Object-based storage is a storage architecture that manages data as objects, as opposed to other storage architectures like file systems which manage data as a file hierarchy and block storage which manages data as blocks within sectors and tracks. In general, each object typically includes the data itself, a variable amount of metadata, and a globally unique identifier. Object storage seeks to enable capabilities not addressed by other storage architectures such as data replication and data distribution at object-level granularity. A distributed data store is a computer network where information is stored on more than one node, often in a replicated fashion. Recently, there are many excellent researches target on energy aware, performance-aware, and trade-of on cloud storage. However, less on the scheduling on the cost-efficient designs. This paper, aims to evaluate the data storage performance with hybrid storage. We try to provide a cost-aware scheme for tier-based hybrid storage. Our scheme can provide a proper placement for data. Thus, users and venders can use lower cost to get the performance that they need.

In this paper, a power allocation algorithm is proposed for multiuser MIMO-OFDM femtocell networks on downlink, where an objective considers the maximization of the SINR of a macro user that is near the femto base station. Since the macro and the femto base stations share the same spectrum, there is a co-channel interference problem. We propose a power allocation method based on genetic algorithms to enhance the SINR of the macro user. In addition, we also utilize the zero-forcing beamforming design to alleviate the co-channel interference. The simulation results show that the proposed genetic algorithm-based power allocation method with zero-forcing beamforming reveals the best performance, and outperforms the method with equal power allocation.

A capacity maximization problem subject to a total power constraint in OFDM-based wireless multicast systems is investigated in this paper. A subcarrier and power allocation method is proposed based on a property observed from the simulation in the multicast system. We design a criterion to efficiently assign subcarriers to multicast groups. Simulation results reveal that the proposed method outperforms the existing suboptimal methods. With the help of the improved mechanism and the designed criterion, the performance of the proposed method is almost the same as that of the optimal solution.

The growing popularity of flash memory is expected to draw attention to the limitations of file-system performance over flash memory. This work was motivated by the modular designs of operating system components such as bus and device drivers. A filter-driver-layered caching design is proposed to resolve the performance gap among file systems and to improve their performance with the considerations of flash memory characteristics. An efficient hybrid tree structure is presented to organize and manipulate the intervals of cached writes. Algorithms are proposed in the merging, padding, and removing of the data of writes. The effectiveness of the proposed approach is demonstrated with some analysis study of FAT-formatted and NTFS-formatted USB flash disks. The proposed cohesive caching policy was implemented as a filter driver in Windows XP/Vista for performance evaluation. In the experiments, a ten-fold or larger performance improvement was usually achieved when the cache size was only 64KB. Other substantial improvements were also observed in the experiments. For example, the proposed design enabled FAT-formatted and NTFS-formatted flash-memory devices to copy Linux image files 93% and 14% faster than conventional flash drives, respectively.

This paper describes an embedded workflow framework (EMWF) that enables flexible personal and home automation and assistive devices and service and social robots (collectively referred to as SISARL) to be built on workflow architecture The process definition language supported by EMWF is called SISARL-XPDL. It consists of a subset of the WfMC standard XML Process Definition Language (XPDL) 2.0, together with elements that implement common mechanisms for robot behavior coordination. SISARL-XPDL definitions of workflows are first translated into standard XPDL and execution directives and then are parsed either directly into binary workflow scripts for execution or into intermediate scripts in C. EMWF provides workflow engines for Linux and Windows CE platforms. The engines are written in C in order to keep their memory footprint and runtime overhead small. Performance data show that the overheads introduced by the engine and workflow data are tolerable for most SISARL devices.

With the popularity of VoIP, Push-To-Talk (PTT) services over the IP networks have gained their momentum. This research aims at essential design issues for the quality of service of PTT services. We explore the relationship between the speech quality and system design issues. MOS (Mean Opinion Score), which is raised by the ITU-T (International Telecommunication Union) as one of the most common benchmarks, is studied under factors in system designs and implementations. In particular, we focus on the analysis of packet delay, jitter, and packet loss for PTT services. Experiments are designed and executed with the considerations of PTT characteristics. Our goal is to provide more understanding of MOS versus PTT and deliver insights in future PTT system designs.

A large-scaled RFID application often needs a highly efficient database system in data processing. This research is motivated by the strong demand of an efficient index structure design for main-memory database systems of RFID applications. In this paper, a signature-based grid index structure is proposed for efficient data queries and storage. An efficient methodology is proposed to locate duplicates and to execute batch deletions and range queries based on application domain knowhow. The capability of the design is implemented in an open source main-memory database system H2 and evaluated by realistic workloads of RFID applications.

This paper presents a power-aware secure routing protocol for wireless embedded networks (WENs). We propose an efficient secure relay scheme called ESRS for ad-hoc-based embedded-network communications. ESRS is a two-phase scheme which includes end-to-end secure channel establishment and relay-behavior monitoring. With end-to-end secure channel establishment, the transmitted data will be protected under an end-to-end secure channel. A power-aware secure route will be also provided in this stage. Then attacks of routing are monitored and controlled in the monitoring phase. As the experimental results are shown, ESRS provides an efficient way to relay data securely for ad-hoc-based embedded network communications.

Energy efficiency remains a critical design issue for wireless sensor networks. How to maintain the network connectivity and, at the same time, to minimize the energy consumption is a very challenging problem. Different from many previous excellent results, we explore the topology control problem for heterogeneous wireless sensor networks. Our objective is to propose distributed topology control algorithms, where only local information is kept for all of the nodes. The experimental results show the strengths of the proposed algorithms in the average edge length and the average link length.

A self-organizing HCMAC neural network was proposed to solve high dimensional pattern classification problems well in our previous work. However, a large amount of redundant GCMAC nodes might be constructed due to the expansion approach of full binary tree topology. Therefore, this study presents a pruning structure of self-organizing HCMAC neural network to solve this problem. Experimental results show the proposed pruning structure not only can largely reduce memory requirement, but also keep fast training speed and has higher pattern classification accuracy rate than the original self-organizing HCMAC neural network does in the most testing benchmark data sets.

This paper presents a self-organizing hierarchical cerebellar model arithmetic computer (HCMAC) neural-network classifier, which contains a self-organizing input space module and an HCMAC neural network. The conventional CMAC can be viewed as a basis function network (BFN) with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear functions. However, the conventional CMAC has an enormous memory requirement for resolving high-dimensional classification problems, and its performance heavily depends on the approach of input space quantization. To solve these problems, this paper presents a novel supervised HCMAC neural network capable of resolving high-dimensional classification problems well. Also, in order to reduce what is often trial-and-error parameter searching for constructing memory allocation automatically, proposed herein is a self-organizing input space module that uses Shannon's entropy measure and the golden-section search method to appropriately determine the input space quantization according to the various distributions of training data sets. Experimental results indicate that the self-organizing HCMAC indeed has a fast learning ability and low memory requirement. It is a better performing network than the conventional CMAC for resolving high-dimensional classification problems. Furthermore, the self-organizing HCMAC classifier has a better classification ability than other compared classifiers.