Big Data in Smart-Cities: Current Research and Challenges

Smart-cities are an emerging paradigm containing heterogeneous network infrastructure, ubiquitous sensing devices, big-data processing and intelligent control systems. Their primary aim is to improve the quality of life of the citizens by providing intelligent services in a wide variety of aspects like transportation, healthcare, entertainment, environment, and energy. In order to provide such services, the role of big-data and its analysis is extremely important as it enables to obtain valuable insights into the large data generated by the smart-cities.  In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. Specifically, first we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the importance of big-data paradigm in a smart-city environment. This is followed by the presentation of a top-level snapshot of the commonly used big-data analytical platforms. Due to the heterogeneity of data being collected by the smart-cities, often with conflicting processing requirements, suitable analytical techniques depending upon the data type are also suggested. In addition to this, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is also proposed that can be applied in any smart-city context. This is complemented by providing the common big-data applications in a smart-city and presentation of ten selected case studies of smart-cities across the globe. Finally, the open challenges are highlighted in order to give future research directions

Contributions to Context-Aware Smart Healthcare: A Security and Privacy Perspective

Les tecnologies de la informació i la comunicació han canviat les nostres vides de manera irreversible. La indústria sanitària, una de les indústries més grans i de major creixement, està dedicant molts esforços per adoptar les últimes tecnologies en la pràctica mèdica diària. Per tant, no és sorprenent que els paradigmes sanitaris estiguin en constant evolució cercant serveis més eficients, eficaços i sostenibles. En aquest context, el potencial de la computació ubiqua mitjançant telèfons intel·ligents, rellotges intel·ligents i altres dispositius IoT ha esdevingut fonamental per recopilar grans volums de dades, especialment relacionats amb l'estat de salut i la ubicació de les persones. Les millores en les capacitats de detecció juntament amb l'aparició de xarxes de telecomunicacions d'alta velocitat han facilitat la implementació d'entorns sensibles al context, com les cases i les ciutats intel·ligents, capaços d'adaptar-se a les necessitats dels ciutadans. La interacció entre la computació ubiqua i els entorns sensibles al context va obrir la porta al paradigma de la salut intel·ligent, centrat en la prestació de serveis de salut personalitzats i de valor afegit mitjançant l'explotació de grans quantitats de dades sanitàries, de mobilitat i contextuals. No obstant, la gestió de dades sanitàries, des de la seva recollida fins a la seva anàlisi, planteja una sèrie de problemes desafiants a causa del seu caràcter altament confidencial. Aquesta tesi té per objectiu abordar diversos reptes de seguretat i privadesa dins del paradigma de la salut intel·ligent. Els resultats d'aquesta tesi pretenen ajudar a la comunitat científica a millorar la seguretat dels entorns intel·ligents del futur, així com la privadesa dels ciutadans respecte a les seves dades personals i sanitàries.Las tecnologías de la información y la comunicación han cambiado nuestras vidas de forma irreversible. La industria sanitaria, una de las industrias más grandes y de mayor crecimiento, está dedicando muchos esfuerzos por adoptar las últimas tecnologías en la práctica médica diaria. Por tanto, no es sorprendente que los paradigmas sanitarios estén en constante evolución en busca de servicios más eficientes, eficaces y sostenibles. En este contexto, el potencial de la computación ubicua mediante teléfonos inteligentes, relojes inteligentes, dispositivos wearables y otros dispositivos IoT ha sido fundamental para recopilar grandes volúmenes de datos, especialmente relacionados con el estado de salud y la localización de las personas. Las mejoras en las capacidades de detección junto con la aparición de redes de telecomunicaciones de alta velocidad han facilitado la implementación de entornos sensibles al contexto, como las casas y las ciudades inteligentes, capaces de adaptarse a las necesidades de los ciudadanos. La interacción entre la computación ubicua y los entornos sensibles al contexto abrió la puerta al paradigma de la salud inteligente, centrado en la prestación de servicios de salud personalizados y de valor añadido mediante la explotación significativa de grandes cantidades de datos sanitarios, de movilidad y contextuales. No obstante, la gestión de datos sanitarios, desde su recogida hasta su análisis, plantea una serie de cuestiones desafiantes debido a su naturaleza altamente confidencial. Esta tesis tiene por objetivo abordar varios retos de seguridad y privacidad dentro del paradigma de la salud inteligente. Los resultados de esta tesis pretenden ayudar a la comunidad científica a mejorar la seguridad de los entornos inteligentes del futuro, así como la privacidad de los ciudadanos con respecto a sus datos personales y sanitarios.Information and communication technologies have irreversibly changed our lives. The healthcare industry, one of the world’s largest and fastest-growing industries, is dedicating many efforts in adopting the latest technologies into daily medical practice. It is not therefore surprising that healthcare paradigms are constantly evolving seeking for more efficient, effective and sustainable services. In this context, the potential of ubiquitous computing through smartphones, smartwatches, wearables and IoT devices has become fundamental to collect large volumes of data, including people's health status and people’s location. The enhanced sensing capabilities together with the emergence of high-speed telecommunication networks have facilitated the implementation of context-aware environments, such as smart homes and smart cities, able to adapt themselves to the citizens needs. The interplay between ubiquitous computing and context-aware environments opened the door to the so-called smart health paradigm, focused on the provision of added-value personalised health services by meaningfully exploiting vast amounts of health, mobility and contextual data. However, the management of health data, from their gathering to their analysis, arises a number of challenging issues due to their highly confidential nature. In particular, this dissertation addresses several security and privacy challenges within the smart health paradigm. The results of this dissertation are intended to help the research community to enhance the security of the intelligent environments of the future as well as the privacy of the citizens regarding their personal and health data

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Cybersecurity issues in software architectures for innovative services

The recent advances in data center development have been at the basis of the widespread success of the cloud computing paradigm, which is at the basis of models for software based applications and services, which is the "Everything as a Service" (XaaS) model. According to the XaaS model, service of any kind are deployed on demand as cloud based applications, with a great degree of flexibility and a limited need for investments in dedicated hardware and or software components. This approach opens up a lot of opportunities, for instance providing access to complex and widely distributed applications, whose cost and complexity represented in the past a significant entry barrier, also to small or emerging businesses. Unfortunately, networking is now embedded in every service and application, raising several cybersecurity issues related to corruption and leakage of data, unauthorized access, etc. However, new service-oriented architectures are emerging in this context, the so-called services enabler architecture. The aim of these architectures is not only to expose and give the resources to these types of services, but it is also to validate them. The validation includes numerous aspects, from the legal to the infrastructural ones e.g., but above all the cybersecurity threats. A solid threat analysis of the aforementioned architecture is therefore necessary, and this is the main goal of this thesis. This work investigate the security threats of the emerging service enabler architectures, providing proof of concepts for these issues and the solutions too, based on several use-cases implemented in real world scenarios

Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence