In today’s digital landscape, high-efficiency video coding (H.265/HEVC) has emerged as the most widely used video coding standard, employing selective encryption schemes to protect the privacy of video content while maintaining efficient compression performance. However, existing coefficient scrambling methods impose a significant computational load, leading to increased bit rate overhead due to encryption, longer execution times, and insufficient safety measures. To address these issues, a new coefficient scrambling scheme based onchaotic mapsis proposed. This approach leverages the pseudorandomness, ergodicity, and sensitivity to initial conditions inherent in chaotic maps to generate highly unpredictable coefficient distributions, thereby strengthening security while preserving low complexity. Unlike conventional scrambling, chaotic maps ensure minimal correlation between encrypted coefficients, enhancing resistance against statistical and differential attacks. Additionally, the scrambling conditions are specifically designed to minimize the impact on the bit rate overhead. Furthermore, when combined with syntax element encryption (SEC), which includes motion vector difference (MVD), quantized transform coefficients (QTC), and luma intraprediction mode (Luma IPM), this method effectively distorts video content. The proposed scheme operates synchronously with slices, ensuring that the decryption of video content remains intact even if some slices are lost. Additionally, a random sequence generated by AES-CTR is incorporated with the H.265 encoded stream to protect against chosen-plaintext attacks. The experimental results indicate that this scheme features high security, compliance with format standards, fast execution times, synchronous updates with slices, and resilience against common attacks, all while achieving a reduced bit rate overhead of 45.13% with a lowered average execution time overhead of 1.91%.

The rapid growth of geographically distributed cloud data centers has intensified the demand for secure, privacy-preserving, and resource-efficient collaboration among mutually untrusted cloud environments. This paper presents BlockFed, a blockchain-empowered federated learning framework designed to enable cyber-threat-resilient collaboration across geographically distributed data centers. In BlockFed, each data center independently trains local models using private workload data and shares only the computed gradients rather than raw data, with a centralized aggregation server through a secure blockchain layer. A Proof-of-Work consensus mechanism is employed to validate gradient transactions and maintain an immutable, tamper-resistant ledger, ensuring trust and integrity among participating entities. The centralized server aggregates blockchain-verified updates to construct a global model, which is iteratively redistributed to support collaborative learning. This integrated learning process enhances task-level resource demand prediction while simultaneously improving system security and operational efficiency across all participating data centers. Extensive simulations on the Google Cluster Dataset show that BlockFed outperforms state-of-the-art baselines, including ISTM, ETP-WE, First-Fit, Best-Fit, and Random-Fit, in resource utilization, power consumption, and active server reduction. BlockFed achieves up to 7-81% higher resource utilization, 41-58% lower power consumption, and 31-65% fewer active servers, while attaining a cyber-threat estimation accuracy of 93.19%.

Digital healthcare is essential to facilitate consumers to access and disseminate their medical data easily for enhanced medical care services. However, the significant concern with digitalization across healthcare systems necessitates for a prompt, productive, and secure storage facility along with a vigorous communication strategy, to stimulate sensitive digital healthcare data sharing and proactive estimation of malicious entities. In this context, this paper introduces a comprehensive quantum-based framework to overwhelm the potential security and privacy issues for secure healthcare data management. It equips quantum encryption for the secured storage and dispersal of healthcare data over the shared cloud platform by employing quantum encryption. Also, the framework furnishes a quantum feed-forward neural network unit to examine the intention behind the data request before granting access, for proactive estimation of potential data breach. In this way, the proposed framework delivers overall healthcare data management by coupling the advanced and more competent quantum approach with machine learning to safeguard the data storage, access, and prediction of malicious entities in an automated manner. Thus, the proposed IQ-HDM leads to more cooperative and effective healthcare delivery and empowers individuals with adequate custody of their health data. The experimental evaluation and comparison of the proposed IQ-HDM framework with state-of-the-art methods outline a considerable improvement up to 67.6%, in tackling cyber threats related to healthcare data security. Note to Practitioners—This paper aims to address the issue of digital healthcare data access, which requires both ease and security. Existing research either focuses solely on safe access or on high security, which often comes with high computational challenges. In this paper, we present a comprehensive approach that takes into account various challenges such as secure data storage, efficient data communication, and the prediction of malicious entities. We have developed a mathematical system to portray the overall management of healthcare data. All techniques proposed in this paper have been implemented using quantum computing and have been tested on four healthcare datasets. Initial experimental results suggest that the proposed approach is feasible. Our techniques can be applied to discover malicious entities and understand the behavior of real-life users in healthcare processes.

