The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).
The outbreak of the covid-19 pandemic has devastated many sectors of each country and led to the development of contact tracing applications for controlling its spread. Contact tracing apps have been promoted to track infected contacts. However, contact tracing has gained significant debate due to its security and privacy concerns. The goal of this study is to examine the most popular contact tracing apps, their impact on pandemic control, as well security and privacy concerns. The multivocal literature review (MLR) brings the results from the state-of-the-art literature. We extracted 23 studies from both formal and grey literature to achieve the research objectives and found several security and privacy threats in the existing contact tracing applications. Additionally, the best practices to address these threats were also identified. We further proposed a preliminary structure of a secure global contact tracing app using blockchain technology.
With the dramatic evolution in networks nowadays, an equivalent growth of challenges has been depicted toward implementing and deployment of such networks. One of the serious challenges is the security where wide range of attacks would threat these networks. Denial-of-Service (DoS) is one of the common attacks that targets several types of networks in which a huge amount of information is being flooded into a specific server for the purpose of turning of such server. Many research studies have examined the simulation of networks in order to observe the behavior of DoS. However, the variety of its types hinders the process of configuring the DoS attacks. In particular, the Distributed DoS (DDoS) is considered to be the most challenging threat to various networks. Hence, this paper aims to accommodate a comprehensive simulation in order to figure out and detect DDoS attacks. Using the well-known simulator technique of NS-2, the experiments showed that different types of DDoS have been characterized, examined and detected. This implies the efficacy of the comprehensive simulation proposed by this study.
The Internet of Things (IoT) has emerged as a new paradigm, and billions of devices are connected with the internet. IoT is being penetrated in major domains of daily life like health care, agriculture, industry, smart homes and monitoring of the environment. The operator of such complex, huge and diverse heterogeneous networks may not even be fully aware of their IoT devices working, activity, behavior and resource utilization etc. The efficient management of IoT devices becomes a challenge for network managers to ensure smooth network operation. Network traffic analysis of IoT devices is a necessary and rudimentary tool to understand the behavior of devices. In this paper firstly, we identify insights of device network traffic, discuss the activity patterns of some IoT devices and present a visual description of the pattern of IoT devices. Secondly, after analyzing the device's behavior, we build and demonstrate a profile of each device based on its activity cycle and traffic patterns information. Thirdly, the K-Means clustering algorithm is used to make clusters of IoT devices using their profile information. The clustering algorithm groups similar devices in a single group. The obtained results clearly describe the patterns of devices which help the network managers to make appropriate network policies for efficient secure network management.
The internet of things (IOT) is a phenomenon of connected devices over the internet to ease human life. It is a system where a separate computing device embedded with sensors is connected to other devices or to the cloud through the different infrastructures of the Internet. The implication of the IOT is still challenging in a geographically distributed environment. Particularly, the main challenges are associated with data privacy and security. In this study, we investigate in the report the risks/issue related to IoT data privacy and security from the existing literature for the last two years and provide a review. We identify a total of seven issues related to IoT data privacy and security. The findings revolved that Privacy, Security, confidentiality, and integrity are the most significant issues for IoT in the current era. The findings of this study provide the researchers with a body of knowledge about the critical issues faced by the users and practitioners of IOT across the globe. Contribution/Originality: In this paper, we conducted the literature review to find out the main challenges that are being faced by challenges related to privacy and security mainly, authentication and access control, confidentiality and integrity IOT devices users and as well as for IOT manufacturer. We highlighted seven, privacy, trust on the device and conducted a questionnaire survey from different organizations and from different research experts and ranked it accordingly.
Healthcare monitoring is a field that caught many researchers from the computer science community in the last decade. In the literature, various levels of people have been considered when proposing a health monitoring system. However, some aspects are still not adequately tackled such as monitoring workers’ health status within confined space where workers would be located in underground environment with less oxygen and a lot of dust. This paper proposes an IoT health monitor system for worker in confined places. The proposed system utilizes four types of microcontroller sensors including LM35 for measuring body temperature, heart beat rate sensor, blood pressure sensor and LPG gas sensor. All the aforementioned sensors are being connected via a GPS module in order to transmit the readings into a smartphone application. A simulation has been conducted to test the proposed sensors where competitive commercial measures have been used as a benchmark. Result of simulation showed that the sensors have fair accuracy that is near-identical to the benchmark.
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