Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Sensing as a Service Model for Smart Cities Supported by Internet of Things

The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.Comment: Transactions on Emerging Telecommunications Technologies 2014 (Accepted for Publication

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Energy patterns for business model innovation

Several transformations are occurring in the energy sector, from new emerging technologies, governmental pressure for decarbonisation and clean energy targets, to new market regulations. This changes impact on how the energy is produced and distributed to consumers, increasing the urge for energy utilities to innovate their traditional established business models, to remain competitive in the energy landscape. Business model innovation is essential to organizational performance, and it has the power to change the logic of entire industries. Business model patterns are a promising tool to support business model innovation, which can be described as solutions to recurrent problems in a business model context. Although there are several published collections of business model patterns, a structured overview on energy patterns in order to support business model innovation in the energy field is missing. In the interest of filling the gap between business models and the energy sector, an energy pattern taxonomy for business model innovation was created. Applying the modified-Delphi card sorting methodology by Paul (2008), in an iterative way, 1 expert in business models and in the energy field was asked to perform a card sorting activity in a model revised and validated by 2 other experts in a previous phase, resulting in a taxonomy with 51 energy patterns organized in 10 meaningful groups. This pattern taxonomy is useful to help energy utilities to innovate and reform their business models, and it can be used from academics and scholars to managers to innovate the business models of their firms.O sector da energia está a sofrer diversas transformações, desde novas tecnologias emergentes, pressões governamentais face a metas de descarbonização e energia limpa, a novas regulações do mercado. Estas mudanças impactam na forma como a energia é produzida e distribuída aos consumidores, aumentado a urgência de inovação das modelos de negócio estabelecidos das utilities energéticas, de forma a manter a sua competitividade no sector. A inovação de modelos de negócio é essencial ao desempenho das organizações, tendo o poder de afectar a lógica de negócio de todo o sector. Padrões de modelos de negócio são uma ferramenta promissora para apoiar a inovação, sendo descritos como soluções já comprovadas para problemas recorrentes. Apesar de existiram diversas colecções de padrões de modelos de negócio na literatura, está em falta uma visão global e estruturada sobre padrões de energia. No interesse de preencher a lacuna entre modelos de negócio e o sector energético, foi criada uma taxonomia de padrões de energia para apoiar inovação no sector. Aplicando a metodologia Delphi modificada (Paul, 2008), de uma forma iterativa, 1 especialista em modelos de negócio e em energia realizou uma actividade de card sortig num modelo revisto e validado por 2 outros especialistas, numa fase anterior, resultando numa taxonomia com 51 padrões organizados em 10 grupos significativos. A taxonomia final resultante será útil para apoiar as utilities energéticas a inovar e reformar os seus modelos de negócio, podendo ser usada desde académicos a gestores com o objectivo de inovar os seus modelos de negócio