Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Towards a secure service provisioning framework in a Smart city environment

© 2017 Elsevier B.V. Over the past few years the concept of Smart cities has emerged to transform urban areas into connected and well informed spaces. Services that make smart cities “smart” are curated by using data streams of smart cities i.e., inhabitants’ location information, digital engagement, transportation, environment and local government data. Accumulating and processing of these data streams raise security and privacy concerns at individual and community levels. Sizeable attempts have been made to ensure the security and privacy of inhabitants’ data. However, the security and privacy issues of smart cities are not only confined to inhabitants; service providers and local governments have their own reservations — service provider trust, reliability of the sensed data, and data ownership, to name a few. In this research we identified a comprehensive list of stakeholders and modelled their involvement in smart cities by using the Onion Model approach. Based on the model we present a security and privacy-aware framework for service provisioning in smart cities, namely the ‘Smart Secure Service Provisioning’ (SSServProv) Framework. Unlike previous attempts, our framework provides end-to-end security and privacy features for trustable data acquisition, transmission, processing and legitimate service provisioning. The proposed framework ensures inhabitants’ privacy, and also guarantees integrity of services. It also ensures that public data is never misused by malicious service providers. To demonstrate the efficacy of SSServProv we developed and tested core functionalities of authentication, authorisation and lightweight secure communication protocol for data acquisition and service provisioning. For various smart cities service provisioning scenarios we verified these protocols by an automated security verification tool called Scyther

The life of a New York City noise sensor network

Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data has been collected that provides an indication of a sensors' health. This telemetry data was analyzed over an 18 month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.Comment: This article belongs to the Section Intelligent Sensors, 24 pages, 15 figures, 3 tables, 45 reference

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations)