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The hydrogen absorption/desorption kinetic properties of MgH2 can be effectively enhanced by doping specific catalysts. In this work, MOFs-derived NiCu@C nanoparticles (∼15 nm) with regular core-shell structure were successfully prepared and introduced into MgH2 (denoted as MgH2NiCu@C). The onset and peak temperatures of hydrogen desorption of MgH2–11 wt.% NiCu@C are 175.0 °C and 282.2 °C, respectively. The apparent activation energy of dehydrogenated reaction is 77.2 ± 4.5 kJ/mol for MgH2–11 wt.% NiCu@C, which is lower than half of that of the as-milled MgH2. Moreover, MgH2–11 wt.% NiCu@C displays great cyclic stability. The strengthening “hydrogen pumping” effect of reversible solid solutions Mg2Ni(Cu)/Mg2Ni(Cu)H4 is proposed to explain the remarkable improvement in hydrogen absorption/desorption kinetic properties of MgH2. This work offers a novel perspective for the design of bimetallic nanoparticles and beyond for application in hydrogen storage and other energy related fields.

Finite mixture of linear regression (FMLR) models are among the most exemplary statistical tools to deal with various heterogeneous data. In this paper, we introduce a new procedure to simultaneously determine the number of components and perform variable selection for the different regressions for FMLR models via an exponential power error distribution, which includes normal distributions and Laplace distributions as special cases. Under some regularity conditions, the consistency of order selection and the consistency of variable selection are established, and the asymptotic normality for the estimators of non-zero parameters is investigated. In addition, an efficient modified expectation-maximization (EM) algorithm and a majorization-maximization (MM) algorithm are proposed to implement the proposed optimization problem. Furthermore, we use the numerical simulations to demonstrate the finite sample performance of the proposed methodology. Finally, we apply the proposed approach to analyze a baseball salary data set. Results indicate that our proposed method obtains a smaller BIC value than the existing method.

Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. These efforts can advance in-season monitoring and decision making to support adaptive management of sustainable wild salmon fisheries.

Over the past years, the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life. However, using machine learning in intelligent networks also presents potential security and privacy threats. A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model. In this survey, we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time. We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms, and analyze the strengths and limitations of corresponding defense methods in a compact form. We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

Abstract Light metal hydrolysis for hydrogen supply is well suited for portable hydrogen fuel cells. The addition of catalysts can substantially aid Mg hydrolysis. However, there is a lack of clear catalytic mechanism to guide the design of efficient catalysts. In this work, the essential role of nanosized catalyst (Ni3Fe/rGO) in activating micro‐sized Mg with ultra‐rapid hydrolysis process is investigated for the first time. Here, an unprecedented content of 0.2 wt% Ni3Fe/rGO added Mg can release 812.4 mL g−1 hydrogen in just 60 s at 30 °C. Notably, an impressive performance with a hydrogen yield of 826.4 mL g−1 at 0 °C in only 30 s is achieved by the Mg‐2 wt% Ni3Fe/rGO, extending the temperature range for practical applications of hydrolysis. Moreover, the four catalysts (Ni3Fe/rGO, Ni3Fe, Ni/rGO, Fe/rGO) are designed to reveal the influence of composition, particle size, and dispersion on catalytic behavior. Theoretical studies corroborate that the addition of Ni3Fe/rGO accelerates the electron transfer and coupling processes and further provides a lower energy barrier diffusion path for hydrogen. Thus, a mechanism concerning the catalyst as migration relay is proposed. This work offers guidelines designing high‐performance catalysts especially for activating the hydrolysis of micro‐sized light weight metals.

Abstract In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field.

