Accompanied by the booming development of artificial intelligence technology, deep learning has been widely used in many fields of cancer, specially in cancer prediction. The dependence on training data for deep learning naturally raises privacy leakage concerns. Federated learning is a promising solution to these issues, but it is limited by the computational capacity of clients. Therefore, this paper has proposed a Split Federated Learning (SFL)-based breast cancer prediction scheme with dynamic client selection aggregation, by aggregating data from multiple healthcare organizations under the premise of privacy protection. First, the split learning is integrated into federated learning to predict breast canner, which contributes to protecting data privacy and reducing the computational burden on client devices. Then, the dynamic client selection aggregation is devised to lower the aggregation communication costs and improve communication efficiency by utilizing Long Short-Term Memory (LSTM) networks to evaluate the availability of each client devices in participating training. Finally, we have conducted extensive experiments on the CAMELYON16 dataset to evaluate the performance of our proposed scheme, and the experimental results have shown that our proposed scheme can converge faster and achieve the lower communication costs.

With the surge in wireless data traffic, integrating millimeter-wave (mmWave) technology into vehicular networks enables high-speed communication. Meanwhile, the rising demand for secure wireless communication drives the use of reconfigurable intelligent surfaces (RIS) to enhance physical layer security (PLS) through intelligent channel control. This paper investigates PLS approaches in multi-RIS-assisted mmWave vehicular communication under stochastic geometry architecture. Taking the dynamically changing and random nature of vehicular network topologies into account, we propose a vehicular network association scheme for a typical vehicle. In this scheme when the quality of the direct link deteriorates due to obstacles or other factors, RIS-assisted communication ensures a more stable connection. By leveraging stochastic geometry theory, a tractable analytical framework is established to evaluate the secrecy performance of the downlink transmission comprehensively. Specifically, the closed-form expressions of connection outage probability (COP) and secrecy outage probability (SOP) are derived. Simulation results demonstrate that introducing RIS into vehicular networks and utilizing the proposed association scheme can significantly improve the security of vehicular networks.

The fronthaul network architecture is the key to dealing with the massive traffic effectively and providing high-quality service, and the multiple base stations (BSs) deployed by it face the gigantic data transmission, which has given the demand for high-capacity communication and information security in the vehicular network. In this paper, we combine the millimeter wave (mmWave) communication and non-orthogonal multiple access (NOMA) technologies to escalate the communication capacity of multiple vehicle users (VUs), and propose a blockage-based cooperative jamming strategy to solve potential security risks in the vehicular network. In particular, with the help of jam-mers selected by this strategy, transmission security is enhanced simultaneously without escalating the instability of connections caused by the time-varying nature of vehicular networks under the NOMA transmission mechanism when the base station (BS) does not fully understand the channel state information (CSI) of VUs. Then we comprehensively analyze the specific distribution of roadways and the distance distribution of VUs under the NOMA strategy, and derive the performance metrics of the network based on the stochastic geometry method. Numerical results show that the proposed cooperative jamming scheme can effectively improve the secrecy performance of the vehicular network.

Unmanned aerial vehicles (UAVs), known for their high flexibility and maneuverability, are regarded as the aerial platforms of future integrated sensing and communication (ISAC) networks. The communication and sensing functions of ISAC share the same spectrum and signal waveform, which often results in communication information being embedded within the sensing waveforms, thereby increasing the risk of information leakage. To enhance the security of UAV-assisted ISAC, we propose a beamforming strategy based on the mutual cooperation between communication and sensing. Specifically, by utilizing the sensing function to process echo signals, we estimate the positions of potential eavesdroppers and obstacles, which supports subsequent obstacle avoidance trajectory planning and physical layer security design. To ensure the transmission secrecy, we introduce artificial noise into the system. By designing the UAV transmit beamforming and the covariance matrix of the artificial noise, we formulate an optimization problem that aims to minimize the signal-to-noise ratio (SNR) received by the eavesdropper. To address this non-convex optimization problem, we propose an optimization algorithm that combines Dinkelbach's transform and semidefinite relaxation (SDR). Simulation results demonstrate that the SNR of eavesdropper remains at a low level throughout the flight of UAV, validating the effectiveness of the proposed scheme.

