699 research outputs found
Home Energy Management System and Internet of Things: Current Trends and Way Forward
Managing energy in the residential areas has becoming essential with the aim of cost saving, to realize a practical approach of home energy management system (HEMS) in the area of heterogeneous Internet-of-Thing (IoT) devices. The devices are currently developed in different standards and protocols. Integration of these devices in the same HEMS is an issue, and many systems were proposed to integrate them efficiently. However, implementing new systems will incur high capital cost. This work aims to conduct a review on recent HEMS studies towards achieving the same objectives: energy efficiency, energy saving, reduce energy cost, reduce peak to average ratio, and maximizing user's comfort. Potential research directions and discussion on current issues and challenges in HEMS implementation are also provided
STAY FLEXIBLE: A PRESCRIPTIVE PROCESS MONITORING APPROACH FOR ENERGY FLEXIBILITY-ORIENTED PROCESS SCHEDULES
The transition of energy supply from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent energy supply and demand. Demand-side energy flexibility, offered particularly by companies, is seen as a promising and necessary approach to address these challenges. Process mining provides significant potential to prevent a deterioration of product quality or process flows due to flexibilization and allows for exploiting monetary benefits associated with flexible process operation. Hence, we follow the design science research paradigm to develop PM4Flex, a prescriptive process monitoring approach, that generates recommendations for pending process flows optimized under fluctuating power prices by implementing established energy flexibility measures. Thereby, we consider company- and process-specific constraints and historic event logs. We demonstrate and evaluate PM4Flex by implementing it as a software prototype and applying it to exemplary data from a heating and air conditioning company, observing considerable cost-savings of 1.42ct per kWh or 7.89%
Context-aware Background Application Scheduling in Interactive Mobile Systems
Department of Computer Science and EngineeringEach individual's usage behavior on mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness provides great opportunities to make the networking and the computing capabilities of mobile systems to be more personalized and more efficient in managing their resources. To this end, we first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find contextual dependencies between application usage patterns, for which we classify contexts autonomously with unsupervised learning methods. Using the knowledge acquired, we develop a context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications for a joint optimization in which the energy saving is maximized and the user discomfort from the scheduling is minimized. Our trace-driven simulations with 96 user traces demonstrate that the context-aware design of CAS enables it to outperform existing process scheduling algorithms. Our implementation of CAS over Android platforms and its end-to-end evaluations verify that its human involved design indeed provides substantial user-experience gains in both energy and application launching latency.ope
An architecture for user preference-based IoT service selection in cloud computing using mobile devices for smart campus
The Internet of things refers to the set of objects that have identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social environments and user context. Interconnected devices communicating to each other or to other machines on the network have increased the number of services. The concepts of discovery, brokerage, selection and reliability are important in dynamic environments. These concepts have emerged as an important field distinguished from conventional distributed computing by its focus on large-scale resource sharing, delivery and innovative applications. The usage of Internet of Things technology across different service provisioning environments has increased the challenges associated with service selection and discovery. Although a set of terms can be used to express requirements for the desired service, a more detailed and specific user interface would make it easy for the users to express their requirements using high-level constructs. In order to address the challenge of service selection and discovery, we developed an architecture that enables a representation of user preferences and manipulates relevant descriptions of available services. To ensure that the key components of the architecture work, algorithms (content-based and collaborative filtering) derived from the architecture were proposed. The architecture was tested by selecting services using content-based as well as collaborative algorithms. The performances of the algorithms were evaluated using response time. Their effectiveness was evaluated using recall and precision. The results showed that the content-based recommender system is more effective than the collaborative filtering recommender system. Furthermore, the results showed that the content-based technique is more time-efficient than the collaborative filtering technique
Exploring the drive-by sensing power of bus fleet through active scheduling
Vehicle-based mobile sensing (a.k.a drive-by sensing) is an important means
of surveying urban environment by leveraging the mobility of public or private
