In recent years, with the advancements in brain science, spiking neural networks (SNNs) have garnered significant attention. SNNs can generate spikes that mimic the function of neurons transmission in humans brain, thereby significantly reducing computational costs by the event-driven nature during training. While deep SNNs have shown impressive performance on classification tasks, they still face challenges in more complex tasks such as object detection. In this paper, we propose SpikingYOLOX, extending the structure of the original YOLOX by introducing signed spiking neurons and fast Fourier convolution (FFC). The designed ternary signed spiking neurons could generate three kinds of spikes to obtain more robust features in the deep layer of the backbone. Meanwhile, we integrate FFC with SNN modules to enhance object detection performance, because its global receptive field is beneficial to the object detection task. Extensive experiments demonstrate that the proposed SpikingYOLOX achieves state-of-the-art performance among other SNN-based object detection methods.

Physical layer security (PLS) in symbiotic radio (SR) systems is primarily considered for passive eavesdropping scenarios. However, overlooking the impact of proactive eavesdroppers poses significant risks. In this paper, we focus on secure transmission in SR systems under proactive eavesdropping conditions. A PLS strategy is investigated for a full-duplex facultative symbiotic radio (FD-FSR) system. First, we introduce an innovative FSR protocol. It allows backscatter devices (BDs) to dynamically switch between cognitive and symbiotic patterns. Next, we develop a multi-device scheduling method. It adaptively assigns BDs as transmitters, cooperators, or jammers to enhance the secrecy rate. We formulate the pattern switching and BD scheduling as a mixed integer programming problem (MIP). To solve this, we first decompose it into binary decision-making and multi-variable optimization sub-problems. Then, a low-complexity two-stage optimization strategy is employed. Numerical results demonstrate that our proposed strategy significantly outperforms existing schemes.

Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learning (FL) stands as one of the fundamental technologies facilitating collaborative model training locally and aggregation, while safeguarding the privacy of vehicle data in VEI. However, traditional FL faces challenges in adapting to vehicle heterogeneity, training large models on resource-constrained vehicles, and remaining susceptible to model weight privacy leakage. Meanwhile, split learning (SL) is proposed as a promising collaborative learning framework which can mitigate the risk of model wights leakage, and release the training workload on vehicles. SL sequentially trains a model between a vehicle and an edge-cloud (EC) by dividing the entire model into a vehicle-side model and an EC-side model at a given cut layer. In this work, we combine the advantages of SL and FL to develop an adaptive split FL scheme for VEC (ASFV). The ASFV scheme adaptively splits the model and parallelizes the training process, taking into account mobile vehicle selection and resource allocation. Our extensive simulations, conducted on nonindependent and identically distributed data, demonstrate that the proposed ASFV solution significantly reduces training latency compared to existing benchmarks, while adapting to network dynamics and vehicles’ mobility.

Along with the rapid development of the fourth industrial revolution, industrial cyber-physical systems (ICPS) are anticipated to achieve precise mapping and management for the physical world by integrating digital sensing and automated control. However, the conflict between limited computing resources and extensive sampling data, combined with severe industrial interference, exacerbates the system’s processing burden and diminishes its accuracy, hindering its ability to meet the low-latency and high-reliability control requirements. To address this issue, this paper investigates an end-edge collaborative control framework to enhance control performance for a short-packet transmission ICPS by providing powerful computation capability. We utilize the age of information (AoI) to characterize the impact of information freshness on control accuracy and construct an AoI-aware control law to assist in data sensing, transmission, and computing strategy design. In addition, we consider the influence of sampling and short-packet decoding errors in AoI-aware control performance to enhance the reliability of sampling and transmission strategies design. A joint optimization scheme of sampling interval, sampling time, computation offloading, and bandwidth allocation based on the block coordinate descent method and game theory is proposed to achieve a tradeoff between the control cost and energy consumption. By considering a real-world trolley inverted pendulum manipulation model, numerical results verify the performance gain of the proposed end-edge collaborative framework and the effectiveness of the presented algorithm.

In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great potential of FL to enhance the architecture of IoMT, the need for effective interference management and the limited energy resources of IoMT devices make the integration of FL into IoMT environments particularly challenging. This study proposes an innovative framework to address these challenges by optimizing power allocation and client selection across participating IoMT devices in the FL process. By employing a Stackelberg game model, our approach orchestrates power allocation among IoMT devices to enhance communication efficiency while adhering to strict differential privacy (DP) standards. Regarding the availability of network state information, we propose non-uniform pricing and uniform pricing strategies, respectively. Then, we derive the optimal interference price and power for the IoMT devices using nonlinear programming and convex optimization. To tackle the issue of energy constraints in IoMT devices, we adopt Lyapunov optimization for adaptive client selection, ensuring sustainable device participation in the FL process over time. In addition, our approach integrates DP to protect patient data, carefully balancing between privacy and the accuracy of the learning model. Our extensive simulations demonstrate marked improvements in privacy preservation, communication efficiency, and energy management efficiency, highlighting the effectiveness of our proposed method over existing solutions.

The International Telecommunication Union defined the requirements for 5G in the International Mobile Telecommunications 2020 (IMT-2020) standard in 2017. Since then, advances in technology and standardization have made the ubiquitous deployment of 5G via satellite a practical possibility, for example, in locations where terrestrial networks (TNs) are not available. However, it may be difficult for satellite networks to achieve the same performance as TNs. To address this, the IMT-2020 requirements for satellite radio interface technology have recently been established. In this paper, these requirements are evaluated through system simulations for the 3rd Generation Partnership Project New Radio non-terrestrial networks with a low Earth orbit satellite. The focus is on the throughput, area traffic capacity, and spectral efficiency requirements. It is observed that the downlink (DL) requirements can be met for user equipment with 2 receive antenna elements. The results also reveal that frequency reuse factor 1 (FRF1) may outperform FRF3 in DL with a dual-antenna setup, which is a surprising finding since FRF3 is typically considered to outperform FRF1 due to better interference reduction. For uplink (UL), 1 transmit antenna is sufficient to meet the requirements by a relatively large margin – a promising result given that UL is generally more demanding.

In the edge computing system, split learning (SL) is an emerging distributed learning approach that allows mobile users (MUs) and edge nodes (ENs) to train the model together without sharing the raw data of the MU. Although MU can preserve its privacy in SL, attacks on the intermediate data at the cut layer for model training can still lead to privacy leakage, which prevents privacy-sensitive MUs from participating in training. Therefore, it is important to implement an effective incentive mechanism to motivate MUs to join SL while preserving their privacy. In this work, from the perspective of maximizing the utility of edge service provider (ESP) while considering the privacy-sensitivity of different MUs, the incentive problem of ESP and MUs is transformed into the utility optimization problem, and an incentive mechanism based on the differential privacy (DP) and contract theory is established to model the interactions between the MUs and EN. The obtained convex optimization problem is obtained through the mathematical derivations, and the optimal contract is presented by solving this problem. The simulation results demonstrate the effectiveness of our proposed privacy preserved incentive mechanism.

