The efforts on the standardization of Non-Terrestrial Networks (NTN) make satellite communication play a pivotal role in 6G era. The integration of satellites and terrestrial networks (TN) provides ubiquitous coverage of 6G coverage. This paper comprehensively investigates the progress of both standardizations and industry construction of satellites. In particular, the focus is on the prospective key technologies of flexible architecture, coverage enhancement and mobility management in 6G NTN. Finally, the challenges in 6G NTN are also illustrated in the aspects on the smooth evolution of the network, the interaction between TN and NTN, and multi-orbit cooperation. The evolution of 6G NTN aims at dealing with these challenges in forthcoming standardization and industry constellation construction.

Task assignment is a critical issue in mobile crowd-sensing, which is aimed to maximize the number of completed tasks subject to budget constraints. However, existing work in this aspect did not consider the correlation between the tasks submitted by the same task requester. That is, tasks in the same subset from the same task requester are often correlated such that they are considered completed only when all of them are completed, and partial completion of them are useless. This requirement largely affects the performance of existing algorithms for the assignment of such partially correlated tasks. In this paper, we formulate the problem of maximizing the total number of completed tasks subject to such correlation and also budget constraints as an integer programming problem. We propose two greedy algorithms, one is requester happiness utility based algorithm and the other is minimum task remaining subset first algorithm. We present design details of both algorithms and deduce their computational complexities. Numerical results demonstrate that these two algorithms can significantly outperform the existing work.

WebAssembly (Wasm), as a compact, fast, and isolation-guaranteed binary format, can be compiled from more than 40 high-level programming languages. However, vulnerabilities in Wasm binaries could lead to sensitive data leakage and even threaten their hosting environments. To identify them, symbolic execution is widely adopted due to its soundness and the ability to automatically generate exploitations. However, existing symbolic executors for Wasm binaries are typically platform-specific, which means that they cannot support all Wasm features. They may also require significant manual interventions to complete the analysis and suffer from efficiency issues as well. In this paper, we propose an efficient and fully-functional symbolic execution engine, named SeeWasm. Compared with existing tools, we demonstrate that SeeWasm supports full-featured Wasm binaries without further manual intervention, while accelerating the analysis by 2 to 6 times. SeeWasm has been adopted by existing works to identify more than 30 0-day vulnerabilities or security issues in well-known C, Go, and SGX applications after compiling them to Wasm binaries.

With the rapid growth of Internet of Things and proliferation of handheld smart devices, mobile crowdsensing has been regarded as an effective sensing paradigm due to its high scalability, low cost, and wide coverage. In this paper, we study hybrid task assignment where semi-opportunistic and participatory users co-exist for task executions while tasks are delay sensitive and have heterogeneous qualities. The design objective is to maximize the total quality of completed tasks subject to a total budget shared by both types of users. We formulate this problem as an integer programming problem. We propose an efficient hybrid users based task assignment algorithm (referred to as HU-TSA), which works in an iterative way as follows. It first selects the top n (initially, n = 1) semi-opportunistic users in terms of quality-cost ratio for task assignment. It then clusters the remaining tasks into different regions based on their closeness and then performs utility based optimized user-region binding and standardized task density based path planning for the participatory users. It repeats the above process over all possible values of n to seek an optimal budget splitting between the two types of users for improved performance. We present the detailed design description of HU-TSA and deduce its computational complexity. Extensive simulations are carried out and the results show the effectiveness of HU-TSA by comparing with existing algorithms.

Mobile edge computing (MEC) is a promising computing paradigm and can effectively reduce the energy consumption and computing costs at mobile devices by offloading computation-intensive and latency-sensitive applications/tasks to edge servers. However, how to achieve cost-effective dependent task offloading and resource allocation subject to application completion time constraint and service configuration constraint at edge side in heterogeneous MEC environments remains a challenge. To address this challenge, in this paper, we study the multi-application dependent task offloading and resource allocation problem in heterogeneous MEC environments for jointly minimizing the energy consumption and computing cost. We first formulate this problem as a mixed integer nonlinear programming (MINLP) problem. We propose a two-stage alternating optimization algorithm. In the first stage, a genetic-based algorithm is proposed to determine an optimized task offloading profile for given transmit power matrix, a look ahead based task scheduling algorithm is designed to obtain an optimized task schedule for the profile. In the second stage, the transmit power allocation problem for a given offloading profile is solved using convex optimization techniques. Extensive simulation results show that the proposed algorithm can effectively reduce the total cost of task executions as compared with baseline algorithms.