Augmented intelligence (AuI) is a concept that combines human intelligence (HI) and artificial intelligence (AI) to leverage their respective strengths. While AI typically aims to replace humans, AuI integrates humans into machines, recognizing their irreplaceable role. Meanwhile, human-in-the-loop reinforcement learning (HITL-RL) is a semisupervised algorithm that integrates humans into the traditional reinforcement learning (RL) algorithm, enabling autonomous agents to gather inputs from both humans and environments, learn, and select optimal actions across various environments. Both AuI and HITL-RL are still in their infancy. Based on AuI, we propose and investigate three separate concept designs for HITL-RL:HI-AI,AI-HI, andparallel-HI-and-AIapproaches, each differing in the order of HI and AI involvement in decision making. The literature on AuI and HITL-RL offers insights into integrating HI into existing concept designs. A preliminary study in an Atari game offers insights for future research directions. Simulation results show that human involvement maintains RL convergence and improves system stability, while achieving approximately similar average scores to traditional$Q$-learning in the game. Future research directions are proposed to encourage further investigation in this area.

Cognitive Internet of Vehicles (CIoV) is an intelligent vehicle network envisioned to opportunistically access spectrum licensed to primary users (PUs) on the premise of not interrupting their normal communications. Dynamic spectrum access enables the CIoV to choose the best possible spectrum for communications based on the outcomes of spectrum data sensing, which can improve the spectrum access performance effectively. In this paper, we enable the CIoV to adapt to various spectrum states through: a) a collaborative big spectrum data sensing scheme to sense a massive amount of spectrum data; and b) a reinforcement learning (RL) based dynamic spectrum access scheme to optimize spectrum selection strategies. Q-learning, which is a popular RL approach, is proposed for underlay, overlay, and collaborative spectrum access modes to allocate spectrum resources to the CIoV intelligently. The Q-learning models, which include the spectrum state vector, the action vector of CIoV, and the spectrum access reward received in different spectrum situations, are defined for the spectrum access modes. A Q-learning based spectrum access algorithm is proposed to improve the communication performance of the CIoV in different spectrum access modes. Simulation results indicate that the collaborative spectrum access mode can achieve higher average throughput, lower interference power and lower communication outage compared with the underlay and overlay spectrum access modes.

Vehicle platoon is a group of vehicles moving in the same direction at a certain speed while maintaining a stable inter-vehicle distance, and so the vehicles are closely connected with each other in a virtual way. While a vehicle platoon can improve traffic efficiency, there are some challenges in maintaining communication among platoon members, especially when some vehicles join an existing platoon that includes long-body, heavy-duty or special vehicles. In order to solve this problem, we propose a spectrum sensing scheduling (SSS) scheme for communication resource management in platooning. First, we propose a three-level platoon architecture, which includes the cloud, road side unit (RSU), and vehicles to increase the communication coverage of a platoon and multiple platoons. Second, the spectrum sensing model, platoon control error model, and platoon communication delay model are established for the platoon delay under the SSS scheme. Third, we propose a greedy algorithm for resource allocation, which is based on the SSS scheme and vehicle-to-vehicle (V2V) communications, to minimize platoon delay. Finally, we simulate platoon delay and platoon safety status of the SSS scheme when some vehicles join an existing platoon. Simulation results show that the proposed SSS scheme reduces the platoon communication delay by around 50% and achieves a smaller platoon error as compared with existing baseline schemes for resource scheduling.

By enabling direct communications between nearby user equipment (UE), device‐to‐device (D2D) communication has become one of the key technologies in 5th generation (5G) mobile networks. D2D communication brings new communication opportunities for mobile devices, especially in a highly dense network. In this paper, D2D communication in the unlicensed spectrum, namely, D2D‐Unlicensed (D2D‐U), is discussed. The use of unlicensed frequency bands can ease the shortage of spectrum resources and improve network performance. However, the D2D‐U in 5G has significant effects on the network performance of existing unlicensed networks sharing the same frequency bands, such as Wi‐Fi and Bluetooth. Therefore, it is necessary to design a fair coexistence scheme for D2D‐U. To understand the coexistence problem, in this paper, we first formulate the network performance of D2D‐U and Wi‐Fi under two different coexistence schemes, namely, listen before talk (LBT) and duty cycle mechanism (DCM). Then, we use computer simulations to investigate a mode selection scheme that switches between these two schemes and point out the best possible solution for the coexistence between D2D‐U and Wi‐Fi.

As we move into the third decade of the 21st century, the 2020s, the unprecedented rate of technological disruption and the short-lived nature of the specifics of engineering state-of-the-art require us to carefully evaluate what it takes to be an effective engineer and what this entails for engineering education and their lifelong learning. While it is true that certain basics of engineering will not change, there will be an increased premium for some skills (such as lifelong learning, meta-learning, collaboration, creativity, critical thinking, communication skills, and cultural/global literacy). 21st-century skills are, as such, timeless skills: it is paradoxically the volatile nature of the modern world that has forced us from ephemeral vocational fads back to these permanently valuable skills. In this full research-to-practice paper, after reporting on the skills that policy think tanks and thought leaders deem necessary for the 21st century, we provide a synthesis in which we describe the pulls and pushes that learners and educators will face in the turbulent times of 2020 and beyond, and how they can thrive in the uncertain future through holistic well-rounded engineering education.

In this paper, we present a novel idea of multipath multicast, which is imperative to bandwidth intensive applications, in the context of multimedia streaming. In addition to congestion control, multipath TCP (MPTCP) has been proposed to establish multiple paths in a network to improve network reliability. Application-layer multicast (ALM) has been proposed to leverage end systems instead of dedicated routers to multicast that is important for an easy large-scale deployment as compared to IP-based multicast. This paper presents our novel idea of multipath multicast in the form of a simple experimental framework called MP-ALM in which we combine the multiplicity feature of MPTCP with the application-layer multicast (ALM). We extensively simulate MP-ALM using ns-3 and use iPerf to generate streaming multicast-MPTCP traffic. Simulation results show that MP-ALM can be beneficial for a better user experience and reduced overall network congestion in the perspective of multicast multimedia streaming.

Electronic textbooks (e-Textbooks) are digitized forms of textbooks which are envisioned to replace existing paper-based textbooks. After intensive literature review, together with interview results, our study has figured out four major IT-based challenges associated with e-Textbooks in its pursuit to replace the traditional textbooks, namely standardizing format of content, improving service reliability, improving quality and accuracy of content, and improving readability. This paper also provides an extensive review on how these challenges have been approached using existing e-Textbook solutions, such as N-Screen services, cloud computing, open market place, P2P between devices and HTML5. For each solution, we develop a usage scenario in which users apply the aforementioned technologies to interact with e-Textbooks and share contents among themselves. This article aims to provide a strong foundation for further investigations into the development and distribution of e-Textbooks for eventual successful adoption of e-Textbooks in school education.

