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With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors’ data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform’s proper operation.

Industrial control systems (ICS) are the backbone for the implementation of cybersecurity solutions. They are susceptible to various attacks, due to openness in connectivity, unauthorized attempts, malicious attacks, use of more commercial off the shelf (COTS) software and hardware, and implementation of Internet protocols (IP) that exposes them to the outside world. Cybersecurity solutions for Information technology (IT) secured with firewalls, intrusion detection/protection systems do nothing much for Operational technology (OT) ICS. An innovative concept of using real operational technology network traffic-based testbed, for cyber-physical system simulation and analysis, is presented. The testbed is equipped with real-time attacks using in-house penetration test tool with reconnaissance, interception, and firmware analysis scenarios. The test cases with different real-time hacking scenarios are implemented with the ICS cyber test kit, and its industrial datasets are captured which can be utilized for Deep packet inspection (DPI). The DPI provides more visibility into the contents of OT network traffic based on OT protocols. The Machine learning (ML) techniques are deployed for cyber-attack detection of datasets from the cyber kit. The performance metrics such as accuracy, precision, recall, F1 score are evaluated and cross validated for different ML algorithms for anomaly detection. The decision tree (DT) ML technique is optimized with pruning method which provides an attack detection accuracy of 96.5%. The deep learning (DL) techniques has been used recently for enhanced OT intrusion detection performances.

Breast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.

Wireless power transfer (WPT) has been extensively studied from various aspects such as far field and near field, operating frequency, coil design, matched capacitance values, misaligned locations of transmitting and receiving coils, distance variance between them, target loads in the specific locations, environment, and operating conditions. This is due to the usefulness of WPT technology in many applications, including the revolutionary method of auto-recharging of unmanned aerial vehicles (UAVs). This paper presents analytical modeling of a WPT-link with two orthogonal transmitting coils arranged to produce an omnidirectional magnetic field suitable for charging a moving rotating load, maximizing energy transfer without any feedback from the receiving end. To achieve a suitable 2D WPT simulation system, as well as an accurate control design, the mutual coupling values in terms of receiver angular rotation are simulated using Ansys software. Power transfer is maximized by using extremum seeking control (ESC), making use of the input power as an objective function with specific parameter values that represent the WPT model to obtain the results. The results shown are those of the input power transmitted by the transmitting-end coils to a load of an orbiting mobile UAV. Based on the simulation results, the controller can achieve maximum power transfer in 100 µs of duration when the speed of the UAV is close to 314 rad/s.

Sand and dust storms are environmental phenomena ,during these storms optical visibility might be decreased, consequently, atmospheric attenuation is clearly noticed.Micro-wave (MW) and Milimeter-wave (mm) propagation is severely affected by dust and sand storms in considerable areas around the world. Suspended dust particles may directly cause attenuation and cross polarization to the Electromagnetic waves propagating through the storm. In this paper, a thorough investigation of dust storm characteristics based onmeasured optical visibility and relative humidity is presented. In addition,the dust storms effects of on Micro-wave and Millimeter-wave propagation have been studied based on data measured Received Signal levels (RSL)and dust storm characteristics synchronously. Analyticaldustattenuationmodels predictions are matched to the measured attenuationdata at 14 GHz and 21 GHz. It has been found that the measured attenuation is approximately ten times higher than the predicted attenuation for both frequencies.

Dust storms affect the performance of microwave links. Several dust storm attenuation prediction models were proposed by researchers based on theoretical assumptions of either single - particle scattering theorems or numerical methods. The climate conditions and signal variations on microwave link operated at 21 GHz were monitored concurrently in Khartoum, Sudan for one year period. It was observed that dust storms are accompanied with increase of relative humidity and decrease in the received signal. The attenuation predicted by theoretical models and the measured attenuation at 21GHz were compared for a dry and humid case. The predicted attenuations in humid condition are varied significantly and increased 5 times more than dry dust condition. From these results it’s obvious that all the theoretical methods are unable to determine the attenuation of microwave propagation in dust storm at 21GHz and it’s essential to improve available models by considering the effect of humidity.

