Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Nowadays,smart electricity grids are managed through advanced tools and techniques.The advent of Artificial Intelligence(AI)and network technology helps to control the energy demand.These advanced technologies can resolve common issues such as blackouts,optimal energy generation costs,and peakhours congestion.In this paper,the residential energy demand has been investigated and optimized to enhance the Quality of Service(QoS)to consumers.The energy consumption is distributed throughout the day to fulfill the demand in peak hours.Therefore,an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption.This model gives priority to consumer preferences while planning the operation of appliances.A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes.Furthermore,the allotment mechanism has been designed to manage the grid appliances through the edge node.The proposed model helps to improve the latency in the grid appliances scheduling process.

Stochastic Learning for Opportunistic Peer-to-Peer Computation Offloading in IoT Edge Computing

Peer-to-peer computation offloading has been a promising approach that enables resourcelimited Internet of Things(IoT)devices to offload their computation-intensive tasks to idle peer devices in proximity.Different from dedicated servers,the spare computation resources offered by peer devices are random and intermittent,which affects the offloading performance.The mutual interference caused by multiple simultaneous offloading requestors that share the same wireless channel further complicates the offloading decisions.In this work,we investigate the opportunistic peer-to-peer task offloading problem by jointly considering the stochastic task arrivals,dynamic interuser interference,and opportunistic availability of peer devices.Each requestor makes decisions on both local computation frequency and offloading transmission power to minimize its own expected long-term cost on tasks completion,which takes into consideration its energy consumption,task delay,and task loss due to buffer overflow.The dynamic decision process among multiple requestors is formulated as a stochastic game.By constructing the post-decision states,a decentralized online offloading algorithm is proposed,where each requestor as an independent learning agent learns to approach the optimal strategies with its local observations.Simulation results under different system parameter configurations demonstrate the proposed online algorithm achieves a better performance compared with some existing algorithms,especially in the scenarios with large task arrival probability or small helper availability probability.

An ID-Based Authenticated Key Agreement Protocol for Peer-to-Peer Computing

Peer-to-peer computing has recently started to gain significant acceptance, since it can greatly increase the performance and reliability of overall system. However, the security issue is still a major gating factor for its full adoption. In order to guarantee the security of data exchanged between two peers in Peer-to-Peer system, this paper comes up with an ID-based authenticated key agreement from bilinear pairings and uses BAN logic to prove the protocol’s security. Compared with other existing protocols, the proposed protocol seems more secure and efficient, since it adopts the static shared Diffie-Hellman key.

Load prediction of grid computing resources based on ARSVR method

Based on the monitoring and discovery service 4 （MDS4） model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then, a time-series autoregressive prediction model is devised. And an autoregressive support vector regression（ ARSVR） monitoring method is put forward to predict the node load of the data grid. Finally, a model for historical observations sequences is set up using the autoregressive （AR） model and the model order is determined. The support vector regression（SVR） model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.

An Improved Grid Security Infrastructure by Trusted Computing

Current delegation mechanism of grid security infrastructure （GSI） can＇t satisfy the requirement of dynamic, distributed and practical security in grid virtual organization. To improve this situation, a TC-enabled GSI is discussed in this paper. With TC-enabled GSI, a practical delegation solution is proposed in this paper through enforcing fine granularity policy over distributed platforms with the emerging trusted computing technologies. Here trusted platform module is treated as a tamper-resistance module to improve grid security infrastructure. With the implement of Project Daonity, it is demonstrated that the solution could gain dynamic and distributed security in grid environment.

Power Grid Islands Service Restoration Based on Cloud Computing

提出了一种基于云计算思想的电网恢复重构方法,利用网络中大量的分布式计算资源加速求取孤岛间网络恢复重构的最优解。利用电力系统分层分布式体系结构将含有多个微网的复杂配电网络视为不同的子云计算区,每个子云计算区根据自身情况将重构的服务请求分解为多个可独立处理的计算块并提交分布的计算节点进行并行处理和结果取优。相关联子云计算区之间可通信以协调不同区域间的负荷转供,最终实现故障恢复重构的目标。算例分析结果验证了该方法的有效性。

“Digital Region” in the Context of the “Grid Computing”

This paper is to construct a “digital local, regional, region” information framework based on the technology of “SIG” and its significance and application to the regional sustainable development evaluation system. First, the concept of the “grid computing” and “SIG” is interpreted and discussed, then the relationship between the “grid computing” and “digital region” is analyzed, and the framework of the “digital region” is put forward. Finally, the significance and application of “grid computing” to the “region sustainable development evaluation system” are discussed.

Performance Predictable ServiceBSP Model for Grid Computing

This paper proposes a performance prediction model for grid computing model ServiceBSP to support developing high quality applications in grid environment. In ServiceBSP model, the agents carrying computing tasks are dispatched to the local domain of the selected computation services. By using the IP （integer program） approach, the Service Selection Agent selects the computation services with global optimized QoS （quality of service） consideration. The performance of a ServiceBSP application can be predicted according to the performance prediction model based on the QoS of the selected services. The performance prediction model can help users to analyze their applications and improve them by optimized the factors which affects the performance. The experiment shows that the Service Selection Agent can provide ServiceBSP users with satisfied QoS of applications.

