Space-air-ground integrated network (SAGIN) represents a pivotal architecture for the future evolution of global mobile communications. However, its inherent high dynamics and stochastic nature pose significant challenges to conventional routing mechanisms. Moreover, the vast spatial-scale openness of SAGIN makes it particularly vulnerable to eavesdropping attacks. This paper presents a novel sleeping multi-armed bandit (SMAB) framework, designed to enable secure routing in SAGIN. Specifically, we first establish channel models for all types of links in SAGIN. Then, we theoretically analyze the statistical properties of secrecy capacity and end-to-end (E2E) delay for message transmission over arbitrary routes, and formulate the secure routing problem to maximize cumulative secure transmission throughput under the delay constraint. The uncertainty in the network state of SAGIN, along with the complexity of the optimization objective and constraint (non-convex, non-linear, and coupled), renders the solution to the secure routing problem highly intractable. To this end, we leverage the MAB model to transform the secure routing problem into a budget-constrained arm-pulling problem and introduce the “sleeping” mode to capture route unavailability due to intermittent link failures. To effectively balance route exploration and exploitation, we further apply the upper confidence bound (UCB) method to design the SMAB-based secure routing algorithm (SMAB-SR), and derive its regret upper bound theoretically. Finally, extensive simulations verify that the SMAB-SR algorithm exhibits significant advantages in E2E secure transmission throughput compared to benchmarks and can maintain highly effective across various SAGIN configurations.

The deep learning-based semantic communication system (DeepSC) is designed to improve the efficiency and accuracy of information transmission, by leveraging joint source-channel coding techniques to extract relevant semantic features. However, existing research on attack methods targeting DeepSC has primarily focused on text and image domains, leaving the speech domain largely unexplored. To this end, this paper proposes Iterative Semantic Gradient Update (ISGU), a novel approach for crafting physical layer adversarial attacks on DeepSC for speech transmission (DeepSC-ST). Specifically, we introduce a joint loss that combines the semantic similarity loss with Connectionist Temporal Classification loss to expedite the generation process of targeted attacks against the DeepSC-ST. In addition, we design an algorithm to generate adversarial examples, enhancing their imperceptibility by meticulously controlling the perturbation power added to the input speech. Extensive experiments indicate that ISGU is capable of rapidly generating highly covert adversarial examples, with a notably high attack success rate.

Semantic communication, an emerging AI-driven communication paradigm, offers great potential for multimodal data delivery in space-ground integrated networks (SGINs). However, the dynamic nature of SGINs presents severe challenges for path selection, making it difficult to ensure the quality of service (QoS) at the semantic level. To this end, we propose in this paper a novel path selection scheme in space-ground multimodal semantic communication networks based on the sleeping multi-armed bandit (MAB) approach. Specifically, we first model the approximate semantic entropy and semantic rate, formulating an optimal path selection problem that integrates link state information and semantic data transmission volume. Then, we convert the path selection problem into a sleeping MAB problem and meticulously design an upper confidence bound (UCB)-based algorithm to solve it, called Periodic Probability Sleeping Path Selection (PPSPS), which copes with the dynamic feature of SGINs. We further theoretically verify the bounded regret of the PPSPS algorithm, indicating that it can ensure good semantic communication QoS. Simulation results demonstrate the superiority of the proposed path selection scheme compared to traditional reinforcement learning methods.

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks.

In mobile edge networks, federated learning (FL) has garnered substantial attention as a distributed machine learning framework with significant advantages for protecting user privacy. Due to the limited resources of wireless bandwidth, such FL-based applications are quite susceptible to Distributed Denial-of-Service (DDoS) attacks. Prior solutions either rely on centralized mechanisms that require complete information about all participants or are customized to specific systems. However, these solutions are either obsolete or ineffective given the new properties of FL. In this work, we first formulate a DDoS mitigation problem on bandwidth allocation for FL within mobile edge networks. Considering interactions between various network components and users, we propose anEvolutionaryGame andDouble-sidedAuction-based framework, termed EGDA, which consists of EG-based and DA-based mechanisms for user-bandwidth allocation (UBA) and server-bandwidth allocation (SBA), respectively. Specifically, to address DDoS attacks on UBA, we design an EG-based approach with minimum latency for FL under limited information. The proposed EG-based allocation algorithm is proven to be stable and achieve the evolutionary equilibrium. To mitigate DDoS attacks on SBA, we study an approach of DA with social welfare maximization while protecting the privacy of participants. Then, an iterative DA-based allocation algorithm is developed to be convergent and satisfy desirable economic properties. Extensive evaluation demonstrates that EGDA mitigates DDoS attacks effectively and efficiently.

