CDNPatch: a cost‐effective failover mechanism for hybrid CDN‐P2P live streaming systems

Among the most well‐established live media distribution technologies is content delivery network (CDN), which improves user‐perceived quality of service by delivering content from proxy servers deployed at the Internet's edge. In recent years, CDN providers started to tap into their subscribers' peer‐to‐peer (P2P) capacity to alleviate their server costs. Under the inherent peer dynamics, a major challenge of these hybrid CDN‐P2P systems is to provide efficient failure recovery with good quality of service guarantees at a reduced server cost. In this work we propose a cost‐effective failover solution named CDNPatch to address the aforementioned problem. CDNPatch enables peers to periodically precompute a few backup content suppliers by efficient information exchange and maintenance algorithms, and leverages auxiliary CDN servers and an economic server provisioning algorithm to reduce the chance of playback interruption occurring to peers. Our simulation results show that CDNPatch can mask the impact of peer dynamics of 3 real P2P systems, namely, SOPCast, PPStream, and PPTV, with 100 % failure recovery success rate and a failure recovery time less than 1 second at a cost of small P2P communication overhead of less than 1 kilobits per second, while using only 10%, 21%, and 51%, respectively, of the pure CDN scheme's server consumption.     

Consortium blockchains-based deep deterministic policy gradient algorithm for optimal electricity trading among households

To achieve higher energy utilization and lower generation cost for renewable sources ( e. g. , wind and solar energy), much work hasbeen focusedon demand response in smart grid (SG). Nonetheless, most existing studies consider energy trading with utility company which results in high energy loss from high voltage to low voltage and privacy leakage. Besides, there are relatively few researches devoted to electricity scheduling and price optimum among households without a third party. To cope with these issues, a novel deep deterministic policy gradient (DDPG)-based energy trading method with consortium blockchain (DETCB) is introduced. Firstly, in order to enhance system security, executing energy transaction among households is on the basis of consortium blockchain, which leads to not only anonymous trade but also public account. Moreover, the primary target from the aspect of the system is apparently the maximal social welfare, thus exploiting an iterative decision-making method combined with DDPG algorithm by non-profit controllers to obtain optimal trading prices and carry out optimal electricity allocation. To this end, security analysis demonstrates that DETCB contributes to creating a secure, stable and trustful environment. Furthermore, the excellent performance concerning social welfare, algorithm efficiency, and transaction energy sum is shown by numerical results.     

Power and cost aware localized routing with guaranteed delivery in unit graph based ad hoc networks

In a localized routing algorithm, each node currently holding a message makes forwarding decision solely based on the position information about itself, its neighbors and destination. In a unit graph, two nodes can communicate if and only if the distance between them is no more than the transmission radius, which is the same for each node. This paper proposes localized routing algorithms, aimed at minimizing total power for routing a message or maximizing the total number of routing tasks that a network can perform before a partition. The algorithms are combinations of known greedy power and/or cost aware localized routing algorithms and an algorithm that guarantees delivery. A shortcut procedure is introduced in later algorithm to enhance its performance. Another improvement is to restrict the routing to nodes in a dominating set. These improvements require two‐hop knowledge at each node. The efficiency of proposed algorithms is verified experimentally by comparing their power savings, and the number of routing tasks a network can perform before a node loses all its energy, with the corresponding shortest weighted path algorithms and localized algorithms that use fixed transmission power at each node. Significant energy savings are obtained, and feasibility of applying power and cost‐aware localized schemes is demonstrated. Copyright © 2004 John Wiley & Sons, Ltd.     

Performance optimization for smart grid blockchainintegrated with fog computing using DDQN

In order to solve the energy crisis and pollution problems, smart grid is widely used. However, there are many challenges such as the management of distributed energy during the construction. Blockchain, as an emerging technology, can provide a secure and transparent solution to the decentralized network. Meanwhile, fog computing network is considered to avoid the high deployment cost. The edge servers have abundant computing and storage resources to perform as nodes in grid blockchain. In this paper, an innovative structure of smart grid blockchain integrated with fog computing are proposed. And a new consensus mechanism called scalable proof of cryptographic selection (SPoCS) is designed to adapt the hybrid networks. The mechanism not only includes a special index, contribution degree, to measure the loyalty of fog nodes and the probability of being a function node, but also has flexible block interval adjustment method. Meanwhile, the number of function nodes (validating nodes and ordering nodes) can also be adjusted. And a deep reinforcement learning (DRL) method is used to select the appropriate quantity to improve the performance under the strict constraints of security and decentralization. The simulation shows the scheme performs well in the throughput, cost and latency.     

