Big Data in Smart-Cities: Current Research and Challenges

Abstract

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, environment, and energy. In order to provide such services, the role of big-data is important. In this article, we investigate the state-of-art research efforts directed towards big-data analytics in a smart-city context. First, we present a big-data centric taxonomy for the smart-cities to bring forth a generic overview of the big-data paradigm in a smart-city environment. Second, we present 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 suggested. Additionally, a generic four-tier big-data framework comprising of the sensing hub, storage hub, processing hub and application hub is presented that can be applied in any smart-city context. This is complemented by providing the common big-data applications and presentation of ten selected case studies of smart-cities across the globe. Finally, open challenges are highlighted in order to give future research directions.

IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues

“…Furthermore, the data accessibility must be guaranteed so that the system can freely access the citizens as well as allowing them to modify and correct. Upon the citizens' contribution, broader information angles could be achieved as it will be easier to collect more data from citizens [18].…”

Big Data: Analytical Solutions, Research Challenges and Trends

“…Analytical solutions based on big data aim to get insights and extract correlations relationships to improve services provided to residents by optimizing the usage of resources, and managing maintenance activities, as well as to support the decision-making processes. Many challenges are facing the development process of city-level smart information services like the integration of generated datasets from various city domains, and analysis process as well [95,96]. The analysis of these resources offers many benefits of smart cities, particularly three main areas attest to why data-driven innovation is crucial to the future of urban life as follows [26,97,98]: 1. intelligent traffic management through the proper utilization of historical data; 2. promoting public safety by using predictive analytics that helps to examine historical and geographical data to recognize when and where crimes occur; 3. managing smart cities infrastructure, the evaluation of the current situation helps to enhance city planning, effective spending, and maintain sustainability.…”

Data delivery performance on MongoDB generated by WSN over high data load

“…19, No. 4, August 2021: 1169 -1176 1170 generated will be very large in a short time [10]. One of the many communication protocols that can save resources is message query telemetry protocol (MQTT) [11], [12].…”