This work studies the joint transmit power control and receive beamforming in uplink rate splitting multiple access (RSMA)-based low earth orbit (LEO) satellite networks, using both generative diffusion model and proximal policy optimization (PPO) learning framework. In particular, using RSMA, interference is partially decoded and partially treated as noise, thereby improving the spectral efficiency, while the dynamics and uncertainty in LEO satellite networks would pose challenges to the real-time power control and receive beamforming optimization. First, a long-run sum data rate maximization problem is formulated, subject to the individual data rate requirement, and then the Markov decision process (MDP) is used to model it. Second, on the basis of MDP, a generative diffusion model-based proximal policy optimization (PPO) framework is proposed, where a denoising network is taken as the actor network in PPO to output the optimal continuous policy, thereby facilitating the hyperparameter tuning and improve the sample efficiency. Finally, experiments are conducted to show advantages of merging diffusion model into PPO, in terms of larger spectral efficiency, by comparing proposed framework with benchmarks.

Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) systems have emerged as a promising technology with the capability to expand terrestrial networks. UAVs, working as edge computing nodes and mobile base stations, can be deployed closer to user equipment (UEs). However, with the rapid increase of UEs, the scarcity of spectrum resources and computing resources has become a critical challenge for future mobile communication systems. Additionally, the inherent characteristics of wireless transmission and untrusted broadcasting pose significant security and privacy concerns for multi-UAV networks. To address these issues, this paper presents a blockchain-based resource trading mechanism (BRTM) and a double auction-based resource trading algorithm (DARA) for multi-UAV edge computing systems. It combines blockchain technology with double auction theory to ensure the security and fairness of resource trading. The relations between UEs and UAVs as a two-stage Stackelberg game is formulated and a pricing-based incentive strategy is proposed. The proposed scheme encourages active participation from both UEs and UAVs while maximizing the sum of their utilities. The security assessment and numerical outcomes show that the proposed method is effective and outperforms other benchmark schemes.

Utilizing the unmanned aerial vehicle (UAV) for task offloading over a large geographic area offers a promising solution to guarantee information freshness, i.e., Age of Information (AoI), in many of Internet of Things (IoT) applications. However, the energy limitations of both ground devices (GDs) and UAV wireless networks necessitate intelligent management of energy resources, as continuous energy consumption is involved in data sensing, transmission, and computation. Incorrect decision-making can exhaust the UAV’s energy prematurely, endangering the efficacy of task offloading missions and potentially causing damage to the UAV itself. In this article, we investigate the problem of task offloading in an UAV-aided wireless powered edge computing system with a focus on enhancing information freshness while ensuring the UAV’s energy-safety. To minimize the average AoI, we propose to jointly optimize GD wireless charging power, UAV flight trajectory, and offloading decisions. To prevent premature energy depletion in UAV operations, we formulate the optimization problem as a constrained Markov decision process (CMDP). Then, we introduce a novel safe deep Q-network (SDQN) algorithm, leveraging Lyapunov equations to derive an optimal strategy, which can strictly ensure that the actions of the UAV does not exceed its energy consumption limit. Through extensive simulations, we demonstrate the effectiveness of our proposed algorithm in minimizing AoI under energy consumption constraints.

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance.

In this paper, we propose a novel age-of-information (AoI)-aware waveform design scheme for the cooperative joint radar-communications (JRC) system, called AoI-aware Online Prediction (A-OnP) scheme. To be specific, we optimize the power allocation of the orthogonal frequency division multiplexing (OFDM) signal. We aim to maximize the radar mutual information (RMI) with considering the communication data rate (CDR) and AoI performance. Specifically, we design a cognitive operating framework for the JRC system, with a particular emphasis on the closed-loop signal processing for online prediction of the radar target scattering coefficient (TSC). Then, considering the obtained TSC prediction result and corresponding communication performance requirement, we optimize the power allocation of the transmit waveform and the signal-to-interference-plus-noise ratio (SINR) threshold of the communication users. Accordingly, we propose a constraints-splitting coordinate descent (CS-CD) method to solve the formulated non-convex problem by strategically splitting the sum-constraints and assign a quota to each channel, where the allocation criteria is automatically decided during iteration. Simulation results demonstrate that, the cooperative radar-centric communication-constrained (RC-CC) waveform outperforms the separately optimized radar-optimal plus communication-optimal (RO-CO) waveform. Additionally, the A-OnP scheme can increase RMI while meeting the communication CDR and AoI requirements.

Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Bearing in mind the importance of AoI and energy consumption during the data collection process, we present a multi-objective optimization problem to minimize the AoI and UAV energy consumption. To explore the tradeoff between AoI and energy consumption, we jointly optimize the collection time, the UAV trajectory, and the duration of time slots. Due to the non-convexity of the formulated problem, we divide the main problem into three sub-problems and address them by leveraging successive convex approximation (SCA) and Lagrangian dual methods. Finally, we design a multi-variable fixed algorithm to iteratively solve the three sub-problems. Simulations are carried out to investigate the tradeoff between AoI and UAV energy consumption, revealing that reducing AoI and energy consumption simultaneously is unattainable. Furthermore, the convergence and validity of the proposed algorithm are presented and analyzed.

Future vehicle localization will be committed to improving the positioning accuracy and energy efficiency of localization systems in the intelligent transportation. Recently, reconfigurable intelligent surface (RIS) as an emerging technology has gained widespread attention and is favorable to enhance the performance of vehicle localization systems because of its capacity of customizing the wireless channel. In this paper, in order to minimize the transmit power, we consider the joint active and passive beamforming problem of RIS-assisted vehicle localization system under the constraints of the localization accuracy and the phase shift parameters of the RIS. Specifically, we establish the model of RIS-assisted vehicle localization system and derive the Cramér-Rao bound (CRB) as the localization performance metric. Next, for the scenario of single vehicle localization, we derive the optimal RISs’ phases, and obtain the optimal solution for joint active and passive beamforming based on semidefinite programming relaxation of the non-convex beamforming problem and the corresponding equivalent analysis. Lastly, aimming to the scenario of multiple vehicles localization, we transform the nonconvex joint active and passive beamforming problem into semidefinite programming (SDP) and geometric programming (GP) form subproblems through alternating optimization. Simulation results verify the feasibility of the proposed methods.

