Autonomous Underwater Vehicles (AUVs) have been becoming excellent platforms in marine environmental monitoring and data collection missions. However, it is challenging to obtain real-time data (e.g., video and high definition images) from AUV swarms, as traditional methods have limitations, such as low-data-rate communication and restricted mobility. To tackle these challenges, in this paper, we exploit complementary strengths of acoustic-optical technology, as well as the mobility of unmanned aerial vehicles (UAVs), and propose a cooperative cross-boundary multi-modal (CCM) communication scheme, in which multiple UAVs carrying acoustic-optical communication nodes serve as mobile sinks for swarms of AUVs. In this scheme, to deal with the imbalanced coverage problem, we design a cooperative movement strategy (CMS) algorithm for multiple UAVs based on the multi-agent reinforcement learning (MARL) approach, combined with particle filtering and task assignment to UAVs for achieving dynamic coverage of AUVs. Furthermore, to achieve high data-rate transmission dealing with optical beam link misalignment problem caused by interference from currents and movement of AUVs, we design an adaptive pointing adjustment (APA) algorithm to construct a stable optical link by controlling the pointing and divergence angles of optical beam for reliable high-speed data transmission. Through extensive simulations using open-source AUV trajectory datasets and ocean current datasets, we demonstrate the effectiveness of the proposed scheme with robust network performance under dynamic maritime conditions. The code is available at https://github.com/SDUST-smartocean/CCM.

Federated learning (FL) in symbiotic Internet of Things (IoT) networks is a promising collaborative paradigm that utilizes IoT devices to co-train machine learning models, promising to accelerate edge intelligence for 6G. Existing studies on heterogeneous FL in IoT networks focus mainly on the differences in link capacity, ignoring the fundamental impact of channel fluctuation on model transmission and communication energy consumption. FL in symbiotic IoT networks still faces the challenges of heterogeneous and dynamic wireless links and inter-round competition of limited resource allocation, significantly impacting energy efficiency and learning performance. To address this issue, we first model wireless channel fading and dynamics for FL over symbiotic IoT networks and develop a joint optimization model for energy efficiency and learning performance. Then, we propose a novel heterogeneous-aware client sampling scheme to achieve energy-efficient training by exploiting prompt channel state tracking to predict energy consumption and update the deviation of the energy budget of each client promptly to select the optimal set of clients for each training phase. Finally, extensive experiments show that our proposed client sampling scheme significantly outperforms the existing methods and improves energy efficiency by up to$1.6\times $.

Owing to flexible management and low overhead, wireless physical layer security (PLS) has been applied to support many critical applications (e.g., data dissemination) of unmanned aerial vehicles (UAVs)-based mobile communication networks in emergency scenarios. Although the impact of static network scenarios or receiver mobility on PLS has been well studied, there is no much work that studies the impact of UAV-based transmitter mobility on PLS. To fill this gap, in this paper, we investigate PLS of a scenario, where a random mobile UAV-based transmitter transmits information to a static ground entity under Rayleigh fading channel. More specifically, we consider a communication system, in which a mobile UAV hovers over a region to collect information and then disseminates this information to a static ground network entity in a confidential manner under the presence of an eavesdropper. Because of popularity and practicality, the UAV is assumed to hover following the random way point (RWP) mobility model. We investigate the secrecy characteristics of the UAV under steady running state in terms of ergodic secrecy capacity (ESC), positive secrecy capacity probability (PSCP) and secrecy outage probability (SOP) for the communication between the UAV and the receiver. We then investigate the secrecy performance of the proposed system while considering the pause time of the RWP mobility model adopted by the UAV. We further extend our proposed theoretical model to other realistic scenarios, including the presence of multiple cooperative and non-cooperative eavesdroppers, and study the PSCP and SOP metrics of the corresponding system. Furthermore, we propose three types of secrecy improvement strategies for the considered communication model. We strike a good trade-off between the secrecy improvement and transmit outage probability. Extensive simulations have been conducted to validate our theoretical analysis as well as the effectiveness of the proposed secrecy improvement strategies.

The collaboration between unmanned aerial vehicles (UAV s) and unmanned surface vehicles (USV s) is critical in mar-itime search scenarios, in which UAV s can leverage high altitude and wide field of view providing real-time target information, while USV s have longer endurance capability of performing precise operations for surface targets. However, UAV s are limited by their battery capacity, which reduces their search duration and range. Furthermore, the traditional fixed charging stations necessitate UAV s to return for recharging, resulting in mission interruptions and decreased search efficiency. To address this problem, we propose an optimized scheduling scheme for UAV-USV cooperative search, in which USV s act as mobile charging stations to provide wireless charging services for UAV s to increase cooperative search mission duration with uninterrupted search execution. Firstly, the USV trajectory optimization problem under the target search constraint is formulated to minimize its energy consumption and maximize the UAV energy utilization. Then, we model the problem as a partially observable Markov decision process (POMDP) and design a scheduling algorithm leveraging the multi-agent deep deterministic policy gradient (MADDPG) method for long endurance UAV-USV collaborative search mission under energy constraints. Numerical simulations confirm the effectiveness of the proposed scheduling scheme.

Maritime data transmission with unmanned aerial vehicles (UAVs) in maritime Internet of Things (MIoT) systems has received increasing attention due to its flexibility and low cost. To further improve the efficiency of maritime data transmission between the UAVs and the maritime buoys, optical communication is considered as a promising technique because of its low latency and high bandwidth. However, optical communication encounters the challenge of beam pointing alignment, particularly in maritime data transmission involving wave disturbance and drift of buoy, which deteriorates and even interrupts the line-of-sight (LOS) optical transmission. To tackle the challenge, this paper proposes DERLOC, a reliable data transmission solution based on deep reinforcement learning (DRL). We first provide the optimization analysis of reliable data transmission and formulate the data transmission procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity. Afterwards, we propose a beam pointing adjustment algorithm based on the soft actor-critic (SAC) approach to alleviate the performance deterioration caused by waves. Then, we analyze the drift characteristic of a buoy and develop a method which enables UAV to predict the position and determine an optimal movement control strategy for ensuring the effectiveness of beam pointing and maintaining stable LOS communication. Through extensive simulations and real-time data validation, the results demonstrate that DERLOC is effective and enables a reliable data transmission via optical links.

