28,721 research outputs found
Integrated optimization of smart home appliances with cost-effective energy management system
Smart grid enables consumers to control and schedule the consumption pattern of their appliances, minimize energy cost, peak-to-average ratio (PAR) and peak load demand. In this paper, a general architecture of home energy management system (HEMS) is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residential customers. The optimization problem is developed under the time of use pricing (TOUP) scheme. To optimize the formulated problem, a powerful meta-heuristic algorithm called grey wolf optimizer (GWO) is utilized, which is compared with particle swarm optimization (PSO) algorithm to show its effectiveness. A rooftop photovoltaic (PV) system is integrated with the system to show the cost effectiveness of the appliances. For analysis, eight different cases are considered under various time scheduling algorithm
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
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Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art
Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP
Heuristic usability evaluation on games: a modular approach
Heuristic evaluation is the preferred method to assess usability in games when experts conduct this
evaluation. Many heuristics guidelines have been proposed attending to specificities of games but
they only focus on specific subsets of games or platforms. In fact, to date the most used guideline to
evaluate games usability is still Nielsen’s proposal, which is focused on generic software. As a
result, most evaluations do not cover important aspects in games such as mobility, multiplayer
interactions, enjoyability and playability, etc. To promote the usage of new heuristics adapted to
different game and platform aspects we propose a modular approach based on the classification of
existing game heuristics using metadata and a tool, MUSE (Meta-heUristics uSability Evaluation
tool) for games, which allows a rebuild of heuristic guidelines based on metadata selection in order
to obtain a customized list for every real evaluation case. The usage of these new rebuilt heuristic
guidelines allows an explicit attendance to a wide range of usability aspects in games and a better
detection of usability issues. We preliminarily evaluate MUSE with an analysis of two different
games, using both the Nielsen’s heuristics and the customized heuristic lists generated by our tool.Unión Europea PI055-15/E0
Efficient energy management for the internet of things in smart cities
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities
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