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Distributed Resource Allocation in Blockchain-Based Video Streaming Systems With Mobile Edge Computing
Abstract
Blockchain-based video streaming systems aim to build decentralized peer-to-peer networks with flexible monetization mechanisms for video streaming services. On these blockchain-based platforms, video transcoding, which is computational intensive and time consuming, is still a major challenge. Meanwhile, the block size of the underlying blockchain has significant impacts on the system performance. Therefore, this paper proposes a novel blockchain-based framework with adaptive block size for video streaming with mobile edge computing (MEC). First, we design an incentive mechanism to facilitate the collaborations among content creators, video transcoders and consumers. In addition, we present a block size adaptation scheme for blockchain-based video streaming. Moreover, we consider two offloading modes, i.e., offloading to the nearby MEC nodes or a group of device-to-device (D2D) users, to avoid the overload of MEC nodes. Then, we formulate the issues of resource allocation, scheduling of offloading, and adaptive block size as an optimization problem. We employ a low-complexity alternating direction method of multipliers (ADMM)-based algorithm to solve the problem in a distributed fashion. Simulation results are presented to show the effectiveness of the proposed scheme.
... It makes cloud computing service and utilities more accessible to the end user and is marked by swift processing and rapid application response time. Fast processing and rapid response time are necessary for applications currently developed with internet connectivity like surveillance, virtual reality, and real-time traffic monitoring [11][12]. End users typically execute these apps on their low-end mobile devices while the processing and main services are done on cloud servers. ...
... A DFHNNLM is a type of neural network model that incorporates fuzzy logic to control classification uncertainty and employs hyperspheres to depict clusters in high-dimensional space. When data points are not clearly separable, it is particularly advantageous [11] [12]. The architecture of DFHNNLM is shown in Fig. 2. Fig. 3. Architecture of DFHNNLM The architecture consists of six layers which are broadly described as follows. ...
... This layer will help in improving the generalization efficiency by reducing the pattern space. f) Output layer (Final Layer)-The output layer is nothing but class layer which consists of two neurons, one for anomalous and other for Normal output [12]. ...
IoT devices have been extensively utilized on numerous smart applications such as smart city, healthcare, and Industry. Since IoT devices possess tiny computing power and not capable to compute large volumes of data, in spite of the advantages of IoT, it also possesses inherent drawbacks like latency, bandwidth limitation, reliability concerns, and security risks. Edge computing counteracts these drawbacks by processing the data locally and implemented for processing this much huge sensors data on cloud. The Edge will process the data closer to where it is created so that processing may be accelerated and latency can be reduced, again in Edge Computing a variety of irregularities in data generation are generated by the increasing heterogeneity and complexity of edge devices due to their limitations. Anomaly detection is a crucial task in edge computing systems, where identifying unusual or deviant patterns of data is essential to ensuring system security and reliability. An original Deep Fuzzy Hypersphere neural network learning model (DFHNNLM) is proposed in this paper for effective anomaly detection in edge computing tasks. The proposed method outperforms current state-of-the-art for anomaly detection with existing deep learning techniques. Proposed model is suitable for any anomaly dataset like ECG5000, NSL-KDD. According to the experimental results, the DFHNNLM outperforms both deep learning and conventional machine learning methods in anomaly detection, achieving improvements in F1-score, accuracy, precision, and recall.
... Spanning from blockchain-based mobile edge computing [1,2], vehicle management [3,4], to smart factory operations [5], wireless blockchain networks have surfaced as a viable way to create strong and dispersed wireless communication infrastructures for several blockchain applications. Especially in large-scale situations like the "internet of things (IoT)" [6], these networks save maintenance expenses, improve security and scalability, relieve the stress on high-load nodes, and minimise the possibility of single points of failure. ...
... Integrated on the junction of blockchabuilt and edge computbuiltg, Liu et al. [2] offer a distributed resource allocation approach for blockchabuilt-based video streambuiltg systems with cell edge computbuiltg. Aimbuiltg to enhance the qualitybuilt of video streambuiltg servicesbuilt, their work tackles the difficulties of resource allocation optimisation built dynamic and diverse network settbuiltgs. ...
... At last, the design enables transaction throughput study by looking at the data flow inside the network and keeping transaction data in the BC. By using the natural security characteristics of blockchain technology and the allotted character of wireless communication, this design guarantees the strength and integrity of wireless Blockchain [2,3] networks. ...
offers a new way to solve the problem state block access management in wireless blockchain networks. It underlines the need state strong access control systems in guaranteeing the security and integrity state distributed wireless communication. Using smart contracts for authentication and authorisation, the suggested approach combines access control systems with blockchain architecture. This device seeks to improve security while reducing latency and overhead. Considering throughput, latency, and energy efficiency among other elements, a mathematical model is created to examine system performance. Comprehensive simulations and studies show the viability and superiority state the suggested method in terms state security, efficiency, and scalability when compared to current options. by offering a strong and fast answer for block access control, essential for guaranteeing trust, integrity, and confidentiality in decentralised wi-fi environments, the research helps to push the in wireless blockchain networks.
... A new protocol has been developed in [12] to provide desired security features. Strategies for optimizing task offloading and data confidentiality protection are presented in [13] and [14]. ...
Integration of cyber-physical surveillance systems (CPSS) into the Internet of Vehicles (IoV) paradigm represents a transformative approach to enhancing transportation safety and efficiency. This article discusses the design, implementation, and application of CPSS as part of IoV ecosystems. Leveraging advancements in sensor technologies, communication protocols, and data analytics, CPSS within IoV enables real-time monitoring, analysis, and response to road conditions, incidents, and emergencies. Our research explores the architecture and functional capabilities of CPSS, including sensor deployment, data fusion, anomaly detection, and decision support mechanisms. We investigate the synergistic interaction between CPSS and IoV platforms, facilitating seamless data exchange, collaboration, and compatibility between automotive and infrastructural domains. Additionally, we discuss potential applications of CPSS in traffic management, law enforcement, emergency response, and urban planning, emphasizing its role in enhancing transportation safety, optimizing resource allocation, and preventing congestion and accidents. Through empirical evaluations and thematic studies, we demonstrate the effectiveness, scalability, and societal impact of integrating CPSS into IoV ecosystems. This research contributes to the development of intelligent transportation systems and underscores the transformative potential of CPSS within the IoV context.
... There have been several proposals for architectural models for storing videos based on edge computing [17] and storage models that use data transfer protocols in peer-to-peer networks [11,18]. In Ref. [19], a decentralized storage approach is proposed in which the videos manipulated in the computational solution developed are stored in a blockchain network. ...
Streaming platforms have revolutionized the digital entertainment industry, but challenges and research opportunities remain to be addressed. One current concern is the lack of transparency in the business model of video streaming platforms, which makes it difficult for content creators to access viewing metrics and receive payments without the intermediary of third parties. Additionally, there is no way to trace payment transactions. This article presents a computational architecture based on blockchain technology to enable transparency in audience management and payments in video streaming platforms. Smart contracts will define the business rules of the streaming services, while middleware will integrate the metadata of the streaming platforms with the proposed computational solution. The proposed solution has been validated through data transactions on different blockchain networks and interviews with content creators from video streaming platforms. The results confirm the viability of the proposed solution in enhancing transparency and auditability in the realm of audience control services and payments on video streaming platforms.
... In [28][29][30][31], the authors have discussed on various latest techniques such as blockchain, machine learning, deep reinforcement learning, distributed ledger etc. and suggested that the integration of blockchain and machine learning may bring significant benefits in communication and networking system [32]. The authors have used above mentioned techniques in different wireless networks and discussed the benefits of integrating of blockchain to artificial intelligence [33]. ...
Mobile Ad-hoc network is characterized as a network of the self-configuring type that comprises several mobile nodes and can be set up easily without needing any kind of infrastructure. The basic assumption taken in the design of MANET's routing protocols is the consideration of all mobile nodes as trusted nodes. However, some malicious nodes drop few data packets in the network which are very hard to detect. These nodes are referred to as gray-hole nodes that launch the gray-hole attack. In MANET, the two types of gray-hole attacks are sequence number-based gray-hole and smart gray-hole attacks. Many existing schemes deal with gray-hole attacks either during the path discovery stage or the data transmission stage. In this paper, a protocol called as Dual Security Ad-hoc On-Demand Distance Vector (DS-AODV) is proposed which consists of two security mechanisms. The first mechanism is used against the sequence number-based gray-hole attack during the path discovery stage whereas the second mechanism is used against the smart gray-hole attack during the transmission of data phase in which the monitoring nodes monitor the activities of their neighboring nodes. The effectiveness of the proposed scheme is validated in the NS-2.35 simulator and the results obtained through the NS-2 simulator tool demonstrate that DS-AODV with 4 numbers of IDS has better performance as compared with state of an art approach with 98.20% packet delivery rate and 19.73 kbps throughput.
... It utilizes Deep Reinforcement Learning techniques to optimize the performance of vehicular networks. [134] This paper explores the use of Blockchain and Mobile Edge Computing for optimizing resource allocation in video streaming systems. It likely discusses the benefits of decentralization in resource management. ...
Wireless Sensor Network (WSN) is an engrossing area of research due to its various applications and the adaptability into increasingly sophisticated system frameworks. The challenges experienced in WSN configuration , are mostly related to it's stringent constraints, which incorporate energy, transmission capacity, memory, computational abilities and to the necessities of the specific application. Over the last decade, a substantial number of attempts have been made to examine and address congestion-oriented problems using a range of methods. Owing to the high energy utilization and data processing needs, the use of traditional algorithms has usually been degraded. In this sense, contemporary researchers have begun using optimization approaches in the field of wireless sensor networks. However, efforts for congestion control technique in sensor networks using optimization model has not yet been done to any great extent in the literature. The goal of this study is to explore various prevalent optimization method that have been used for congestion control in WSNs. We have also analyzed the multifaceted nature inborn in various optimization problems, which would also suggest future directions to the researchers working in this ambit.
... Furthermore, the consideration of vehicle reputations is crucial to the preservation of data sharing quality. In order to ensure the confidentiality of users, use of blockchain technology is employed to establish a decentralized and secure video system with peer-to-peer access [28] . ...
p>Mobile edge computing (MEC) is a very promising paradigm that facilitates efficient processing and analysis of Internet of Things (IoT) data at the network edge. MEC is a cloud-based platform that offers a range of online resources and sophisticated mobile apps to users of mobile devices. The continuing trend among mobile users is the growing need for accessing current apps and cloud-based services on their mobile computing devices (MCDs) with high data transfer rates and low latency. MCD’s frequently experience situations where they are either overwhelmed with excessive resource demands or insufficiently used due to imbalanced requests for resources. Offloading strategies play a crucial role in optimizing the efficiency of real-time data processing and analysis. This study proposes an ETOSP: Energy-efficient Task Offloading Strategy based on Partial Offloading in Mobile Edge Computing framework for efficient resource management as a solution to address the aforementioned difficulty. The application of the genetic algorithm is employed to produce strategies that provide balanced resource allocation for the purpose of identifying the most optimum offloading approach. The performance evaluation of ETOSP is shown through simulated experiments.</p
... In [21], for the purpose of enhancing the security and stability of cache management, the authors design a new edge cache scheme based on block chain. [22] proposes an incentive scheme for blockchain-based video flow that makes video conversion and user collaboration more secure and reliable. In [23], the authors propose a service-oriented blockchain system architecture to make task offloading between boundary servers more secure in the MEC environment. ...
Blockchain technology is getting more and more attention due to its decentralization, independence and security features. However, in wireless networks it faces a computational challenge: the proof-of-work problem. Mobile edge computing (MEC) leads to a vaild scheme by providing cloud computing capabilities to mobile devices. Non-orthogonal multiple access (NOMA) exploits the diversity properties in the power domain to further increase system throughput and spectral efficiency. In this paper, we suggest a new NOMA-based MEC wireless blockchain network to minimize system energy consumption through task offloading decision optimization, user clustering, computing resource and transmit power allocation. In order to effectively figure out this non-convex problem, we first propose a offloading decision and user clustering algorithm, and then propose a computing resource allocation algorithm based on user Quality of Service (QoS) requirements. Finally, the transmission power can be easily determined. The numerical simulation results verify that the proposed joint optimization algorithm can effectively decrease the system energy consumption.
... For example, the authors in [45] demonstrated that a classifier that recognizes smiling faces also leaks information about the relative attractiveness of the individuals in its training set [45]. [37], model arrival rate, and on-device learning rate [36]. ...
Federated Learning (FL) has gained widespread popularity in recent years due to the fast booming of advanced machine learning and artificial intelligence, along with emerging security and privacy threats. FL enables efficient model generation from local data storage of the edge devices without revealing the sensitive data to any entities. While this paradigm partly mitigates the privacy issues of users’ sensitive data, the performance of the FL process can be threatened and reach a bottleneck due to the growing cyber threats and privacy violation techniques. To expedite the proliferation of the FL process, the integration of blockchain for FL environments has drawn increasing attention from academia and industry. Blockchain has the potential to prevent security and privacy threats with its decentralization, immutability, consensus, and transparency characteristics. However, if the blockchain mechanism requires costly computational resources, then the resource-constrained FL clients cannot be involved in the training. Considering that, this survey focuses on reviewing the challenges, solutions, and future directions for the successful deployment of blockchain in resource-constrained FL environments. We comprehensively review variant blockchain mechanisms suitable for the FL process and discuss their trade-offs for a limited resource budget. Further, we extensively analyze the cyber threats that could be observed in a resource-constrained FL environment, and how blockchain can play a key role in blocking those cyber attacks. To this end, we highlight some potential solutions for the coupling of blockchain and FL that can offer high levels of reliability, data privacy, and distributed computing performance.