Distributed Vehicle Sharing System (DVSS) leverages emerging technologies such as blockchain to create a secure, transparent, and efficient platform for sharing vehicles. In such a system, both efficient matching of users with available vehicles and optimal pricing mechanisms play crucial roles in maximizing system revenue. However, most existing schemes utilize user-to-vehicle (two-sided) matching and pricing, which are unrealistic for DVSS due to the lack of participation of service providers. To address this issue, we propose in this paper a novel Three-sided stable Matching with an optimal Pricing (TRIMP) scheme. First, to achieve maximum utilities for all three parties simultaneously, we formulate the optimal policy and pricing problem as a three-stage Stackelberg game and derive its equilibrium points accordingly. Second, relying on these solutions from the Stackelberg game, we construct a three-sided cyclic matching for DVSS. Third, as the existence of such a matching is NP-complete, we design a specific vehicle sharing algorithm to realize stable matching. Extensive experiments demonstrate the effectiveness of our TRIMP scheme, which optimizes the matching process and ensures efficient resource allocation, leading to a more stable and well-functioning decentralized vehicle sharing ecosystem.

Air-ground integrated networks (AGINs) are promising to provide wide-coverage, high-capacity, and low-latency communication services, and thus have been attracting increasing attention from industry and academia recently. However, the open and dynamic nature of AGINs makes them vulnerable to eavesdropping attacks, posing a major challenge in ensuring end-to-end information transmission security. To this end, we propose in this paper a secure routing scheme in AGINs based on the multi-armed bandit (MAB) approach. Specifically, we first model the secrecy transmission performance for the ground-to-ground links and ground-to-air links. Based on this, we then formulate the end-to-end secure route selection problem and convert it into a budget-constrained MAB problem, where each arm is associated with a corresponding reward and cost. We further design a Secure Route Upper Confidence Bound (SRUCB) algorithm to solve the MAB problem, which copes with the scenario where the locations of eavesdroppers and jammers are unknown, and can be proven to have a bounded regret. Numerical results demonstrate the superiority of the proposed routing scheme compared to several online learning algorithms.

Benefiting from the booming of Big Data and artificial intelligence (AI) technologies, data-as-a-service is gradually transforming into knowledge-as-a-service. Extracting knowledge from massive raw data is becoming a popular paradigm to save network resources and improve efficiency, and establishing knowledge markets is receiving increasing attention from academia and industry. In this paper, we propose a one-stop knowledge acquisition ecosystem termed BWKA that covers the whole process from upper-layer knowledge trading to underlying knowledge generation. In the knowledge trading process, the knowledge-as-a-service platform (KSP) is the buyer and publishes knowledge demands to multiple local knowledge sellers (LKSs). In the knowledge generation process, each LKS aggregates data from its sensors and then trains data into knowledge according to the KSP's requirements. We resort to blockchain technology and provide a series of tailored operating rules and functions to protect the truthfulness of data gathering and the fairness of knowledge trading. In addition, we introduce incentive mechanisms to stimulate selfish and rational entities in the BWKA ecosystem to participate in knowledge acquisition. To analyze the strategic interactions among entities theoretically, we develop a nested hierarchical game model, where the upper-layer knowledge trading is evaluated based on the Contract Theory, and the lower-layer knowledge generation is formulated as a two-stage Stackelberg game. By solving the nested hierarchical game in a backward inductive way, we identify the optimal strategy for each entity in closed form. Experiments on the Ethereum blockchain and simulation results demonstrate the practical operability and outstanding performance of the BWKA ecosystem.