基于区块链的社会物联网可信服务管理框架

针对当前社会物联网可信服务管理中存在的中心化程度高、交易不透明和易受攻击等问题,提出了一种基于区块链的社会物联网可信服务管理框架。该框架通过区块链的去中心化特性在服务请求者和服务提供者之间直接建立信任关系,利用智能合约产生并管理新的交易,实现交易过程透明化并减少管理维护成本。同时,区块链的共识机制可用于交易验证并防范恶意攻击和篡改,减少网络攻击和恶意节点的欺骗。最后,分析了该框架在实际构建中面临的主要挑战。     

Energy‐efficient partner selection in cooperative wireless networks: a matching‐theoretic approach

In this paper, the problem of stable energy‐efficient partner selection in cooperative wireless networks is studied. Each node aims to be paired with another node so as to minimize the total energy consumption required to meet a target end‐to‐end signal‐to‐noise ratio requirement and thus maintain quality of service. Specifically, each node ranks every other node in the network according to their energy saving achievable through cooperation. Two polynomial time complexity algorithms based on the stable roommates matching problem are proposed through which nodes are paired according to their preference lists. The first algorithm, denoted Irving's stable matching, may not always have a stable solution. Therefore, the second algorithm—which is a modified version of Irving's algorithm and denoted maximum stable matching—is proposed to find the maximum number of stable disjoint pairs. Simulation results are provided to validate the efficiency of the proposed algorithms in comparison with centralized energy‐efficient partner selection as well as other matching algorithms, yielding a trade‐off between stability and total energy consumption, but comparable symbol error rate performance and network sum rate. Copyright © 2016 John Wiley & Sons, Ltd.     

Lifetime evaluation in energy‐efficient rectangular ad hoc wireless networks

Ad hoc wireless network nodes are typically battery‐powered, therefore energy limit is one of the critical constraints of ad hoc wireless networks' development. This paper evaluates the network lifetime of a rectangular network model that achieves energy efficiency by optimizing the node radio range based on the geographical adaptive fidelity (GAF) topology management protocol (Proceedings of ACMMobil'01, July 2001; 70–84). We derive the optimal transmission range of nodes and analyze both static and dynamic traffic scenarios in both equal‐grid and adjustable‐grid rectangular GAF models, where the results show that the adjustable‐grid model saves 78.1% energy in comparison with the minimum energy consumption of equal‐grid model. The impact of node density on both equal‐grid and adjustable‐grid models is investigated to achieve grid‐lifetime balance among all grids to optimize the entire network lifetime. The lifetime estimation results show that without node density control the adjustable‐grid model prolongs the entire network lifetime by a factor of 4.2 compared with the equal‐grid model. Furthermore, the adjustable‐grid model with node density control is able to prolong the entire network lifetime by a factor of 6 compared with the equal‐grid model. Copyright © 2010 John Wiley & Sons, Ltd.     

X‐Region: A framework for location privacy preservation in mobile peer‐to‐peer networks

While enjoying various LBS (location‐based services), users also face the threats of location privacy disclosure. This is because even if the communications between users and LBS providers can be encrypted and anonymized, the sensitive information inside LBS queries may disclose the exact location or even the identity of a user. The existing research on location privacy preservation in mobile peer‐to‐peer (P2P) networks assumed that users trust each other and directly share location information with each other. Nonetheless, this assumption is not practical for most of the mobile P2P scenarios, for example, an adversary can pretend to be a normal user and collect the locations of other users. Aiming at this issue, this paper presents x‐region as a solution to preserve the location privacy in a mobile P2P environment where no trust relationships are assumed amongst mobile users. The main idea is to allow users to share a blurred region known as x‐region instead of their exact locations so that one cannot distinguish any user from others inside the region. We propose a theoretical metric for measuring the anonymity property of x‐region, together with three algorithms for generating an x‐region, namely, benchmark algorithm, weighted expanding algorithm, and aggressive weighted expanding algorithm. These algorithms achieve the anonymity and QoS requirements with different strategies. Our experiments verify the performance of the algorithms against three key metrics. Copyright © 2013 John Wiley & Sons, Ltd.     