Driver distraction, a major cause of traffic crashes, is reported to reduce driving performance and be detected with vehicle behavioral features. It also induces physiological responses. Time and frequency-domain features of physiological signals have been used to study distraction, but they are susceptible to residual noise and tend to overlook complexity. Moreover, the resampling problem arises while analyzing physiological signals at multiple time scales. This paper proposes a novel framework based on multiscale entropy on absolute time scales (MSaE) and bidirectional long short-term memory (BiLSTM) network to mine the distraction information in multi-modality physiological signals and detect distraction automatically. Firstly, an entropy-based resampling method is adopted to find the suitable downsampling rates of electroencephalography (EEG), electrocardiogram (ECG), and electromyography (EMG). Then, calculating entropy with absolute time scales instead of relative time scales in a sliding window is utilized to explore the fluctuations of each signal while distraction. Afterward, ReliefF is selected from conventional feature selectors to identify the optimal feature set for each signal. Finally, BiLSTM with time dependency is designed to detect driver distraction with the selected feature set. The results illustrate significant distinctions in the MSaE of multiple physiological signals between normal and distracted driving. Additionally, MSaE, superior to traditional features, is selected as the most discriminative feature for each signal in distraction mining. Furthermore, the accuracy is further improved by about 8%, incorporating multi-modality features rather than vehicle behavioral features. This study indicates the potential of employing various signals to understand and detect driver distraction effectively.

Recent advancements in the Internet of Medical Things (IoMT) have significantly influenced the development of smart healthcare systems. Mobile edge computing (MEC)-assisted federated learning (FL) has emerged as a promising technology for providing fast, efficient, and reliable healthcare services while ensuring patient privacy. However, concerns about the privacy and security of sensitive information often make patients hesitant to share their data. Moreover, MEC servers face challenges accessing the necessary radio resources for data transmission. To address these issues, designing an effective incentive mechanism that encourages healthcare user participation in FL and facilitates resource provision from the base station (BS) is vital. This work proposes an efficient and privacy-preserving incentive scheme that considers the interaction among the BS, MEC servers, and MEC users in the MEC-assisted FL healthcare system. Utilizing the Stackelberg game model, we investigate the allocation of transmit power, determination of differential privacy (DP) budgets for MEC users, reward strategies, radio resource demands for MEC servers, and pricing for radio resources at the BS. Furthermore, we analyze the Stackelberg equilibrium and empirically validate the effectiveness of our proposed scheme using a real-world medical dataset.

Digital twin (DT) technology enables the replica of physical objects and environmental statuses of a physical system, which can be used for further simulation, analysis, and prediction. By combining DT with mobile edge computing (MEC), a new paradigm called digital twin edge networks (DITEN) is able to fill the gap between physical edge networks and digital systems and provide novel services to physical devices. Due to possible failure in data sensing, balancing sensing time and successful sensing rate needs to be considered to ensure the freshness of collected data in DITEN. Additionally, an optimal data scheduling policy is necessary to ensure efficient communication between physical devices and the edge server while maintaining the accuracy of DT. Therefore, this paper proposes a joint optimization problem for the sensing and communication for DITEN. Due to the nonconvex nature of the formulated problem, we decompose the original problem into three subproblems, and an iterative optimization algorithm is proposed to minimize the system overhead of DNT realization. The effectiveness of the proposed method is evaluated through extensive simulations.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform power allocation approach in the spectrum-sharing dual-functional radar-communication (DFRC) systems, with a particular emphasis on improving radar recognition performance while considering the specific communication requirements of individual users. Specifically, the radar mutual information (RMI) is maximized in terms of allocating the radar power to the OFDM subcarrier within the limits of the power constraints and meeting the age of information (AoI) expectation requirements. By considering the impact of radar interference, we measure the AoI performance of individual users using the transmission outage probability. Then, an iterative constraints-splitting (ICS) method is developed to find the optimal radar power allocation results from the formulated non-convex problem by transforming it into an equivalent convex problem using an iteratively determined factor. Simulation results demonstrate that the proposed individual user AoI preference-based approach can improve RMI performance while meeting the AoI communication requirements of individual users. Additionally, higher RMI and more stable AoI performance can be achieved while maintaining total communication performance by allocating more sub-carriers to fewer users.

Due to the ongoing standardization and deployment activities, satellite networks will be supplementing the 5G and beyond Terrestrial Networks (TNs). For the satellite communications involved to be as efficient as possible, techniques to achieve that should be used. Multi-Connectivity (MC), in which a user can be connected to multiple Next Generation Node Bs simultaneously, is one such technique. However, the technique is not well-researched in the satellite environment. In this paper, an algorithm to activate MC for users in the weakest radio conditions is introduced. The algorithm operates dynamically, considering deactivation of MC to prioritize users in weaker conditions when necessary. The algorithm is evaluated with a packet-level 5G non-terrestrial network system simulator in a scenario that consists of a TN and transparent payload low earth orbit satellite. The algorithm outperforms the benchmark algorithms. The usage of MC with the algorithm increases the mean throughput of the users by 20.3% and the 5th percentile throughput by 83.5% compared to when MC is turned off.

Benefiting from the deployment flexibility and the line-of-sight (LoS) channel conditions, unmanned aerial vehicle (UAV) has gained tremendous attention in data collection for wireless sensor networks. However, the high-quality air-ground channels also pose significant threats to the security of UAV-aided wireless networks. In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the remote ground base station (BS). First, during the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective of maximizing the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate of transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

IoT devices have been widely utilized to detect state transition in the surrounding environment and transmit status updates to the base station for system operations. To guarantee the accuracy of system control, age of information (AoI) is introduced to quantify the freshness of the sensory data and meet the stringent timeliness requirement. Due to the limited computing resources, the status update can be offloaded to the mobile edge computing (MEC) server for execution. Since status updates generated by insufficient sensing operations may be invalid and lead to additional processing time, a joint data sensing and processing optimization problem needs to be considered. Therefore, this work formulates an NP-hard problem that considers the freshness of the status updates and energy consumption of the IoT devices. Subsequently, the problem is decomposed into sampling, sensing, and computation offloading optimization problems. To optimize the system overhead, a multi-variable iterative system cost minimization algorithm is proposed. Simulation results illustrate the efficacy of our method in decreasing the system cost, and indicate the influence of sensing and processing under different scenarios.

In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the base station (BS). First, for the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective to maximize the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize compression strategy and weighted device selection, which can significantly reduce the size of uploaded data and decrease the communication time. We perform a series of experiments on the MNIST/CIFAR-10 datasets, in both lID and non-lID data settings. We compare the communication time of different aggregation schemes, in terms of iteration rounds and target accuracy. Simulation results demonstrate that the uploading time of the proposed scheme is up to 4.3 times shorter than other existing ones. Experiments on an end - to-end FL framework also verify the communication efficiency of CE-FedPA in a real-world setting.

