Limited energy in each node is the major design constraint in wireless sensor networks (WSNs), especially in mine tunnel scenario where the WSNs are required to work perpetually. To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. To keep the sensor nodes working perpetually, one fundamental question is how to design the charging scheme. Considering the special tunnel scenario, this paper proposes a Complete Feasible Charging Strategy (CFCS) to ensure the whole WRSNs is working perpetually. We divide the whole WRSN into several subnetworks and use several mobile chargers (MCs) to charge every subnetwork periodically and orderly. For a subnetwork, we formulate the main problem as a charging time distribution problem. A series of theorems are deduced to restrict the charging configurations, and a group nodes mechanism is proposed to expand the scale of the WRSNs. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results demonstrate which of the CFCS boundary theorems is correct and that our proposed CFCS can keep the WRSNs working perpetually. Furthermore, our Nodes-Grouped mechanism can support more nodes in WRSN compared to the state-of-the-art baseline methods.

Satellite–terrestrial integrated communication systems (STICSs) are envisioned to provide ubiquitous, seamless connectivity in next-generation (6G) wireless communication networks for massive-scale Internet of Things (IoT) deployments. This global coverage extends beyond densely populated areas to remote regions (e.g., polar zones, open oceans, deserts) and disaster-prone areas, supporting diverse IoT applications, including remote sensing, smart cities, intelligent agriculture/forestry, environmental monitoring, and emergency reporting. Random access signals, which constitute the initial transmission from access IoT devices to base station for unscheduled transmissions or network entry in terrestrial networks (TNs), encounter significant challenges in STICSs due to inherent satellite characteristics: wide coverage, large-scale access, substantial round-trip delay, and high carrier frequency offset (CFO). Consequently, conventional TN preamble designs based on Zadoff–Chu (ZC) sequences, as used in 4G LTE and 5G NR systems, are unsuitable for direct deployment in 6G STICSs. This paper first analyzes the challenges in adapting terrestrial designs to STICSs. It then proposes a CFO-resistant preamble design specifically tailored for STICSs and details its detection procedure. Furthermore, a dedicated root set selection algorithm for the proposed preambles is presented, generating an expanded pool of random access signals to meet the demands of increasing IoT device access. The developed analytical framework provides a foundation for performance analysis of random access signals in 6G STICSs.

Due to the high mobility and low cost, unmanned aerial vehicle (UAV)-mounted edge computing (UMEC) provides an efficient way to provision computing offloading services for Internet of Forestry Things (IoFT) applications in forest areas without sufficient infrastructure. Multiple IoFT applications can be consolidated into fewer UAV-mounted servers to improve the resource utilization and reduce deployment costs with the precondition that all applications’ Quality of Service (QoS) can be met. However, most existing application placement schemes in UMEC did not consider the dynamic nature of the aggregated computing resource demand. In this paper, the resource allocation and application placement problem based on fine-grained cooperative overbooking in UMEC is studied. First, for the two-tenant overbooking case, a Two-tenant Cooperative Resource Overbooking (2CROB) scheme is designed, which allows tenants to share resource demand violations (RDVs) in the cooperative overbooking region. In 2CROB, an aggregated-resource-demand minimization problem is modeled, and a bisection search algorithm is designed to obtain the minimized aggregated resource demand. Second, for the multiple-tenant overbooking case, a Proportional Fairness-based Cooperative Resource Overbooking (PF-MCROB) scheme is designed, and a bisection search algorithm is also designed to obtain the corresponding minimized aggregated resource demand. Then, on the basis of PF-MCROB, a First Fit Decreasing-based Cooperative Application Placement (FFD-CAP) scheme is proposed to accommodate applications in as few servers as possible. Simulation results verify that the proposed cooperative resource overbooking schemes can save more computing resource in cases including more tenants with higher or differentiated resource demand violation ratio (RDVR) thresholds, and the FFD-ACP scheme can reduce about one third of necessarily deployed UAVs compared with traditional overbooking. Thus, applying efficient cooperative overbooking in application placement can considerably reduce deployment and maintenance costs and improve onboard computing resource utilization and operating revenues in UMEC-aided IoFT applications.

Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.

Forests play a critical role in maintaining global ecological balance, regulating climate, and supporting biodiversity. Effective forest management and monitoring relies on the deployment of large-scale wireless sensor networks (WSNs) for real-time data collection, enabling the protection of ecosystems and the early detection of environmental changes. However, such massive deployments pose serious challenges with increasingly scarce radio resources. Sparse code multiple access (SCMA), a non-orthogonal multiple access (NOMA) technique, has been identified as a promising solution for facilitating wireless communications among numerous distributed sensors in large-scale WSNs with improved spectral efficiency. This is essential for application scenarios involving a substantial number of terminal devices, including forest monitoring and management. Codebook design is a critical issue for SCMA systems. It is closely related to the detection performance at the receiver, which in turn has a direct effect on the communication coverage or quality of service (QoS) for the terminal devices. This paper investigates the symbol error rate (SER) performance of SCMA systems over AWGN channels and derives its theoretical upper bound. The optimization objectives for each stage of codebook design are mathematically analyzed for a single resource element (RE), a single device, and multi-device, multi-RE scenarios. On this basis, a lightweight stepwise codebook design scheme is proposed in this paper. Simulation results demonstrate that the proposed codebooks can maintain fairness among devices while guaranteeing detection performance.

Hand-drawn circuit diagrams remain common in education, maintenance, and early-stage design and are often photographed for storage, sharing, and reuse. Recovering electrically meaningful structure from such camera-acquired images is difficult because irregular strokes, wire discontinuities, crossings, symbol–text interference, and imaging artifacts can disrupt valid circuit topology. We therefore formulate the task as topology recovery with semantic completion rather than symbol recognition alone. To solve it, we propose a topology-consistent structural parsing framework that integrates multi-source visual perception, wire connected-component-guided connectivity reasoning, and explicit endpoint semantic recovery for direction-sensitive and multi-terminal components. On an independent benchmark of 1317 hand-drawn circuit diagrams, the proposed method achieves a 95.14% strict image-level end-to-end success rate. The recovered structures are further exported as Simulation Program with Integrated Circuit Emphasis (SPICE)-compatible netlists for downstream simulation and verification. These results support a practical vision-based image acquisition and processing workflow for converting camera-acquired hand-drawn circuit images into machine-readable and simulation-ready circuit representations.