A management architecture for client‐defined cloud storage services

Cloud service providers offer virtual resources to users, who then pay for as much as they use. High‐speed networks help to overcome the limitation of geographical distances between clients and cloud servers, which encourage users to adopt cloud storage services for data backup and sharing. However, users use only a few cloud storage services because of the complexity of managing multiple accounts and distributing data to store. In this paper, we propose the client‐defined management architecture (CLIMA) that redefines a storage service by coordinating multiple cloud storage services from clients. We address practical issues of coordinating multiple cloud service providers using a client‐based approach. We implement a prototype as a realization of CLIMA, which achieves both reliability and privacy protection using erasure code and higher performance by optimally scheduling data transmission. We use our prototype to evaluate the benefits of CLIMA on commercial cloud storage service providers. Finally, CLIMA empowers clients to increase the manageability and flexibility of cloud storage services. Copyright © 2015 John Wiley & Sons, Ltd.     

A hierarchical framework of dynamic relay selection for mobile users and profit maximization for service providers in wireless relay networks

Although extensive research has been carried out on the issue of how to optimally select relays in wireless relay networks, relay selection for mobile users is still a challenging problem because of the requirement that the dynamic selection should adapt to user mobility. Moreover, because the selected relays consume their energy on relaying data for the users, it is required that the users have to pay for this relay service. The price of selecting relays will affect the users' decisions. Assuming that different relays can belong to different service providers, we consider the situation that the service providers can strategically set the prices of their relays to maximize their profits. In this paper, we jointly study the dynamic relay selection for mobile users and profit maximization for service providers. Also, we design a Stackelberg‐game hierarchical framework to obtain the solution. At the lower level, we investigate the relay selection problem for the mobile users under given prices of selecting the relays. It is formulated as a Markov decision process problem with the objective to minimize the mobile user's long‐term average cost (which consists of the payment to the relay service and the cost due to packet loss), and solved by applying the linear programming technique. At the upper level, we study the game of setting relay prices for the service providers, with the knowledge that the mobile users will make relay selections based on their given prices. Nash equilibrium is obtained as the solution. Our results can help to provide a guidance for service providers to compete for providing relay services. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimization‐based workload distribution in geographically distributed data centers: A survey

Energy efficiency is a contemporary and challenging issue in geographically distributed data centers. These data centers consume significantly high energy and cast a negative impact on the energy resources and environment. To minimize the energy cost and the environmental impacts, Internet service providers use different approaches such as geographical load balancing (GLB). GLB refers to the placement of data centers in diverse geolocations to exploit variations in electricity prices with the objective to minimize the total energy cost. GLB helps to minimize the overall energy cost, achieve quality of service, and maximize resource utilization in geo‐distributed data centers by employing optimal workload distribution and resource utilization in the real time. In this paper, we summarize various optimization‐based workload distribution strategies and optimization techniques proposed in recent research works based on commonly used optimization factors such as workload type, load balancer, availability of renewable energy, energy storage, and data center server specification in geographically distributed data centers. The survey presents a systemized and a novel taxonomy of workload distribution in data centers. Moreover, we also debate various challenges and open research issues along with their possible solutions.     

基于平衡定价和成本梯度的科学工作流调度策略

 科学工作流调度在自治网格和竞争市场环境下变得极具挑战性.为了兼顾科学用户和服务提供者的需要,提出效用网格环境下市场驱动的科学工作流调度框架:使用平衡定价机制推导出多个资源约束情况下的服务价格,最大化服务提供者的利润和实现资源的最优分配;成本优化映射策略引入成本梯度因子作为服务选择的标准,提高调度算法的优化能力和优化速度.性能模拟表明该框架不仅实现了较高的资源节点收益和资源利用率,还可在保证用户QoS要求的前提下优化不同类型科学工作流的执行成本.     