The increased, widespread, unwarranted, and unaccountable use of Closed-Circuit TeleVision (CCTV) cameras globally has raised concerns about privacy risks for the last several decades. Recent technological advances implemented in CCTV cameras, such as Artificial Intelligence (AI)-based facial recognition and Internet of Things (IoT) connectivity, fuel further concerns among privacy advocates. Machine learning and computer vision automated solutions may prove necessary and efficient to assist CCTV forensics of various types.In this paper, we introduce and release the first and only computer vision models are compatible with Microsoft common object in context (MS COCO) and capable of accurately detecting CCTV and video surveillance cameras in street view, generic images, and video frames.Our best detectors were built using 8,387 images, which were manually reviewed and annotated to contain 10,419 CCTV camera instances, and achieved an accuracy rate of up to 98.7%. This work proves fundamental to a handful of present and future applications that we discuss, such as CCTV forensics, pro-active detection of CCTV cameras, providing CCTV-aware routing, navigation, and geolocation services, and estimating their prevalence and density globally and on geographic boundaries.

It is estimated that over 1 billion Closed-Circuit Television (CCTV) cameras are operational worldwide. The advertised main benefits of CCTV cameras have always been the same; physical security, safety, and crime deterrence. The current scale and rate of deployment of CCTV cameras bring additional research and technical challenges for CCTV forensics as well, as for privacy enhancements.This paper presents the first end-to-end system for CCTV forensics and feasible privacy-enhancing applications such as exposure measurement, CCTV route recovery, CCTV-aware routing/navigation, and crowd-sourcing. For this, we developed and evaluated four complex and distinct modules (CCTVCV [1], OSRM-CCTV [2], BRIMA [3], CCTV-Exposure [4]), all of which are novel, unique, peer-reviewed, and can be used either separately or within an integrated end-to-end system such as CCTV-FullyAware. We release all our artefacts as open-source/open data. We hope our work will bootstrap policy-driving discussions and large-scale applications such as CCTV forensics and privacy-enhancing technologies.

Joint localization and communication systems have drawn significant attention due to their high resource utilization. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided simultaneously localization and communication system. We first determine the sum squared position error bound (SPEB) as the localization accuracy metric for the presented localization-communication system. Then, a joint RIS discrete phase shifts design and subcarrier assignment problem is formulated to minimize the SPEB while guaranteeing each user’s achievable data rate requirement. For the presented non-convex mixed-integer problem, we propose an iterative algorithm to obtain a suboptimal solution by utilizing the Lagrange duality as well as penalty-based optimization methods. Simulation results are provided to validate the performance of the proposed algorithm.

Driver distraction diverting drivers’ attention to unrelated tasks and decreasing the ability to control vehicles, has aroused widespread concern about driving safety. Previous studies have found that driving performance decreases after distraction and have used vehicle behavioral features to detect distraction. But how brain activity changes while distraction remains unknown. Electroencephalography (EEG), a reliable indicator of brain activities has been widely employed in many fields. However, challenges still exist in mining the distraction information of EEG in realistic driving scenarios with uncertain information. In this paper, we propose a novel framework based on Multi-scale entropy (MSE) in a sliding window and Bidirectional Long Short-term Memory Network (BiLSTM) to explore the distraction information of EEG to detect driver distraction based on multi-modality signals in real traffic. Firstly, MSE with sliding window is implemented to extract the EEG features to determine the distraction position. Statistical analysis of vehicle behavioral data is then performed to validate driving performance indeed changes around distraction position. Finally, we use BiLSTM to detect driver distraction with MSE and other traditional features. Our results show that MSE notably decreases after distraction. Consistent with the result of MSE, driving performance significantly deviates from the normal state after distraction. Besides, BiLSTM performance of MSE outperforms other entropy-based methods and is better than behavioral features. Additionally, the accuracy is improved again after adding MSE feature to behavioral features with a 3% increasement. The proposed framework is useful for mining brain activity information and driver distraction detection applications in realistic driving scenarios.

In Low Earth Orbit (LEO) satellites, which run in polar orbit, the area of overlap among beams becomes wider as the latitude of satellites increases, which leads to intolerable interference and extra energy consumption. To minimize the onboard power with QoS requirements, we propose an energy optimization model with considering power allocation, user association and dynamic beam ON/OFF operation jointly. Moreover, the frequent beam ON/OFF operations lead to the large number of user handovers, so handover cost is also considered in the model. The original problem is decomposed into two levels due to the high coupling of variables and the successive convex approximation is employed. A low complexity greedy ON/OFF iteration is proposed to adapt to dynamic topology of LEO. Simulation results show that the proposed scheme can effectively reduce the system energy consumption.

Recently emerging software-defined networking allows for centralized control of the network behavior enabling quick reactions to security threats, granular traffic filtering, and dynamic security policies deployment making it the most promising solution for today’s networking security challenges. Software-defined networking coupled with network function virtualization extends conventional security mechanisms such as authentication and authorization, traffic filtering and firewalls, encryption protocols and anomaly-based detection with traffic isolation, centralized visibility, dynamic flow control, host and routing obfuscation, and security network programmability. Virtualized security network functions may have different effects on security benefit and service quality, thus, their composition has a great impact on performance variance. In this study, we focus on solving the problem of optimal security function chaining with the help of reinforcement machine learning. In particular, we design an intelligent defense system as a reinforcement learning agent which observes the current network state and mitigates the threat by redirecting network traffic flows and reconfiguring virtual security appliances. Furthermore, we test the resulting system prototype against a couple of network attack classes using realistic network traffic datasets.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the warden can move. In this paper, we propose a covert communication scheme against a mobile warden, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the covert outage probability (COP) limit. First, we analyze the monotonicity of the COP to obtain the optimal location the warden can move. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. Under this worst situation, we first maximize the connection probability over the transmit-to-jamming power ratio within the maximum allowed COP for a fixed transmission rate. Then, the Newton's method is applied to maximize the connection throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Static and dynamic binary analysis techniques are actively used to reverse engineer software's behavior and to detect its vulnerabilities, even when only the binary code is available for analysis. To avoid analysis errors due to misreading op-codes for a wrong CPU architecture, these analysis tools must precisely identify the Instruction Set Architecture (ISA) of the object code under analysis. The variety of CPU architectures that modern security and reverse engineering tools must support is ever increasing due to massive proliferation of IoT devices and the diversity of firmware and malware targeting those devices. Recent studies concluded that falsely identifying the binary code's ISA caused alone about 10% of failures of IoT firmware analysis. The state of the art approaches detecting ISA for executable object code look promising, and their results demonstrate effectiveness and high-performance. However, they lack the support of publicly available datasets and toolsets, which makes the evaluation, comparison, and improvement of those techniques, datasets, and machine learning models quite challenging (if not impossible). This paper bridges multiple gaps in the field of automated and precise identification of architecture and endianness of binary files and object code. We develop from scratch the toolset and datasets that are lacking in this research space. As such, we contribute a comprehensive collection of open data, open source, and open API web-services. We also attempt experiment reconstruction and cross-validation of effectiveness, efficiency, and results of the state of the art methods. When training and testing classifiers using solely code-sections from executable binary files, all our classifiers performed equally well achieving over 98% accuracy. The results are consistent and comparable with the current state of the art, hence supports the general validity of the algorithms, features, and approaches suggested in those works.

