Energy-Efficient QoE-Driven Strategies for Context-Aware RAT Selection

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

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Clustering in Wireless Sensor Networks

Internet and Distributed Computing Advancements ◽

10.4018/978-1-4666-0161-1.ch008 ◽

2012 ◽

pp. 197-211

Author(s):

Enamul Haque ◽

Norihiko Yoshida

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Routing Protocols ◽

Energy Efficient ◽

Wireless Sensor ◽

Body Sensor Networks ◽

Context Aware ◽

Context Aware Computing ◽

Adaptive Clustering ◽

Clustering Data

Applications of Wireless Sensor Networks (WSN) have been expanded from industrial operation to daily common use. With the pace of development, a good number of state-of-the-art routing protocols have been proposed for WSN. Among many of these protocols, hierarchical or cluster-based protocol technique is adopted from the wired network because of its scalability, better manageability, and implicit energy efficiency. In this chapter, the authors have surveyed Low Energy Adaptive Clustering Hierarchy, Power-Efficient Gathering in Sensor Information Systems, Adaptive Periodic Threshold-Sensitive Energy Efficient Sensor Network, and Hybrid Energy-Efficient Distributed Routing Protocols. These protocols exhibit notable characteristics and advantages compared to their contemporaries. Again, context aware computing and applications have been greatly emphasized in recent articles by renowned technologists. This approach is considered as a momentous technology that will change the way of interaction with information devices. Accordingly, context aware clustering technique carries a great deal of importance among WSN routing protocols. Therefore, the authors have investigated noteworthy context aware routing protocols such as: Context Adaptive Clustering, Data-Aware Clustering Hierarchy, Context-Aware Clustering Hierarchy, and Context-Aware Multilayer Hierarchical Protocol. Their investigation and analysis of these protocols has been included in this chapter with useful remarks. Context awareness is considered an integral part of Body Sensor Networks (BSN), which is one kind of WSN. Thus, the authors have also discussed issues related to context aware techniques used in BSN.

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Stable-Matching-Based Energy-Efficient Context-Aware Resource Allocation for Ultra-Dense Small Cells

Advances in Wireless Technologies and Telecommunication - Interference Mitigation and Energy Management in 5G Heterogeneous Cellular Networks ◽

10.4018/978-1-5225-1712-2.ch002 ◽

2017 ◽

pp. 29-57

Author(s):

Zhenyu Zhou ◽

Zheng Chang ◽

Chen Xu ◽

Tapani Ristaniemi

Keyword(s):

Resource Allocation ◽

Energy Efficient ◽

Small Cells ◽

Context Aware ◽

Delay Sensitive ◽

Optimum Energy ◽

Significant Performance ◽

Promising Solution ◽

Nonlinear Fractional Programming ◽

Efficient Matching

Implementing caching to ultra-densely deployed small cells provides a promising solution for satisfying the stringent quality of service (QoS) requirements of delay-sensitive applications with limited backhaul capacity. With the rapidly increasing energy consumption, in this chapter, the authors investigate the NP-hard energy-efficient context-aware resource allocation problem and formulate it as a one-to-one matching problem. The preference lists in the matching are modeled based on the optimum energy efficiency (EE) under specified matching, which can be obtained by using an iterative power allocation algorithm based on nonlinear fractional programming and Lagrange dual decomposition. Next, on account of the Gale-Shapley algorithm, an energy-efficient matching algorithm is proposed. Some properties of the proposed algorithm are discussed and analyzed in detail. Moreover, the authors extend the algorithm to the matching with indifferent and incomplete preference lists. Finally, the significant performance gain of the proposed algorithm is demonstrated through simulation results.

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