Mobile crowdsensing (MCS) has emerged as a popular and promising paradigm for solving challenging problems by utilizing collective wisdom and resources. However, the system architecture and operational rules for MCS have not been well-defined, and obtaining accurate and reliable results from conflicting data collected by workers is difficult due to discrepancies in sensor quality and privacy protection requirements. In this paper, we combine the methodologies of Dynamic Truth Discovery (DTD), Combinatorial Multi-Armed Bandit (CMAB), and Multi-Attribute Reverse Auction to develop a novel MCS ecosystem, with the objective of maximizing the sensing accuracy-aware utility under the budget constraint. We first establish the data collection model by jointly considering the task completion duration as well as the deviation caused by both endogenous errors and privacy protection-oriented injected noise. Then, we theoretically evaluate the accuracy of truth discovery and quantify the contribution of each worker to MCS to form the worker selection criterion. As the qualities of workers are initially unknown, the platform faces the exploration-exploitation dilemma. Therefore, we apply CMAB to transform the worker recruitment problem into a combinatorial arm-pulling problem and elaborately design an Upper Confidence Bound (UCB) algorithm to achieve a desirable exploration-exploitation tradeoff. Moreover, we design an auction-based payment method for the platform, stimulating workers to provide their quoted price honestly while enabling individual rationality. Extensive simulations and comparison results demonstrate the feasibility and effectiveness of our proposed MCS ecosystem.

As a new distributed data training framework, federated learning (FL) has attracted increasing attention owing to its advantages of preserving data privacy and low communication cost. However, in a wireless FL network, due to the limited bandwidth resources, when a large number of clients participate in FL, the communication burden is too heavy. Therefore, efficient bandwidth allocation is critical to facilitate the application of wireless FL. In this paper, we investigate bandwidth allocation in wireless FL networks with the objective of minimizing the latency of FL services. We first analyze and formulate the latency minimization problem. Then, considering that the computing and transmitting capabilities of each client cannot be completely and truly acquired, we develop an evolutionary game (EG) framework to model the dynamic process of bandwidth allocation in wireless FL. We further show the optimal bandwidth allocation solution is equivalent to the evolutionary equilibrium (EE) obtained by the replicator dynamics in the EG model, and prove the EE is asymptotically stable. With the help of these results, we propose the EG-based bandwidth allocation algorithm, which enables the latency of FL services to be reduced by performing the replicator dynamics iteratively. Numerical simulations are provided to demonstrate the evolutionary behaviors in the EG-based bandwidth allocation Algorithm.

In the era of big data, the unprecedented growth of data has spawned the commercial application of data trading markets in the Internet of Vehicles (IoV), while also posing challenges to their economic feasibility. In this paper, we propose a data-energy trading architecture in IoV consisting of a market operator, electric vehicles (EVs), and roadside units (RSUs), where RSUs exchange energy for data collected by EVs, and the market operator solves the data/energy allocation problem to maximize social welfare. However, due to the information asymmetry and fragmentation in the market, it is difficult to determine the optimal data and energy trading amount. To this end, we design an iterative double-sided auction (IDA) mechanism to regulate the interactive behaviors among the trading entities, where the market operator gathers local information from RSUs and EVs, and gradually adjusts the submitted bids of two sides to reach the desired payment and reward rules. The proposed IDA-based data-energy trading algorithm is convergent and satisfies the economic properties of efficiency, incentive compatibility, individual rationality, and budget balance. Numerical results demonstrate the performance of the proposed IDA-based data-energy trading architecture in IoV.

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided EH CR networks (EH-CRNs) in a multichannel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient BC (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Achieving higher spectrum utilization, auction-based mechanisms has been regarded as a popular tool in dynamic spectrum access (DSA). Recently, Sybil attacks in auction-based DSA mechanisms have been investigated, where a cheating bidder can manipulate an auction by submitting bids under multiple fake identities. Existing Sybil-proof mechanisms in DSA are limited to prevent Sybil attacks from primary users (PUs) or secondary users (SUs). However, both of PUs and SUs may perform Sybil attacks in DSA, i.e., double Sybil attacks. The challenge of solving the double Sybil attacks is that fictitious identities and fake bids can directly affect allocation results, but the malicious bidders cannot be straightforwardly distinguished from all bidders. To resist the double Sybil attacks, we propose STEAM, the first double Sybil-proof and two-dimensional Truthful spEctrum Auction Mechanism for DSA. Specifically, STEAM merges suspicious buyers based on geographic characteristics and sorts sellers by a bid-independent sorting method to minimize the impact of untruthful bids and Sybil attacks on the allocation results. Theoretical analysis and extensive evaluations prove that STEAM is double Sybil-proof, two-dimensional truthful, individual rational and budget-balanced, while the performance loss in various metrics within 8% compared to the existing auction-based mechanisms.

In this paper, we focus on a multi-hop decentralized wireless network consisting of legitimate nodes, adversary wardens, and friendly but selfish jammers, and investigate the routing design for achieving covert communication. For a pair of source and destination nodes, we first provide theoretical analysis for a given route between them to reveal how the covertness performance is related to the jamming power of jammers in the network. Then, we design an incentive mechanism that stimulates selfish jammers to supply artificial jamming to protect communication covertness, by granting them rewards from the source. A two-stage Stackelberg game framework is developed to analyze the strategic interactions between the source and jammers, and so as to determine the optimal settings of rewards and jamming power. Based on these results, we formulate a shortest weighted path-finding problem to identify the optimal route for covert communication between the source and destination, which can be solved efficiently by employing Dijkstra's algorithm. Simulation results demonstrate the performance of the proposed incentive routing scheme.

This paper investigates secure communication in a two-hop relaying system based on physical layer security. The relay employs time-switching simultaneous wireless information and power transfer (SWIPT) to harvest energy and receive information from the source, and then transmits the source’s information and its own information to the destination. A passive eavesdropper exists and wiretaps information transmission over both hops. Under the general system configuration, we first provide performance modeling to reveal the secrecy rate of source and relay as well as identify their utilities. Then, we analyze the hierarchical competition behaviors between the source and relay from a game-theoretic perspective. In particular, we develop a Stackelberg game-based analytical framework to determine the optimal strategies for the source and relay by deriving the Stackelberg equilibrium. Furthermore, we summarize the feasible conditions of utilizing SWIPT-enabled relaying for secure communication and propose the end-to-end transmission scheme accordingly. Extensive numerical results are presented to demonstrate the performance of the proposed SWIPT-enabled relaying system.

Abstract This paper focuses on the issue of joint time and power allocation in multi‐channel energy harvesting CR networks (EH‐CRNs), where the multi‐antenna secondary transmitter (ST) opportunistically accesses the licensed subchannels by a hybrid overlay/underlay transmission approach. To improve spectrum efficiency and energy efficiency of the EH‐CRNs, the ST scavenges energy from the radio‐frequency signal radiated by the primary transmitter, and exploits the harvested energy for data transmission through subchannels of different states in overlay/underlay mode simultaneously. Moreover, under the interference power constraint, energy constraint, and maximum power constraint, the secondary throughput is improved by optimising the allocation of subchannels, the time scheduling between energy harvesting and data transmission, and the power allocation of the ST among different subchannels. A subchannel allocation scheme with low time complexity is proposed, and the secondary throughput optimisation problem is formulated with respect to the time scheduling and power allocation of the ST. Then it is proved the problem is convex, and the problem is solved by a proposed joint time and power allocation algorithm. Numerical results show that the proposed scheme has an advantage of secondary throughput over the other schemes. Finally, the impacts of key relevant factors on the secondary throughput are explored.

The efficient buffer space management in intermittently connected Internet of Things (IC-IoT) is of great importance for data delivery performance guarantee in such networks. This article considers two typical buffer space management policies for IC-IoT, i.e., buffer-space sharing (BS) and buffer-space allocation (BA). The BS policy allows the buffer space of each device to be fully shared by the exogenous packets and the packets from other devices, while the BA policy divides the buffer space into the source buffer and relay buffer for storing the two kinds of packets separately. With the help of the queueing theory and Markov chain theory, we develop a theoretical framework to capture the sophisticated queueing processes for the buffer space under either BS or BA policy, which enables the limiting distribution of the buffer occupation state to be determined. We then provide theoretical modeling for throughput and expected end-to-end delay to evaluate the fundamental performance of the IC-IoT under the BS and BA policies. Finally, extensive simulation and numerical results are presented to validate theoretical models and to demonstrate the effects of BS and BA policies on the IC-IoT performance.

Blockchain technology is a promising approach for solving the security and personal privacy problems in Internet applications. The successful commercial deployment of Blockchain markets relies on a comprehensive understanding of the economic and strategic interactions among different entities involved. In this paper, we focus on a blockchain market consisting of a blockchain platform (BP), multiple miners, and blockchain users (BUs), and formulate their interactions as a three-stage Stackelberg game. In Stage I, the BP strategizes the rewards granted to the miners, so as to attract the miners to contribute more computing power used for improving the security and privacy of the blockchain. In Stage II, each miner strategizes its computing power individually for winning the mining compe-tition, which is modeled as a non-cooperative game. In Stage III, the BUs strategize the transaction fee to acquire a corresponding service experience. With the objective of utility maximization, we develop a theoretical framework to analyze the hierarchical interactive behaviors among the entities in a backward inductive way. By solving the Stackelberg equilibrium, we determine the optimal strategies of entities in closed-form. Numerical results are provided to demonstrate the performance of the strategic interactions in the blockchain market.

Mobile Social Networks have become one of the most convenient services for users to share information everywhere. This crowdsourced information is often meaningful and recommended to users, e.g., reviews on Yelp or high marks on Dianping, which poses the threat of Sybil attacks. To address the problem of Sybil attacks, previous solutions mostly use indirect/direct graph model or clickstream model to detect fake accounts. However, they are either dependent on strong connections or solely preserved by servers of social networks. In this paper, we propose a novel predictable approach by exploiting users' custom patterns to distinguish Sybil attackers from normal users for the application of recommendation in mobile social networks. First, we introduce the entropy of spatial-temporal features to profile the mobility traces of normal users, which is quite different from Sybil attackers. Second, we develop discriminative entropy-based features, i.e., users' preference features, to measure the uncertainty of users' behaviors. Third, we design a smart Sybil detection model based on a binary classification approach by combining our entropy-based features with traditional behavior-based features. Finally, we examine our model and carry out extensive experiments on a real-world dataset from Dianping. Our results have demonstrated that the model can significantly improve the detection accuracy of Sybil attacks.

This article focuses on the secure routing problem in the decentralized Internet of Things (IoT). We consider a typical decentralized IoT scenario composed of peer legitimate devices, unauthorized devices (eavesdroppers), and selfish helper jamming devices (jammers), and propose a novel incentive jamming-based secure routing scheme. For a pair of source and destination, we first provide theoretical modeling to reveal how the transmission security performance of a given route is related to the jamming power of jammers in the IoT. Then, we design an incentive mechanism with which the source pays some rewards to stimulate the artificial jamming among selfish jammers, and also develop a two-stage Stackelberg game framework to determine the optimal source rewards and jamming power. Finally, with the help of the theoretical modeling as well as the source rewards and jamming power setting results, we formulate a shortest weighted path-finding problem to identify the optimal route for secure data delivery between the source-destination pair, which can be solved by employing the Dijkstra's or Bellman-Ford algorithm. We prove that the proposed routing scheme is individually rational, stable, distributed, and computationally efficient. Simulation and numerical results are provided to demonstrate the performance of our routing scheme.

This paper focuses on the QoS-aware secure routing design based on the physical layer security technology for a multi-hop wireless network consisting of legitimate nodes, malicious eavesdroppers, and selfish jammers. We first provide theoretical modeling for a given route to reveal how its end-to-end security/QoS performance is related to the transmitting power of legitimate nodes along the route and the jamming power of jammers in the network. We then design an incentive mechanism that stimulates jammers to generate artificial jamming for security enhancement, and also develop a non-cooperative game framework to resolve the jamming power setting issue here. Based on the security/QoS performance modeling of the route and jamming power setting, we further propose a theoretical framework to determine the optimal transmitting power of nodes along the route such that its optimal transmission security can be achieved under a QoS constraint. Finally, with the help of the power setting results of the given route, we formulate a shortest weighted path-finding problem to identify the optimal route for data delivery in the network, which can be solved by employing the Bellman-Ford or Dijkstra's algorithm. It is demonstrated that the proposed routing scheme is individually rational, stable, distributed and computationally efficient.

Dynamic spectrum access (DSA) is a promising platform to solve the spectrum shortage problem, in which auction based mechanisms have been extensively studied due to good spectrum allocation efficiency and fairness. Recently, Sybil attacks were introduced in DSA, and Sybil-proof spectrum auction mechanisms have been proposed, which guarantee that each single secondary user (SU) cannot obtain a higher utility under more than one fictitious identities. However, existing Sybil-poof spectrum auction mechanisms achieve only Sybil-proofness for SUs, but not for primary users (PUs), and simulations show that a cheating PU in those mechanisms can obtain a higher utility by Sybil attacks. In this paper, we propose TSUNAMI, the first Truthful and primary user Sybil-proof aUctioN mechAnisM for onlIne spectrum allocation. Specifically, we compute the opportunity cost of each SU and screen out cost-efficient SUs to participate in spectrum allocation. In addition, we present a bid-independent sorting method and a sequential matching approach to achieve primary user Sybil-proofness and 2-D truthfulness, which means that each SU or PU can gain her maximal utility by bidding with her true valuation of spectrum. We evaluate the performance and validate the desired properties of our proposed mechanism through extensive simulations.

Real-time surgery workflow automatic detection as computer-assisted surgery systems has become an emerging trend due to improving patient safety during surgery. Currently, the convolutional neural networks can show the best performance for content-based video analysis of surgical workflow. In this paper, a novel solution of surgery workflow detection during the procedure was presented, the edge information of original phases from video frames was extracted and then employed to train together with original phases by using a ResNet. Finally, the methods were evaluated on cataract-101 dataset, a publicly available dataset for surgical phase analysis, on which a maximum accuracy of 90.1% was reached. Additionally, the accuracy of 3% improvement was achieved when compared with the method of no processing the data by edge detection. It is shown that using the edge information of original images could improve the performance of surgical phase recognition, because it can be complementary information for original images to recognize the surgical workflow. This paper shows valuable potential to develop modern medical diagnosis and treatment in automating workflow recognition, and the edge processing of original phases for recognition images can also produce new features to assist the network to recognize the original images. Furthermore, the technology studied in this paper can also be used in other video analysis tasks, or classification of image tasks.

A lot of works have been done to demonstrate that buffer-aided relaying can achieve a significant performance gain in cooperative wireless networks. However, the additional delay introduced by buffer has been largely neglected in available works, which is of significant importance for delay-sensitive networks. In this paper, we consider a two-hop buffer-aided relaying system suffering from eavesdropping, where every packet owns a limited lifetime. In order to satisfy a specific secrecy rate of the system, this paper proposes a novel security and lifetime (SELI)-aware relay selection scheme by balancing the security and lifetime constraints. Furthermore, to address the problem of the heterogeneous packets queuing in the buffer, the approach of Markov chain is embedded to model the packet occupancy process. With the help of this complete framework, we derive the exact expressions of performance metrics, including reliable outage probability, packet discarding probability and secrecy throughput. Finally, extensive simulation and numerical results are provided to validate our analysis and illustrate the proposed scheme can efficiently reduce the packet discarding ratio.