Large-scale computing systems in the modern era distribute tasks into smaller units that can be executed simultaneously to speed up job completion and decrease energy usage. However, cloud computing systems encounter a significant challenge called the Long Tail problem, where a small subset of slow-performing tasks hinders the overall progress of parallel job execution. This behavior leads to longer service response times and reduced system efficiency. This paper introduces a novel approach called Stochastic Gradient Descent with Momentum-driven Neural Network to analyze and classify heterogeneous tasks as either stragglers or non-stragglers. The straggler tasks are further categorized into Resource Hunter and Long-Tail stragglers based on their specific resource requirements. A traffic management policy is implemented to schedule and assign resources among user job requests, considering the task category, to achieve parallelism and improve sustainability within the cloud infrastructure. Extensive simulations are conducted using the Google Cluster Dataset (GCD) to assess the effectiveness of the proposed framework. The results obtained from these simulations are then compared to state-of-the-art techniques. The experimental findings demonstrate significant reductions in power consumption, carbon emissions, active servers, conflicting servers, and VM migration up to 55.16%, 49.76%, 35%, 25.7%, and 87.29%, respectively. Moreover, there has been an enhancement in resource utilization by up to 78.31%, accompanied by a decrease in execution time of up to 67.74%.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

Cloud computing has become inevitable for every digital service which has exponentially increased its usage. However, a tremendous surge in cloud resource demand stave off service availability resulting into outages, performance degradation, load imbalance, and excessive power-consumption. The existing approaches mainly attempt to address the problem by using multi-cloud and running multiple replicas of a virtual machine (VM) which accounts for high operational-cost. This paper proposes a Fault Tolerant Elastic Resource Management (FT-ERM) framework that addresses aforementioned problem from a different perspective by inducing high-availability in servers and VMs. Specifically,(1)an online failure predictor is developed to anticipate failure-prone VMs based on predicted resource contention;(2)the operational status of server is monitored with the help of power analyser, resource estimator and thermal analyser to identify any failure due to overloading and overheating of servers proactively; and(3)failure-prone VMs are assigned to proposed fault-tolerance unit composed of decision matrix and safe box to trigger VM migration and handle any outage beforehand while maintaining desired level of availability for cloud users. The proposed framework is evaluated and compared against state-of-the-arts by executing experiments using two real-world datasets. FT-ERM improved the availability of the services up to 34.47% and scales down VM-migration and power-consumption up to 88.6% and 62.4%, respectively over without FT-ERM approach.

We propose a joint rate and power adaptation scheme to maximize the decoding quality for scalable video coding (SVC)-based video transmissions over multi-input multioutput (MIMO) systems. The rate adaptation in our proposed scheme includes selection of the best modulation and coding schemes, set of spatial channels, number of SVC layers (source coding rates), and their corresponding application layer forward error correction (APP-FEC) coding rates. The power adaptation involves the proper allocation of the power to each antenna in the MIMO system. SVC-based video transmissions require unequal error protection (UEP) for different SVC layers due to the inter-layer dependency. In most of the previous works, the bit stream of each particular SVC layer is allocated to one spatial channel and the UEP is achieved by transmitting the more important SVC layers through the spatial channels with higher channel gains. However, in our proposed scheme, the bit stream of each particular SVC layer is distributed to multiple spatial channels so that additional diversity gain can be exploited by applying APP-FEC. The UEP can also be achieved by allocating different APP-FEC coding rates on each video layer. Moreover, transmit power allocation is also effectively and jointly determined to improve the system performance. The effectiveness and favorable performance of our proposed scheme are shown by simulations with H.264 SVC traces of high-definition video clips over MIMO systems.

