Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute ...various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.
The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources ...and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.
Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of ...vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.
The mineral-organo composites control the speciation, mobility and bioavailability of heavy metals in soils and sediments by surface adsorption and precipitation. The dynamic changes of soil mineral, ...organic matter and their associations under redox, aging and microbial activities further complicate the fate of heavy metals. Over the past decades, the wide application of advanced instrumental techniques and modelling has largely extended our understanding on heavy metal behavior within mineral-organo assemblages. In this review, we provide a comprehensive summary of recent progress on heavy metal immobilization by mineral-humic and mineral-microbial composites, with a special focus on the interfacial reaction mechanisms of heavy metal adsorption. The impacts of redox and aging conditions on heavy metal speciations and associations with mineral-organo complexes are discussed. The modelling of heavy metals adsorption and desorption onto synthetic mineral-organo composites and natural soils and sediments are also critically reviewed. Future challenges and prospects in the mineral-organo interface are outlined. More in-depth investigations are warranted, especially on the function and contribution of microorganisms in the immobilization of heavy metals at the complex mineral-organo interface. It has become imperative to use the state-of-the-art methodologies to characterize the interface and develop in situ analytical techniques in future studies.
•We provide recent progress on heavy metal immobilization by mineral-humic and mineral-microbial composites.•The impacts of redox and aging on heavy metal associations with mineral-organo complexes are discussed.•The modellings of heavy metals reaction onto synthetic mineral-organo composites and natural soils are reviewed.•Future challenges and prospects on the mineral-organo interfacial reactions are outlined.•More studies are needed on the contribution of microorganisms in the immobilization of heavy metals in complex systems.
As an emerging communication platform in the Internet of Things, IoV is promising to pave the way for the establishment of smart cities and provide support for various kinds of applications and ...services. Energy management in IoV has been attracting an upsurge of interest in both academia and industry. Currently, green IoV mainly focuses on two aspects: energy management of battery- enabled RSUs and EVs. However, these two issues are always resolved separately while ignoring their interactions. This standalone design may cause energy underutilization, a mismatch between traffic demands and energy supplies, as well as high deployment and sustainable costs for RSUs. Therefore, the integration of energy management between battery-enabled RSUs and EVs calls for comprehensive investigation. This article first provides an overview of several promising research fields for energy management in green IoV systems. Given the significance of efficient communications and energy management, we construct an intelligent energy-harvesting framework based on V2I communications in green IoV communication systems. Specifically, we develop a three-stage Stackelberg game to maximize the utilities of both RSUs and EVs in V2I communications. After that, a real-world trajectory-based performance evaluation is provided to demonstrate the effectiveness of our scheme. Finally, we identify and discuss some research challenges and open issues for energy management in green IoV systems.
Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. ...As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the evergrowing demand for service quality of automobiles, the way to provide users with a range of security-related and user-oriented vehicular applications has become significant. This paper concentrates on the design of a service access system in SIoVs, which focuses on a reliability assurance strategy and quality optimization method. First, in lieu of the instability of vehicular devices, a dynamic access service evaluation scheme is investigated, which explores the potential relevance of vehicles by constructing their social relationships. Next, this work studies a trajectory-based interaction time prediction algorithm to cope with an unstable network topology and high rate of disconnection in SIoVs. At last, a cooperative quality-aware system model is proposed for service access in SIoVs. Simulation results demonstrate the effectiveness of the proposed scheme.
Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local ...computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices' battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.
Currently, vehicles have the abilities to communicate with each other autonomously. For Internet of Vehicles (IoV), it is urgent to reduce the latency and improve the throughput for data transmission ...among vehicles. This article proposes a deep learning based transmission strategy by exploring trirelationships among vehicles. Specifically, we consider both the social and physical attributes of vehicles at the edge of IoV, i.e., edge of vehicles. The social features of vehicles are extracted to establish the network model by constructing triangle motif structures to obtain primary neighbors with close relationships. Additionally, the connection probabilities of nodes based on the characteristics of vehicles and devices can be estimated, by which a content sharing partner discovery algorithm is proposed based on convolutional neural network. Finally, the experiment results demonstrate the efficiency of our method with respect to various aspects, such as message delivery ratio, average latency, and percentage of connected devices.
Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the ...scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.
•Distribution of Pb in soils was estimated by extraction, MSM, XANES and SEM-EDS.•SOM and metal oxides are main scavengers for exogenous Pb.•Fe/Al/Ca competition restricted the formation of organic ...bound Pb.•Fe/Al oxides promoted the transformation of Pb species to residual fractions.
The interactions of soil components have profound impacts on the speciation, bioavailability and transformation of heavy metals. However, these interactions have not been well included in multi-surface model (MSM) as most of the models adopt component additive method. Here, an incubation experiment was conducted with three contrasting soils spiked with 200 mg/kg Pb and rice straw to investigate the impact of mineral-organic interactions on Pb speciation and to validate the MSM for Pb in soils. With the aid of chemical extraction and instrumental analysis (XANES and SEM-EDS), the results show that metal oxides and soil organic matter are the main scavengers for Pb, accounting for the stabilization of exogenous Pb by ∼ 40% − 80% and ∼ 13% − 30%, respectively. The accumulation of the most stable residual Pb was driven by Fe/Al oxides, which was fostered by organic matter through the formation of amorphous Fe/Al oxides. Unexpectedly, the introduction of straw promoted the activation of metal oxides and the competition from Fe/Al/Ca ions reduced the binding of Pb by soil organic matter. Simulation of organic-Fe/Al/Ca interactions largely improved the accuracy of the MSM model results for the prediction of Pb speciation distribution. Overall, this study highlights that mineral-organic interactions play important role in the stabilization of exogenous Pb in soils, while incorporate of these interactions into MSM is recommended in future heavy metal studies.
PDF
