint partition, to implement the labelled RFS density recursion. The joint partition method derives a joint prediction, partition and update formulation of the MD filtering and extends the Gibbs sampler to provide global optimal results with low computational cost. The authors’ algorithm is demonstrated on an OTHR simulation compared with the previous approach, such as the MD probability hypothesis density filter and the multipath cardinality-balance multi-target multi-Bernoulli filter.
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