We propose a forward error correction (FEC) coding rate adaptation scheme which maximizes the quality of experience (QoE), for scalable video coding (SVC) based video transmissions over multi-input multi-output (MIMO) systems. The proposed scheme adaptively selects the best set of spatial channels, number of video layers and their corresponding FEC coding rate according to channel state information (CSI) from the receiver. Unlike previous work, our proposed scheme distributes the FEC encoded bit streams to multiple spatial channels so that additional diversity gains can be obtained. Due to the complexity of the optimization, we decompose the original problem into several sub-problems, which can then be solved by a heuristic algorithm. The optimal solution can be found by choosing the best among all the candidate solutions obtained from the sub-problems. The effectiveness and superb performance of our proposed scheme can be demonstrated by many simulations with different videos and channel conditions.

We propose an application (APP) layer scheduling scheme for scalable video transmissions over multiple radio access technologies (multi-RATs). More specifically, the proposed scheme adaptively adjusts the transmission parameters based on estimated network characteristics so that the decoding quality of experience (QoE) at user end is maximized. These parameters include number of transmitted video layers (source coding rate), the APP layer forward error correction (FEC) redundancy for each video layer (channel coding rate), and the transmission data rates in both cellular network and wireless local area network (WLAN). The network conditions are estimated by real-time protocol (RTP) and real-time control protocol (RTCP). Since the proposed scheme is an APP layer design, it can be easily implemented without changing the configurations of lower layers (e.g., transport or MAC layers). Simulations are conducted in network simulator 3 (NS-3), and demonstrate the effectiveness of our proposed scheme.

In this paper, we propose a near optimal power allocation scheme, which maximizes the quality of experience (QoE), for scalable video coding (SVC) based video transmissions over multi-input multi-output (MIMO) systems. This scheme tries to optimize the received video quality according to video frame-error-rate (FER), which may be caused by either transmission errors in physical (PHY) layer or video coding structures in application (APP) layer. Due to the complexity of the original optimization problem, we decompose it into several sub-problems, which can then be solved by classic convex optimization methods. Detailed algorithms with corresponding theoretical derivations are provided. Simulations with real video traces demonstrate the effectiveness of our proposed scheme.

Though erasure codes are widely adopted in high fault tolerance storage systems, there exists a serious small-write problem. Many algorithms are proposed to improve the small-write performance in RAID systems, but not considering the network bandwidth usage. However, the network bandwidth is expensive in cloud distributed storage systems. In this paper, we proposed an ECL-based (E-MBR codes, Caching and Logging-based) technique to improve the small-write performance without using extra network bandwidth. Experimental results show that the proposed algorithm outperforms the competing algorithm.

In cloud computing, resource subscription is an important procedure which enables customers to elastically subscribe to IT resources based on their service requirements. Resource subscription can be divided into two categories, namely long-term reservation and on-demand subscription. Although customers need to pay the upfront fee for a long-term reservation contract, the usage charge of reserved resources is generally much cheaper than that of the on-demand subscription. To provide a better Internet service by using cloud resource, service operators will expect to make a trade-off between the amount of long-term reserved resources and that of on-demand subscribed resources. Therefore, how to properly make resource provision plans is a challenging issue. In this paper, we present a two-phase algorithm for service operators to minimize their service provision cost. In the first phase, we propose a mathematical formulae to compute the optimal amount of long-term reserved resources. In the second phase, we use the Kalman filter to predict resource demand and adaptively change the subscribed on-demand resources such that provision cost could be minimized. We evaluated our solution by using real-world data. Our numerical results indicated that the proposed mechanisms are able to significantly reduce the provision cost.

In wireless multicast services, a base station (BS) requires channel quality indicator (CQI) feedback from subscribers in order to determine a proper modulation and coding scheme (MCS). However, as the group sizes increase, the required feedbacks also increase, which will cause more signaling overheads in uplink. This paper proposes an efficient CQI feedback resource allocation scheme for wireless video multicast applications. More specifically, when a BS is serving several multicast groups simultaneously, our algorithm determines the proper sizes of feedback sets for each multicast group so that the system performance, in terms of average number of multicast service supported subscribers, is maximized. Simulation results not only well demonstrate the advantages of our proposed scheme, but also match with theoretical analyses.