Abstract Diagnosability is often considered as an important factor for measuring the self-diagnostic ability of network systems. However, classic system-level diagnosis focuses only on processor faults and ignores the objective reality of communication faults. Under real circumstances, missing edges and node failures usually occur simultaneously in multiprocessor systems (called hybrid fault circumstances). Therefore, it is important to study the diagnosability of multiprocessor systems under hybrid fault circumstances. In this paper, we propose several diagnosabilities of interconnection networks with missing edges and faulty nodes. By exploring some important relationships between diagnosability and the minimum degree of a network under hybrid fault circumstances, we present and prove the diagnosability of several classic interconnection networks, including BC (bijective connection) networks, star graphs, folded hypercubes, exchanged hypercubes, exchanged crossed cubes, k-ary n-cubes, bubble-sort star graphs and balanced hypercubes, with missing edges and broken-down nodes under the PMC (Preparata, Metze and Chien) and MM* (Maeng and Malek) models.

Mobile crowdsensing (MCS) has been an effective sensing paradigm by utilizing the smart devices carried by mobile users to complete sensing tasks at different locations. An important problem in MCS is how to achieve effective task assignment in the context of opportunistic sensing, where mobile users are selectively recruited to perform tasks in an opportunistic way. However, most existing work in this aspect suppose there are only one service platform and further the sensing qualities of users are known a priori. In this article, we study the task assignment when there are multiple service platforms and further the sensing qualities of users are unknown a priori. The design objective is to maximize the overall sensing qualities of finished tasks at all platforms. For this purpose, we build a multiplatform cooperation framework and formulate the task quality maximization problem in this case as a 0–1 integer linear programming (ILP) problem. We propose a multiplatform-cooperation-based task assignment mechanism (MCTA). MCTA includes two phases. The first phase establishes stable cooperation relationship among platforms while respecting their respective cooperation willingness, and for this phase, we propose a cross-platform cooperation relationship construction algorithm. The second phase performs effective online task assignment, and for this phase, we propose two online multiarmed bandit (MAB) with sleeping -arms-based user selection algorithms using local and global learning, respectively, based on whether cross-platform user-sensing-quality learning is allowed. We derive the regrets of the proposed algorithms and prove that MCTA has the properties of cooperation stability and computation efficiency. Extensive simulation results show the high performance of our proposed MCTA mechanism as compared with the existing work.

Mobile crowdsensing has become a promising sensing paradigm with the popularization of mobile devices. In this paper, we focus on an opportunistic mobile crowdsensing scenario where there are multiple task requesters and users, who move in an opportunistic way in the target environment. When a task requester encounters a user, he can assign some of his held tasks to the user and receive corresponding task results when they re-encounter sometime later. In this paper, we study how to minimize the largest makespan of all requesters for the task result collections. To address this issue, we propose a cluster based largest makespan sensitive online task assignment (C-LOTA) algorithm. C-LOTA first performs two-phase clustering which clusters the users into different clusters, one for each task requester, based on their relativeness to the task requesters and also the task workloads at different requesters. C-LOTA then iteratively performs greedy intra-cluster task assignment such that largest task is firstly assigned and the first idle user always takes the task, until all tasks are assigned. We present the detailed algorithm design of C-LOTA. We deduce its computation complexity. Simulation results show that C-LOTA can achieve much better performance compared with existing work.

Space-Air-Ground Integrated Network is the trend for 6G network development. It could integrate many advantages from terrestrial network, satellites and high altitude platform stations, improving the coverage and service quality significantly. Meanwhile, breaking the barriers of heterogeneous networks and realizing the cooperation between them pose new challenges for network operation. This paper firstly analyzes the current situation and expounds technical researches and commercial achievements from the perspectives of standardization and industry. Secondly, we focus on network architecture, network sharing and private network, mainly investigating the operation and mutual enabling key technologies in Space-Air-Ground Integrated Network. Finally, we point out the challenges from “time-frequency-space” dimensions.