Ensuring secrecy information transmission with enhanced energy efficiency is one of the critical issues in unmanned aerial vehicles (UAVs)-assisted wireless systems owing to the open nature of wireless channels and the limited battery power. In this article, the secrecy energy efficiency (SEE) is investigated for a more critical scenario, where an UAV acts as a bidirectional relay exchanging information between the two ground users and a malicious terminal attempts to implement eavesdropping and interfering on the confidential information simultaneously. Multiple parameters, such as time scheduling, power allocation, UAV trajectory within a given flight cycle, and both multiple access (MA) and broadcast (BC) phases are optimized jointly to maximize the system SEE. To address this nonconvex optimization problem involved with coupled variables tightly, we employ the block coordinate descent (BCD) method and develop an iterative algorithm that leverages successive convex approximation (SCA) and the Dinkelbach algorithm to obtain the optimal solution. Meanwhile, simulation results are presented to demonstrate the superiority of our proposed scheme in terms of SEE.

To implement high-speed wireless communication from the deep ocean to the sea surface leveraging multiple autonomous underwater vehicles (AUVs) with underwater wireless optical communication (UWOC) technique is an emerging and promising technology that enables real-time data collection for accurate underwater exploration and monitoring, e.g., coordinated moving targets tracking. However, multi-hop UWOC is more susceptible to beam misalignment and positional uncertainty caused by external interference in the harsh marine environments. To address these challenges, we design a cooperative movement scheme for multiple AUVs based on deep reinforcement learning (DRL) approach to realize robust and reliable optical communication under mobile target tracking scenarios. In this scheme, we first model the optical channel with optical noise and then analyze the link performance of multiple AUVs to meet the bit error rate (BER) requirements. Afterwards, we map the cooperative optical communication problem to a Markov decision process (MDP) by incorporating the extended Kalman filter (EKF) technique to enhance effective communication. Finally, we propose a cooperative control strategy for multiple mobile AUVs based on deep deterministic policy gradient (DDPG). Through extensive simulations, it is demonstrated that the proposed algorithm is effective in achieving reliable underwater optical communication under mobile scenarios.

In marine wireless sensor networks, swarms of unmanned aerial vehicles (UAVs) based optical communication system can be leveraged to transmit underwater real-time monitoring data which enables a variety of potential maritime missions, such as video streaming for underwater surveillance and target tracking. However, due to the high directionality of optical beams and the mechanical instability of UAVs caused by the wind, the optical link via UAVs as relays is very fragile. To deal with the challenge, in this study, we model the problem as a partially observed Markov decision process (POMDP), and propose a novel link maintenance scheme based on the cooperative multi-agent deep deterministic policy gradient (MADDPG) approach to control the swarms of UAVs, which integrates the UAV dynamics model and the optical communication model. In this scheme, multiple UAVs act as agents controlling their own states in real-time under the complex wind field, in order to keep mechanical stability through cooperation, and dynamically maintain the reliability of the optical links which maximize communication performance to achieve reliable end-to-end optical communication and reduce energy consumption. Through numerical simulations, it demonstrates that the proposed optimization scheme is effective and achieves robust performance in terms of communication quality and energy consumption.

In conventional federated learning (FL), multiple edge devices holding local data jointly train a machine learning model by communicating learning updates with a centralized aggregator without exchanging their data samples. Owing to the communication and computation bottleneck at the centralized aggregator and inaccurate learning model caused by the non-independent and identically distributed (IID) data, we here consider a two-tier FL network, in which Internet of Things (IoT) nodes are the core clients that hold data, the model aggregators at the middle tier are the low altitude aerial platforms (UAVs), and the model aggregator at the top-most layer is the high-altitude aerial platform (UAV with relatively high altitude). Under the assumption that each IoT node has parallel computing ability, we study the energy-efficient computation and communication resource allocation in such a network within some time budget. Upon formulating the problem as an optimization problem, we solve the computation and communication resource allocation problems as the separate subproblems within a time frame, and then propose an iterative algorithm to solve the entire problem jointly. More specifically, we solve both the energy-efficient computation and communication resource allocation subproblems using the dual decomposition technique, and then apply a bisection search-based recursive technique to solve the entire energy efficiency problem jointly. Moreover, we propose offline and online client scheduling schemes that not only select the optimal edge nodes for association but also assign workload to each client based on the data quality and workload constraint. With real data, extensive simulations are conducted to verify the effectiveness of the proposed resource allocation scheme. The results further reveal that the learning performance not only is dependent on the computation and communication energy consumption of the FL process but also the model divergence weight owing to the non-IID data at client IoT nodes.

Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in distributed computing. A popular method to tackle this problem is to use a random sample of the big dataset to compute an approximate result as an estimation of the true result computed from the entire dataset. In this paper, instead of using a single random sample, we use multiple random samples to compute an ensemble result as the estimation of the true result of the big dataset. We propose a distributed computing framework to compute the ensemble result. In this framework, a big dataset is represented in the RSP data model as random sample data blocks managed in a distributed file system. To compute the ensemble clustering result, a set of RSP data blocks is randomly selected as random samples and clustered independently in parallel on the nodes of a cluster to generate the component clustering results. The component results are transferred to the master node, which computes the ensemble result. Since the random samples are disjoint and traditional consensus functions cannot be used, we propose two new methods to integrate the component clustering results into the final ensemble result. The first method uses component cluster centers to build a graph and the METIS algorithm to cut the graph into subgraphs, from which a set of candidate cluster centers is found. A hierarchical clustering method is then used to generate the final set of$k$cluster centers. The second method uses the clustering-by-passing-messages method to generate the final set of$k$cluster centers. Finally, the$k$-means algorithm was used to allocate the entire dataset into$k$clusters. Experiments were conducted on both synthetic and real-world datasets. The results show that the new ensemble clustering methods performed better than the comparison methods and that the distributed computing framework is efficient and scalable in clustering big datasets.

Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

In this paper, we consider the anti-jamming problem for the uplink scenario in federated learning (FL) networks under the uncertainty condition of jamming channels. We first consider a single-carrier single-user FL network, in which the channel gain of the jammer is known to the FL user in probabilistic terms. We formulate the interaction between the FL user and the jammer by a Bayesian Stackelberg game. Upon the derivation of the best response strategy for both the players, we prove the existence and uniqueness of the equilibrium state. We further extend the game formulation for the multi-user multi-channel case, in which multiple FL users access the global aggregator using multiple channels. Similar to the single-user case, in this game, the jammer acts as the leader and the FL network works as the follower. Since the derivation of the optimal strategy is intractable due to the non-convexity nature of the problem, we provide a suboptimal algorithm that provides an equilibrium state for both the players. Extensive simulations on real data set are conducted to verify the effectiveness of the proposed games for both the single and multi-user cases. The results reveal that the priori probabilities of jamming channels greatly affect the strategies of both the players as well as the equilibrium state.

Optical wireless communication (OWC) is an emerging technology for direct communication through the water-air interface. However, due to the high directionality of optical beams and the harsh oceanic environment, it faces significant challenges to achieve the alignment and preserve the link availability, as the waves cause beam deflection and the mobility of the transceivers makes the link worse. To tackle these challenges and achieve reliable optical communication between autonomous underwater vehicles and unmanned aerial vehicles, we propose a deep reinforcement learning algorithm assisted by an extended Kalman filter to solve the alignment issue. To improve the reliability of communication, we present an algorithm to obtain the optimal beam divergence angle to maximize the link availability. The numerical simulations demonstrate that the proposed scheme achieves better performance in terms of energy consumption and alignment accuracy, and the link availability is increased by 25% compared to that without adjustment.

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, calledaerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

With the increasing demand for security in many sectors, such as defense and health systems, developing secure Internet of Things (IoT) networks is a matter of great urgency. Looking at a potential solution for secure IoT systems, we investigate the physical layer security of cooperative nonorthogonal multiple access (NOMA) systems. After decoding information signal, the idea that a strong IoT node can serve as a relay node for other weak IoT nodes in enhancing their signal reception reliability, is known as cooperative NOMA. We consider both single-antenna and multiantenna aided transmission scenarios, where the base station (BS) communicates with two IoT nodes of different strengths. In the multiantenna scenario, artificial noise (AN) is generated at the BS and the strong IoT node for improving the security of the system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both the IoT nodes under both the single-antenna and multiantenna aided scenarios. For the single-antenna scenario, we show that the power optimization at the BS and the strong IoT node can enhance the secrecy performance to some extent. For this case, we further study the secrecy diversity order of the overall system, which is mainly determined by the IoT node with the worse channel condition. For the multiantenna scenario, we derive the asymptotic secrecy outage probability (SOP) when the number of antennas tends to infinity. Extensive simulations have been conducted to verify the accuracy and effectiveness of the proposed analytical derivations. The presented results verify that the security performance of the cooperative NOMA-based IoT network can be improved through an appropriate power control scheme and by generating AN at the BS and the strong IoT node. The simulation results further illustrate that the asymptotic SOP is close to the exact one.

Unmanned aerial vehicles (UAVs) are one of the effective means to provide emergency communication services in post-disaster areas. In this article, we consider data dissemination in post-disaster areas, where all Internet of Things (IoT) nodes may not have data needs all the time. The energy consumption in data dissemination is one of the key metrics to pay attention to since the charging facilities for UAVs may be limited due to the destruction of existing infrastructure. In addition, UAVs have limited endurance or lifetime, so unnecessarily flying over IoT nodes that may not have data is time consuming. Therefore, given the energy budget and data requirements of the IoT nodes, we formulated a data dissemination problem using multiple UAVs in a post-disaster area while optimizing their trajectory, mission completion time, and energy consumption. After time discretization, the formulated problem is a mixed-integer nonconvex problem and thus difficult to solve in general. For this reason, we jointly use the bisection search technique and the block coordinate descent (BCD) method to solve the entire problem while aiming to optimize the trajectory and mission completion time of the considered UAV as well as the overall energy consumption. In each iteration of the BCD method, we solve the user association, trajectory optimization, and power optimization subproblems one after the other in an alternating fashion. To solve each subproblem, we employ the geometric programming (GP)-based optimization technique that transforms the variables and constraints. Regarding the initial trajectory of the UAV, we utilized dynamic programming techniques based on unsaturated data requirements of IoT nodes. We performed extensive simulations in many realistic environments to verify the effectiveness and efficiency of the proposed data dissemination scheme in post-disaster scenarios.

Due to its inherent ability in reducing hardware cost and power consumption while maintaining high system capacity, hybrid precoding is deemed as one of the key technologies in the upcoming 5G/6G millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, it is challenging to design high performance hybrid precoders/combiners with low computational complexity. In this paper, based on the singular value decomposition (SVD) technique and the concept of equivalent channel, joint hybrid precoding strategies with high spectral-efficiency and low complexity are proposed for both single-user and multi-user massive MIMO systems. Specifically, for single-user massive MIMO scenarios, after transforming the design of hybrid beamforming into the problem of maximizing the square of sum eigenvalues for an equivalent channel, a two-stage successive method is conceived to design the analog precoder and combiner jointly, and the corresponding equivalent channel is constructed. Then, the digital precoding and combining operations are realized directly by applying the SVD technique to the matrix of equivalent channel. Meanwhile, the hybrid precoding strategy is extended to the multi-user scenario for achieving high performance resultant from multi-user diversity. Extensive simulations are conducted to verify the effectiveness of the precoding/combing schemes. The results show that our proposed schemes can achieve superior performance with lower complexity compared to the existing ones.