With the recent progress in the development of low-budget sensors and machine-to-machine communication, the Internet-of-Things has attracted considerable attention. Unfortunately, many of today's smart devices are rushed to market with little consideration for basic security and privacy protection making them easy targets for various attacks. Unfortunately, organizations and network providers use mostly manual workflows to address malware-related incidents and therefore they are able to prevent neither attack damage nor potential attacks in the future. Thus, there is a need for a defense system that would not only detect an intrusion on time, but also would make the most optimal real-time crisis-action decision on how the network security policy should be modified in order to mitigate the threat. In this study, we are aiming to reach this goal relying on advanced technologies that have recently emerged in the area of cloud computing and network virtualization. We are proposing an intelligent defense system implemented as a reinforcement machine learning agent that processes current network state and takes a set of necessary actions in form of software-defined networking flows to redirect certain network traffic to virtual appliances. We also implement a proof-of-concept of the system and evaluate a couple of state-of-art reinforcement learning algorithms for mitigating three basic network attacks against a small realistic network environment.

The escalation of the Internet of Things applications has put on display the different sensor data processing methods. The sensor data compression is one of the fundamental methods to reduce the amount of data needed to transmit from the sensor node which is often battery powered and operates wirelessly. Reducing the amount of data in wireless transmission is an effective way to reduce overall energy consumption in wireless sensor nodes. The methods presented and tested are suitable for constrained sensor nodes with limited computational power and limited energy resources. The methods presented are compared with each other using compression ratio and inherent latency. Latency is an important parameter in on-line applications. The improved variation of the linear regression-based method called RT-LRbTC is tested and it has proved to be a potential method to be used in a wireless sensor node with a fixed and predictable latency. The compression efficiency of the compression algorithms is tested with real measurement data sets.

To accommodate the explosively growing demands for mobile traffic service, wireless network virtualization is proposed as the main evolution towards 5G. In this work, a novel contract theoretic incentive mechanism is proposed to study how to manage the resources and provide services to the users in the wireless virtualized networks. We consider that the infrastructure providers (InPs) own the physical networks and the mobile virtual network operator (MVNO) has the service information of the users and needs to lease the physical radio resources for providing services. In particular, we utilize the contract theoretic approach to model the resource trading process between the MVNO and multiple InPs. Two scenarios are considered according to whether the information (such as the radio resource they can provide) of the InPs are globally known. Subsequently, the corresponding optimal contracts regarding the user association and transmit power allocation are derived to maximize the payoff of the MVNOs while maintaining the requirements of the InPs in the trading process. To evaluate the proposed scheme, extensive simulation studies are conducted. It can be observed that the proposed contract theoretic approach can effectively stimulate InPs' participation, improve the payoff of the MVNO, and outperform other schemes.

This paper proposes a threat detection system based on Machine Learning classifiers that are trained using darknet traffic. Traffic destined to Darknet is either malicious or by misconfiguration. Darknet traffic contains traces of several threats such as DDoS attacks, botnets, spoofing, probes and scanning attacks. We analyse darknet traffic by extracting network traffic features from it that help in finding patterns of these advanced threats. We collected the darknet traffic from the network sensors deployed at SURFnet and extracted several network-based features. In this study, we proposed a framework that uses supervised machine learning and a concept drift detector. Our experimental results show that our classifiers can easily distinguish between benign and malign traffic and are able to detect known and unknown threats effectively with an accuracy above 99%.

Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only genuine users data for training and validation. We designed a cross validation procedure with artificially generated impostor samples that improves the learning process yet allows fair comparison to previous works. We evaluated the methods using the CMU keystroke dynamics benchmark dataset. Both proposed approaches outperformed the previous state-of-the-art results for the CMU dataset for unsupervised learning.

The explosively growing demands for mobile traffic services bring both challenges and opportunities to wireless networks. Wireless network virtualization is proposed as the main evolution path toward the forthcoming fifth generation (5G) cellular networks. In this paper, we propose a software defined and virtualized (SDV) wireless network architecture for enabling multi-flow transmission with multiple infrastructure providers (InPs) and multiple mobile virtual network operators (MVNOs). In order to ensure the heterogeneity, we formulate the virtual resource allocation problem with diverse QoS requirements as a social welfare maximization problem with distance-related transaction cost. Due to hidden information of InPs and MVNOs for the auctioneer, we introduce a shadow price for ensuring desirable economic properties and total welfare for the system. Simulations are conducted with different system configurations to show the effectiveness and the energy efficiency performance of the proposed SDV wireless network framework and iterative double auction mechanism.

Low power nodes have been a hot topic in research, standardization, and industry communities, which is typically considered under an umbrella term called heterogeneous networking. In this paper, we look at the problem of optimal deployment of low power nodes that could be either small cells connected via the wired backhaul or relays that utilize the same spectrum and the wireless access technology to get connected to the core network. We present that even though both relay and small cell nodes should be located somewhere at the cell edge, their optimal coordinates are not the same since relays have a limitation that comes from a link between a relay and the master base station.

The explosively growing demands for mobile traffic service bring both challenges and opportunities to wireless networks, among which, wireless network virtualization is proposed as the main evolution towards 5G. In this paper, we first propose a Software Defined Network (SDN) based wireless virtualization architecture for enabling multi-flow transmission in order to save capital expenses (CapEx) and operation expenses (OpEx) significantly with multiple Infrastructures Providers (InPs) and multiple Mobile Virtual Network Operators (MVNOs). We formulate the virtual resource allocation problem with diverse QoS requirements as a social welfare maximization problem with transaction cost. Due to the high computational complexity of formulated problem and hidden information of InPs and MVNOs for SDN controller, we introduce the shadow price for ensuring the desirable economic properties as well as the total welfare of system. Simulations are conducted with different system configurations to show the effectiveness of the proposed SDN based wireless virtualization framework and double auction mechanism.

In this paper, resource allocation (RA) problem in heterogeneous Software Defined Network (SDN) with infrastructure sharing platform among multiple network service providers (NSPs) is studied. The considered problem is modeled as a reverse combinatorial auction (R-CA) game, which takes competitiveness and fairness of different NSPs into account. The heterogeneous RA associated with personal QoS requirement problem is optimized by maximizing the social welfare, which is demonstrated to be total system throughput. By exploiting the properties of iterative programming, the resulting non-convex Winner Determination Problem (WDP) is transformed into an equivalent convex optimization problem. The proposed R-CA game is strategy- proof and proved to be with low computational complexity. Simulation results illustrate that with SDN controller sharing environment, the proposed iterative ascending price Vickrey (IA-PV) algorithm converges fast and can obtain nearly optimal system throughput. It is also demonstrated to be robust with density changing, enable higher fairness and ensure individual profit among different NSPs. With the fairness guaranteed, this infrastructure sharing SDN platform can attract more NSPs to participate, in order to achieve more profit and cost reduction.