Mobile crowdsensing is a new paradigm for data collection by utilizing the mobility of sensor-rich hand-held smart devices. One of the key challenges in mobile crowdsensing is how to effectively assign tasks to mobile users in an online manner. In this paper, we study the online task assignment problem in mobile crowdsensing where each task has specific time window for its sensor data collection. The objective is to maximize the total profit of the platform in whole sensing period. We first model the crowdsensing system and formulate the profit maximization problem under study. To address this problem, we propose two heuristic algorithms, one is bipartite-match-based algorithm (BMA) using Kuhn-Munkres algorithm and the other improves the first by using data offloading for data upload cost reduction, if applicable. We present detailed algorithm design for both algorithms and deduce their computational complexities. Finally, simulation results validate the effectiveness of our proposed algorithms.

With the rapid development of mobile smart devices, mobile crowdsensing has become an attractive paradigm for sensor data collection. In a mobile crowdsensing system, the platform can publish a set of tasks and then recruit suitable mobile users to accomplish these tasks. In this paper, we study the budget-constrained task assignment problem for mobile crowdsensing. We assume users can choose to take different transportations for task execution, and different choices have different task coverages, travel expenses, and travel time. We model the crowdsensing system and formulate the budget-constrained task assignment problem under study. We prove this problem is NP-hard. To address this problem, we propose a Value/Reward Maximum First heuristic algorithm (VRMF). We present the detailed algorithm design and deduce its computational complexity. Simulation results validate the effectiveness of our proposed algorithm.

With the widespread of smart devices, mobile crowdsensing has become an attractive way to perceive and collect sensing data. In this paper, we focus on studying AP-assisted task assignment in mobile crowdsensing. The objective is to effectively reduce the average or worst-case makespan of tasks. We focus on a scenario that a task requester needs the assistance of mobile users for task accomplishment while they can meet directly or via APs in an opportunistic manner. We model the crowdsensing system and then formulate the problems under study. We then propose an AP-assisted average makespan sensitive online task assignment (AP-AOTA) algorithm and an AP-assisted largest makespan sensitive online task assignment (AP-LOTA) algorithm. In the proposed algorithms, task assignment at each step considers both the inter-encountering time between requester and each user and that between them while going through APs. We present design details of the proposed algorithms. We derive their computational complexities to be O(mn2), where m is the number of tasks and n is the number of users. Finally, trace-driven simulation results show that the proposed algorithms outperform existing work.

Considering that the invasive weed optimization (IWO) algorithm and the harmony search (HS) algorithm are inclined to fall into local optima with low convergence precision when they are used to deal with complex function optimization problems, this paper proposes a hybrid algorithm, HS–IWO algorithm, which is combined HS algorithm and IWO algorithm. We introduce strategies such as fixing the number of seeds, reinitializing limit solutions, multi-individual global HS, parameter optimization, etc. In order to make the two algorithms take advantage of their merits, they are mixed organically in this paper. Through tests on some complex functions of benchmark, the experimental results display that the HS–IWO algorithm has the efficiency and robustness of the algorithms. It is an optimization algorithm that is highly effective and stable, especially to be applied to the optimization of complicated functions compared with other intelligent optimization algorithms.

With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.

The total atmospheric precipitable water vapor amount (TWV) is a key variable for the study of the Earth's climate. This paper develops an algorithm to estimate the TWV over clear skies from the Medium Resolution Spectral Imager (MERSI) data in the near-IR channels. The MODTRAN 4 code is used to simulate the top of the atmospheric radiances for the MERSI channels. The results show that the proposed algorithm is suitable to estimate TWV form the absorbing channel centered at 0.940 μm and the atmospheric window channels centered at 0.865 μm and centered at 1.030 μm by the radiances over the clear pixels, with relative differences in the range of 10%-15%.