The integration of wireless power transfer (WPT) and mobile edge computing (MEC) provides an effective solution for overcoming the energy and computational limitations of Internet of Things (IoT) devices by enabling them to harvest energy from radio frequency signals and offload data to edge servers. A crucial challenge in wireless powered MEC (WP-MEC) networks is how to efficiently optimize offloading decisions and resource allocation to enhance overall system performance. In this paper, we investigate the partial offloading strategy within a WP-MEC network consisting of multiple HAPs. The optimization problem is formulated as a Mixed-Integer Non-linear Programming (MINLP) problem with variables of WPT duration, offloading decisions and energy allocation. To solve this problem, we propose a deep reinforcement learning (DRL)-based framework, which employs a neural network architecture combining convolutional and fully connected layers to output offloading decisions. Additionally, we design an optimization algorithm for joint optimization of WPT duration and offloading proportions. Numerical results demonstrate the proposed method achieves better performance than the existing DRL methods, which demonstrates the efficiency of the proposed method.

The massive low earth orbit (LEO) satellite-empowered Internet of Things (MLS-IoT) network has many advantages including high capacity, low latency, large network coverage, and high reliability. However, the openness and broadcasting on satellite communication links pose huge challenges to protect the security of data delivery, especially to defend against eavesdropping. Leveraging the density of LEO satellite deployment and developing efficient secure communication schemes with the aid of physical layer security (PLS) technique deserve further exploration. To the best of our knowledge, it is an almost untouched issue to employ satellites to transmit jamming signal in MLS-IoT systems since lots of available researches in this line mainly considered cooperative jamming only on ground networks. For this purpose, we propose in this paper a PLS based secure communication enhancement scheme in MLS-IoT network. A jamming scheduling and resource allocation problem is presented for the secrecy rate maximization by dynamically switching the roles of satellites on sub-channels for transmitting private information or jamming signal. An iterative strategy is devised to jointly optimizing jamming scheduling, device association, channel assignment, and power allocation. Numerical results validate that our presented optimization approach could significantly improve the system secrecy rate and achieve the optimal jamming scheduling and resource allocation decisions.

Wireless-powered mobile edge computing (WPMEC) has gained significant attention for enabling energy-sustainable and low-latency Internet of Things (IoT) applications in domains such as smart cities and industrial automation. Heterogeneous computing resources, which encompass devices with diverse computational capabilities, are essential to support adaptive task offloading and resource-efficient service provisioning. However, integrating wireless power transfer (WPT) with mobile edge computing (MEC) in dynamic environments remains challenging, particularly in the efficient coordination of heterogeneous resources. This paper investigates a distributed optimization problem in a wireless-powered heterogeneous MEC network, where base station association, binary offloading decisions, and distributed resource allocation are closely intertwined. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and propose a novel decentralized solution framework that decomposes it into two subproblems: deep reinforcement learning is employed to optimize base station selection and task offloading strategies, while convex optimization techniques, including the Lagrangian method, are used to allocate bandwidth and computational resources at the base stations. Simulation results demonstrate that the proposed approach significantly enhances the energy efficiency and computational performance of wireless edge computing compared to existing benchmarks, confirming its practical value for resource-constrained MEC systems.

Covert communication can reduce the security overhead of messages in New Radio (NR) Vehicle-to-Everything (V2X) and provide a higher level of privacy assurance. However, it also introduces a tradeoff between covertness and transmission. In this paper, we explore the tradeoff between covertness and transmission in NR V2X communications and propose a covert scheme, Covert Semi-Persistent Scheduling (C-SPS). To elucidate this tradeoff in the traditional case, we derive the theoretical results based on stochastic geometry, which accounts for the fundamental processes of semi-persistent scheduling, including transport block re-selection and resource collision. Based on our derivation, the optimal setting of C-SPS is obtained to maximize the covertness of message delivery while satisfying the transmission requirements of NR V2X. More importantly, we assess the covert threat posed by intelligent adversaries equipped with the Large Language Model (LLM) capable of reasoning based on the tailored chain of thought. Finally, we evaluate the effectiveness of C-SPS in balancing the tradeoff under both traditional case and LLM-assisted case.

