No abstract

Pluvial flash floods in urban areas are becoming increasingly frequent due to climate change and human actions, negatively impacting the life, work, production and infrastructure of a population. Pluvial flooding occurs when intense rainfall overflows the limits of urban drainage and water accumulation causes hazardous flash floods. Although flash floods are hard to predict given their rapid formation, Early Warning Systems (EWS) are used to minimize casualties. We performed a systematic review to define the basic structure of an EWS for rain flash floods. The structure of the review is as follows: first, Section 2 describes the most important factors that affect the intensity of pluvial flash floods during rainfall events. Section 3 defines the key elements and actors involved in an effective EWS. Section 4 reviews different EWS architectures for pluvial flash floods implemented worldwide. It was identified that the reviewed projects did not follow guidelines to design early warning systems, neglecting important aspects that must be taken into account in their implementation. Therefore, this manuscript proposes a basic structure for an effective EWS for pluvial flash floods that guarantees the forecasting process and alerts dissemination during rainfall events.

We are living in an era of big data, where the process of generating data is continuously been taking place with each coming second. Data that is more varied and extremely complex in structure (unstructured/semi-structured) with problems of indexing, sorting, searching, analyzing and visualizing are major challenges of today's organizations. Big data is always defined by its 5-v characteristics which are Volume, Velocity, Veracity, Variety, and Value. Almost each data model comprising big data is dependent on these 5-v characteristics. A large number of researches have been done on velocity and volume, but the complete and efficient solution for the variety is still not available in the markets. Traditional solutions provided by DBMS generally use multidimensional data type. However, many new data types cannot be compatible with these traditional systems. Big Data is a general problem affecting different fields, whether it is business, economic, social security or scientific research. To analyze huge data sets in order to get insights and find patterns in data is called big data analytics. Big data analytics is the need of every corporate and state of the art organization to look forward and make useful decisions. This paper comprises of discussion on current issues, opportunities, trends, and challenges of big data aimed to discuss variety in more detail. An efficient solution for the big data variety problem will be discussed.

The Internet of things (IoT) is an active area in the current research community due to the improvement in mobile computing and wireless networks. Currently, the IoT is involved in many fields like smart cities, smart health monitoring, smart tracking, and smart factory; therefore, it is introducing new research opportunities and industrial revolutions. Smart health, in particular, is very important and trendy domain for researchers and practitioners due to its continuous monitoring of health of patients. The objective of smart health is to provide medical facilities to patients at anytime and anywhere. The smart health monitoring systems are mostly connected with the wireless network medium that is extremely vulnerable for threats. However various attacks are observed that can endanger these health monitoring applications and systems. These attacks include Denial of Service (DoS) Attack, Fingerprint and Timing-based Snooping, Router Attack, Select and Forwarding attack, Sensor attack and Replay Attack. In this paper, we discuss these attacks with their impact on health monitoring systems with some suggestive measures from our research findings.

Cloud Technology is a most challenging modern area in the field of modern technologies in which assets (e.g., CPU and capacity) can be rented and discharged by the clients through internet on-demand basis. The cloud computing has been giving virtual computing services to a little, medium and extensive industries, and services, for example, infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Cloud computing has a great combination with the agile software development as a research area. Many researchers worked in Agile Cloud field. The software industries are using the agile methodology for efficient software development need some platform to get quick feedback from the client. Therefore, the agile-cloud is a great combination for it but due to security reasons that directly influence the less adoption of cloud in software industries. This paper reports the survey results of software industries. The total of seven IT industries and many professionals was involved in this paper. However, this paper also contributes and reveals how existing issues can affect agile-cloud adoption for efficient software development. Furthermore, we do not find any type of survey conducted in Pakistan's software industries-related to cloud-agile adoption.INDEX TERMS Agile-cloud computing, industries adoption, challenges and future directions.