Approximation Algorithms for Capacitated Minimum Forest Problems in Wireless sensor Networks with a Mobile Sink

To deploy a wireless sensor network for the purpose of large-scale monitoring, in this paper, we propose a heterogeneous and hierarchical wireless sensor network architecture. The architecture consists of sensor nodes, gateway nodes, and mobile sinks. Th

Efficient Virtual Network Embedding Via Exploring Periodic Resource Demands

Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%

Collusion-Resistant Repeated Double Auctions for Relay Assignment in Cooperative Networks

Cooperative communication effectively enhances the channel capacity of wireless networks by allowing some single-antenna nodes to relay data for other nodes. In such a communication scheme, choosing appropriate relay nodes is critical to maximize the overall network performance. In this paper, we consider the assignment problem of relay nodes in a cooperative wireless network, where physical relay infrastructures and relay supporting services (relay assignment) are independently operated by different selfish entities, each of which is driven by its own benefit. We first formulate the problem as a repeated double auction by taking into account the benefits of all entities in the system. That is, we consider a system consisting of a set of source-to-destination pairs, relay nodes, group agents, and the auctioneer, where source nodes are grouped into different groups and each group is represented by a group agent. The source nodes and group agents seek opportunities to maximize their own benefits through untruthful bidding, colluding with each other, and so on. We then show that these behaviors will jeopardize the social benefit of all entities in the system. To mitigate the effect of such behaviors, we devise a truthful repeated double auction that is able to bound the collusion probability of each entity. We finally conduct experiments by simulations to evaluate the performance of the proposed auction mechanism. Empirical results show that the proposed auction is effective in collusion-resistance with bounded collusion probabilities. To our best knowledge, this is the first auction mechanism for relay assignment in wireless networks that is truthful, collusion-resistant, budget-balance and individual-rational

Minimizing the operational cost of data centers via geographical electricity price diversity

Data centers, serving as infrastructures for cloud services, are growing in both number and scale. However, they usually consume enormous amounts of electric power, which lead to high operational costs of cloud service providers. Reducing the operationa

Collusion-Resistant Repeated Double Auctions for Cooperative Communications

Deployment of relay nodes to existing wireless net-works recently has received much attention since the channel capacity from sources to destinations through the cooperation of relay nodes is greatly enhanced. However, choosing appropriate relay nodes i

Network Lifetime Maximization in Delay-Tolerant Sensor Networks with a Mobile Sink

In this paper we investigate the network lifetime maximization problem in a delay-tolerant wireless sensor network with a mobile sink by exploiting a nontrivial tradeoff between the network lifetime and the data delivery delay. We formulate the problem as a joint optimization problem that consists of finding a trajectory for the mobile sink and designing an energy-efficient routing protocol to route sensing data to the sink, subject to the bounded delay on data delivery and the given potential sink location space. Due to NP-hardness of the problem, we then propose a novel optimization framework, which not only prolongs the network lifetime but also improves the other performance metrics including the network scalability, robustness, and the average delivery delay. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm against other heuristics. The experimental results demonstrate that the proposed algorithm outperforms the others significantly in terms of network lifetime prolongation

Minimizing Remote Monitoring Cost of Wireless Sensor Networks

In this paper we consider a remote monitoring scenario where the monitoring center is geographically located far away from the region of the deployed sensor network, and the sensing data by the sensors is transmitted to the monitoring center through a th

Data locality-aware big data query evaluation in distributed clouds

With more and more businesses and organizations outsourcing their IT services to distributed clouds for cost savings, historical and operational data generated by the services have been growing exponentially. The generated data that are referred to as big data, stored at different geographic datacenters, now become an invaluable asset to these businesses and organizations, as they can make use of the data through analysis to identify business advantages and make strategic decisions. Big data analytics thus has been emerged as a main research topic in cloud computing. To efficiently evaluate a big data analytic query in a distributed cloud consisting of multiple datacenters at different geographic locations interconnected by the Internet, it poses great challenges: (i) the source data of the query typically are located at different datacenters; and (ii) the resource demands of the query may be beyond the supplies of any single datacenter at that moment. In this paper, we formulate an online query evaluation problem for big data analytic queries in distributed clouds, with an objective to maximize the query acceptance ratio while minimizing the accumulative query evaluation cost, for which we first propose a novel metric to model the usages of different resources in the distributed cloud, by incorporating the capacities and workloads of different datacenters and links, as well as resource demands of different queries. We then devise effi- cient online algorithms for query evaluations under both unsplittable and splittable source data assumptions. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are promising, and outperform other heuristics at 95% confidence intervals