A Business Model Guideline of Electricity Utility Systems Based on Blockchain Technology in Thailand: A Case Study of Consumers,Prosumers and SMEs

This paper presents a business model guideline of electricity utility systems based on blockchain technology in Thailand: a case study of consumers, prosumers and SMEs. Because the resource management of electricity utility systems in Thailand presently are based on enhanced single buyer or a centralized system. However, a free-trade energy market is likely to open or occur in the near future. Thus, a preparation of changing or adapting such a free-trade energy market using disruptive technology is an important point to study the aforementioned disruptive technology (i.e. blockchain technology). The future business of the free-trade energy market is related to the policy of the government (Thailand 4.0). It will support technology development and innovation to encourage the potential and readiness and to compete for foreign countries. In this scoping study, the business model guideline of electricity trading with peer to peer (P2P) is proposed to utilize the case study of consumers and prosumers/SMEs in Thailand based on a decentralized system. The P2P trading is directly the buying and selling of electricity between both consumers and prosumers. It is not a passed intermediary; otherwise, an intermediate process can be cut by this trading in order to save and reduce electricity costs. Preliminary expected impacts of cost structure analysis are summarized in terms of comparisons between the existing electricity trading and the blockchain technology. The results of such preliminary expected impacts show that costs to consumers, prosumers and SMEs obtained from the use of blockchain technology are possibly lower than those of EGAT, PEA, and MEA in the future. In addition, the business model guideline is completely discussed by theory in disruptive innovation.

     

LTE系统中基于负载的能量有效性调度策略

针对绿色通信中如何能在保证一定服务质量的前提下降低能量消耗这一关键问题,根据带宽交换能量的思想,综合考虑系统吞吐量和用户间公平性,提出了LTE 下行传输中基于负载的资源调度策略,通过降低频谱效率换得能量效率的提高,以达到节能的目的。仿真结果表明,所提的两个算法在满足用户速率要求的同时都能在很大程度上降低发射功率,且保证了用户间的公平性。     

Sparse least mean mixed‐norm adaptive filtering algorithms for sparse channel estimation applications

Sparse least‐mean mixed‐norm (LMMN) algorithms are developed to improve the estimation performance for sparse channel estimation applications. Both the benefits of the least mean fourth and least mean square algorithms are utilized to exploit a type of sparse LMMN algorithms. The proposed sparse‐aware LMMN algorithms are implemented by integrating an l ₁‐norm or log‐sum function into the cost function of traditional LMMN algorithm so that they can exploit the sparse properties of the broadband multi‐path channel and achieve better channel estimation performance. The proposed sparse LMMN algorithms are equal to adding an amazing zero‐attractor in the update equation of the traditional LMMN algorithm, which aim to speed up the convergence. The channel estimation performance of the proposed sparse LMMN algorithms are evaluated over a sparse broadband multi‐path channel to verify their effectiveness. Simulation results depict that the sparse LMMN algorithms are superior to the previously reported sparse‐aware least mean square/fourth, least mean fourth and least mean square and their corresponding sparse‐aware algorithms in terms of both the convergence and steady‐state behavior when the broadband multi‐path channel is sparse. Copyright © 2016 John Wiley & Sons, Ltd.     

CE2RV: Commissioned energy-efficient virtualization for large-scale heterogeneous wireless sensor networks

The Internet of Things (IoT) comprises sensor networks, intelligent things, devices, and humans for heterogeneous services and applications. Energy constraints in conventional wireless networks impact IoT performance resulting in service failures. For reducing the adverse impact of energy, this article introduces a commissioned energy-efficient resource virtualization (CE2RV) scheme. This proposed scheme classifies the sensor nodes as fast and slow-depleting for identifying service failures. The fast-depleting nodes are discontinued from the service replications, and the remaining energy high-node-connected resources are identified. Such resources are virtualized for thwarting the existing energy failures over various services. The node classifications are performed using a tree-learning algorithm. The classifications are performed for node replacement and service virtualization under different energy depletion rates. This is required for preventing sensor network disconnections between the users and service providers. The classification is required for overcoming multiple virtualizations between common nodes across different service providers. The proposed scheme's performance is analyzed using the metrics of service disconnections, energy utilization, energy efficiency, and service delay.     