ABSTRACT Cognitive radio (CR), which is the next generation wireless communication system, enables unlicensed users or secondary users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or primary users (PUs) so that bandwidth availability improves at the SUs, which helps to improve overall spectrum utilization. Collaboration is an intrinsic characteristic of CR to improve network performance. For instance, in collaborative spectrum sensing, SU hosts generate sensing outcomes, and collaborate amongst themselves through making final decisions at a decision fusion center in order to improve the accuracy of spectrum sensing. The requirement to collaborate has inevitably opened doors to various forms of attacks by malicious SUs, and this critical issue can be addressed using trust and reputation management (TRM), and so this is the focus of this article. Generally speaking, TRM detects malicious SUs, including honest SUs that turn malicious. Hence, TRM is of paramount importance in most kinds of schemes that require collaboration in CR networks. Our contribution in this article is as follows. This article provides an extensive survey on the application of TRM in various schemes in CR networks in order to ameliorate the effects of malicious SUs in collaboration. The discussion is presented with respect to a TRM taxonomy, various approaches to achieve TRM, various attack models, as well as the challenges and characteristics associated with TRM. Because of the significance of TRM in collaboration, this article presents a wide range of open issues to warrant further research in this area. Copyright © 2013 John Wiley & Sons, Ltd.

Cognitive radio (CR) has been introduced to allow unlicensed users, or better known as secondary users (SUs), to exploit underutilised licensed spectrum owned by the primary users (PUs). The SUs perform channel sensing to check the state of the PUs in order to use the channel without interfering with the PUs' activities. It is possible for malicious users or attackers to exploit channel sensing resulting in biased sensing decisions benefiting selfish SUs or attackers. In SIN 2011, a secure cooperative sensing protocol was proposed by Rifà-Paus and Gamgues to address this issue. In this paper, we study their protocol and discuss possible issues in the protocol, including the possibility of creating a rogue fusion centre and replaying session authentication. We also briefly examine the practical threats from more powerful adversanes capable of jamming the channels. We suggest several potential mitigations including use of well-established authenticated key exchange and standard entity authentication mechanisms.

Cognitive Radio (CR) is a key technology for improving the utilization level of the overall radio spectrum in wireless communications. It is able to sense and change its transmission and reception parameters adaptively according to spectrum availability in different spectrum channels. The Cognition Cycle (CC) is a state machine that is embodied in each CR host that defines the mechanisms related to achieving context awareness and intelligence including observation, learning, and action selection. The CC is the key element in the design of various applications in CR networks such as Dynamic Channel Selection (DCS), scheduling and congestion control. In this paper, Reinforcement Learning (RL) is employed to implement the CC in mobile CR networks. Previous works consider static networks with homogeneous channels. This paper analyzes the performance of RL as an approach to achieve context awareness and intelligence in regard to DCS in mobile CR networks with heterogeneous channels. Our contribution in this paper is to show whether RL is an appropriate tool to implement the CC. The results presented in this paper show that RL is a promising approach.

Proyiding that licensed or Primary Users (PUs) are oblivious to the presence of unlicensed or Secondary Users (SUs), Cognitive Radio (CR) enables the SUs to use underutilized licensed spectrum (or white spaces) opportunistically and temporarily conditional on the interference to the PUs being below an acceptable level. Context awareness and intelligence enable the SU to sense for and use the underutilized licensed spectrum in an efficient manner. This paper investigates various learning mechanisms for achieving context awareness and intelligence with respect to Dynamic Channel Selection (DCS) in CR networks. The learning mechanisms are Adaptation (Adapt), Window (Win), Adaptation-Window (AdaptWin), and Reinforcement Learning (RL). The DCS scheme helps SU base station to select channel adaptively for data transmission to its SU host in static and mobile centralized CR networks. The purpose is to enhance quality of service, particularly throughput and delay (in terms of number of channel switches), in the presence of channel heterogeneity. Our contribution is to investigate simple and yet pragmatic learning mechanisms for CR networks. Simulation results reveal that RL, AdaptWin and Win achieve approximately similar and the best possible network performance, followed by Adapt, and finally Random, which does not apply learning and serves as baseline.