Underwater wireless optical communication (UWOC) has received widespread attention recently due to its advantages in terms of high bandwidth, low power consumption and low delay. However, unlike underwater acoustic communication systems, the underwater wireless optical communication system is limited by its short communication range. In order to enlarge its application domain, it requires relaying and routing technology to achieve long-distance high bandwidth reliable transmissions (e.g., real-time video streaming). Although pre-deploying large number of sensor nodes is one of the possible solutions, this is impractical in response to emergency events such as oil pipeline leakage and shipwreck recovery. To this end, in this paper, we leverage swarms of autonomous underwater vehicles (AUVs) to build underwater optical communication links to transfer real-time video streaming. We first model the optical transmission channel considering solar noise, and then calculate both the optical communication angle and the reliable communicating range that satisfy a pre-defined bit error rate (BER). We then formulate the deployment optimization problem while targeting on minimizing both deployment energy consumption and delay. We also take positioning errors of AUVs into account to guarantee reliable communications. We provide an energy efficient algorithm to solve the formulated deployment problem. Through extensive simulations, we show that the algorithm effectively improves the deployment performance and achieves reliable communications.

Mobility is one of the most fascinating features of unmanned aerial vehicles (UAVs) to support many critical applications (e.g., data dissemination) in emergency scenarios. Communication information in such applications could be confidential, and hence an effective security technique is required to mitigate the presence of an eavesdropper to some extent. On the other hand, blessed by the flexibility in the management and low overhead, physical layer security has received a significant attention in the recent years. To this end, we consider a communication system, in which a sensor-equipped mobile UAV hovers over a region to collect information and then disseminate this information to static a ground network entity in a confidential manner. Because of the popularity and practicality, the UAV is assumed to hover following the random way point (RWP) mobility model. We investigate the secrecy characteristics of the UAV under steady-state running state in terms of positive secrecy capacity probability and secrecy outage probability for the communication between the UAV and the receiver. Because of the intractable singleton analysis owing to the UAV mobility, we derive four separate closed form expressions of these performance metrics in four quadrants of conventional coordinate systems. We then investigate the secrecy performance of the UAV while considering the pause time of its RWP model. Furthermore, we propose two types of secrecy improvement strategies for the considered communication model. We strike a good trade-off between the secrecy improvement and transmit outage probability. Extensive simulations have been conducted to validate our theoretical analysis.

Being a beneficial service in emergency situations, UAV-aided relay communications have received tremendous attentions in the recent years. In this paper, we consider a three-node UAV-based relay network, in which a mobile UAV establishes communication between two remaining nodes in the system. Despite numerous works available for such a system on the optimization of trajectory and other communication resources, none of these have addressed the delay performance of the system at the granular packet level. Furthermore, it is already established that the equipment of buffer at the relay node provides more flexibility in the delivery of packets through the exploitation of better channel quality. On the other hand, with the continued popularity of multimedia and similar other applications, it is very likely that the source node in the system receives bursty traffic from different external networks. Given that the source and UAV nodes have finite packet-level buffers and the trajectory of the UAV is known, under a bursty traffic model, we aim to study the average end-to-end packet delay and buffer overflow performance of the system. While capturing the predictable channel variation due to the movement of the UAV, we establish a queuing model for the source and UAV nodes based on the stochastic process. Then, from the dynamics of the established queuing model, we derive the average end-to-end packet delay and queue overflow probability of such a system. Through extensive numerical simulation, we justify the accuracy and effectiveness of the proposed analytical model while comparing with three static deployment scenarios of the UAV. Through providing sufficient analytical evidence as well as the numerical results, we exhibit that a mobile relay system outperforms the static relay one in terms of both the delay and buffer overflow metrics.

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30-minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works. Moreover, EchoWrite provides favorable user experience of entering texts.

Despite extensive research efforts, underwater sensor networks (UWSNs) still suffer from serious performance issues due to their inefficient and uncoordinated channel access and resource management. For example, due to the lack of holistic knowledge on the network resources, existing decentralized routing protocols fail to provide globally optimal performance. On the other hand, Software Defined Networking (SDN), as a promising paradigm to provide prominent centralized solutions, can be employed to address the aforementioned issues in UWSNs. Indeed, SDN brings unprecedented opportunities to improve the network performance through the development of advanced algorithms at controllers. In this paper, we study the routing problem in such a network with new features including centralized route decision, global network-state awareness, seamless route discovery while considering the optimization of several long-term global performance metrics. We formulate the entire routing problem of a multi-modal UWSN as an optimization problem while considering the interference phenomenon of ad hoc scenarios and some long-term global performance metrics of an ideal routing protocol. Our formulated problem nicely captures all possible flexibilities of a sensor node no matter it has the full-duplex or half-duplex functionality. Upon the formulation, we recognize the NP-hard nature of the problem for all possible scenarios. We adopt a rounding technique based on the convex programming relaxation concept to solve the formulated routing problem that considers full-duplex scenarios, whereas we solve the problem for half-duplex scenarios using a greedy method upon interpreting it as a submodular function maximization problem. Through extensive simulation via our Python-based in-house simulator, we verify that our proposed globally optimal routing scheme always outperforms three existing decentralized routing protocols (each of these protocols are selected from each of three prominent protocol types, i.e., flooding, cross-layer information and adaptive machine learning based, respectively) in terms of reliability, latency, energy efficiency, lifetime and fairness.

Signature is one of the most prevailing identity authorization approaches. It is yet inconvenient to use in real life in the sense that a majority of existing signature verification approaches rely on additional digital signing devices. In this paper, we propose a portable device-free signature verification system named SilentSign which makes use of acoustic sensors (i.e., microphone and speaker) embedded in smart devices to enable secure and convenient signature verification service. The basic idea is to leverage acoustic signals to measure the distance variation of the tip of the pen while signing. We carefully design the signal modulation scheme, develop a phase-based distance measurement technique, and train the verification model for high performance and robustness. Compared with conventional digital signing systems, SilentSign allows users to sign more invisibly and conveniently. We conduct extensive experiments involving 35 participants to evaluate SilentSign. Results show that SilentSign can achieve 98.2% AUC and 1.25% EER.