... In [28][29][30][31], the authors have discussed on various latest techniques such as blockchain, machine learning, deep reinforcement learning, distributed ledger etc. and suggested that the integration of blockchain and machine learning may bring significant benefits in communication and networking system [32]. The authors have used above mentioned techniques in different wireless networks and discussed the benefits of integrating of blockchain to artificial intelligence [33]. ...
Mobile Ad-hoc network is characterized as a network of the self-configuring type that comprises several mobile nodes and can be set up easily without needing any kind of infrastructure. The basic assumption taken in the design of MANET's routing protocols is the consideration of all mobile nodes as trusted nodes. However, some malicious nodes drop few data packets in the network which are very hard to detect. These nodes are referred to as gray-hole nodes that launch the gray-hole attack. In MANET, the two types of gray-hole attacks are sequence number-based gray-hole and smart gray-hole attacks. Many existing schemes deal with gray-hole attacks either during the path discovery stage or the data transmission stage. In this paper, a protocol called as Dual Security Ad-hoc On-Demand Distance Vector (DS-AODV) is proposed which consists of two security mechanisms. The first mechanism is used against the sequence number-based gray-hole attack during the path discovery stage whereas the second mechanism is used against the smart gray-hole attack during the transmission of data phase in which the monitoring nodes monitor the activities of their neighboring nodes. The effectiveness of the proposed scheme is validated in the NS-2.35 simulator and the results obtained through the NS-2 simulator tool demonstrate that DS-AODV with 4 numbers of IDS has better performance as compared with state of an art approach with 98.20% packet delivery rate and 19.73 kbps throughput.
... As a result, the rising costs of computing are an inevitable issue. It is conceivable to provision compute transmission services with reduced delay and energy effectiveness by deploying MEC servers at the edge of networks [39]. The BSs with MEC servers near the devices running blockchain applications can outsource complex calculation chores. ...
Internet of Things (IoT) technology has uncovered a wide range of possibilities in several industrial sectors where smart devices are capable of exchanging real-time data. Machine-to-machine (M2M) data exchange provides a new method for connecting and exchanging data among machine-oriented communication entities (MOCE). Conspicuously, network services will be severely affected if the underneath IoT infrastructure is disrupted. Moreover, it is difficult for MOCEs to re-establish connectivity automatically. Conspicuously, in the current paper, an analysis is performed regarding potential technologies including unmanned aerial vehicles, blockchain, and mobile edge computing (MEC) that can enable the secure establishment of M2M communications networks that have been compromised to maintain the secure transmissible data. Furthermore, a Markov decision process-based joint optimization approach is proposed for blockchain systems that aims to elevate computational power and performance. Additionally, the dueling deep Q-network (DDQ) is incorporated to address the dynamic and complex optimization issue so that UAV selection is ensured to maximize performance. The results of experimental simulation with several statistical attributes suggest that the proposed framework can increase throughput optimally in comparison to state-of-the-art techniques. Additionally, a performance measure of reliability and stability depicts significant enhancement for the proposed framework.
... Because task offloading operations for MEC primarily consist of local execution, overall offloading, and partial offloading, the current study uses two task offloading models: the overall offloading model and the partial offloading model [8]. The overall offloading model and the partial offloading model both have advantages and disadvantages; the overall offloading model is relatively simple, and the algorithm is simple to implement, but resource utilization is low. ...
Developing an effective task offloading strategy has been a focus of research to improve the task processing speed of IoT devices in recent years. Some of the reinforcement learning-based policies can improve the dependence of heuristic algorithms on models through continuous interactive exploration of the edge environment; however, when the environment changes, such reinforcement learning algorithms cannot adapt to the environment and need to spend time on retraining. This paper proposes an adaptive task offloading strategy based on meta reinforcement learning with task latency and device energy consumption as optimization targets to overcome this challenge. An edge system model with a wireless charging module is developed to improve the ability of IoT devices to provide service constantly. A Seq2Seq-based neural network is built as a task strategy network to solve the problem of difficult network training due to different dimensions of task sequences. A first-order approximation method is proposed to accelerate the calculation of the Seq2Seq network meta-strategy training, which involves quadratic gradients. The experimental results show that, compared with existing methods, the algorithm in this paper has better performance in different tasks and network environments, can effectively reduce the task processing delay and device energy consumption, and can quickly adapt to new environments.
5G (Fifth Generation) technology represents a significant advancement in telecommunications, facilitating industry innovation and advancement for applications across various sectors. It enhances speed, minimizes latency, and optimizes connectivity. By leveraging task offloading, 5G networks maximize the efficiency of both computational and network resources, ensuring faster, more reliable data delivery and enabling the high-performance requirements of modern applications. However, offloads are complex because task migrations are highly dynamic and must balance the offloading performances. Multi-Access Edge Computing (MEC) is an approach that addresses the processing limitations of low-powered computers by transferring calculations and jobs to cloud platforms. It uses networks’ edges to move tasks to the nearest data centers, reducing latencies and increasing connection speeds. In addition to data security, MEC has restrictions when delivering information to edge nodes from current voice-only to data-driven communication networks. Framed as classification challenges in MEC, this paper uses blockchain technology to guarantee data security and privacy for task offloads. Through our proposed secure and robust dynamic task-offloading strategy Secured Adaptive Generalized Edge (SAGE), the work emphasizes on reinforcement learning (RL) for judgments on task offloads from mobile devices to edge networks considering the energy limitation and network latency. The schema efficiently derives near-optimal task-offloading decisions by leveraging contextual bandits to enhance the task-offloading paradigm in edge computing. The offloads use blockchains-based smart contracts for ensuring security and SDN-based dynamic computation offloads and migrations. Simulations under synthetic and actual circumstances are conducted to demonstrate that the suggested schema can efficiently adapt to dynamic settings in its learning process, hence enabling faster convergence and near-optimal delay performances. SAGE considers knowledge of delay in queuing, offloading cost, and trust, therefore reducing average task offloading delays by 36% when compared to other existing systems. The Dynamic Edge Server Offload (DESO) algorithm identifies harmful Edge servers (ESs) reducing the frequency of job offloading delays and failures, where results demonstrate reductions in task durations by 30%, with different arrival rates of offloaded tasks and energy consumptions compared to random and uniform offloading schemes.
