Editorial for IEEE access special section on theoretical foundations for big data applications : challenges and opportunities

Big data is one of the hottest research topics in science and technology communities, and it possesses a great application potential in every sector for our society, such as climate, economy, health, social science, and so on. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, and manage. We can conclude that big data is still in its infancy stage, and we will face many unprecedented problems and challenges along the way of this unfolding chapter of human history.

A highly practical approach toward achieving minimum data sets storage cost in the cloud

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.

A market-oriented hierarchical scheduling strategy in cloud workflow systems

A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.

A high embedding capacity image watermarking method with rank-based embedder and decoder

High embedding capacity is desired in digital image watermarking. In this paper, we propose a novel rank-based image watermarking method to achieve high embedding capacity. We first divide the host image into blocks. Then the 2-D discrete cosine transform (DCT) and zigzag scanning is used to construct the coefficient sets with a secret key. After that, the DCT coefficient sets are modified using a rank-based embedding strategy to insert the watermark bits. A buffer is also introduced during the embedding phase to enhance the robustness. At the decoding step, the watermark bits are extracted by checking the ranks of the detection matrices. The proposed method is host signal interference (HSI) free, invariant to amplitude scaling and constant luminance change, and robust against other common signal processing attacks. Experimental results demonstrate the effectiveness of the proposed method.

Robustness and embedding capacity enhancement in time-spread echo-based audio watermarking

In echo-based audio watermarking methods, poor robustness and low embedding capacity are the main problems. In this paper, we propose a novel time-spread echo method for audio watermarking, aiming to improve the robustness and the embedding capacity. To improve the robustness, we design an efficient pseudonoise (PN) sequence and a corresponding decoding function. Compared to the conventional PN sequence used in time-spread echo hiding based method, more large peaks are produced during the autocorrelation of the proposed PN sequence. Our decoding function is designed to utilize these peaks to improve the robustness. To enhance the embedding capacity, multiple watermark bits are embedded into one audio segment. This is achieved by varying the delays of added echo signals. Moreover, the security of the proposed method is further improved by scrambling the watermarks at the embedding stage. Compared with the conventional time-spread echo-based method, the proposed method is more robust to conventional attacks and has higher embedding capacity. The effectiveness of our method is illustrated by simulation results.

Control layer resource management in SDN-IoT networks using multi-objective constraint

Software Defined Networking (SDN) and Internet of Things (IoT) integration has thrown many critical challenges. Specifically, in heterogeneous SDN-IoT ecosystem, optimized resources utilization and effective management at the control layer is very difficult. This mainly affects the application specific Quality of Service (QoS) and energy consumption of the IoT network. Motivated from this, we propose a new Resource Management (RM) method at the control layer, in distributed SDN-IoT networks. This paper starts with reasons that why at control layer RM is more complex in the SDN-IoT ecosystem. After-that, we highlight motivated examples that necessitate to investigate new RM methods in SDN-IoT context. Further, we propose a novel method to compute controller performance. Theoretical analysis is conducted to prove that the proposed method is better than the existing methods.

Matching cognitive styles to computer-based instruction : an approach for enhanced learning in electrical engineering

An approach aimed at enhancing learning by matching individual students' preferred cognitive styles to computer-based instructional (CBI) material is presented. This approach was used in teaching some components of a third-year unit in an electrical engineering course at the Queensland University of Technology. Cognitive style characteristics of perceiving and processing information were considered. The bimodal nature of cognitive styles (analytic/imager, analytic/verbalizer, wholist/imager and wholist/verbalizer) was examined in order to assess the full ramification of cognitive styles on learning. In a quasi-experimental format, students' cognitive styles were analysed by cognitive style analysis (CSA) software. On the basis of the CSA results the system defaulted students to either matched or mismatched CBI material. The consistently better performance by the matched group suggests potential for further investigations where the limitations cited in this paper are eliminated. Analysing the differences between cognitive styles on individual test tasks also suggests that certain test tasks may better suit certain cognitive styles.

Nanocrystalline Ge Flash Memories: Electrical Characterization and Trap Engineering

Conventional floating gate non-volatile memories (NVMs) present critical issues for device scalability beyond the sub-90 nm node, such as gate length and tunnel oxide thickness reduction. Nanocrystalline germanium (nc-Ge) quantum dot flash memories are fully CMOS compatible technology based on discrete isolated charge storage nodules which have the potential of pushing further the scalability of conventional NVMs. Quantum dot memories offer lower operating voltages as compared to conventional floating-gate (FG) Flash memories due to thinner tunnel dielectrics which allow higher tunneling probabilities. The isolated charge nodules suppress charge loss through lateral paths, thereby achieving a superior charge retention time. Despite the considerable amount of efforts devoted to the study of nanocrystal Flash memories, the charge storage mechanism remains obscure. Interfacial defects of the nanocrystals seem to play a role in charge storage in recent studies, although storage in the nanocrystal conduction band by quantum confinement has been reported earlier. In this work, a single transistor memory structure with threshold voltage shift, Vth, exceeding ~1.5 V corresponding to interface charge trapping in nc-Ge, operating at 0.96 MV/cm, is presented. The trapping effect is eliminated when nc-Ge is synthesized in forming gas thus excluding the possibility of quantum confinement and Coulomb blockade effects. Through discharging kinetics, the model of deep level trap charge storage is confirmed. The trap energy level is dependent on the matrix which confines the nc-Ge.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

Analysis of twelve electrical and electronics engineering programs

This paper investigates undergraduate programs in electrical and electronics engineering offered by twelve universities in Australia, Asia, Europe, and America. The investigation focuses on the structure and content of the programs, and the contact hour and assessment of the subjects involved in the programs. The investigation is carried out in four stages: selection of universities, collection of data, analysis of data, and formulation of outcomes. A list of subjects is created based on the content of the programs. The average percentage coverage of each subject in the twelve programs is calculated. The subjects are then grouped into nine program components. The average percentage coverage of each component per university is calculated. For each component, the total number of contact hours for lecture, tutorial, and practical is calculated. Also, the average percentage of four assessment methods for each component is found. Discussions on the outcome of the investigation are presented.<br />

Making PBL teams successful : a study on student diversity in a first year electrical engineering programme

Problem based learning (PBL) is a group learning environment that involves a radical change in the way students learn and the role that academic staff play in facilitating learning. The PBL approach claims to build extended technological and social understandings as it offers a context for development of autonomous learners. It has an emphasis on collective and individual learning motivation and decision-making behaviours.<br /><br />In this paper, we present the responses of students to the heterogeneous characteristic of PBL teams in a first year electrical engineering degree course at an Australian University. The learning cultures in PBL teams that emerge as a result of the diverse characteristics of teams are also presented in this paper.<br /><br />A number of PBL teams were observed and interviewed throughout their first year course with their consent. Analysis of the data collected about students&rsquo; learning and outcomes in PBL teams informed the ways in which individual students approach their learning, the ways in which they control, regulate and direct their learning individually and as a group and the extent to which they participate, engage and thereby learn in the course.<br /><br />It is evident that some students have a strong influence on the behaviour of other students in their team. These students also influenced what is learnt as a team, the ways in which they interrelated, worked as a team and problem solved in changing circumstances. Therefore, when designing student teams for PBL academics should not assume that a mono-cultural group or a mixed-ability group of students will work successfully together. We think that the results of this research inform both the design of PBL courses and the facilitation of PBL groups to accomplish successful group learning outcomes.