transport vehicles. Buses, for their extensive spatial coverage and reliable
operations, have received much attention in drive-by sensing. Existing studies
have focused on the assignment of sensors to a set of lines or buses with no
operational intervention, which is typically formulated as set covering or
subset selection problems. This paper aims to boost the sensing power of bus
fleets through active scheduling, by allowing instrumented buses to circulate
across multiple lines to deliver optimal sensing outcome. We consider a fleet
consisting of instrumented and normal buses, and jointly optimize sensor
assignment, bus dispatch, and intra- or inter-line relocations, with the
objectives of maximizing sensing quality and minimizing operational costs,
while serving all timetabled trips. By making general assumptions on the
sensing utility function, we formulate the problem as a nonlinear integer
program based on a time-expanded network. A batch scheduling algorithm is
developed following linearization techniques to solve the problem efficiently,
which is tested in a real-world case study in Chengdu, China. The results show
that the proposed scheme can improve the sensing objective by 12.0%-20.5%
(single-line scheduling) and 16.3%-32.1% (multi-line scheduling), respectively,
while managing to save operational costs by 1.0%. Importantly, to achieve the
same level of sensing quality, we found that the sensor investment can be
reduced by over 33% when considering active bus scheduling. Comprehensive
comparative and sensitivity analyses are presented to generate managerial
insights and recommendations for practice.Comment: 32 pages, 13 figures, 8 table
Investigations into Elasticity in Cloud Computing
The pay-as-you-go model supported by existing cloud infrastructure providers
is appealing to most application service providers to deliver their
applications in the cloud. Within this context, elasticity of applications has
become one of the most important features in cloud computing. This elasticity
enables real-time acquisition/release of compute resources to meet application
performance demands. In this thesis we investigate the problem of delivering
cost-effective elasticity services for cloud applications.
Traditionally, the application level elasticity addresses the question of how
to scale applications up and down to meet their performance requirements, but
does not adequately address issues relating to minimising the costs of using
the service. With this current limitation in mind, we propose a scaling
approach that makes use of cost-aware criteria to detect the bottlenecks within
multi-tier cloud applications, and scale these applications only at bottleneck
tiers to reduce the costs incurred by consuming cloud infrastructure resources.
Our approach is generic for a wide class of multi-tier applications, and we
demonstrate its effectiveness by studying the behaviour of an example
electronic commerce site application.
Furthermore, we consider the characteristics of the algorithm for
implementing the business logic of cloud applications, and investigate the
elasticity at the algorithm level: when dealing with large-scale data under
resource and time constraints, the algorithm's output should be elastic with
respect to the resource consumed. We propose a novel framework to guide the
development of elastic algorithms that adapt to the available budget while
guaranteeing the quality of output result, e.g. prediction accuracy for
classification tasks, improves monotonically with the used budget.Comment: 211 pages, 27 tables, 75 figure
NILM techniques for intelligent home energy management and ambient assisted living: a review
The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.AgĂȘncia financiadora:
Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve
01/SAICT/2018/39578
Fundação para a CiĂȘncia e Tecnologia through IDMEC, under LAETA:
SFRH/BSAB/142998/2018
SFRH/BSAB/142997/2018
UID/EMS/50022/2019
Junta de Comunidades de Castilla-La-Mancha, Spain:
SBPLY/17/180501/000392
Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project):
TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio
An Optimal Ride Sharing Recommendation Framework for Carpooling Services
Carpooling services allow drivers to share rides with other passengers. This helps in reducing the passengersâ fares and time, as well as traffic congestion and increases the income for drivers. In recent years, several carpooling based recommendation systems have been proposed. However, most of the existing systems do no effectively balance the conflicting objectives of drivers and passengers. We propose a Highest Aggregated Score Vehicular Recommendation (HASVR) framework that recommends a vehicle with highest aggregated score to the requesting passenger. The aggregated score is based on parameters, namely: (a) average time delay, (b) vehicleâs capacity, (c) fare reduction, (d) driving distance, and (e) profit increment. We propose a heuristic that balances the incentives of both drivers and passengers keeping in consideration their constraints and the real-time traffic conditions. We evaluated HASVR with a real-world dataset that contains GPS trace data of 61,136 taxicabs. Evaluation results confirm the effectiveness of HASVR compared to existing scheme in reducing the total mileage used to deliver all passengers, reducing the passengersâ fare, increasing the profit of drivers, and increasing the percentage of satisfied ride requests
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