Strength training is becoming increasingly popular among all age groups, as it helps the participants increase muscle strength, improve body flexibility, reduce health risks, and reshape physical forms. However, strength training imposes strict regulations on gestures and requires professional instruction in real time for the sake of body-building efficiency and safety. For this purpose, in this article, we propose a novel low-cost system named iCoach, to provide real-time monitoring and coaching service for strength training participants. Specifically, we design and implement a smart fitness glove, which can be seamlessly equipped with a pervasive inertial unit. With this customized but low-cost device, we can recognize various training programs, detect nonstandard behaviors while exercising, and assess exercising qualities of a user. Our primary experimental results show that iCoach can recognize 15 sets of training programs, detect three common nonstandard behaviors, and assess the quality of training with high accuracy and reliability.

Nowadays, smart wristbands have become one of the most prevailing wearable devices, as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to type on. Yet, by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which first leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rates, ViType designs a set of novel mechanisms, including a fine-grained feature extraction to process the vibration signals, and a runtime calibration and adaptation scheme to recover from the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust against various confounding factors. The average recognition accuracy is 95 percent with an initial training sample size of 20 for each key. The accuracy is 1.54 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98 percent on average during one month.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30- minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works.

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand "landmarks'', especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, "overclocks'' its sampling rate to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96%) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.4%), and under various practical usage disturbances.

Wearable wristbands have become prevailing in the recent days because of their small and portable property. However, the limited size of the touch screen causes the problems of fat fingers and screen occlusion. Furthermore, it is not available for users whose hands are fully occupied with other tasks. To break this bottleneck, we propose a portable, hand-free and secure text-entry system, called FaceInput, which firstly uses a single small form factor sensor to accomplish a practical user input via facial vibrations. To sense the tiny facial vibration signals, we design and implement a double-stage amplifier whose maximum gain is 225. To enhance the input accuracy and robustness, we design a set of novel schemes for FaceInput based on the Mel-frequency cepstral coefficient (MFCC) concept and a hidden Markov model (HMM) to process the vibration signals, and an online calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects during the period of one month. The results demonstrate that FaceInput can be successful to sense the tiny facial vibrations and robust to fight against various confounding factors. The average recognition accuracy is 98.2%. Furthermore, by enabling the runtime calibration and adaptation scheme that updates and enlarges the training data set, the accuracy can reach 100%.

A key feature of the non-orthogonal multiple access (NOMA) technique is that users with better channel conditions have prior information about the messages of other users. The technique to exploit the prior knowledge of strong users in order to improve the performance of weak users is known as cooperative NOMA. In this paper, we study the physical layer security in such a cooperative NOMA system. In order to reduce the complexity, the considered system in this paper has two users. Through the cooperative NOMA concept, the performance of the weak user is enhanced by the strong user. Given that there is an eavesdropper in the system that can hear all transmissions, we study the secrecy rate of the strong and the weak users. More specifically, we make an attempt to derive the secrecy outage probability (SOP) of both the users. Due to the intractable nature of the exact analysis for the weak user, we provide the closed form expression for the SOP of this user in high SNR regime while keeping the exactness for the strong user. Through numerical simulations, we verify the correctness of our analytical derivations under different scenarios. Besides, we provide the insights of achieving optimal secrecy performance in such a system.

Recently, we have seen the unprecedented development in unmanned aerial vehicles (UAVs) from different aspects. Accordingly, an increasing number of applications have emerged based on UAVs. Among which, placing UAVs as Aerial Base Stations (ABSs) has received considerable interest in both the industrial and academic community. Existing solutions focus on the optimization of the UAV deployment problem for static user topology using the control information obtained from the Terrestrial Base Station (TBS), that makes hard for the controller to make real-time decisions. To break this stalemate, we propose a SemI-DistributEd system, named SIDE, for the UAV self-deployment. In SIDE, we introduce a mechanical equilibrium based approach, named EMech, via which the UAV positions are self-adapted according to users' attraction (e.g., user distance and traffic demand) within their transmission range. To facilitate the EMech, we propose a fine-grained area splitting strategy, termed KDivision, that partitions the service area in accordance with the user density. Finally, an area merging technique, namely RMerge, is exploited to approximately optimize the positions of the UAVs assisted by an Utility Function that strikes a balance amid the network performance and economic cost. We conduct field experiments to validate the feasibility of EMech. Extensive simulation results show that the proposed SIDE finds the optimal number of assigned UAVs, which not only reduces the cost of the system significantly, but also improves the achievable rate up to 74.6% compared to the existing solutions while consuming almost the same energy level.

Atmospheric humidity is a significantly important factor in our daily lives, as it is closely bound up with agriculture, industrial production, human health and so on. Therefore, efficient and precise humidity measurement techniques are indispensable. However, the existing off-the-shelf techniques, including the dry and wet bulb hygrometer, humidity sensor as well as WiFi based detector, all fail to achieve a sensitive, accurate and convenient humidity measurement, especial for a large scale deployment. In this paper, we observe that different levels of water vapor have certain impact on millimeter wave (mmWave) signals in indoor environments. Accordingly, we propose an fine-grained environmental humidity sensing technology via wireless signals in the mmWave band. However, mmWave signals are not only sensitive to humidity, but also other environmental factors, such as oxygen. To establish a linear relationship between humidity and mm Wave signal propagation, we exploit a subspace projection technique to remove the environmental noise. Upon extracting the humidity-associated features in the noise-free signal, we utilize support vector machine (SVM) to model the humidity measurement classifier of a certain place. Extensive experiments have been conducted in different scenarios in order to verify the effectiveness of the proposed system. Results show that the average accuracy of humidity measurement is up to 85 % when the humidity interval is 3 %, and is 95 % when the humidity interval is 5%. We further show that the proposed method is very sensitive to the humidity dynamics and is 63.2 times faster compared to the traditional hygrometers.

