Abstract-A rapidly emerging trend in the IT landscape is the uptake of large-scale datacenters moving storage and data processing to providers located far away from the end-users or locally deployed servers. For these large-scale datacenters power efficiency is a key metric, with the PUE (Power Usage Effectiveness) and DCiE (Data Centre infrastructure Efficiency) being important examples. This paper proposes a belief rule based expert system to predict datacenter PUE under uncertainty. The system has been evaluated using real-world data from a data center in the UK. The results would help planning construction of new datacenters and the redesign of existing datacenters making them more power efficient leading to a more sustainable computing environment. In addition, an optimal learning model for the BRBES demonstrated which has been compared with ANN and Genetic Algorithm; and the results are promising.

The aim of this paper is to present the findings of a PhD research (Heinzl, 2007)

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

Abstract

Abstract-The main goal of this proposed project is to harness the emerging IoT technology to empower elderly population to selfmanage their own health, stay active, healthy, and independent as long as possible within a smart and secured living environment. An integrated open-sourced IoT ecosystem will be developed. It will encompass the entire data lifecycle which involves the following processes: data acquisition, data transportation; data integration, processing, manipulation and computation; visualisation; data intelligence and exploitation; data sharing; data storage. This innovative cloud-based IoT ecosystem will provide a one-stop shop for integrated smart IoTenabled services to support older people (greater or equal to 65 years old) who live alone at home (or care homes). Another innovation of this system is the design and implementation of an integrated IoT gateway for wellebing wearable and home automation system sensors with varying communication protocols. The SMART-ITEM system and services will appropriately address the following (i) smart health and care; (ii) smart quality of life; (iii) SMART-ITEM social community. The development of the system will be based on the User Centred Design methodology so as to ensure active user engagement throughout the entire project lifecycle and necessary standards as well as compliances will be adhered to (e.g. security, trust and privacy) in order to enhance user acceptance.

Substantial numbers of Indonesian women are seeking employment as domestic workers in Malaysia in order to escape poverty and unemployment and to be able to support their families back home. Most Indonesian domestic workers in Malaysia face unpleasant working conditions with long working hours and no freedom to move or communicate; some find themselves in a situation of abuse. In many cases, the decision to work abroad is made without being properly informed about what to expect. Furthermore, most of the Indonesian migrant domestic workers do not know about process and procedures and are not aware of their rights and the possibilities of seeking assistance when problems occur. In order to empower the target group, relevant information need to be disseminated. Current strategies do not seem to achieve the desired effect. Many of the affected women come from remote areas, are poor and have a low level of education; therefore, their skills to make use of written or even digital information are limited. Appropriate strategies are suggested to utilise traditional and commonly used information dissemination channels such as cultural performances, group discussions and radio. Educational measures should be combined with aspects of local entertainment culture in order to attract attention and to provoke identification with the issues discussed. Further research is necessary to actually develop an appropriate information dissemination strategy with regard to the target group and to evaluate its benefits by conducting pilot projects.

Greening of Data Centers could be achieved through energy savings in two significant areas, namely: compute systems and cooling systems. A reliable cooling system is necessary to produce a persistent flow of cold air to cool the servers due to increasing computational load demand. Servers' dissipated heat effects a strain on the cooling systems. Consequently, it is necessary to identify hotspots that frequently occur in the server zones. This is facilitated through the application of data mining techniques to an available big dataset for thermal characteristics of High-Performance Computing ENEA Data Center, namely Cresco 6. This work presents an algorithm that clusters hotspots with the goal of reducing a data centre's large thermal-gradient due to uneven distribution of server dissipated waste heat followed by increasing cooling effectiveness.

The International Telecommunications Union has provided vital statistics on ICT use, which evidently shows an increasing trend of universal growth in ICT uptake. According to ITU, in 2013, there are almost as many mobile-cellular subscriptions as people in the world. This is attributed to the mobile revolution, which delivers ICT applications in education, business, government, banking, health, and so on. Thus, the concept of mobile computing has revolutionized people's lives such that, nowadays, any information can be accessed and transmitted anytime and anywhere from any device. This new way of interacting with services basically has two main underlying drivers: (1) availability of innovative mobile devices (e.g., smartphones and tablets) and (2) emerging ubiquitous and affordable wireless networks. According to IndustryWeek, mobile computing is accelerating in organizations as well and mobile technology is viewed as providing the biggest boost to businesses. By 2020, the number of connected devices globally is expected to reach 80 billion and augmented reality facilitated via mobile devices will penetrate most sectors: leisure, social media, retail, restaurants, property, transport, and so on.However, mobile computing is also a power-hungry technology. Therefore, there is a pressing need for the deployment of sustainable mobile computing and communication which focus on reduced energy consumption of its mobile ICT infrastructure and CO2 emissions in order to uphold the three pillars of sustainability: environmental, economic, and social. This is made possible if sustainable mobile computing platforms appropriately address the following challenges: extension of battery life, reduction of heat dissipation from components, power consumption, energy efficient wireless communications, efficient resource management, and sustainable power architecture. Some of the energy efficient techniques in sustainable mobile computing are as follows: (i) hardware: homogenous and heterogeneous multiprocessor system-on-chip, integration of multicore processors and multiple accelerometers into mobile system-on-chip, contextaware and energy aware operating systems, energy aware virtual memory (PAVM), application processor integration, and optimized scheduling in multiprocessor architectures; (ii) Wi-Fi communication: energy aware cellular data scheduling and energy-delay trade-off and dynamic voltage and frequency scaling (DVFS); (iii) sensors: accuracy and energy trade-off; (iv) applications: energy efficient computation off-loading, multithreaded applications, and green software design patterns.The scope of this special issue is in line with recent contributions from academia and industry on the recent activities that tackle the technical challenges making mobile computing and communications more sustainable. For the current issue, we are pleased to introduce a collection of papers covering a range of topics such as frameworks for multicore mobile computing; architectures for IoT with mobile fog servers; energy efficient schemes ...