Relay networking is an appealing option for operators to solve coverage issues and to improve performance without a need to deploy more complex macro sites. In this paper we look at several options for re-transmission mechanisms that are already adopted in wireless standards, such as 3GPP LTE-Advanced and IEEE 802.16. In particular, we consider a hop-by-hop HARQ operation without any ARQ on top of it, and also with ARQ working in the end-to-end and hop-by-hop modes. We run extensive simulations for the aforementioned configurations and present our results indicating that HARQ should be ideally complemented by the ARQ mechanism. As for the exact ARQ mode, the hop-by-hop ARQ provides better performance that however comes at its own implementation cost.

Today, rapid growth in the amount of malicious software is causing a serious global security threat. Unfortunately, widespread signature-based malware detection mechanisms are not able to deal with constantly appearing new types of malware and variants of existing ones, until an instance of this malware has damaged several computers or networks. In this research, we apply an anomaly detection approach which can cope with the problem of new malware detection. First, executable files are analyzed in order to extract operation code sequences and then n-gram models are employed to discover essential features from these sequences. A clustering algorithm based on the iterative usage of support vector machines and support vector data descriptions is applied to analyze feature vectors obtained and to build a benign software behavior model. Finally, this model is used to detect malicious executables within new files. The scheme proposed allows one to detect malware unseen previously. The simulation results presented show that the method results in a higher accuracy rate than that of the existing analogues.

Attacks against web servers and web-based applications remain a serious global network security threat. Attackers are able to compromise web services, collect confidential information from web data bases, interrupt or completely paralyze web servers. In this study, we consider the analysis of HTTP logs for the detection of network intrusions. First, a training set of HTTP requests which does not contain any attacks is analyzed. When all relevant information has been extracted from the logs, several clustering and anomaly detection algorithms are employed to describe the model of normal users behavior. This model is then used to detect network attacks as deviations from the norms in an online mode. The simulation results presented show that, compared to other data mining algorithms, the method results in a higher accuracy rate.

Low power nodes have been a hot topic in research, standardization, and industry communities, which is typically considered under an umbrella term called heterogeneous networking. In this paper we look at the problem of deploying optimally low power nodes in the context of relay networking, when an operator connects low power nodes (or small cells) via the wireless backhaul that uses the same spectrum and the same wireless access technology. We present an analytical model that can calculate optimal coordinates for low power nodes based on the input parameters, such as preferred number of nodes, their transmission power, parameters of the environment etc. The analytical calculations are complemented by extensive dynamic system level simulations, by means of which we analyze overall system performance for the obtained coordinates. We also show that even relatively marginal deviations from optimal coordinates can lead to worse system performance.

Employing a Blind Source Separation (BSS) algorithm is one of the mechanisms used in extracting unobserved signals from observed mixtures in signal processing. Direct Sequence - Code Division Multiple Access (DS-CDMA) is mature and prominent in spreading code assisted spread spectrum based multiple access communication techniques. Mitigation of deteriorative effects caused within the air interface of DS-CDMA is aimed by trying to remove the jamming signal. A pair of algorithms derived with the aid of BSS schemes are presented in this paper. In the short code model time correlation properties of the channel is taken advantage for BSS. Two energy functions of receive signal are used with the iterative fixed point rule in determining the filter coefficients. The methods are tested in a downlink channel. Equal Gain Combining (EGC) is used to treat the channel parameter values. They are important mechanisms due to simplicity and applicability to High Speed Packet Access (HSPA) based systems.

This paper presents an information-theoretic approach to variable selection for prediction of laboratory measurements of paper quality. Along with a well-known Principal Component Analysis we considered techniques for variable selection based on the classical Shannon Mutual Information and a novel Maximal Information Coefficient. A multilayer perceptron neural model was used to predict quality measurements and compare feature selection techniques. The suggested approach was tested on real industrial data obtained form a pilot paper machine. The presented results show that information-theoretic techniques perform better compared to Principal Component Analysis, providing higher accuracy results.

In this research, online detection of anomalous HTTP requests is carried out with Growing Hierarchical Self-Organizing Maps (GHSOMs). By applying an n-gram model to HTTP requests from network logs, feature matrices are formed. GHSOMs are then used to analyze these matrices and detect anomalous requests among new requests received by the webserver. The system proposed is self-adaptive and allows detection of online malicious attacks in the case of continuously updated web-applications. The method is tested with network logs, which include normal and intrusive requests. Almost all anomalous requests from these logs are detected while keeping the false positive rate at a very low level.

Mitigation of inter-carrier (ICI) and inter-symbol interference (ISI) in the systems based on Orthogonal Frequency Division Multiplexing (OFDM) has been a popular research topic already for several decades. Utilisation of signal bases with ameliorated time-frequency (TF) localisation is one of the methods to increase interference robustness. However, this approach results in more complicated signal processing. In this paper we theoretically derive and formulate computationally efficient algorithms for modulation of discrete finite-dimensional signals constructed from well-localised bases. These results can be used directly for software implementation. In addition, we present the comparison of computing load for straightforward matrix multiplication, for developed algorithm and for Fast Fourier Transform (FFT) used in OFDM. This study shows that the number of operations can be significantly reduced allowing signal processing with a great number of sub-carriers and time shifts in a reasonable time.

Retrieval process of original symbols of a spread spectrum based communication system is tried to be improved by Gradient Algorithm (GA) and Blind Source Separation (BSS) principles. Two simple schemes, based on two energy functions are presented. Time correlation properties of the channel are used as advantages in developing the filter coefficients for the receiver. Direct Sequence - Code Division Multiple Access (DS-CDMA) technique based system setup is used for algorithm testing purposes. This is identified as one of the most stable spread spectrum communication technique where most of the technologies developed for that are highly compatible with High Speed Packet Access (HSPA) transmission technique as well. A transmission operated in a synchronous, downlink wireless channel is used to evaluate performance of the algorithms. Better performance than basic transmission can be demonstrated with these combined schemes under these conditions. Even though it is not quantified, low processing complexity of these algorithms can be identified as an important property of them.

The IEEE 802.16 technology defines the ARQ mechanism that enables a connection to resend data at the MAC level if an error is detected. In this paper, we analyze the ARQ aware scheduling for the 802.16 base station. In particular, we consider how the BS scheduler can account for the ARQ block size, absence of the ARQ block rearrangement, and the ARQ transmission window. We propose a set of constraints that can be applied to any base station scheduler algorithm. To test them, we run a number of simulation scenarios. The simulations results confirm that the ARQ aware scheduling can improve the overall performance.