Wi-Fi Mesh Networks (WMNs) as a popular platform can be used for the construction of dynamic backhaul networks especially over small cells, which is an indispensable part of the 5G technology. Finding the optimal single-path flow routing solution over the multi-hop backhaul networks is a classic NP-hard problem and there are non-trivial drawbacks such as packet re-ordering to implement multi-path routing as a practical solution. However, Software Defined Networking (SDN) as an emerging paradigm can provide a great opportunity to implement fast and efficient solutions for routing the network flows over WMNs. In this paper, we propose a randomized single-path flow routing that can be applied to SDN-aware WMNs. The randomized nature of our introduced solution avoids the complexities of implementing a multi-path flow routing and it presents a viable routing scheme that guarantees certain performance bounds. In addition, it considers the key characteristics of wireless networks and it can be employed for multi-channel multi-radio WMNs. Through numerical results, we have shown that our solution follows the theoretical, tighter and more general performance bounds. Moreover, in contrast to most of the prior studies, the performance of the proposed solution is not only evaluated through a real testbed (in terms of the aggregated throughput and protocol overhead) but also compared with some of the most popular WMN routing protocols.

Nowadays, smart wristbands have become one of the most prevailing wearable devices as they are small and portable. However, due to the limited size of the touch screens, smart wristbands typically have poor interactive experience. There are a few works appropriating the human body as a surface to extend the input. Yet by using multiple sensors at high sampling rates, they are not portable and are energy-consuming in practice. To break this stalemate, we proposed a portable, cost efficient text-entry system, termed ViType, which firstly leverages a single small form factor sensor to achieve a practical user input with much lower sampling rates. To enhance the input accuracy with less vibration information introduced by lower sampling rate, ViType designs a set of novel mechanisms, including an artificial neural network to process the vibration signals, and a runtime calibration and adaptation scheme to recover the error due to temporal instability. Extensive experiments have been conducted on 30 human subjects. The results demonstrate that ViType is robust to fight against various confounding factors. The average recognition accuracy is 94.8% with an initial training sample size of 20 for each key, which is 1.52 times higher than the state-of-the-art on-body typing system. Furthermore, when turning on the runtime calibration and adaptation system to update and enlarge the training sample size, the accuracy can reach around 98% on average during one month.

Currently, one of the main enablers for network evolution is software-defined networking (SDN), where the control plane is decoupled from the data plane. A controller, as a (logically) centralized entity in the control plane, is the Achilles' heel of SDN resilience since its failure would affect the proper functioning of the entire network. The resilience of the control plane is strongly linked to the controller placement problem, which deals with the positioning and assignment of controllers to the forwarding devices (i.e., switches). A resilient controller placement problem needs to assign more than one controller to a switch while it satisfies certain quality of service requirements. In this paper, we propose a solution for such a problem that, unlike most of the former studies, takes both the switch-controller/inter-controller latency requirements and the capacity of the controllers into account to meet the traffic load of switches. The proposed algorithms, one of which has a polynomial-time complexity, adopt a clique-based approach in graph theory to find high-quality solutions heuristically. It is evaluated with real wide area network (WAN) topologies and the corresponding results are extensively analyzed. The resultant studies equip the service providers with helpful insights into the design of a resilient software-defined WAN.

Non-orthogonal multiple access (NOMA) is envisioned to be one of the most beneficial technologies for next generation wireless networks due to its enhanced performance compared with other conventional radio access techniques. Although the principle of NOMA allows multiple users to use the same frequency resource, due to decoding complication, the information of users in practical systems cannot be decoded successfully if many of them use the same channel. Consequently, assigned spectrum of a system needs to be split into multiple subchannels in order to multiplex that among many users. Uplink resource allocation for such systems is more complicated compared with the downlink ones due to the individual users' power constraints and the discrete nature of subchannel assignment. In this paper, we propose an uplink subchannel and power allocation scheme for such systems. Due to the NP-hard and non-convex nature of the problem, the complete solution, that optimizes both subchannel assignment and power allocation jointly, is intractable. Consequently, we solve the problem in two steps. First, based on the assumption that the maximal power level of a user is subdivided equally among its allocated subchannels, we apply many-to-many matching model to solve the subchannel-user mapping problem. Then, in order to enhance the performance of the system further, we apply iterative water-filling and geometric programming two power allocation techniques to allocate power in each allocated subchannel-user slot optimally. Extensive simulation has been conducted to verify the effectiveness of the proposed scheme. The results demonstrate that the proposed scheme always outperforms all existing works in this context under all possible scenarios.

Localization in complex indoor environments with RF signal is a challenging task. Via this technique, the signal is easily affected by obstacles and environmental noise due to the broadcast nature of RF signal transmission. In this paper, we observe that seismic signal is more oriented than the RF signal, and thus is more suitable for localization in an complex indoor environment with obstacles. Motivated by this observation, we propose a device-free passive indoor localization system, namely Floc, that can fight against environmental impact and achieve high localization accuracy. FLoc is composed of three modules, which are sensing module that collects seismic signal from footsteps, footstep detection module that remove the environmental impact and recover the clean footsteps, and localization module that leverages seismic signal from footsteps for positioning. We implement FLoc on credit-card sized single-board computer Raspberry Pi equipped with geophones. To verify the effectiveness of our system, we conduct extensive experiments for different scenario in a complex indoor environment with the area of 6 × 8 square meter. The experimental results demonstrates that Floc can achieve up to 7cm localiztion accuracy on average, and can outperform the existing acoustic signal-based localization techniques.

