Workflow scheduling plays a critical role in optimizing completion time and throughput in distributed cloud environments, leveraging the parallelism of heterogeneous computing resources. However, existing workflow scheduling algorithms often fall short due to heuristic limitations and the challenges in adaptability within heterogeneous settings, leading to suboptimal scheduling solutions. In this article, we present a novel deep reinforcement learning (DRL) framework tailored for continuous workflow scheduling in heterogeneous environments. First, we propose an intelligent scheduler that updates the policy network through interactions with a multitenant environment, triggered by scheduling events. Next, the framework incorporates a graph attention network (GAT) and a self-attention multilayer perceptron (MLP) to preprocess the workflow topology and embed dynamic features of ready tasks and available processors into the state input at each scheduling step. Additionally, a k-dimensional tree-based k-nearest neighbors (kNN) algorithm is employed to map the output action vector to a pair of executed ready task and processor, facilitating the transition from continuous to discrete action spaces and addressing challenges associated with dynamic action spaces. Experimental results demonstrate that our method converges effectively in continuous workflow scheduling scenarios and significantly outperforms the best-known methods in terms of average makespan and load balancing efficiency.

As a key enabling technology of the Internet of Thing (IoT), WiFi sensing has undergone noteworthy advancements and brought significant improvement to prevailing IoT systems and applications. The past few years have witnessed growing efforts in WiFi sensing, which is widely applied in various applications, such as indoor localization, human activity recognition, physiological signal monitoring, and so on. However, these techniques are also maliciously used by attackers to eavesdrop on legitimate users and even tamper the sensing results. Fortunately, these attack techniques in turn promote the advancement of WiFi sensing techniques, especially defense techniques. In this study, we carried out a comprehensive survey to systematically summarize the works related to the topic of attacks and defenses on WiFi sensing technology. Firstly, we summarize the existing surveys in related areas and highlight our unique novelty. Then, we introduce the concept of the core topic of this survey and provide a taxonomy to distinguish different kinds of attack and defense techniques, respectively, that is, active and passive attack techniques as well as active and passive defense techniques. Furthermore, existing works in each category are grouped and introduced in detail, respectively.

In the modern era of big data, large-scale graph computing has become challenging because of the dramatic rise in graph data size. Graph edge partitioning (GEP) is a crucial preprocessing step to distributed graph platforms, yet it is challenging to partition the large-scale graphs. GEP has shown better partition quality than the graph vertex partitioning for the graph’s skewed degree distribution. Existing GEP approaches are classified into two as stream and offline. The former category assigns edges to the partitions based on the previously received edge information. It has less partitioning quality and is affected by stream order compared to the latter while supporting big graph partitioning. The latter uses complete knowledge of a graph during partitioning and hence has a better partitioning quality than the former; however, it does not support large-scale graphs. In this study, we propose a novel OffStream partitioning approach (OSPA) and hybrid graph edge partitioner OffStreamNH. OSPA leverages both the offline and stream graph partitioning approaches through stateful partitioning by introducing a state layer. This stateful partition state is recorded while offline is partitioning its input graph. It contains partial knowledge of previously partitioned data and is used by the stream partitioner. The OffStreamNH uses Neighborhood Expansion (NE) and Higher Degree Replicated First (HDRF) algorithms for the offline and online; respectively, with minor modifications of both algorithms. Experimental results show that OffStreamNH outperforms the state of the art stream partitioners in terms of replication factor, load balance and tolerates the effect of stream orders.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

The integration of satellite communications with mobile edge computing (MEC) into space-air-ground integrated networks, known as satellite edge computing (SEC), has become a crucial research field for future communication systems to provide extensive global coverage services. This paper investigates the joint dependent task offloading and resource allocation problem for remote Internet-of-Things (IoT) applications within the SEC architecture. The proposed system leverages uncrewed aerial vehicles (UAVs) as mobile access points and edge servers and utilizes low-earth orbit (LEO) satellites and ground stations as cloud computing resources. Multiple applications with dependent tasks from IoT devices (IoTDs) are modeled as directed acyclic graphs (DAGs). To address the challenges of reducing the system cost in UAV-assisted SEC, we first propose a one-to-many matching algorithm to associate IoTDs with UAVs. Then, a multi-application task sequence algorithm is devoted to merging the multiple DAGs and sorting the task order. Finally, a graph-aware asynchronous multi-agent reinforcement learning approach empowers the agents to autonomously discover optimal offloading and resource allocation strategies. Extensive simulations based on real-world datasets demonstrate the effectiveness of the proposed approach in minimizing the system costs while meeting application latency requirements, outperforming other benchmark algorithms.