The ability to rapidly and effectively monitor the alpine grassland aboveground biomass (AG-AGB) is very important to grassland resource management and sustainable use. The present satellite-driven models for estimating AG-AGB still need be improved due to incomplete monitoring indicators and a lack of sufficient in situ measurements. Thus, this study proposes a new AG-AGB estimation model based on MODIS and SRTM data for improving the estimation accuracy of AG-AGB. In this new model, first six estimation indicators were obtained from 33 spectral and environmental indicators using Extreme Gradient Boosting (XGBoost) and correlation analysis. Second, multiple linear regression (MLR), a back propagation neural network (BPNN), a support vector machine (SVM), and the random forest (RF) algorithm were used to build the new model based on the long-term AG-AGB field measurements and the corresponding to six estimating indicators for 6128 samples. Third, the new model was used to map the spatial distribution of the AG-AGB in Qinghai Province, China. And, the trends of the AG-AGB changes from 2005 to 2018 were analyzed using Sen and Mann-Kendall trend analysis and Hurst exponent analysis. The results show that 1) the six estimation indicators (EVI, radiation, altitude, B5/B7, latitude, and precipitation) were confirmed to be important for estimating the AG-AGB. 2) Among the MLR, BPNN, SVM, and RF, the model based on MODIS and SRTM data constructed using the RF algorithm was determined to be the best new model, with an R2 of 0.938 and a relative root mean square error (RRMSE) of 19.88%, for accurately estimating the AG-AGB based on 6128 samples. 3) Moreover, a set of 1200 samples was determined to be the lowest sample size for constructing the new model, with an R2 of nearly 0.923 and an RRMSE of 25%. 4) The AG-AGB can be more accurately mapped using the new model (R2 of 0.800 and RRMSE of 23.91%), indicating that the new model based on MODIS and SRTM data has the potential to map the AG-AGB over a large area. And over the last 14 years, the AG-AGB overall trends were that the areas with increasing AG-AGB (67.65%) were much larger than those with decreasing AG-AGB (32.35%), and the area of the sustainable AG-AGB increase accounts for 39.75% of the total alpine grassland area in northeastern and southwestern parts of Qinghai Province. The proposed estimation model based on MODIS and SRTM data is more suitable, greatly improves the accuracy of AG-AGB modeling, and provides a scientific basis for the reasonable use and management of alpine grassland resources.

Finite battery capacity limits the network lifetime of wireless sensor networks, and thus severely impedes the deployment of large scale sensor networks. To prolong the lifetime, researchers utilize mobile chargers to recharge sensors with external power sources. In this paper, we study both periodic charging time scheduling and charging path planning with multiple chargers. First, we present an efficient slot-based periodic charging time scheduling algorithm with both a fine-grained node classification scheme to prevent unnecessary visits of energy-sufficient nodes, and a balanced charging task assignment scheme to avoid charging starvation. To further enhance charging efficiency, we also propose a charging path planning algorithm, which enables parallel power replenishment with multiple chargers. The simulation results show that our algorithms are effective and competitive when compared with existing algorithms.

The last decade has witnessed unmanned aerial vehicles (UAVs) emerging as a powerful platform for various industrial and civilian applications. However, refrained by limited battery capacities, the hovering time of UAVs is still limited, impeding them from achieving remote tasks, such as wide area inspection. To deal with such long-range applications, a common sense solution is to employ vehicles to transport, launch, and recycle UAVs. Efficient routing and scheduling for UAVs and vehicles can greatly reduce time consumption and financial expenses incurred in long-range inspection. Nevertheless, prior works in vehicle-assisted UAV inspection considered only one UAV, and was incapable of concurrently serving multiple targets distributed in an area. Leveraging multiple UAVs to serve multiple targets in parallel can significantly enhance efficiency and expand service areas. Therefore, in this paper, we propose a novel hybrid genetic algorithm (HGA), which supports the cooperation of one vehicle and multiple drones for wide area inspection applications. HGA allows multiple UAVs to launch and recycle in different locations, minimizing time wastage for both the vehicle and UAVs. Performance evaluation is presented to demonstrate the effectiveness and efficiency of our algorithm when compared with existing solutions.

Nowadays video content has contributed to the majority of Internet traffic, which brings great challenge to the network infrastructure. Fortunately, the emergence of edge computing has provided a promising way to reduce the video load on the network by caching contents closer to users.But caching replacement algorithm is essential for the cache efficiency considering the limited cache space under existing edge-assisted network architecture. To investigate the challenges and opportunities inside, we first measure the performance of five state-of-the-art caching algorithms based on three real-world datasets. Our observation shows that state-of-the-art caching replacement algorithms suffer from following weaknesses: 1) the rule-based replacement approachs (e.g., LFU,LRU) cannot adapt under different scenarios; 2) data-driven forecast approaches only work efficiently on specific scenarios or datasets, as the extracted features working on one dataset may not work on another one. Motivated by these observations and edge-assisted computation capacity, we then propose an edge-assisted intelligent caching replacement framework LSTM-C based on deep Long Short-Term Memory network, which contains two types of modules: 1) four basic modules manage the coordination among content requests, content replace, cache space, service management; 2) three learning-based modules enable the online deep learning to provide intelligent caching strategy. Supported by this design, LSTM-C learns the pattern of content popularity at long and short time scales as well as determines the cache replacement policy. Most important, LSTM-C represents the request pattern with built-in memory cells, thus requires no data pre-processing, pre-programmed model or additional information. Our experiment results show that LSTM-C outperforms state-of-the-art methods in cache hit rate on three real-traces of video requests. When the cache size is limited, LSTM-C outperforms baselines by 20%~32% in cache hit rate. We also show that the training and predicting time of one iteration are $8.6~ms$ and $300~\mu s$ on average respectively, which are fast enough for online operations.