A distributed battery recovery aware topology control algorithm for wireless sensor networks

Battery recovery effect is a phenomenon that the available capacity of a battery could increase if the battery can sleep for a certain period of time since its last discharging. Accordingly, the battery can work for a longer time when it takes some rests between consecutive discharging processes than when it works all the time. However, this effect has not been considered in the design of energy‐efficient topology control algorithms for wireless sensor networks. In this paper, we propose a distributed battery recovery effect aware connected dominating set constructing algorithm (BRE‐CDS) for wireless sensor networks. In BRE‐CDS, each network node periodically decides to join the connected dominating set or not. Nodes that have slept in the preceding round have priority to join the connected dominating set in the current round while nodes that have worked in the preceding round are encouraged to take sleep in the current round for battery recovery. Detailed algorithm design is presented. The computational complexity of BRE‐CDS is deduced to be O(D²), where D is node degree. Simulation results show that BRE‐CDS can significantly prolong the network lifetime as compared with existing work. Copyright © 2016 John Wiley & Sons, Ltd.     

Deterministic K‐means secure coverage clustering with periodic authentication for wireless sensor networks

Innovative and emerging developments in sensor networks are proven to be the backbone for real‐time applications such as satellite communications, military and border area surveillance systems, health care systems, traffic monitoring systems, seismic and underwater monitoring systems, and agriculture and habitat environment systems. Coverage and clustering techniques enable the sensor network to operate in group‐based and region‐based communication and thus save the node energy. Energy‐efficient protocols save the node energy and increase the network life cycle in a resource‐constrained sensor network. Cluster head (CH) node manages and controls the operations such as network topology, coverage area, and routing paths (multi‐paths and fault‐tolerant paths) of the network. In this paper, we present deterministic K‐means secure coverage clustering (K‐SCC) with periodic authentication. The proposed protocol uses coverage clustering technique with periodic authentication between the CH node and sensor nodes to establish the secure channel in the network. Maximum cover of K nodes is maintained in the secure coverage cluster to achieve authenticated communication between the sensor nodes in the network. The proposed K‐SCC protocol is compared with the existing protocols such as deterministic‐SCC and random‐SCC protocols. Simulation results indicate that the proposed K‐SCC protocol achieves an average of 84% coverage ratio (cluster/sensor node ratio) as compared with 62% coverage ratio in the existing SCC protocols. Simulations also indicate that the proposed K‐SCC protocol consumes 20% less energy as compared with the existing SCC protocol. Copyright © 2015 John Wiley & Sons, Ltd.     

Hierarchical location service for wireless sensor networks with mobile sinks

In wireless sensor networks (WSNs), a mobile sink can help eliminate the hotspot effect in the vicinity of the sink, which can balance the traffic load in the network and thus improve the network performance. Location‐based routing is an effective routing paradigm for supporting sink mobility in WSNs with mobile sinks (mWSNs). To support efficient location‐based routing, scalable location service must be provided to advertise the location information of mobile sinks in an mWSN. In this paper, we propose a new hierarchical location service for supporting location‐based routing in mWSNs. The proposed location service divides an mWSN into a grid structure and exploits the characteristics of static sensors and mobile sinks in selecting location servers. It can build, maintain, and update the grid‐spaced network structure via a simple hashing function. To reduce the location update cost, a hierarchy structure is built by choosing a subset of location servers in the network to store the location information of mobile sinks. The simulation results show that the proposed location service can significantly reduce the communication overhead caused by sink mobility while maintaining high routing performance, and scales well in terms of network size and sink number. Copyright © 2009 John Wiley & Sons, Ltd.     

Localized energy efficient routing in mobile ad hoc networks

We consider the problem of localized energy aware routing in mobile ad hoc networks. In localized routing algorithms, each node forwards a message based on the position of itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we propose new localized energy aware routing algorithms called OLEAR. The algorithms have very high packet delivery rate with low packet forwarding and battery power consumption. In addition, they ensure good energy distribution among the nodes. Finally, packets reach the destination using smaller number of hops. All these properties make our algorithm suitable for routing in any energy constrained environment. We compare the performance of our algorithms with other existing energy and non‐energy aware localized algorithms. Simulation experiments show that our algorithms present comparable energy consumption and distribution to other energy aware algorithms and better packet delivery rate. Copyright © 2006 John Wiley & Sons, Ltd.