In the IEEE 802.16 networks, the base station allocates resources to subscriber stations based on their QoS requirements and bandwidth request sizes. A subscriber station can send a bandwidth request when it has an uplink grant allocated by the base station or by taking part in the contention resolution mechanism. This paper presents analytical calculations for parameters that control the contention resolution process in the IEEE 802.16 networks. In particular, the backoff start/end values and the number of the request transmission opportunities are considered. Simulation results confirm the correctness of theoretical calculations. They also reveal that the adaptive parameter tuning results in a better throughput when compared to a static configuration. At the same time, all the timing requirements are met.

In the IEEE 802.16 networks, the base station allocates resources to subscriber stations based on their QoS requirements and bandwidth request sizes. A subscriber station can send a bandwidth request when it has an uplink grant allocated by the base station or by taking part in the contention resolution mechanism. This paper presents analytical calculations for parameters that control the contention resolution process in the IEEE 802.16 networks. In particular, the backoff start/end values and the number of request transmission opportunities are considered. The simulation results confirm the correctness of theoretical calculations. They also reveal that the adaptive parameter tuning results in a better throughput when compared to a static configuration. At the same time, all the timing requirements are met.

In the IEEE 802.16 networks, a subscriber station can use the contention slots to send bandwidth requests to the base station. The contention resolution mechanism is controlled by the backoff start/end values and a number of the request transmission opportunities. These parameters are set by the base station and are announced to subscriber stations in the management messages. In the case of the VoIP services, it is critical that the contention resolution occurs within the specified time interval to meet the VoIP QoS requirements. Thus, it is the responsibility of the base station to set correct contention resolution parameters to ensure the QoS requirements. This paper presents analytical calculations for the parameters that control the contention resolution process in the IEEE 802.16 WiMAX networks. The simulation results confirm the correctness of theoretical calculations. They also reveal that the adaptive parameter tuning results in a better resource utilization when compared to the static configuration. At the same time, all the delay requirements are ensured.

The physical and logical structures of next generation network and service environment is complex and requires increasingly sophisticated and complicated tools to be fully controllable and well managed. The main problem that has risen is that the old telemanagement model has not enough flexibility to manage rapidly and constantly changing network environment. 3G and 4G networks are basically IP based and the knowledge of IP type traffic management is somewhat new and challenging to telecom vendors. The present way of controlling and managing telecom systems is to use non real time off-line PM (performance monitoring) and tools. Time periods between tuning can now be some days to some weeks or even months. Modern 3/4G networks actually need constant real time control to overcome for example fast and unpredictable video streaming or call type traffic bursts. To ease the difficult situation and solve the problem we have studied and developed a new control and management concepts and methods to monitor, control and manage in real time any magnitude of network and system environments. This paper presents the basic concept of the management system in big operator environments consisting of any kind of network elements

Flow-based fast handover for mobile IPv6 (FFHMIPv6) is a new handover method designed for mobile IP networks. The FFHMIPv6 uses the flow-state information and packet encapsulation to enable reception of packets simultaneously with the address registration process of mobile IPv6. Analysis and performance evaluation of FFHMIPv6 shows it handles the handover delay more efficiently than the basic mobile IPv6, hierarchical MIPv6, as well as fast handovers for MIPv6 handover mechanisms. However, if FFHMIPv6 is to be the primary mechanism for handover in mobile IP networks it has to be secure, efficient as well as reliable alternative to the existing handover mechanisms. In this paper, we present the security analysis and assessment of FFHMIPv6. We also recommend solutions to the identified security vulnerabilities and defense mechanisms to FFHMIPv6. The major security threat of FFHMIPv6 is related to the process of Flow-based fast handover binding update (FFHBU).

This paper presents adaptive resource sharing model that uses a revenue criterion to allocate network resources in an optimal way. The model ensures QoS requirements of data flows and, at the same time, maximizes the total revenue by adjusting parameters of the underlying scheduler. Besides, the adaptive model eliminates the need to find the optimal static weight values because they are calculated dynamically. The simulation consists of several cases that analyse the model and the way it provides the required QoS guarantees. The simulation reveals that the installation of the adaptive model increases the total revenue and ensures the QoS requirements for all service classes

This paper presents adaptive algorithm for updating the weights of a WFQ scheduler. The algorithm provides maximum revenue for the network operator, while the QoS requirements for the connections in different service classes are satisfied. The operation of the algorithm is verified in ns-2 simulator environment. The simulations show that the algorithm works well with TCP traffic and provides appropriate service for all service classes.

Multicast is a tempting possibility for many broad- band services. It makes possible to deliver one data-stream to several receivers simultaneously. IP-Multicast is based on an open group concept. This means that it is possible for all the users to join the group and thus receive the data. The open concept is also the main reason why multicast has not been taken in wider use. There is two different solution to solve this problem, group access control and multicast data encryption. Group access control mechanisms focuses on restricting the group membership at the users edge device. Traffic encryption scheme relies on end-to-end encryption, so a key management architecture is also needed. We introduce our own proposal for a multicast group access control mechanism. Our mechanism consists of user authentication and dynamic access control list configuration. Our proposal is protocol independent and thus easy to take in use in various content delivery network environments.

IEEE 802.16 standard defines the wireless broadband access network technology called WiMAX. WiMAX introduces several interesting advantages, and one of them is the support for QoS at the MAC level. For these purposes, the base station must allocate slots based on some algorithm. We propose a simple, yet efficient, solution for the WiMAX base station that is capable of allocating slots based on the QoS requirements, bandwidth request sizes, and the WiMAX network parameters. To test the proposed solution, we have implemented the WiMAX MAC layer in the NS-2 simulator. Several simulation scenarios are presented that demonstrate how the scheduling solution allocates resources in various cases. Simulation results reveal the proposed scheduling solution is ensures the QoS requirements of all the WiMAX service classes and shares fairly free resources achieving the work-conserving behaviour.

In this paper we propose an adaptive packet scheduling model for networks providing quality of service. The model minimizes the weighted mean delays of the connections for specified pricing classes (gold, silver, and bronze), while also maximizing the revenue of the service provider. In this scenario a call admission control (CAC) mechanism is used for guaranteeing specified mean delay characteristics for the different service classes. A closed form analytic and optimal solution is derived for the weights of the scheduler. The scenario is independent of the statistical assumptions for the connections, and is therefore robust against erroneous estimates of the traffic characteristics

In this paper we present a packet scheduling method which guarantees bandwidth of the connection and optimizes revenue of the network service provider. A closed form formula for updating the adaptive weights of a packet scheduler is derived from a revenue-based optimization problem. The weight updating procedure is fast and independent on the assumption of the connections' statistical behavior. The features of the algorithm are simulated and analyzed with a call admission control (CAC) mechanism. We also show in context with the CAC procedure a mechanism for guaranteeing a specified mean bandwidth for different service classes.