The Internet of Things (IoT) is widely regarded as a key component of the Internet of the future and thereby has drawn significant interests in recent years. IoT consists of billions of intelligent and communicating “things”, which further extend borders of the world with physical and virtual entities. Such ubiquitous smart things produce massive data every day, posing urgent demands on quick data analysis on various smart mobile devices. Fortunately, the recent breakthroughs in deep learning have enabled us to address the problem in an elegant way. Deep models can be exported to process massive sensor data and learn underlying features quickly and efficiently for various IoT applications on smart devices. In this article, we survey the literature on leveraging deep learning to various IoT applications. We aim to give insights on how deep learning tools can be applied from diverse perspectives to empower IoT applications in four representative domains, including smart healthcare, smart home, smart transportation, and smart industry. A main thrust is to seamlessly merge the two disciplines of deep learning and IoT, resulting in a wide-range of new designs in IoT applications, such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection. We also discuss a set of issues, challenges, and future research directions that leverage deep learning to empower IoT applications, which may motivate and inspire further developments in this promising field.

Conventional sensing methodologies for smart home are known to be labor-intensive and complicated for practical deployment. Thus, researchers are resorting to alternative sensing mechanisms. Wi-Fi is one of the key technologies that enable connectivity for smart home services. Apart from its primary use for communication, Wi-Fi signal has now been widely leveraged for various sensing tasks, such as gesture recognition and fall detection, due to its sensitivity to environmental dynamics. Building smart home based on Wi-Fi sensing is cost-effective, non-invasive, and enjoys convenient deployment. In this paper, we survey the recent advances in the smart home systems based on the Wi-Fi sensing, mainly in such areas as health monitoring, gesture recognition, contextual information acquisition, and authentication.

Finite network lifetime severely limits the usage of sensor networks. To prolong the lifetime, researchers adopt mobile chargers to recharge sensors with external power sources. Prior studies customarily assumed that a mobile charger sequentially visits and charges sensors. However, this method is inefficient, since it normally incurs considerable charging waiting time. To address the limitation, in this paper, we propose to replenish sensors with a mobile worker carrying multiple portable chargers. The worker simultaneously charges multiple sensors in a small region by deploying the chargers onto the sensors. We claim that this parallel charging mode significantly reduces waiting time incurred per sensor and thus improves charging efficiency. Based on the novel design, this paper contributes two novel approaches called periodic multi-charger (PMC) and on-demand multi-charger (OMC). The PMC offers guaranteed and sustainable power supplies via periodic charging schedules, while the OMC derives charging schedules in real time to adapt to the dynamism in energy consumption patterns. Both the algorithms achieve high charging efficiency by simultaneously charging sensors with multiple chargers in each charging round. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches.

Recent years have witnessed significant advances in indoor positioning. Existing approaches either require cumbersome site surveys or customized hardware, thus hindering their popularity. In this paper, we propose Stationary peers-Assisted indoor Positioning (SAP), a practical indoor positioning solution that is highly scalable and easy-to-deploy. SAP greatly alleviates the pain of fingerprint collection by smartly leveraging the relatively stationary people, namely stationary peers that are largely available in common indoor environments to assist positioning. Once SPs' locations and the relative distance between SPs and a target are obtained, SAP can apply trilateration to locate the target. SAP incorporates three key modules: a novel accelerometer-based filter that can accurately identify SP, an enhanced fingerprint-based positioning method that can accurately pinpoint SPs' locations, and a robust acoustic ranging method. We implement a prototype of SAP on the Android platform and evaluate its performance in representative real-world environments. SAP achieves an 80% positioning error of 2.2 m, which is comparable to the most existing smartphone-assisted approaches.

Combining powerful sensors and near ubiquitous distribution, the smartphone has become an irreplaceable part of modern day life. Using its pervasive sensing capabilities, the smart-phone guides us to our destination with precise step-by-step directions, advises us on what to have for lunch, and improves our photography skills through stabilizing our camera. Using the popular augmented reality (AR) smartphone app Pokemon Go as a case study, we explore the world of pervasive sensing. In this paper, we show both the current state of the art that enable applications such as Pokemon Go to thrive, as well as the limitations and opportunities inherent in current pervasive sensing applications.