In large-scale dynamic network environments, optimizing the computation offloading and resource allocation strategy is key to improving resource utilization and meeting the diverse demands of User Equipment (UE). However, traditional strategies for providing personalized computing services face several challenges: dynamic changes in the environment and UE demands, along with the inefficiency and high costs of real-time data collection; the unpredictability of resource status leads to an inability to ensure long-term UE satisfaction. To address these challenges, we propose a Knowledge-Defined Networking (KDN)-based Adaptive Edge Resource Allocation Optimization (KARO) architecture, facilitating real-time data collection and analysis of environmental conditions. Additionally, we implement an environmental resource change perception module in the KARO to assess current and future resource utilization trends. Based on the real-time state and resource urgency, we develop a Deep Reinforcement Learning-based Adaptive Long-term Computation Offloading and Resource Allocation (AL-CORA) strategy optimization algorithm. This algorithm adapts to the environmental resource urgency, autonomously balancing UE satisfaction and task execution cost. Experimental results indicate that AL-CORA effectively improves long-term UE satisfaction and task execution success rates, under the limited computation resource constraints.
This paper presents a quantitative study on the performance of an asynchronous chain model [32] during its theoretical design stage. The asynchronous chain [32] in this study is asynchronous along with adaptively-sized blocks in a proactive manner, whereas the conventional chain controls the block posting in a strictly synchronous manner to the fixed-sized blocks. The model of the adaptive chain, with a rather “reactively” dynamic size of blocks as shown in [31], can be compared versus the proposed model which is asynchronous with a “proactively” dynamic size of the blocks. It is assumed in this paper that variable bulk arrivals (VBA) of transactions in Poisson distribution and asynchronous bulk posting (ABS) of transactions off a block potentially in different capacity in exponential time, referred to as VBAABS. Basic numerical simulations results have been reported in [32] primarily for feasibility validation purpose. In our earlier conference version [32] we have presented the work with focus on development and validation of the performance model in a quantitative manner with extensive numerical simulations to demonstrate the efficacy of the proposed asynchronous chain model, and in this paper we extend the work to also include substantially new works such as an extensive comparative study in performance versus other blockchain models such as the baseline chain model [30] (see Section 5.1) and the adaptive chain model [31] (5.2). Then, a summary by each chain model in consideration, is provided for clarity (5.3). Furthermore, a new simulation is conducted with a focus on the performance of microtransactions as a type of transactions to be commonly expected in the gaming decentralized applications to demonstrate the benefit from the proactive asynchrony of block postings (5.4). Lastly, the implementation results and analysis are shown based on the Ethereum open source as reported in [32].
The integration of Machine Learning (ML) and blockchain technology emphasizes their combined potential to revolutionize various sectors, including banking, healthcare, supply chain management, and security. By integrating the data-driven capabilities of ML with blockchain's secure, transparent, and decentralized platform, the study explores how these technologies can enhance efficiency, data privacy, and resource allocation. A particular focus is given to the challenges of transaction throughput, energy consumption, and interoperability within blockchain networks. The research also delves into blockchain-based frameworks, especially their integration with Mobile Edge Computing (MEC) and Non-Orthogonal Multiple Access (NOMA) technologies, to improve system performance and reduce latency. A novel NOMA-based MEC wireless blockchain system is proposed, optimizing several network parameters. The proposed system is evaluated against Resource Utilization, System Performance and Blockchain-Specific metrics. The paper concludes by outlining future research directions
Edge-assisted mobile crowdsensing (EMCS) has gained significant attention as a data collection paradigm. However, existing incentive mechanisms in EMCS systems rely on centralized platforms, making them impractical for the decentralized nature of EMCS systems. To address this limitation, we propose CHASER, an incentive mechanism designed for blockchain-based EMCS (BEMCS) systems. In fact, CHASER can attract more participants by satisfying the incentive requirements of budget balance, double-side truthfulness, double-side individual rationality and also high social welfare. Furthermore, the proposed BEMCS system with CHASER in smart contracts guarantees the data confidentiality by utilizing an asymmetric encryption scheme, and the anonymity of participants by applying the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). This also restrains the malicious behaviors of participants. Finally, most simulations show that the social welfare of CHASER is increased by approximately when compared with the state-of-the-art approaches. Moreover, CHASER achieves a competitive ratio of approximately 0.8 and high task completion rate of over 0.8 in large-scale systems. These findings highlight the robustness and desirable performance of CHASER as an incentive mechanism within the BEMCS system.
This work studies video transcoding and multicast in an edge computing network (ECN), where routers are furnished with computing resources. For example, the recent IPv6-SRv6 paradigm enables programmable networking that works in concert with such hardware innovations, provisioning application-defined route selection and realizing compute&forward functionalities. A prominent class of user applications in ECN is video streaming. Edge routers may replicate and transcode video streams while delivering them toward end users for customized services, up to node processing and link transmission capacities. We design an auction-based online optimization framework DOP, which comprises of three components: video access management, dynamic price function design, as well as transcoding multicast tree (TMT) optimization. We formulate the online social welfare maximization problem, and design an efficient primal-dual framework that simultaneously makes video transcoding, multicast routing and resource pricing decisions. Through rigorous theoretical analysis, we prove DOP guarantees truthfulness and achieves a good competition ratio. Extensive simulations further verify that social welfare increases by about 12.9%, in comparison to benchmark algorithms.
The popularization of the Internet and the rise of social media enable people to share and access information rapidly, leading to a significant increase in the importance of public opinion analysis. However, public opinion analysis still needs help with issues, such as data security, data sharing, and data credibility, which may result in information leakage and inaccurate analysis results. This article proposes a secure management method for public opinion analysis to address these issues. First, a group key management framework is proposed, and then device grouping and mutual authentication methods are used to determine device identity, addressing the data security issue. A dynamic key generation algorithm is proposed to solve the data sharing problem, and a group key management algorithm is implemented to manage the entire lifecycle of group keys, addressing the data credibility issue and establishing a more secure data processing, and storage environment for public opinion analysis. This article utilizes OpenEthereum to build a blockchain network and conducts simulation experiments on the proposed solution. After comprehensive evaluation, it is confirmed that the solution is efficient and scalable.