Cognitive Radio (CR) is a next-generation wireless communication system that exploits underutilized licensed spectrum to optimize the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of CR hosts in the absence of fixed network infrastructure. Context-awareness and intelligence are key characteristics of CR networks that enable the CR hosts to be aware of their operating environment in order to make an efficient and optimal joint action. Applying our extended Payoff Propagation (PP) mechanism in DCRN helps the CR hosts to achieve an efficient and optimal joint action in a cooperative and distributed manner through learning. The PP is suitable to be applied in most schemes in DCRN that requires context-awareness and intelligence such as Dynamic Channel Selection (DCS), scheduling, and congestion control. We investigate the performance of the PP in respect to DCS, and show that it is able to converge to an efficient and optimal joint action in a distributed manner including a DCRN with cyclic topology; furthermore we show that fast convergence is possible.

Cognitive Radio (CR) is a next-generation wireless communication system that enables unlicensed users to exploit underutilized licensed spectrum to optimize the utilization of the overall radio spectrum. A Distributed Cognitive Radio Network (DCRN) is a distributed wireless network established by a number of unlicensed users in the absence of fixed network infrastructure such as a base station. Context awareness and intelligence are the capabilities that enable each unlicensed user to observe and carry out its own action as part of the joint action on its operating environment for network-wide performance enhancement. These capabilities can be applied in various application schemes in CR networks such as Dynamic Channel Selection (DCS), congestion control, and scheduling. In this paper, we apply Reinforcement Learning (RL), including single-agent and multi-agent approaches, to achieve context awareness and intelligence. Firstly, we show that the RL approach achieves a joint action that provides better network-wide performance in respect to DCS in DCRNs. The multi-agent approach is shown to provide higher levels of stability compared to the single-agent approach. Secondly, we show that RL achieves high level of fairness. Thirdly, we show the effects of network density and various essential parameters in RL on the network-wide performance.

Traditional static spectrum allocation policies have been to grant each wireless service exclusive usage of certain frequency bands, leaving several spectrum bands unlicensed for industrial, scientific and medical purposes. The rapid proliferation of low-cost wireless applications in unlicensed spectrum bands has resulted in spectrum scarcity among those bands. Since most applications in Wireless Sensor Networks (WSNs) utilize the unlicensed spectrum, network-wide performance of WSNs will inevitably degrade as their popularity increases. Sharing of under-utilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is a new paradigm in wireless communication that allows sensor nodes as the unlicensed users or Secondary Users (SUs) to detect and use the under-utilized licensed spectrum temporarily. Given that the licensed or Primary Users (PUs) are oblivious to the presence of SUs, the SUs access the licensed spectrum opportunistically without interfering the PUs, while improving their own performance. In this paper, we propose an approach to build Cognitive Radio-based Wireless Sensor Networks (CR-WSNs). We believe that CR-WSN is the next-generation WSN. Realizing that both WSNs and CR present unique challenges to the design of CR-WSNs, we provide an overview and conceptual design of WSNs from the perspective of CR. The open issues are discussed to motivate new research interests in this field. We also present our method to achieving context-awareness and intelligence, which are the key components in CR networks, to address an open issue in CR-WSN.

Cognitive radio (CR) is a novel and promising paradigm for next-generation wireless communication. It is able to sense and change its transmission and reception parameters adaptively according to spectrum availability at different channels. The cognition cycle (CC) is a state machine that is embodied in each CR that defines the mechanisms related to achieving context-awareness and intelligence including observation, orientation, learning, planning, decision making, and action selection. The CC is the key element in the design of various schemes in CR networks such as dynamic channel selection (DCS), scheduling and congestion control. Hence, a good implementation of the CC is of paramount importance. In this paper, reinforcement learning (RL) is employed to implement the CC. The main focus is to analyze the performance of RL as an approach to achieving context-awareness and intelligence in regard to DCS. The contributions of this paper are twofold. Firstly, we seek to justify whether RL is an appropriate tool to implement the CC. Secondly, we seek to understand the effects of changes on RL parameters on network performance. In addition, we propose solutions for the problems associated with the application of RL in DCS. The results presented in this paper show that RL is a promising approach.