As an essential technology for constructing green passive Internet of Things (IoT) network, backscatter communication (BackCom) enables battery-free IoT devices to deliver information by modulating and reflecting incident carriers. Nevertheless, the energy harvesting at an IoT device is constrained by its distance from the radio-frequency (RF) emitter, and the double-fading phenomenon significantly restricts the achievable performance of BackCom-based IoT network. Existing research has revealed that intelligent reflecting surface (IRS) and dynamic unmanned aerial vehicle (UAV) are promising solutions to overcome the current bottleneck, and their combined application in IoT network for BackCom remains in the preliminary exploration phase. Most studies focus more on traditional fixed RF emitters, which lack the flexibility needed for efficient energy transmission and are not suitable for infrastructure blank areas or hard-to-maintain regions. Motivated by this, an IRS-aided UAV-powered green IoT network is proposed in this paper, where the UAV acts as a mobile power beacon to energize multiple IoT nodes on the ground in a time-division multiple address manner, and an IRS is deployed in the scenario to enhance the BackCom performance. To guarantee reliable data transfer under energy harvesting constraint, we maximize the minimum average throughput among all IoT nodes for BackCom during the UAV flight duration by optimizing the node communication scheduling, power splitting coefficient, UAV trajectory and IRS phase shift. Specifically, we employ a four-stage alternating optimization method to decouple the optimization variables and solve for each variable independently. Finally, extensive experimental results verify the superior performance of our proposal in improving throughput over other benchmark schemes.

By leveraging IoT data, Business Process Management (BPM) systems can sense the physical world situation and make more accurate decisions proactively, but the current BPM systems lack the effective method to take good use of IoT data. In this paper, a situation-aware dynamic binding method for IoT services targeting proactive BPM is proposed to meet this requirement. The method first establishes a prediction model to predict the bindable IoT services under given situations by constructing an encoder-decoder structure network model using bi-directional gated recurrent units (Bi-GRU) and incorporating an attention mechanism. Then our method creates a Dynamic IoT Service Task (DIT) model for BPM. This enables the BPM system to integrate the prediction model with dynamic switching of IoT services and a parallel running architecture for multiple IoT services. A prototype system is developed and a case study is conducted to evaluate the performance of the method. The study focuses on the safety supervision scenario for the transportation of hazardous Liquefied Natural Gas (LNG) by sea. Results show that the prediction model proposed in this paper outperforms other models on multiple indicators. Also, the case study and experimental results verify the effectiveness of the method.

With the development of IoT and AI, better knowledge and information can be learned and extracted from IoT sensory data which enables business systems to proactively provide services to customers. This paper is the first study that attempts to forecast high-level business events from raw IoT sensory event data to improve the proactivity of services and applications. We propose a deep learning based business event forecasting framework, i.e., IoT2BE, which extracts prior knowledge to identify business events from IoT sensory data, extracts features in multi-views including the spatial and temporal view, generates spatio-temporal business event embeddings, and uses a seq2seq model with attention to predict the future business events. Extensive experiments are based on two datasets including one real-world maritime ship trajectory dataset and one publicly available raw sensor dataset from a smart home environment. The results demonstrate that our framework can be effectively applied in various business scenarios.

Inter-segment gateway selection, as a critical issue for data delivery from ground segment to satellite via air network segment, is of great challenges for the design of space-air-ground converged networks (SAGCNs), especially for the transmission energy optimization due to the presence of unreliable or lossy wireless link in air network. To the best of our knowledge, it is an entirely new problem, since existing works on gateway selection mainly focused on one single network segment and gave no consideration to other segments. It is also noted that, we are the first to study the gateway selection problem with the objective of minimizing the transmission energy. Toward this end, in this paper, we formulate the issue of inter-segment gateway selection as a constrained optimization problem and propose two algorithms, i.e., an optimal enumeration algorithm (OEA) and a simulated annealing based algorithm (SOA). Extensive experiments based on different link error rate and relative velocity settings have been conducted and as validated by our numerical results, OEA can obtain an optimal result with extremely high computational complexity and SOA is able to achieve a near-optimal solution with much lower computational complexity.