As a foundational technology for managing decentralized systems like smart grids and healthcare systems, blockchain is attracting a lot of interest. However, owing to excessive resource requirements and low scalability with frequent‐intensive transactions, its use in resource‐constrained mobile devices is restricted. To let mobile devices offload processing tasks to cloud resources, edge computing can be used. For scalability and secure transactions, the integration with edge computing guarantees consistent access, distributed computing, and non‐compromised storage. To accomplish effective integration, critical challenges such as reliability, adaptability, and resource planning must be resolved. Despite these efforts, further study is still needed to address issues with confidentiality, flexibility, and reliability to develop a workable, secure decentralized data storage system. This study utilizes edge computing to build an Internet of Things (IoT) architecture that satisfies the safety and scalability necessary criteria for integration along with the integration of peer‐to‐peer and blockchain technologies. Already‐existing blockchain and related technologies have also been investigated to suggest solutions that address issues like secrecy, reliability, and scalability in order to successfully incorporate blockchain in IoT systems. Our experimental findings indicate that achieving low rates of stale blocks and promoting decentralization requires a meticulous selection of block sizes and generation intervals. It is essential to maintain block creation intervals as short as practically possible, and utilizing blocks with a size below 1 MB is advisable.
In mobile edge computing (MEC), each user chooses and then offloads the task to an edge server, whereas data security is a concern in MEC due to the lack of trust between users and edge servers. Blockchain is introduced to provide a reliable environment for MEC. In blockchain-based MEC, edge servers are used as the nodes in both MEC and blockchain. After processing the users’ tasks, the edge servers upload the results and other task-related information to the blockchain. The edge servers simultaneously execute two kind of tasks, i.e., the tasks offloaded by the users and the blockchain tasks. Therefore, the user offloading decision affects the processing latency of MEC tasks, and there is a trade-off between the resource allocation for MEC and blockchain tasks. However, most existing studies optimize the resource allocation for blockchain and MEC individually, which leads to the suboptimal performance of blockchain-based MEC. In this paper, we study the problem of user offloading decision and the computing resource allocation of edge servers for MEC and blockchain tasks, with the objective to minimize the total processing delay of MEC and blockchain tasks. We propose an algorithm for joint computing resource allocation for MEC and blockchain (JMB). Theoretical analysis proves that JMB is a 3.16-approximation algorithm. Simulation results show that JMB can effectively reduce the delay in blockchain-based MEC.
This chapter investigates the revolutionary convergence of data science with the Internet of Things (IoT). It demonstrates how data science approaches like advanced analytics, machine learning, and artificial intelligence (AI) may improve the capabilities of IoT devices and systems. This confluence enables data-driven decision-making, process improvement, and the empowerment of numerous businesses by deriving valuable insights from the enormous data created by IoT devices. The chapter also discusses the ethical issues surrounding data privacy and security, emphasizing responsible behaviors to support the responsible and long-term development of Intelligent IoT applications. The chapter illustrates the potential of this convergence to disrupt industries, build smart cities, and nurture a more linked and efficient future through real-world examples.
In the Industrial Internet of Things (IIoT), data sharing is crucial for promoting the intelligent development of industrial production. To achieve effective data supervision, introducing blockchain into traditional cloud-based data-sharing frameworks has attracted widespread attention. However, existing blockchain-based data-sharing schemes still have issues with security and efficiency. Therefore, we propose a blockchain-enabled data-sharing scheme based on proxy re-encryption. First, the scheme considers both storage and access authentication, guaranteeing data sources’ trustworthiness and preventing data misuse. Second, the scheme uses an on-chain and off-chain cooperative storage mechanism, saving the storage resources of the blockchain. Third, the scheme supports data packing, which effectively improves data storage efficiency. The security analysis shows that our scheme satisfies the security requirements. Finally, we build a blockchain platform using the hyperledger fabric. The performance evaluation shows that our scheme is more advantageous regarding computational overhead than other related schemes.
Vehicular networking technology is advancing rapidly, and one promising area of research is blockchain-based vehicular edge computing to enhance resource allocation and data security. This paper aims to optimize resource allocation and data security in vehicular edge computing by leveraging blockchain technology, thereby improving the overall system efficiency and reliability. By integrating the computational, storage, and communication capabilities of vehicles with blockchain, efficient utilization of edge computing and fair resource allocation are achieved. Additionally, data encryption techniques are introduced to ensure data security and privacy protection. Experimental results demonstrate that the system can automatically identify and allocate the most suitable edge computing nodes, thereby enhancing the responsiveness and quality of computational tasks. The blockchain-based vehicular edge offloading system not only optimizes the performance of vehicular networking systems and enhances user experience but also strengthens data security and privacy protection, providing a novel solution for the development of the vehicular networking industry.
With the large‐scale deployment of communication and computation units, the conventional radio access network has been gradually transformed into a sophisticated Cyber‐Physical System. Since the access network is put forward higher expectations for ultra‐reliable and low‐latency services, Short‐Packet Communications (SPC) based on Finite Blocklength Codes can be adopted to further decrease network latency and enhance communication reliability. Considering the interaction among the service provider, the network operator, and users, a three‐stage Stackelberg game is constructed to dynamically arrange communication and computation resources. In this paper, the tripartite interaction problem is modelled as a generalized Nash equilibrium problem, and the reverse induction method is employed to achieve the sub‐game equilibrium. To solve the problem of maximizing tripartite utilities, the authors propose an iterative algorithm for jointly managing resource allocation and tripartite pricing. Simulation results show that compared with the benchmark, the proposed scheme can effectively improve the utilities of users, the service provider, and the network operator and achieve a win–win situation.
The emergence of communication-based train control (CBTC) system within urban rail transport has improved the efficiency of safe train operations. At the same time, the CBTC system enhances the reliability of the train system and lowers latency of the data transmission. Nevertheless, there are still certain critical issues that need to be considered in the CBTC: 1) limited coverage and high maintenance costs of wayside equipment; 2) multiple ground devices configuration and complex system architecture; and 3) insufficient computing capacity of the train leads to heavy latency and energy consumption. To cope with the above problems, in this paper, the multi-hop ad hoc network coexisting with train-to-train communication and train-to-wayside communication is applied to simplify the networking architecture, together with the employment of mobile edge computing (MEC) servers to provide massive computing and communication resources for trains. Meantime, a new CBTC framework is developed for computing offloading and resource allocation via multi-hop ad hoc network and MEC. Offloading decisions, offloading ratio, computing and communication resource allocation are integrated to minimize latency and energy consumption. Furthermore, the proposed problem is a mixed- integer non-convex problem that is transformed into a solvable convex problem, and the consensus alternating direction method of multipliers-based (ADMM) algorithm is employed to solve the problem. The simulation results show that our proposed method has remarkable advantages over other schemes.