Low-power lossy networks performance relies heavily on the wireless node battery status. Furthermore, Routing Protocol for Low-Power and Lossy Network routing protocol was not optimally designed with sustainable energy consumption in mind to suit these networks. Prolonging the lifespan of these networks is of utmost priority. This article introduces a solar energy harvesting module to power energy-constrained network devices and quantifies the effect of using harvested energy on prolonging their network lifetime when Routing Protocol for Low-Power and Lossy Network routing protocol is used. Simulation of the new developed module is conducted in three different scenarios using Contiki Cooja simulator sporting Zolertia Z1 motes. Furthermore, the harvested energy used was fed from a Cooja-based Simulation model of actual PV supercapacitor circuit design. All battery levels were set to 1% of their total capacity for all nodes in the network to speed up observing the energy harvesting effect. The performance evaluation results showed that the network with no-energy harvesting operated for time duration of 4:08:04 time units (i.e. hour:minute:second) with a dramatic decrease in connection between nodes in the network. However, the same network, when using the harvested energy to back up the battery operation, lasted for 6:40:01 in time units with improved connectivity, a total extended network lifetime of 2:31:97-time units. Furthermore, for the Routing Protocol for Low-Power and Lossy Network routing metrics, OF0 outperformed ETX in term of throughput, packet delivery ratio, energy consumption, and network connectivity. Results indicate that the developed harvested energy module fits perfectly for any Cooja-based simulation and mimics actual photovoltaic-based supercapacitor battery. It should also help researchers introduce and quantify accurately new energy consumption-based routing metrics for Routing Protocol for Low-Power and Lossy Network.

Microwave (MW) and millimeter-wave (mmW) propagation are severely affected by dust storms and sand storms in arid and semi-arid areas. Electromagnetic waves may suffer from attenuation due to suspended particles during a dust storm. This paper proposes an empirical model to predict the attenuation due to dust storms based on a one-year measurement of visibility, humidity and their effects on MW links in Sudan. Signal strength variations on two operational MW links at 14 and 22 GHz as well as visibility were monitored simultaneously. The model is developed empirically using measured attenuation and measured storm characteristics (e.g., visibility, dielectric constant, frequency, and moisture content). The predicted attenuation from the proposed empirical model is compared with the attenuation at frequencies ranging from 7.5 to 40 GHz measured at different locations, and good agreement is found. Additionally, this method is characterized by simplicity and capability to predict reliable dust storm attenuation for a wide range of frequencies and moisture levels.

Sand and dust storms are environmental phenomena, during these storms optical visibility might be decreased, consequently, atmospheric attenuation is clearly noticed. Micro-wave (MW) and Milimeter-wave (mm) propagation is severely affected by dust and sand storms in considerable areas around the world. Suspended dust particles may directly cause attenuation and cross polarization to the Electromagnetic waves propagating through the storm. In this paper, a thorough investigation of dust storm characteristics based on measured optical visibility and relative humidity is presented. In addition, the dust storms effects of on Micro-wave and Millimeter-wave propagation have been studied based on data measured Received Signal levels (RSL)and dust storm characteristics synchronously. Analytical dust attenuation models predictions are matched to the measured attenuation data at 14 GHz and 21 GHz. It has been found that the measured attenuation is approximately ten times higher than the predicted attenuation for both frequencies.

Several models were proposed to predict the attenuation of microwave signals due to sand and dust storms. Those models were developed based on theoretical assumptions like Rayleigh approximation, Mie equations or numerical methods. This paper presents a comparison between attenuation predicted by three different theoretical models with measured attenuation at 14 GHz. Dielectric constant of dust particles is one of the important parameter in prediction models. This constant is estimated from measured dust samples and is utilized for predictions. All models are found largely underestimating the measurement. Humidity is also monitored and has been observed higher during dust storm. Hence dielectric constants are re-estimated with relative humidity conditions using available conversion model. The prediction has a great impact of humidity and predicted attenuations are found much higher in humid than dry dust condition. However, all models underestimate the measurement even considering 100% of relative humidity. Hence it is recommended to investigate the models by considering humidity and other environmental factors that change during dust storm. &nbsp

Dust storms can degrade visibility and increase atmospheric attenuation. Therefore, microwave (MW) propagation is severely affected by dust storms in many parts of the world. Air-born dust particles may affect electromagnetic waves during a dust storm. In this paper airborn dust particles effects are studied based on measured visibility. Recent analytical and numerical models results are compared to the measured at 14 GHz. Consequently, measured attenuation is significantly greater than the predicted using recent analytical and numerical models.

A field study on dust storm effect on Microwave links in arid area covering a wide range of radio frequency was conducted for seven months in Khartoum, Sudan. The study involves computerized monitoring of Transmitted and Received signal levels of (TRSL) of six Microwave links operating at Ku (14GHz) and Ka (21GHz) bands as well as an automatic weather station. This paper presents a description of the experiment and reports preliminary results of measuring dust storms as they affect the radio wave propagation at Ku and Ka bands. The effect of dust storms on propagation is studied by measuring the storm parameters; visibility and induced attenuation due to these storms. The preliminary result shows that the higher frequency is more affected by dust storms than the lower. In addition, the attenuation increases with lengths of the link.