In this paper we present a common triggering mechanism (cross-layer framework) and logic to gather link state and quality related information from the link layers of different access technologies. This information may he utilized by upper layer protocols and applications to react to changes of the link layer. Examples of such a utilization is movement detection, handover decision and application adaptation. The access technology dependent events and parameters, when necessary, are converted into access technology independent triggers and hints. The presented prototype architecture, link information provider (LIP), is currently tested with the VERHO vertical handover controller, which enables intelligent policy-based handover decisions according to several input parameters from the user, application, link layer, etc. Currently, LIP supports certain general link layer parameters, which can he gathered from the operating system and network interface drivers. The system is designed to be easily extended.

We introduce mechanisms, which offer soft handover support between two different IP subnets for the mobile devices. The mobility support protocol that was developed tries to solve the same problem as mobile IP, that is, how to manage up to date information about the location of a moving node and to keep its connections established during its movement. It uses soft handover and relies on accurate routing information of routers participating in the protocol. The protocol works entirely in user space (layer 7, 6 and 5 of the OSI model) and considers the handover only in layer 3.

Different applications, such as voice over IP and video-on-demand, need different quality of service parameters (e.g., guaranteed bandwidth, delay, and latency) from the networks. The customers with different needs pay different prices to the service provider, who must share resources in a plausible way. In a router, packets are queued using a multi-queue system, where each queue can correspond to one service class. This paper presents an adaptive weighted fair queue based algorithm for channel allocation. The weights in gradient type algorithms are adapted by using revenue as a target function.

In the future converged wireless and wired networks will be required to provide support to a number of different types of traffic, each with its own particular characteristics and quality of service parameters including e.g. guaranteed bandwidth, jitter, and latency. The customers of different classes pay different prices to the service provider, who must share resources in a plausible way. Differentiation can be implemented by using a multiqueue system, where each queue corresponds to one service class. In this paper, an adaptive weighted round robin (WRR) based algorithm for traffic allocation is presented and studied in the single node case. The weights in the adaptive gradient type WRR algorithm are updated using revenue as a target function. Due to the adaptive nature of the algorithm, it can operate in the nonstationary environments. In addition, it is nonparametric and deterministic in the sense that any assumptions about call density functions or duration distributions are not made.

In this paper we present an algorithm for balancing traffic flows between lightpaths in WDM networks. The load balancing is based on classification of the traffic flows and the use of state information of the links and optical cross-connects. Higher priority traffic flows are directed to less loaded lightpaths thus providing them with higher throughput and lower transport delay than is available for lower priority flows. The load levels of the lightpaths are tuned dynamically to support the required quality of service. Additionally, the algorithm ensures that the aggregate load of the incoming data flows does not exceed the overall capacity of the parallel lightpaths.

This paper proposes a model to maximize revenue while serving customers with different quality-of-service (QoS) requirements. A providers' goal is to share resources between active customers to ensure all QoS requirements. At the same time, a provider is interested in maximizing the revenue. Since the amount of active users varies a provider functioning can be optimized by allocating different portions of resources. The proposed model is based on the weighted fair queue policy, which is extended so that the usage-based revenue criterion can be used to dynamically adapt weights. The model is flexible in that different services are grouped into service classes and are given different performance characteristics. It guarantees the QoS requirements and maximizes the revenue by manipulating weights of the WFQ model. The simulation of the proposed model considers a single node with several service classes. It is shown that the total revenue can be significantly improved when compared to a non-adaptive approach.

This paper presents an adaptive scheduling and call admission control model for the revenue maximization. The algorithm shares limited resources to different traffic flows in a fair way, and at the same time it maximizes the revenue of the service provider. Used algorithm is derived from the linear type of revenue target function, and closed form globally optimal formula is presented. The method is computationally inexpensive, while still producing maximal revenue. Due to the simplicity of the algorithm, it can operate in the highly nonstationary environments. In addition, it is nonparametric and deterministic in the sense that it uses only the information about the number of users and their traffic flows, not about call density functions or duration distributions. One possible implementation target to the algorithm could be justify it to work with IEEE 802.11e.

In this paper we analyze how well 3G traffic classes can survive with 802.11e's QoS properties, when packet sizes and channel error rates are varied. We try to find out the best packet size and channel error rate combinations for each 3G traffic classes by using different wireless and wired network configurations.

This paper introduces a model that can be used to share link capacity among customers under different kind of traffic conditions. This model is suitable for different kind of networks like the 4G networks (fast wireless access to wired network) to support connections of given duration that requires a certain quality of service. We study different types of network traffic mixed in a same communication link. A single link is considered as a bottleneck and the goal is to find customer traffic profiles that maximizes the revenue of the link. Presented allocation system accepts every calls and there is not absolute blocking, but the offered data rate/user depends on the network load. Data arrival rate depends on the current link utilization, user's payment (selected CoS class) and delay. The arrival rate is (i) increasing with respect to the offered data rate, (ii) decreasing with respect to the price, (iii) decreasing with respect to the network load, and (iv) decreasing with respect to the delay. As an example, explicit formula obeying these conditions is given and analyzed.

Pricing of the future multimedia services in the 3G/4G networks will play a key role from operator's point of view to achieve the maximum revenue and maximizing ROY. On the other hand pricing of the various new services is a very important issue to subscribers and especially the pricing versus the acceptance of services will be a very delicate and important matter that must be dealt very gently. This paper introduces models for the 3G/4G service pricing including QoS.

This paper presents a new methodology based on economic models to provide Quality of Service (QoS) guarantees to competing traffic classes (classes of sessions) in packet networks. We consider an economic model of a packet network where resources are priced. As the demand for network services accelerates, users' satisfaction to the service level might decrease due to congestion at the network nodes. To prevent this, efficient allocation of network resources, such as available bandwidth and switch capacity, is needed. By using so-called user profile as well as utility (e.g. data rate) functions, it is possible to allocate data rates and other utilities using arbitrary number of QoS classes, say 0.01,..., 10. Maximal capacity of the data network infrastructure is exploited by using a dynamic allocation strategy.

The low cost and variety of future clients (e.g., PDAs, laptops, pagers, printers, and specialized appliances) will result in a larger number of client devices per user. Thin clients will have fast processors, but little or no disk storage so that they will download most of their data and executables. The existence of cheap client hardware with high-resolution graphics and high-quality audio together with high-bandwidth networks will probably lead to applications with increasing demands on network and server performance. We focus on the development of a high-performance, cache-based architecture that is general enough to support most type of server applications. Such an architecture should enable the server to achieve very close to the maximum performance that the architecture can achieve. In addition, customizability and handling heterogeneous objects are also relevant to the architecture because it must be general enough to support a wide variety of applications with different QoS needs.