This paper study the secure optimal QoS routing (SOQR) in wireless ad hoc networks (WANETs) based on the physical layer security techniques. Specifically, we consider a multi-hop WANET with malicious eavesdroppers and cooperative jammers, and formulate the SOQR as an optimization problem. To deal with this problem, we first derive the closed-form expressions of connection outage probability (COP) and secrecy outage probability (SOP) for any given end-to-end path, which serve as the performance metrics of communication QoS and transmission security, respectively. Then, we explore the minimum COP conditioned on that SOP is below a pre-specified threshold and obtain the corresponding achievable power allocation strategy. With the help of analysis of a given path, we further propose the SOQR algorithm which selects the secure path between a pair of source and destination nodes in a distributed manner to achieve the optimal QoS performance. Finally, numerical simulations are conducted to validate the efficiency of our theoretical results, as well as to illustrate the QoS-security tradeoffs.

Recently extensive works have been devoted to the performance analysis of physical layer security in wireless communication systems. However, the combination of physical layer security and quality of service (QoS) for route selection in multi-hop wireless ad hoc networks (WANETs) still remains an open technical challenge. As an initial step towards this end, this paper focuses on a multi-hop WANET with two typical transmission schemes amplify-and-forward (AF) and decode-and-forward (DF), and explores the route selection with the consideration of both security and QoS. We first derive the closed-form expressions of secrecy outage probability (SOP) and connection outage probability (COP) for a single hop link, and further extend the results to an end-to-end route. Then we conduct the performance comparison between the AF scheme and DF scheme. Finally, based on both the SOP and COP of a route, we formulate the route metric and propose a flexible route selection algorithm which enables us to select the suitable route for message delivery according to different security and QoS requirements.

This paper focuses on a class of important two-hop relay mobile ad hoc networks (MANETs) with limited-buffer constraint and any mobility model that leads to the uniform distribution of the locations of nodes in steady state, and develops a general theoretical framework for the end-to-end (E2E) delay modeling there. We first combine the theories of fixed-point (FP), quasi-birth-and-death process, and embedded Markov chain to model the limiting distribution of the occupancy states of a relay buffer, and then apply the absorbing Markov chain theory to characterize the packet delivery process, such that a complete theoretical framework is developed for the E2E delay analysis. With the help of this framework, we derive a general and exact expression for the E2E delay based on the modeling of both packet queuing delay and delivery delay. To demonstrate the application of our framework, case studies are further provided under two network scenarios with different MAC protocols to show how the E2E delay can be analytically determined for a given network scenario. Finally, we present extensive simulation and numerical results to illustrate the efficiency of our delay analysis as well as the impacts of network parameters on delay performance.

Since the seminal work of Grossglauser and Tse [1], the two-hop relay algorithm and its variants have been attractive for mobile ad hoc networks (MANETs) due to their simplicity and efficiency. However, most literature assumed an infinite buffer size for each node, which is obviously not applicable to a realistic MANET. In this paper, we focus on the exact throughput capacity study of two-hop relay MANETs under the practical finite relay buffer scenario. The arrival process and departure process of the relay queue are fully characterized, and an ergodic Markov chain-based framework is also provided. With this framework, we obtain the limiting distribution of the relay queue and derive the throughput capacity under any relay buffer size. Extensive simulation results are provided to validate our theoretical framework and explore the relationship among the throughput capacity, the relay buffer size and the number of nodes.

An analytical approach to quantifying the routing overhead in wireless ad hoc networks is presented in this paper. We find that in addition to the traditional control overhead and sub-optimal routing overhead, the retransmissions of discarded packets due to buffer overflow in receiver nodes on a route will consume extra bandwidth, which increasing the routing overhead. In this paper, we focus on the impact of packet loss process, analytical expressions for routing overhead and minimal packet loss rate are also derived. A simulation comparing retransmission-aware routing and a hypothetical optimal reactive routing protocol is used as a supplement of our theory, which shows that there still has a great potential to reduce the overhead to improve the network capacity.

Through sensing and utilizing available network resources, cognitive network can obviously increase network performance. In this paper, a distributed on-demand routing protocol with traffic awareness (TACR) is proposed for cognitive wireless network. This routing protocol establishes the path based on the cognition and reasoning of traffic loads in a network and it also meet quality of service (QoS) requirements by introducing autonomous intelligence-Q-learning. Simulation results show that the TACR routing protocol shortens the average end-to-end delay significantly and also improves the average throughput.