This paper proposes an adaptive mode and modulation coding switching (AMMCS) scheme for multiple-input multiple-output (MIMO) multicasting system. More specifically, in addition to the adaptively chosen modulation and error coding, two types of MIMO modes are adaptively switched, i.e., between spatial multiplexing (SM) mode, which can achieve higher data rate, and spatial diversity (SD) mode, which can provide higher received signal-to-noise ratio (SNR). Analytical equations for scheduling rate and average spectral efficiency are derived for both SD and SM modes in a 2×2 MIMO system. Simulation results well justify our analytical equations and also demonstrate that our proposed scheme can achieve higher scheduling rate when SNR is low and higher spectral efficiency when SNR is high.

Although random access operations are desirable for on-demand video streaming in peer-to-peer systems, they are difficult to efficiently achieve due to the asynchronous interactive behaviors of users and the dynamic nature of peers. In this paper, we propose a network coding equivalent content distribution (NCECD) scheme to efficiently handle interactive video-on-demand (VoD) operations in peer-to-peer systems. In NCECD, videos are divided into segments that are then further divided into blocks. These blocks are encoded into independent blocks that are distributed to different peers for local storage. With NCECD, a new client only needs to connect to a sufficient number of parent peers to be able to view the whole video and rarely needs to find new parents when performing random access operations. In most existing methods, a new client must search for parent peers containing specific segments; however, NCECD uses the properties of network coding to cache equivalent content in peers, so that one can pick any parent without additional searches. Experimental results show that the proposed scheme achieves low startup and jump searching delays and requires fewer server resources. In addition, we present the analysis of system parameters to achieve reasonable block loss rates for the proposed scheme.

This paper presents an embedded debugging/ performance monitoring engine (EDPME), which is capable of collect run time characteristics, detect AHB on-chip bus protocol error/inefficiency, and capture on-chip AHB bus traces at various abstraction levels with compression ratio up to 98% for a low cost tile-based 3D graphics SoC development.

This paper presents an 8.69 Mvertices/s, 278 Mpixels/s, 15.7 mm2tiled-based 3D graphics SoC HW/SW supporting OpenGL ES 1.0 running at 139 MHz. The SoC also includes embedded circuitry to monitor run time characteristics, detect bus protocol error/inefficiency, and capture bus traces at various abstraction levels with compression ratio up to 98%.

In this paper, a receiver driven overlap Forward Error Correction (FEC) scheme, which offers unequal importance protection of video layers, is proposed to protect H.264/SVC video over the Internet. A mathematical model is provided to calculate video quality in terms of subjective Video Quality Metric (VQM). With the provided mathematical model, the video server prepares for receivers a lookup table, which lists all optimal combinations of video and FEC layers under various available downlink bandwidth and packet drop rates. By table lookup, a receiver can determine an optimal combination of video and FEC layers to subscribe to maximize the subjective video quality based on its available downlink bandwidth and packet drop rates.

To eliminate packet loss accumulation and overcome bursty packet loss problems caused by peer departures in peer-to-peer (P2P) streaming systems, a multisource structure combining with a distributed forward error correction (FEC) scheme is proposed. In the proposed structure, each peer connects to multiple parents according to the prespecified FEC packets ensemble and each parent forwards partial streaming packets to the peer. If one or few parents fail, other parents can still provide remaining part of streaming packets that can be used to recover the missing packets by using packet level FEC scheme. Packet loss probability and packet loss accumulation from parent peers to child peers in tree-based P2P streaming systems are investigated. The analysis and NS2 simulation results show that packet loss probability is reduced and furthermore the packet loss accumulation can be eliminated when an appropriate FEC protection is used.

In this paper, we propose a scheme for peer-to-peer (P2P) live streaming with multi-source multicast and forward error correction. In our scheme, there is a control topology for membership management, and a multi-source multicast tree for data delivery. The control topology facilitates peers to locate multiple sources for media content, and the multisource multicast tree make the system adaptive to node churn and packet loss. Simulation results show that the performance of our proposed method is significantly better than that of BitTorrent-Like (BT-Like) systems.