A study on dust storm effect on Microwave links in arid area covering a wide range of radiofrequency was running for one year in Khartoum, Sudan. The study involves computerized monitoring of Transmitted and Received signal levels of two Microwave links operating at (14GHz) and (21GHz) as well as an automatic weather station. This paper presents a description of the experiment and reports results of measuring dust storms as they affect the radio wave propagation specially frequencies above 10GHz. The effect of dust storms on propagation is studied by measuring the storm parameters; visibility and induced attenuation due to these storms. The results have been compared with the attenuation prediction models. It has been shown that the measured attenuation caused by the dust storms at 0.01% availability is about eight and eleven times the calculated attenuation using Zain and Chu models at 14.5GHz respectively. Whereas, the measured attenuation due to dust storms at 21.2GHz and 0.01% availability is about ten and fourteen times the calculated attenuation using Zain and Chu models respectively.

Wireless communication service providers are currently facing challenges due to the congested frequencies spectrum which has imposed the use of higher and higher frequencies. However, higher frequency bands are more sensitive to weather condition and the microwave signal attenuation due to atmospheric particles increases rapidly at higher frequency bands. Consequently dust storms and all related phenomena may cause signal attenuation which can consequently limit the performance of wireless communication systems specially frequencies above 10GHz for arid area. The aim of this paper is to show that real dust storm is a complex phenomena which is difficult to be described by the theoretical physical or mathematical models. An empirical dust storm prediction model based on the long term statistical observations of dust storm properties and its corresponding microwave signal levels will be a step forward to provide microwave link designers with a precise tool to rely on. This paper has proposed a research framework to collect necessary data from Khartoum, Sudan and develop an empirical attenuation prediction model.

Dust storms are significant meteorological phenomena which attenuates signal level especially at Ku band and higher frequencies with direct impact on telecommunications system performance. Few prediction models have been developed to calculate the attenuation due to dust storm in dB/km. All prediction models have assumed that the intensity of dust storm is uniformly distributed around the area covered by the dust storm. It has been found that the dust storm intensity is not uniformly distributed along the horizontal path. However, to predict the total attenuation due to dust storm that a microwave link might suffer, an adjustment factor is needed to account for the variation of dust storm intensity along the propagation path. In this paper an adjustment factor which reflects the variation of dust storm horizontally (terrestrial links) has been derived based on the analysis of the horizontal dust storm behavior. It has been found that the estimated attenuation with the horizontal adjustment factor is very close to the measured attenuation than without using the horizontal adjustment factor.

In this paper an adjustment factor which reflects the vertical variation of dust storm (earth satellite links) has been derived based on the analysis of the vertical dust storm behavior. The paper has been divided to four parts. First an introduction to the dust storm phenomena and its effect on microwave signal attenuation. Secondly, observations of the behavior of the vertical dust storm intensity variation. Thirdly, the vertical path adjustment factor has been estimated. Lastly, a conclusion summarized the results and recommended further work. It has been found that the predicted attenuation without the vertical path adjustment factor is much higher than with the vertical path adjustment factor. Therefore, the vertical path adjustment factor will provide optimum utilization for the transmission resources.

Dust storm effect on microwave signal attenuation has attracted many researchers recently. Many prediction models have been developed in order to predict microwave signal attenuation during the dust storm. Most of the dust storm prediction models have ignored the variation of the particle size inside the dust storms. Therefore, their predicted results didn't reflect those have been measured. In this paper two of the dust prediction models [1–2] have been investigated based on particle size statistical distributions (normal, exponential and lognormal) [3–8]. The proposed modification reflects the actual variation of the dust particle size distribution in these prediction models. The predicted attenuation based on lognormal particle size distribution have been compared with the measured data and found close agreement.

Dust storms are meteorological phenomena which occur for a percentage of time in arid and semi arid areas especially at African Sahara and Middle East. Measurements at existing microwave links have showed dust storms can potentially result in serious attenuation in signal level especially at Ku band and higher frequencies with direct impact on telecommunications system performance. Only a limited amount of research has been carried out and the available data was very scarce. Few prediction models have been developed to estimate microwave signal attenuation during the dust storm based on scattering theory and approximation of dust properties. However, real dust storm is a complex phenomenon which is difficult to be described by theoretical physical or mathematical models [5–6]. In this paper, an evaluation of the existing attenuation prediction models has been done based on the measured dust storm properties and measured attenuation in Sudan.