Conflagration is one of the major disasters that threatens human life and property. If the proper action is not taken in detecting the symptom of conflagration events ahead of time, the number of such disasters will keep increasing. An effective solution in this context will alleviate many fire-related global problems to a great extent. Although fire detectors are not available in many places, WiFi networks are increasingly prevalent nowadays. Motivated by the previous works that used WiFi signals for the purpose of environment monitoring and activity recognition, we make an attempt to use WiFi signals to detect fire. Through several experiments, we find that fire influences the transmission of wireless signals uniquely, and consequently it affects the amplitude and phase of the resultant Channel State Information (CSI). Based on this observation, in this paper, we propose a device-free fire detection system, namely Wi-Fire, using commercial WiFi devices. To the best of our knowledge, this is the first work that leverages CSI of radio frequency (RF) signal to detect fire events using existing wireless infrastructure without requiring any additional device. We implement our proposed system on desktop computers equipped with commercial 802.11n network interface cards (NICs). Comprehensive experiments have been conducted for different scenarios in different environments to verify the effectiveness of our proposed system. The results verify that the fire detection accuracy of this training-based system is up to 96.67% on average.

Underwater sensor networks have many applications ranging from ocean monitoring, undersea exploration, target tracking, coastal surveillance, to disaster prevention. In multi-application scenarios, the network might need to handle different types of packets to satisfy the requirements of diverse data transmission metric. For example, multimedia-based applications may include different multimedia packets, such as voice, compressed images, even video streams with different quality of experience. To meet the requirements of such applications, in this paper, we propose a hybrid adaptive routing scheme (HARS) for drifting restricted floating ocean sensor networks (DR-OSNs), which exploits both surface wireless and underwater communication channels to fulfill different performance requirements. We evaluate the performance of the routing scheme and investigate the factors which affect the scheme. The simulation results demonstrate that the scheme achieves a reasonable performance for different communication channels and packet delivery.

Blind mobility aid is a primary part in the daily life of blind people. Although plenty of systems or devices are invented to make the navigation of blind people easier, those are generally expensive and hardly affordable for them. To solve these issues, we introduce ABAid, a novel system designed for blind or visually impaired people to navigate, with commercial off-the-shelf (COTS) mobile devices. Based on in-depth acoustic localization and gyroscope techniques, this system is not only the means of huge convenience to carry, but also is capable of detecting obstacles before reaching them. In our experiments designed to detect the distance of wall, the proposed system achieves 3.24% average error rate. It can further measure the direction of wall, and the average error in this case is 2.73°. With high accuracy and stable measurement, ABAid is able to help blind people move independently in fairly uncomplicated scenarios.

The wearable devices are small and easy to carry but typically with poor interaction experience. For example, Apple iWatch does not support instant text message input feature because of the lack of keyboard availability on the tiny touch screen. To address this problem, we develop a novel system, termed iKey, which enables users to use the back of one of their hands as virtual keyboard for wearable wristbands. iKey recognizes keystrokes based on a location-based training model via body vibration. We will demonstrate a real time functional prototype of iKey in this demo.

In recent years, dramatic growth of mobile data traffic has left the operators no choice but to consider Wi-Fi networks as an economic complementary solution. To achieve this, WLANs require to adopt some of the key features of carrier-grade operators, such as centralized resource management. As an emerging paradigm, Software Defined Networking (SDN) can be used to provide salient centralized network solutions for Wi-Fi infrastructures. In fact, applying SDN to different wireless platforms, e.g., Wi-Fi Mesh Networks (WMNs), brings unprecedented opportunities to improve the network performance by employing more sophisticated algorithms at SDN controllers. Moreover, it should be noted that traffic engineering over WMNs incorporates tightly correlated steps including association control, gateway selection and flow routing which are individually NP-hard problems. In this paper, we present an agile and fine-grained access provisioning solution via bridging the cellular and Wi-Fi technologies that empowers us to address the users demand by steering data flows on different tiers of WMNs. In contrast to the prior work, we present a detailed unified formulation for joint gateway selection and flow routing in Multi-Channel Multi-Radio (MCMR) WMNs that considers the key attributes of wireless networks. The functionality of the presented solution is evaluated through various experiments with extensive numerical results.

With the development of acoustic localization schemes using mobile devices, keystroke detection has received tremendous attention from the academia and industry. Currently, most of the existing systems focus on the recognition of a single keystroke and they are limited by several restrictions. In this paper, we consider the idea of signal variation caused by the combination of two combined keystrokes, and propose an acoustic-based scheme that can detect the combined keystrokes effectively. Our system exploits the blind signal separation technique to deal with the mixed signals, resultant from typing two separate keys simultaneously. Then, we apply feature extraction and pattern recognition algorithms to recognize the combined keystrokes. Extensive experiments have been conducted in a laboratory environment with mobile phones equipped with two microphones. Our results show that for several combinations of two keystrokes, on average, we can achieve 78.4% recognition accuracy.

Because of its outstanding performance in extending coverage and reducing transmit power, wireless relay cooperation is deemed as one of the promising techniques to realize green-broadband communication in the future. In such relay cooperative systems, optimal power allocation not only can prolong the lifetime of users, but also is an effective way to reduce radiation in the environment. In this paper, for amplify-and-forward-relay cooperative networks, where multiple communication pairs share a common relay, we propose a novel power allocation scheme, which has considerably lower complexity compared with the existing solution schemes. The objective of this paper is to minimize sum-source-power consumption under the conditions that the transmit power of all nodes in the network is constrained and their predetermined target signal-to-noise ratios are satisfied. By providing sufficient analytical evidence, we study the optimality, convergence, and computational complexity of our proposed scheme. Through numerical simulation, we further justify the effectiveness and efficacy of our scheme compared with the existing works.

Coordination among users is inevitable in wireless communication for efficient medium access. Even though the data rate of individual user increases significantly, the performance of wireless network does not grow up accordingly due to the high MAC coordination overhead. In this paper, we present VFA, namely virtual frame aggregation, to achieve high coordination efficiency by amortizing the overhead over multiple transmissions. VFA provides a novel way to construct a winner cluster and allow the winners to transmit without interruption. Specifically, in a multicarrier network, every contending node chooses a subcarrier and the nodes are ordered by the index of the chosen subcarrier. When there are some subcarriers chosen by two or more nodes, an additional slot is exploited to reorder the collided nodes. Finally, all ordered nodes form a cluster and the transmissions are issued sequentially and uninterruptedly. Simulation results show that usually two slots are enough to construct a sufficiently large winner cluster. Moreover, VFA achieves a notable throughput gain over IEEE 802.11 as high as 120% with better fairness under various scenarios.