Cloud gaming is a new way to deliver high-quality gaming experience to gamers anywhere and anytime. In cloud gaming, sophisticated game software runs on powerful servers in data centers, rendered game scenes are streamed to gamers over the Internet in real time, and the gamers use light-weight software executed on heterogeneous devices to interact with the games. Due to the proliferation of high-speed networks and cloud computing, cloud gaming has attracted tremendous attentions in both the academia and industry since late 2000's. In this paper, we survey the latest cloud gaming research from different aspects, spanning over cloud gaming platforms, optimization techniques, and commercial cloud gaming services. The readers will gain the overview of cloud gaming research and get familiar with the recent developments in this area.

How to mitigate such pressure has become a critical challenge for global grid enterprises.Empowered by the development of IoT techniques,high-speed communications,and AI-chips,traditional grid enterprises are trying to adopt these new techniques to solve the challenge,especially in heterogeneous data collection,energy scheduling,and AI-enabled monitoring.Firstly,the background of smart grid and the history of edge computing were introduced.Then,edge computing enabled solutions were illustrated in three typical grid scenarios.Finally,two edge computing platforms at home and abroad were introduced.The main technologies and application scenarios of them were also analyzed.

In recent years, P2P file sharing has been widely embraced and becomes the largest application of the Internet traffic. And the development of automobile industry has promoted a trend of deploying Peer-to-Peer (P2P) networks over vehicle ad hoc networks (VANETs) for mobile content distribution. Due to the high mobility of nodes, nodes' limited radio transmission range and sparse distribution, VANETs are divided and links are interrupted intermittently. At this moment, VANETs may become Vehicle-based Delay Tolerant Network (VDTNs). Therefore, this work proposes an Optimal Fragmentation-based Multimedia Transmission scheme (OFMT) based on P2P lookup protocol in VDTNs, which can enable multimedia files to be sent to the receiver fast and reliably in wireless mobile P2P networks over VDTNs. In addition, a method of calculating the most suitable size of the fragment is provided, which is tested and verified in the simulation. And we also show that OFMT can defend a certain degree of DoS attack and senders can freely join and leave the wireless mobile P2P network. Simulation results demonstrate that the proposed scheme can significantly improve the performance of the file delivery rate and shorten the file delivery delay compared with the existing schemes.

It becomes increasingly important to obtain the accurate and spontaneous runtime network behavior for further studies on wireless sensor networks. However, the existing testbeds cannot appropriately match such requirements. A High-accuracy Nonintrusive Networking Testbed (HINT) is proposed. In HINT, the interconnected chip-level signals are passively captured with auxiliary test boards and the captured data are transferred in additional networks to test server. The test server of HINT collects all the test data and depicts the full network behavior. HINT supports networking test, protocol verification, performance evaluation and so on. The experiments show that HINT transparently gathers accurate runtime data and does not disturb the spontaneous behavior of sensor networks. HINT is also extendible to different hardware platforms of sensor nodes. Consequently, HINT is an upstanding testbed solution for the future fine-grained and experimental studies on the resource-constrained wireless sensor networks.

One of the most important applications for wireless sensor networks (WSNs) is Data Collection, where sensing data are collected at sensor nodes and forwarded to a central base station for further processing. Since using battery powers and wireless communications, sensor nodes can be very small and easily attached at specified locations without disturbing surrounding environments. This makes WSN a competitive approach for data collection comparing with its wired counterpart. In this paper, we review recent advances in this research area. We first highlight the special features of data collection WSNs, by comparing with wired data collection network and other WSN applications. With these features in mind, we then discuss issues and prior solutions on the data gathering protocol design. Our discussion also covers different approaches for message dissemination, which is a critical component for network control and management and greatly affects the overall performance of a data collection WSN system.

This paper studies the distributed caching managements for the current flourish of the streaming applications in multihop wireless networks. Many caching managements to date use randomized network coding approach, which provides an elegant solution for ubiquitous data accesses in such systems. However, the encoding, essentially a combination operation, makes the coded data difficult to be changed. In particular, to accommodate new data, the system may have to first decode all the combined data segments, remove some unimportant ones, and then reencode the data segments again. This procedure is clearly expensive for continuously evolving data storage. As such, we introduce a novel Cooperative Coding and Caching (C3) scheme, which allows decoding-free data removal through a triangle-like codeword organization. Its decoding performance is very close to the conventional network coding with only a sublinear overhead. Our scheme offers a promising solution to the caching management for streaming data.