In this paper, we propose a multi-source multicast structure combining with a distributed FEC scheme to overcome burst packet loss problems caused by peer departures in P2P live streaming systems. The analytical results show that the burst packet loss can be eliminated by using multi-source structure with appropriate FEC parameters. A prototype system is implemented to verify that the proposed system is practically feasible and effective. The experimental results show the proposed system can effectively overcome the burst packet loss by taking advantage on the multiple source structure with the distributed FEC scheme.

This study proposes a method of inequality-based multiobjective genetic algorithm (MMGA) to solve the aircraft routing problem. The proposed algorithm includes the following features: 1) a method of inequality to confine a genetic algorithm to search a Pareto optimal set in regions of interest with little computing effort; 2) an improved rank-based fitness assignment method to significantly increase the speed of fitness evaluation; and 3) a repairing strategy to relax the infeasible flight schedules to help reduce violations of solutions. The MMGA is successfully applied to solve the aircraft routing problems in a local airline company.

This work derives a generalized video object profit function from the extended weighted transcoding graph to calculate the individual cache profit of certain versions of a video object, and the aggregate profit from caching multiple versions of the same video object. This proposed function takes into account the popularity of certain versions of an object, the transcoding delay among versions, and the average duration of access of each version. Based on the profit function, cache-replacement algorithms are proposed to reduce the startup delay and network traffic by efficiently caching video objects with the most profits. Two kinds of simulations were conducted to evaluate the performance of the proposed algorithms. These simulations exploit partial viewing traces and complete viewing traces, separately. The results demonstrate that the proposed algorithms outperform the competing algorithms by 15%-39% in delay saving ratio and 5%-10% in byte-hit ratio

This paper presents an on-demand connection-oriented multi-channel MAC protocol for ad-hoc networks. The major characteristics of proposed protocol are: (a) each mobile node is equipped with two network interfaces, (b) frame broadcasting is supported and (c) no time synchronization is needed. Compared with other multi-channel MAC protocols, the proposed protocol reduces the cost of channel negotiation by considering the property that a connection generates multiple frames for transmitting. NS-2 is used to evaluate the performance of the proposed protocol. Simulation results show that the proposed protocol can reduce the cost of channel negotiation and increase the network throughput

Abstract In this paper, we present an on‐demand flow regulated routing algorithm (OFRA) for ad hoc wireless networks. The OFRA consists of two parts: an intermediate node load evaluation process and a routing path selection process. The intermediate node load evaluation process evaluates the load efficiency of the intermediate nodes according to bandwidth, data packets and computing capability. The routing path selection process selects the routing path with lower flow and fewer intermediate nodes. The OFRA can prevent intermediate nodes to be overcrowded and distribute traffic load over routing paths more evenly. The simulation result shows that the percentage of blocked routing paths is reduced and the total flow is more balanced and distributed. Copyright © 2006 John Wiley & Sons, Ltd.

The major direct solutions to the three-point perspective pose estimation problems are reviewed from a unified perspective. The numerical stability of these three-point perspective solutions are discussed. It is shown that even in cases where the solution is not near the geometric unstable region considerable care must be exercised in the calculation. Depending on the order of the substitutions utilized, the relative error can change over a thousand to one. This difference is due entirely to the way the calculations are performed and not to any geometric structural instability of any problem instance. An analytical method is presented which produces a numerically stable calculation.>

Solutions for four different pose estimation problems are presented. Closed-form least-squares solutions are given to the overconstrained 2D-2D and 3D-3D pose estimation problems. A globally convergent iterative technique is given for the 2D-perspective-projection-3D pose estimation problem. A simplified linear solution and a robust solution to the 2D-perspective-projection-2D-perspective-projection pose-estimation problem are also given. Simulation experiments consisting of millions of trials with varying numbers of pairs of corresponding points and varying signal-to-noise ratios (SNRs) with either Gaussian or uniform noise provide data suggesting that accurate inference of rotation and translation with noisy data may require corresponding point data sets with hundreds of corresponding point pairs when the SNR is less than 40 dB. The experimental results also show that the robust technique can suppress the blunder data which come from outliers or mismatched points.>