Relaying is an emerging technique for 3G/4G high bandwidth networks in order to improve the capacity of edge nodes. As the deployment cost is high, there might be a few number of relay nodes in the cell which can help the edge nodes to transmit their data. From this perspective, one of the key problems in a relay equipped node is to make decision which edge nodes to be helped and how much power need to be disseminated among them in order to maximize the system capacity. This problem is formulated as an optimization problem given individual node and total available power constraints. The objective function of the formulated problem is non-convex, and we solve this using geometric programming (GP)-based method. Since the solution of this problem is computationally expensive, we propose a low complexity suboptimal solution. Having noticed the selfless nature of the sources in the centralized solution, we also provide a game theoretical solution. Two separate Stackelberg games are required to solve this power allocation problem. Moreover, given the total power constraint, a centralized entity is necessary to connect these two games. For assigning power among the sources, the centralized entity plays the buyer level game, whereas the sources act as power sellers. On the other hand, to disseminate relay power among the sources, roles of the players are just interchanged. Besides, before staring the game, the centralized entity determines, of total power, how much is for the transmit operation of the sources and how much is for their relay operation. We show that there is a unique Stackelberg Equilibrium (SE) for both games under certain convergence condition. Finally, the proposed game theoretical solution can achieve comparable performance in terms of resource allocation with the centralized optimal one.

Relaying is one of the key techniques adopted by third‐generation partnership project long‐term evolution (LTE) advanced as part of 4G cellular technologies, aiming to increase coverage and capacity of networks especially for the edge nodes. The authors have considered the uplink scheduling of LTE networks with the help of positioned relay nodes which have fixed routing configuration. The entire problem is projected as a constrained convex optimisation formulation and for the solution purpose, subgradient method is adopted. Revealing the guiding principles of optimal solution, a few suboptimal scheduling algorithms are proposed to allocate resource blocks across all nodes with the help of existing work. Deploying a large number of relays may not be useful to basic user nodes, and hence, the proposed schemes are adaptive and have ability to distinguish useful relays from not‐useful ones. In addition to system throughput maximisation, for ensuring fairness across user nodes, the authors have proposed scheduling techniques which are the outcome of Nash bargaining solution. Numerical calculations and results have been shown to justify that relay nodes can potentially improve system's performance at low load, whereas at high load they remain inactive because of their inability to contribute.

TDD/TDMA based secondary network with cognitive capability is considered here. Primary network is WiFi based where the secondary network operates. DCF protocol has been followed by the users of WiFi network. Exact operating mechanism of DCF protocol is complex. Given this complexity, it is difficult for a secondary user to fully exploit the spectrum hole unused by WiFi users. We propose a novel opportunistic channel access scheme for the overlay network. Based on some valid arguments, we have justified the optimal transmission strategy of secondary user. Analytical derivation has been given for both types of user when the later acts over the former. Through numerical results, we have justified that the proposed strategy achieves far better performance affecting the original users' performance slightly.

Video streaming over wireless links is a challenging issue due to the stringent quality-of-service requirement of video traffic and the contention nature of wireless media with limited channel resources. Various wireless Media Access Control (MAC) protocols have been proposed, and most of them follow either the contention-based or contention-free approach. In this paper, we propose to stream video traffic over both contention-based and contention-free MAC protocols, exampled by WiMedia UWB Prioritized Contention Access (PCA) and Distributed Reservation Protocol (DRP), and then evaluate the performance of such an approach with extensive simulation. The novelty of our approach is to use both PCA and DRP for every single video stream by reserving well below the peak data rate and handling traffic burst through reduced contention. The simulation results further reveal the tradeoff between hard and soft DRP with single or dual buffer on the performance of video streaming.

Relaying is one of the key techniques considered by the 3GPP LTE-Advanced as part of 4G cellular technologies, aiming to increase the coverage and capacity of the network especially for the edge nodes. In this work, we have considered the uplink scheduling of an LTE network with the help of positioned relay nodes. We have projected the entire problem as a constrained optimization problem. As a solution, we have considered sub-gradient based approach to divide the utility maximization of all nodes, including relay nodes, in the cell into sub-problems of utility optimization of each individual node. Based on this solution we have proposed a scheduling algorithm to allocate resource blocks across all nodes. Numerical calculations and results have been shown to justify that relay nodes can potentially achieve the trade-off between throughput and fairness.

To reserve or not for bursty video traffic over wireless access networks has been a long-debated issue. For uplink transmissions in infrastructure-based wireless networks and peer-to-peer transmissions in mesh or ad-hoc networks, reservation can ensure the Quality-of-Service (QoS) provisioning at the cost of a lower degree of resource utilization. Contention-based Medium Access Control (MAC) protocols are more flexible and efficient in sharing resources by bursty traffic to achieve a higher multiplexing gain, but the performance may degrade severely when the network is congested and collisions occur frequently. More and more wireless standards adopt a hybrid approach, which allows the coexistence of resource reservation and contention-based MAC protocols. However, how to cost-effectively support video traffic using hybrid MAC protocols is still an open issue. In this paper, we first propose how to use hybrid MAC protocols to support video streaming over wireless networks. Then, we quantify the performance of video traffic over wireless networks with contention-only, reservation-only, and hybrid MAC protocols, respectively. Admission regions for video streams with these three approaches are obtained. Using the standard WiMedia MAC protocols as an example, extensive simulations with a commonly-used network simulator (NS-2) and real video traces are conducted to verify the analysis. The analytical and simulation results reveal the tradeoff between reservation and contention-based medium access strategies, and demonstrate the effectiveness of the hybrid approach.