This paper studies artificial intelligence (AI) aided communication and computing resource allocation in a vehicular network that supports blockchain-enabled video streaming. Our study aims to improve the operating efficiency and to maximize the transcoding rewards for blockchain based vehicular networks. Our resource allocation policy considers the vehicular mobility, which is modelled with a highly-realistic Semi-Markov renewal process, as well as the real-time video service delay constraints. We propose a multi-timescale actor-critic-reinforcement learning framework to tackle these grand challenges. We also develop a prediction model for the vehicular mobility by using analysis and classical machine learning, which alleviates the heavy signaling and computation overheads due to the vehicular movement. A mobility-aware reward estimation for the large timescale model is then proposed to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the developed theoretical findings in this paper and the significant performance gains due to our proposed multi-timescale framework.
The Fifth-Generation Mobile Networks (5G) contribute to the growth of mobile applications and services and the increase of connected Internet of Things (IoT) devices by providing an infrastructure for upcoming necessities. One of the crucial scenarios in 5G and IoT is the Internet of Healthcare Things (IoHT), where many unexplored services emerge to improve the patient’s quality of life. However, there are challenges for infrastructure requirements to support data traffic growth while promoting a reduction in energy usage. Edge Computing (EC) and Network Slicing (NS) are two supporting paradigms for data-intensive and low-latency applications that enable the host of virtualized resources in 5G. However, the arrangement of computing resources in different levels and nodes is a critical challenge, followed by constraints, requirements, and performance goals. Furthermore, since IoHT applications present demanding necessities regarding latency and throughput, such as high-quality video streaming, virtual and augmented reality, computer vision, and intelligent signal processing, the adoption of application placement strategies is essential to improve the performance of these services. This chapter presents a review of EC and NS in the context of IoHT for 5G.KeywordsFifth-Generation Mobile Networks (5G)Edge Computing (EC)Network Slicing (NS)Internet of Healthcare Things (IoHT)Healthcare
Mobile crowdsourcing is a new computing paradigm that enables outsourcing computation tasks to mobile crowd nodes by means of offloading the tasks from the user to a mobile edge computing (MEC) server. This paper studies the problem of scheduling security-critical tasks of crowdsourcing applications in a multi-server MEC environment. We formulate this scheduling problem as an integer program and propose a family of convergent grey wolf optimizer (CGWO) metaheuristic algorithms to seek for the best scheduling solutions. Our proposed CGWO uses a task permutation to represent a candidate solution to the formulated scheduling problem, and employs a probability-based mapping scheme to map each search agent in grey wolf optimizer (GWO) onto a valid task permutation. We introduce a new position update strategy for generating the next generation of grey wolf population after each round of search. With this strategy, we prove our proposed CGWO guarantees its convergence to the global best solution. More importantly, we provide a thorough analysis on the movement trajectories of grey wolves during the evolutionary procedure, in order to determine appropriate parameter values such that CGWO would not be trapped in local optima. Experimental results justify the superiority of CGWO metaheuristics over the standard GWO in solving the crowdsourcing task scheduling problem.
This article establishes an edge computing architecture based on blockchain sharding to apply blockchain technology at the edge layer. The tasks offloaded to the edge layer are performed by a set of edge server nodes for consensus operations, which improves the security of offloading. At the same time, the nodes and offloaded tasks are divided into multiple shards, and the typical practical Byzantine fault tolerance (PBFT) consensus is performed in each shard to improve scalability. In order to avoid the impact of malicious nodes in an unreasonable sharding scheme on network security and performance, we model the many-objective optimization sharding problems, which includes reducing consensus latency, energy consumption, sharding failure probability, and improving sharding throughput. Moreover, we design a many-objective evolutionary algorithm based on information entropy (MaOEA-IE) to solve the model. In the process of population environment selection, the distribution information of individuals is mapped to a Gaussian function, and we aggregate the distribution functions of all individuals and calculate the information entropy to measure the distribution of the population. The simulation results of benchmark test problems and model solving problems show that the solution set of MaOEA-IE has the best convergence, diversity, and comprehensive performance. This article provides an idea for the formulation of blockchain sharding scheme.
In mobile edge computing (MEC), task offloading and resource allocation are two important issues that are inextricably linked. However, existing studies have either ignored the mobility of mobile users (MUs) during task offloading or the allocation of profits between two parties during the allocation of limited resources (i.e., the resource competition). In this paper, we jointly optimized these two problems. First, to reduce the task offloading delay and the service interruption due to movement, we develop a mobility-aware model, based on which we propose the MWBS algorithm to select the appropriate offloading base station (BS) for MUs. Second, considering the resource competition and the delay constraint of the task, we develop a double auction model and then propose the DARA algorithm, which efficiently allocates the BS resources and maximizes the total system revenue (i.e., social welfare) through a multi-session auction. Finally, we combine MWBS and DARA to propose the BS resource allocation algorithm called MD-BSRA in mobile scenarios. Simulation results show that MD-BSRA can effectively improve task offload success rate, total system revenue and resource utilization while reducing offload delay and service interruption.
While edge computing has the potential to offer low-latency services and overcome the limitations of traditional cloud computing, it presents new challenges in terms of trust, security, and privacy (TSP) in IoT environments. Cooperative edge computing (CEC) has emerged as a solution to address these challenges through resource sharing among edge nodes. However, for multi-infrastructure providers, incentive and trust mechanisms among edge nodes are crucial technical issues that must be addressed alongside system latency and reliability to meet performance requirements. In this paper, we propose a blockchain-assisted intelligent edge cooperation system (BIECS) to systematically solve these issues. By leveraging blockchain technology, we construct trust among edge nodes and employ an incentive mechanism for resource sharing among multi-infrastructure providers. We formulate the system performance optimization as a multi-objective joint optimization problem and solve it efficiently through a two-stage strategy for selecting edge nodes. We first design an improved Long Short Term Memory (LSTM) model for resource prediction and then select edge nodes for executing offloaded tasks and handling the corresponding blockchain process related to each task execution. To evaluate the performance of BIECS, we implement the system based on Hyperledger Fabric and design extensive experiments. Our proposed system achieves better performance in terms of system delay, throughput, and resource utilization compared to state-of-the-art schemes for edge cooperation.