Given the highly dynamic traffic loads of mobile Internet of Things (IoT) devices and their stringent quality-ofservice requirements, i.e., access delay particularly, as well as the heterogeneous infrastructures among IoT networks, it is a nontrivial task to efficiently deploy cloudlets among large number of access points (APs) in IoT networks, especially for the access delay and network reliability, since different placement schemes would produce various network performances. To combat this issue, we are motivated to investigate in details the optimal placement of cloudlets to minimize the average access delay by applying software-defined networking (SDN) techniques to provide flexible and programmable management for cloudlets deployment in IoT networks with considering the complicated queuing process at numerous SDN-based APs. An enumerationbased optimal placement algorithm (EOPA) is first proposed as benchmark. Then we propose a ranking-based near-optimal placement algorithm (RNOPA) which is able to dynamically adapt to mobile IoT devices and their traffic loads, by treating each AP as a single server queue and adopting an efficient ranking mechanism. As corroborated by extensive simulation results, RNOPA reports access delay very close to that of EOPA. Note that RNOPA outperforms the famous K-medians clustering algorithm (KMCA) in both of average cloudlet access delay and reliability, while at the cost of a much lower computational complexity than KMCA.

Leveraging the concept of software-defined network (SDN), the integration of terrestrial 5G and satellite networks brings us lots of benefits. The placement problem of controllers and satellite gateways is of fundamental importance for design of such SDN-enabled integrated network, especially, for the network reliability and latency, since different placement schemes would produce various network performances. To the best of our knowledge, it is an entirely new problem. Toward this end, in this paper, we first explore the satellite gateway placement problem to obtain the minimum average latency. A simulated annealing based approximate solution (SAA), is developed for this problem, which is able to achieve a near-optimal latency. Based on the analysis of latency, we further investigate a more challenging problem, i.e., the joint placement of controllers and gateways, for the maximum network reliability while satisfying the latency constraint. A simulated annealing and clustering hybrid algorithm (SACA) is proposed to solve this problem. Extensive experiments based on real world online network topologies have been conducted and as validated by our numerical results, enumeration algorithms are able to produce optimal results but having extremely long running time, while SAA and SACA can achieve approximate optimal performances with much lower computational complexity.

Mobile edge computing (MEC), as an extension of the cloud computing paradigm to the edge network, is a promising solution to provide resource-intensive and time-critical applications to mobile users. It overcomes some obstacles of traditional mobile cloud computing by offering ultra-short latency and less core network traffic. This paper proposes a new framework based on the architecture of MEC to deliver cloud services to the edge. We introduce enumeration based optimal placement algorithm (EOPA) and divide-and- conquer based near-optimal placement algorithm (DCNOPA) to attain minimal data traffic by distributing virtual machine replica copies (VRCs) of applications to the edge network. Simulation results show that compared to the famous K-medians clustering algorithm (KMCA), the performance of DCNOPA is much closer to that of EOPA with lower computational complexity. Furthermore, we investigate the optimal number of VRCs within a given limitation of benefit-to-cost ratio.

This paper analyzes the information theoretic secrecy performance in finite-area wireless networks based on a stochastic geometry framework. Unlike most prior works, which explored the physical layer security with a large number of transmitters, legitimate receivers and eavesdroppers in infinite regions, we consider a finite downlink wireless network composing of a transmitter, a legitimate receiver and several eavesdroppers. The legitimate receiver attempts to receive confidential data from the transmitter in the presence of the eavesdroppers. We present the probabilistic characteristics of the achievable secrecy rates and average secrecy rates in both disk regions and regular L-sided convex polygon regions. As shown by extensive numerical results, the proposed framework could be leveraged to efficiently analyze the secrecy performance of finite-area networks, and give insights for network designers on how to achieve good secrecy performance in finite-area networks.