A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning.

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system's spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.

A Brief Review of Multipath TCP for Vehicular Networks.

Multipath TCP (MPTCP) is one of the most important extensions to TCP that enables the use of multiple paths in data transmissions for a TCP connection. In MPTCP, the end hosts transmit data across a number of TCP subflows simultaneously on one connection. MPTCP can sufficiently utilize the bandwidth resources to improve the transmission efficiency while providing TCP fairness to other TCP connections. Meanwhile, it also offers resilience due to multipath data transfers. MPTCP attracts tremendous attention from the academic and industry field due to the explosive data growth in recent times and limited network bandwidth for each single available communication interface. The vehicular Internet-of-Things systems, such as cooperative autonomous driving, require reliable high speed data transmission and robustness. MPTCP could be a promising approach to solve these challenges. In this paper, we first conduct a brief survey of existing MPTCP studies and give a brief overview to multipath routing. Then we discuss the significance technical challenges in applying MPTCP for vehicular networks and point out future research directions.

Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues.

The vehicular Internet of Things (IoT) comprises enabling technologies for a large number of important applications including collaborative autonomous driving and advanced transportation systems. Due to the mobility of vehicles, strict application requirements, and limited communication resources, the conventional centralized control fails to provide sufficient quality of service for connected vehicles, so a decentralized approach is required in the vicinity to satisfy the requirements of delay-sensitive and mission-critical applications. A decentralized system is also more resistant to the single point of failure problem and malicious attacks. Blockchain technology has been attracting great interest due to its capability of achieving a decentralized, transparent, and tamper-resistant system. There are many studies focusing on the use of blockchain in managing data and transactions in vehicular environments. However, the application of blockchain in vehicular environments also faces some technical challenges. In this paper, we first explain the fundamentals of blockchain and vehicular IoT. Then, we conduct a literature review on the existing research efforts of the blockchain for vehicular IoT by discussing the research problems and technical issues. After that, we point out some future research issues considering the characteristics of both blockchain and vehicular IoT.

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues.

Federated learning (FL) is a distributed machine learning approach that can achieve the purpose of collaborative learning from a large amount of data that belong to different parties without sharing the raw data among the data owners. FL can sufficiently utilize the computing capabilities of multiple learning agents to improve the learning efficiency while providing a better privacy solution for the data owners. FL attracts tremendous interests from a large number of industries due to growing privacy concerns. Future vehicular Internet of Things (IoT) systems, such as cooperative autonomous driving and intelligent transport systems (ITS), feature a large number of devices and privacy-sensitive data where the communication, computing, and storage resources must be efficiently utilized. FL could be a promising approach to solve these existing challenges. In this paper, we first conduct a brief survey of existing studies on FL and its use in wireless IoT. Then we discuss the significance and technical challenges of applying FL in vehicular IoT, and point out future research directions.

SDN-based Handover Scheme in Cellular/IEEE 802.11p Hybrid Vehicular Networks.

With the arrival of 5G, the wireless network will be provided with abundant spectrum resources, massive data transmissions and low latency communications, which makes Vehicle-to-Everything applications possible. However, VANETs always accompany with frequent network topology changes due to the highly mobile feature of vehicles. As a result, the network performance will be affected by the frequent handover. In this paper, a seamless handover schemeis proposed where the Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are employed to adapt to the dynamic topology change in VANETs. The introductionof SDN provides a global view of network topology and centralized control, which enables a stable transmission layer connection when a handover takes place, so that the upper layer performance isnot influenced by the network changes. By employing MEC server, the data are cached in advance before a handover happens, so that the vehicle can restore normal communication faster. In order toconfirm the superiority of our proposal, computer simulations are conducted from different aspects. The results show that our proposal can significantly improve the network performance when ahandover happens.

A Context-Aware Edge-Based VANET Communication Scheme for ITS.

We propose a context-aware edge-based packet forwarding scheme for vehicular networks. The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized manner, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. A reinforcement learning algorithm is used to optimize the last two-hop communications in order to improve the adaptiveness of the communication routes. The proposed scheme selects different edge nodes for different types of communications with different context information such as connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size. We launch extensive simulations to evaluate the proposed scheme by comparing with existing broadcast protocols and unicast protocols for various network conditions and traffic patterns.

Ultrasensitive self-powered cytosensors based on exogenous redox-free enzyme biofuel cells as point-of-care tools for early cancer diagnosis.

An exogenous redox-free, membrane-less enzyme biofuel cell-based ultrasensitive self-powered cytosensing platform was fabricated. With the ultrahigh sensitivity and the merits of not requiring external power sources or exogenous reagents, the device has great potential as a point-of-care tool for early diagnosis of cancer in vivo.

A ternary hybrid of carbon nanotubes/graphitic carbon nitride nanosheets/gold nanoparticles used as robust substrate electrodes in enzyme biofuel cells.

A novel ternary hybrid of carbon nanotubes/graphitic carbon nitride nanosheets/gold nanoparticles was prepared and used as robust substrate electrodes for fabricating membrane-less glucose/O2 enzyme biofuel cells (EBFCs), and a remarkably improved power output was observed for the prepared EBFC.

A nitrogen-doped graphene/gold nanoparticle/formate dehydrogenase bioanode for high power output membrane-less formic acid/O2 biofuel cells.

A high power output, membrane-less formic acid/O2 enzymatic biofuel cell was fabricated, in which nitrogen-doped graphene was proven to be effective for recycling the NAD(+)/NADH cofactor at a nitrogen-doped graphene/gold nanoparticle/formate dehydrogenase bioanode in the catalytic oxidation of formic acid.

EMMNet: sensor networking for electricity meter monitoring.

Smart sensors are emerging as a promising technology for a large number of application domains. This paper presents a collection of requirements and guidelines that serve as a basis for a general smart sensor architecture to monitor electricity meters. It also presents an electricity meter monitoring network, named EMMNet, comprised of data collectors, data concentrators, hand-held devices, a centralized server, and clients. EMMNet provides long-distance communication capabilities, which make it suitable suitable for complex urban environments. In addition, the operational cost of EMMNet is low, compared with other existing remote meter monitoring systems based on GPRS. A new dynamic tree protocol based on the application requirements which can significantly improve the reliability of the network is also proposed. We are currently conducting tests on five networks and investigating network problems for further improvements. Evaluation results indicate that EMMNet enhances the efficiency and accuracy in the reading, recording, and calibration of electricity meters.