Streaming platforms have established themselves in the global economy as a powerful source of revenue generation, owing to the advancement of mobile and broadband networks, as well as the popularity of smartphones. Blockchain technology has been used in the ecosystem of streaming platforms to develop solutions that provide transparency and traceability features about the services provided. This paper investigates how the characteristics of decentralized storage, immutability, and traceability have influenced the business model of streaming platforms. To achieve this goal, it was investigated in state-of-the-art, studies that addressed the development and validation of computational solutions with blockchain technology observing the following criteria: (i) type of network, (ii) data storage description, (iii) payment system with blockchain technology and (iv) transparency in audience metrics using smart contracts. Based on the results found, there is a lack of studies that assess the financial and governance feasibility of maintaining all of a streaming platform’s functionalities using blockchain technology.KeywordsBlockchainSmart contractDecentralized databaseReview studyStreaming platformData storageSoftware applicationsBusiness model transparencyDAppsDistributed Ledger.BlockchainStreaming platformSmart contractsReview study
The dramatically growing trend of vehicles equipped with driving camera recorders has allowed realizing real-time crowdsourced video sharing in vehicular edge computing (VEC). Such cameras can assist in monitoring objects directly in front of and behind the vehicles, enabling them to provide important visual information through real-time video streaming in case of possible accidents. Exploiting the on-board units (OBUs) for VEC can allow drivers and passengers to share and access on-road video surveillance services. However, data security and privacy concerns of video generators (owners) are two key challenges that can severely limit video sharing in a VEC environment. In this article, we propose a blockchain empowered publish/subscribe (P/S) scheme to enable one-to-many secure video sharing in the VEC scenario. Then, we design an attribute-based encryption algorithm with static and dynamic attributes (ABE-SD) to achieve fine-grained access control in a mobile environment. Finally, We utilize permissioned blockchain and smart contracts to record access policy and publish and subscribe events, thus resulting in user self-certification and event traceability. The numerical results indicate that our proposed scheme ABE-SD outperforms traditional centralized CP-ABE methods in terms of encryption and decryption performance. The simulation experiments demonstrated that the proposed video-sharing scheme is secure and efficient.
Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized approach for resource and transaction management. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile environments is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new transactions to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious option to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a demonstrative prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the data training to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the Edge Computing Service Provider. We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue
Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves sub-optimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.
Accompanied by the rapid development of mobile video service requirements, the dramatic increase in video streaming traffic causes a heavy burden for mobile networks. Mobile Edge Computing (MEC) has become a promising paradigm to enhance the mobile networks by providing cloud-computing capabilities within the radio access network (RAN). With the ability of content caching and context awareness, MEC could provide low-latency and adaptive-bitrate video streaming to improve service providing ability of the RAN. In this paper, we propose a MEC enhanced Adaptive BitRate (MEC-ABR) video delivery scheme which combines content caching and adaptive bitrate streaming technology together. The MEC server acts as a controlling component to implement the video caching strategy and adjust the transmitted bitrate version of videos flexibly. A Stackelberg game is formulated to deal with the storage resources occupied by each BS. The joint cache and radio resource allocation is tackled into a matching problem. We propose the Joint Cache and Radio resource Allocation (JCRA) algorithm to solve the matching problem in order to make the cooperation between cache and radio resources. Simulation results reveal that the proposed scheme could improve both the cache hit ratio and the system throughput over other schemes.
Mobile edge computing (MEC) providing Information Technology (IT) and cloud-computing capabilities within the Radio Access Network (RAN) is an emerging technique in fifth generation (5G) networks. MEC can extend the computational capacity of smart mobile devices (SMDs) and economize SMDs’ energy consumption by migrating the computation-intensive task to the MEC server. In this paper, we consider a multi-mobileusers MEC system, where multiple SMDs ask for computation offloading to a MEC server. In order to minimize the energy consumption on SMDs, we jointly optimize the offloading selection, radio resource allocation and computational resource allocation coordinately. We formulate the energy consumption minimization problem as a Mixed Interger Nonlinear Programming (MINLP) problem which is subject to specific application latency constraints. In order to solve the problem, we propose a Reformulation-Linearization-Technique based Branch-and-Bound (RLTBB) method, which can obtain the optimal result or a suboptimal result by setting the solving accuracy. Considering the complexity of RTLBB cannot be guaranteed, we further design a Gini coefficient based greedy heuristic (GCGH) to solve the MINLP problem in polynomial complexity by degrading the MINLP problem into the convex problem. Many simulation results demonstrate the energy saving enhancements of RLTBB and GCGH.
This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their distance from the destination and creates a structured multi-hop configuration in which each group can relay its neighbor's data. The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.
Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.
Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for ℓ1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.
Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have significant effects on video services in next generation mobile networks. In this article, we jointly consider SDMNs, in-network caching, and MEC to enhance the video service in next generation mobile networks. We use a new video experience evaluation standard called U-vMOS, which is a more advanced measurement of the video quality based on the well-known video mean opinion score (vMOS).With the objective of maximizing the mean U-vMOS, an optimization problem is formulated. Due to the coupling of video data rate, computing resource, and traffic engineering (bandwidth provisioning and paths selection), the problem becomes intractable in practice. Thus, we utilize dualdecomposition method to decouple those three sets of variables. By this decoupling, video rate adaptation is performed at users with network assistants. End nodes can schedule computing resource independently. Traffic engineering is performed by the software-defined networking (SDN) controller and base stations (BSs). Furthermore, to address the challenges of dynamic change of network status and the drawbacks caused by the frequent exchange of information, we design a decentralized algorithm based on alternating direction method of multipliers to solve the traffic engineering problem. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.
To support the massive content delivery, a novel virtualized heterogeneous networks framework is proposed to enable content transcoding, caching and multicast. In addition, a virtual multi-resources allocation problem is formulated to jointly optimize the utilities of computing, caching, and communication for the proposed framework. Simulations are conducted to show the effectiveness of the proposed scheme.
Smart contracts might encode legal contracts written in natural language to represent the contracting parties’ shared understandings and intentions. The issues and research challenges involved in the validation and verification of smart contracts, particularly those running over blockchains and distributed ledgers, are explored.
Recent years have witnessed the booming popularity of CLS platforms, through which numerous amateur broadcasters live stream their video contents to viewers around the world. The heterogeneous qualities and formats of the source streams, however, require massive computational resources to transcode them into multiple industrial standard quality versions to serve viewers with distinct configurations, and the delays to the viewers of different locations should be well synchronized to support community interactions. This article attempts to address these challenges and to explore the opportunities with new generation computation paradigms, in particular, fog computing. We present a novel fog-based transcoding framework for CLS platforms to offload the transcoding workload to the network edge (i.e., the massive number of viewers). We evaluate our design through our PlanetLab-based experiment and real-world viewer transcoding experiment.
Dynamic Adaptive Streaming over HTTP (DASH) is currently a widely adopted technology for video delivery over the Internet. DASH offers significant advantages, enabling users to switch dynamically between different available video qualities responding to variations in the current network conditions during video playback. This is particularly interesting in wireless and mobile access networks, which present unexpected and frequent such variations. Moreover, mobile users in these networks share a common radio access link and, thus, a common bottleneck in case of congestion, which may cause user experience to degrade. In this context, the Mobile Edge Computing (MEC) emerging standard gives new opportunities to improve DASH performance, by moving IT and cloud computing capabilities down to the edge of the mobile network. In this paper, we propose a novel architecture for adaptive HTTP video streaming tailored to a MEC environment. The proposed architecture includes an adaptation algorithm running as a MEC service, aiming to relax network congestion while improving user experience. Our mechanism is standards-compliant and compatible with receiver-driven adaptive video delivery algorithms, with which it cooperates in a transparent manner.