Adaptive Trust Management Framework in P2P Grid Computing

In P2P Grid computing systems, the authorization decision is often tackled by two different trust management methods： policy-based approach, where authorization are built on logical rules and verifiable properties encoded in signed credentials, and reputation-based approach, based on collecting, aggregating and disseminating reputation among the peers. However, the overhead caused by proof of compliance on authorization and the absence of certifying authorities may negate the strong and objective security advantages of policy-based approach, whilst vagueness, complexity and inaccurate characterization caused by reputation evolution may eliminate the quantitative and flexible advantages of reputation-based approach. We propose an adaptive trust management framework, which combines the merit of policy proof and reputation evolution such that authorization is aware of not only the strong and objective security traits, but also the calculability and the availability security traits. Finally, the framework of system is proposed.

Existence of Market Equilibria for Grid Computing

Grid computing has emerged as an effective mechanism for allocating globally available surplus computational capacity to applications whose requirements exceed local capacity. It is often viewed as a commodity exchange with additional grid computing specific constraints that may arise due to requirements on multiple resources (e.g., disk space) in addition to computing power. These constraints are related to complementarity and substitution effects among resources, and significantly alter the assumptions typically used for demonstrating the existence of market equilibrium. However, prior work in grid computing has simply assumed that market equilibria exist. Our work fills this gap by studying the existence of market equilibrium under the grid computing environment. To do so, we first establish an economic framework that incorporates the grid computing specific constraints into a commodity market. We next derive some intuitive necessary conditions based on the computing requirements of individual agents. We finally establish the existence of regular markets as a competitive equilibrium, given that these necessary conditions are met and that the agents’ utility functions satisfy some minimal requirements. In the process, we also show existence of competitive equilibrium for the special case of grid computing as a pure exchange economy.

An Improved Task Scheduling Algorithm in Grid Computing Environment

Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved effectively.

Call for Papers The 6^(th) International Conference on Grid and Cooperative Computing

Recent years have seen rapid advances in various grid-related technologies, middleware, and applications. The GCC conference has become one of the largest scientific events worldwide in grid and cooperative computing. The 6th international conference on grid and cooperative computing (GCC2007) Sponsored by China Computer Federation (CCF),Institute of Computing Technology, Chinese Academy of Sciences (ICT) and Xinjiang University ,and in Cooperation with IEEE Computer Soceity ,is to be held from August 16 to 18, 2007 in Urumchi, Xinjiang, China.

The Application of IHA in Grid Cloud Computing Task Decomposition and Scheduling Based on Bionics

Based on the large amount and variations of the power grid task as well as its requirement of real- time performance and economic benefit, we make a further improvement and expansion of IHA (Improved Heuristic Algorithm) on the combination of bionics in genetic engineering and evolution to solve the decomposing and scheduling problems. Firstly, we transform those complex decomposing problems into the operational optimal solution problem by IHA to decrease the rate of running into the local optimal solution [1]. In task scheduling, we classify the sub-tasks by the emergency degree for resource allocation, which not only largely reduces the calculation and resource cost but also improves working efficiency and the speed of execution [2]. Finally, we select optimal scheduling scheme by the Fitness function defined about time and cost.

Towards Attaining Reliable and Efficient Green Cloud Computing Using Micro-Smart Grids to Power Internet Data Center

Energy generation and consumption are the main aspects of social life due to the fact that modern people’s necessity for energy is a crucial ingredient for existence. Therefore, energy efficiency is regarded as the best economical approach to provide safer and affordable energy for both utilities and consumers, through the enhancement of energy security and reduction of energy emissions. One of the problems of cloud computing service providers is the high rise in the cost of energy, efficiency together with carbon emission with regards to the running of their internet data centres (IDCs). In order to mitigate these issues, smart micro-grid was found to be suitable in increasing the energy efficiency, sustainability together with the reliability of electrical services for the IDCs. Therefore, this paper presents idea on how smart micro-grids can bring down the disturbing cost of energy, carbon emission by the IDCs with some level of energy efficiency all in an effort to attain green cloud computing services from the service providers. In specific term, we aim at achieving green information and communication technology (ICT) in the field of cloud computing in relations to energy efficiency, cost-effectiveness and carbon emission reduction from cloud data center’s perspective.

THE STRATEGY OF RESOURCE MANAGEMENT BASED ON GRID COMPUTING

This paper analyzes the defaults of traditional method according to the resource management method of grid computing based on virtual organization. It supports the concept to ameliorate the resource management with mobile agent and gives the ameliorated resource management model. Also pointed out is the methodology of ameliorating resource management and the way to realize in reality.

A Fault Tolerance Algorithm for Resource Discovery in Semantic Grid Computing Using Task Agents

One of the interesting topics in grid computing systems is resources discovery. After the failure of a resource in a chain of resources made for a specific task in grid environment, discovering and finding a new resource that reconstructs the chain is an important topic. In this study, with defining new agent that is called task agent, and by proposing an algorithm, we will increase the fault tolerance against probable failure of a resource in the resource chain.