As the biggest big data, video data streaming in the network contributes the largest portion of global traffic nowadays and in the future. Due to heterogeneous mobile devices, networks, and user preferences, the demands of transcoding source videos into different versions have increased significantly. However, video transcoding is a time-consuming task, and how to guarantee quality-of-service (QoS) for large video data is very challenging, particularly for those real-Time applications that hold strict delay requirement such as live TV. In this paper, we propose a cloud-based online video transcoding (COVT) system aiming to offer economical and QoS guaranteed solution for online large-volume video transcoding. COVT utilizes the performance profiling technique to obtain the different performances of transcoding tasks in different infrastructures. Based on the profiles, we model the cloud-based transcoding system as a queue and derive the QoS values of the system based on the queuing theory. With the analytically derived relationship between QoS values and the number of CPU cores required for transcoding workloads, COVT is able to solve the optimization problem and obtain the minimum resource reservation for specific QoS constraints. A task scheduling algorithm is further developed to dynamically adjust the resource reservation and schedule the tasks so as to guarantee the QoS in runtime. We implement a prototype system of COVT and experimentally study the performance on real-world workloads. Experimental results show that the COVT effectively provisions a minimum number of resources for predefined QoS. To validate the effectiveness of our proposed method under large-scale video data, we further perform simulation evaluation, which again shows that the COVT is capable of achieving economical and QoS-Aware video transcoding in cloud environment.
Adaptive bitrate streaming (ABR) has been widely adopted to support video streaming services over heterogeneous devices and varying network conditions. With ABR, each video content is transcoded into multiple representations in different bitrates and resolutions. However, video transcoding is computing intensive, which requires the transcoding service providers to deploy a large number of servers for transcoding the video contents published by the content producers. As such, a natural question for the transcoding service provider is how to provision the computing resource for transcoding the video contents while maximizing service profit. To address this problem, we design a cloud video transcoding system by taking the advantage of cloud computing technology to elastically allocate computing resource. We propose a method for jointly considering the task scheduling and resource provisioning problem in two timescales, and formulate the service profit maximization as a two-timescale stochastic optimization problem. We derive some approximate policies for the task scheduling and resource provisioning. Based on our proposed methods, we implement our open source cloud video transcoding system Morph and evaluate its performance in a real environment. The experiment results demonstrate that our proposed method can reduce the resource consumption and achieve a higher profit compared with the baseline schemes.
In this paper, we introduce a Reformulation-Linearization Technique-based open-source optimization software for solving polynomial programming problems (RLT-POS). We present algorithms and mechanisms that form the backbone of RLT-POS, including constraint filtering techniques, reduced RLT representations, and semidefinite cuts. When implemented individually, each model enhancement has been shown in previous papers to significantly improve the performance of the standard RLT procedure. However, the coordination between different model enhancement techniques becomes critical for an improved overall performance since special structures in the original formulation that work in favor of a particular technique might be lost after implementing some other model enhancement. More specifically, we discuss the coordination between (1) constraint elimination via filtering techniques and reduced RLT representations, and (2) semidefinite cuts for sparse problems. We present computational results using instances from the literature as well as randomly generated problems to demonstrate the improvement over a standard RLT implementation and to compare the performances of the software packages BARON, COUENNE, and SparsePOP with RLT-POS.
When processing transactions in a block, a miner increases his reward but also decreases his probability to earn any reward because the time needed for his block to reach consensus depends on its size. We show that this leads to a game situation between miners. We analytically solve this game for two miners. Then, we show that miners do not play a Nash equilibrium in the current Bitcoin mining environment, instead, they should not process any transaction. Finally, we show that the situation where no transaction is ever processed would stop being a Nash equilibrium if the transaction fee was multiplied or, equivalently, the fixed reward divided by a factor of about 12.
Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
In this paper we present formulae for contact distributions of a Voronoi tessellation generated by a homogeneous Poisson point process in the d-dimensional Euclidean space. Expressions are given for the probability density functions and moments of the linear and spherical contact distributions. They are double and simple integral formulae, which are tractable for numerical evaluation and for large d. The special cases d = 2 and d = 3 are investigated in detail, while, for d = 3, the moments of the spherical contact distribution function are expressed by standard functions. Also, the closely related chord length distribution functions are considered.
An examination of single-transaction blocks and their effect on network throughput and block size
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A transaction fee market exists without a block size limit
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Sweden) and a start-up in California, USA. He joined Carleton University in 2007, where he is currently a Professor. He received the IEEE Outstanding Service Award in 2016
F. Richard Yu (S'00-M'04-SM'08-F'18) received
the PhD degree in electrical engineering from the University of British Columbia (UBC) in 2003. From
2002 to 2006, he was with Ericsson (in Lund, Sweden) and a start-up in California, USA. He joined
Carleton University in 2007, where he is currently a
Professor. He received the IEEE Outstanding Service
Award in 2016, IEEE Outstanding Leadership Award
in 2013, Carleton Research Achievement Award in
2012, the Ontario Early Researcher Award (formerly
Premiers Research Excellence Award) in 2011, the
He has served as the Technical Program Committee (TPC) Co-Chair of numerous conferences. Dr. Yu is a registered Professional Engineer in the province of Ontario
Excellent Contribution Award at IEEE/IFIP TrustCom 2010, the Leadership
Opportunity Fund Award from Canada Foundation of Innovation in 2009 and
the Best Paper Awards at IEEE ICNC 2018, VTC 2017 Spring, ICC 2014,
Globecom 2012, IEEE/IFIP TrustCom 2009 and Int'l Conference on Networking 2005. His research interests include wireless cyber-physical systems,
connected/autonomous vehicles, security, distributed ledger technology, and
deep learning.
He serves on the editorial boards of several journals, including Co-Editorin-Chief for Ad Hoc & Sensor Wireless Networks, Lead Series Editor for
IEEE Transactions on Vehicular Technology, IEEE Transactions on Green
Communications and Networking, and IEEE Communications Surveys &
Tutorials. He has served as the Technical Program Committee (TPC) Co-Chair
of numerous conferences. Dr. Yu is a registered Professional Engineer in the
province of Ontario, Canada, a Fellow of the Institution of Engineering and
Technology (IET), and a Fellow of the IEEE. He is a Distinguished Lecturer,
the Vice President (Membership), and an elected member of the Board of
Governors (BoG) of the IEEE Vehicular Technology Society.