Robust provisioning of multicast sessions in cognitive radio networks

Today\u27s wireless networks use fixed spectrum over long term and fixed geographical regions. However, spectrum utilization varies by time and location, which leads to temporal and special spectrum underutilization. Therefore, new ways to improve spectrum utilization are needed. Cognitive radio is an emerging technology that enables dynamic sharing of the spectrum in order to overcome spectrum underutilization problem. Users in cognitive radio networks are either primary or secondary users. A primary user is the user who is licensed to use a channel, and has priority to use it over any other user. The secondary user uses a licensed spectrum channel opportunistically when a primary user is idle. Hence, it has to vacate the channel within a certain tolerable interference time when the primary user appears. As a result of this, the secondary user needs to find backup channels to protect the links it is using from primary user\u27s interruption. In this thesis, we concentrate on supporting the multicast service mode using cognitive radio networks. Moreover, we are concerned with supporting this mode of service such that it is robust in the face of failures. The type of failures we are interested in is channel disappearance due to the resumption of activities by primary users. We develop three algorithms which provide robust multicasting in such networks. Our three proposed algorithms are: 1) multicast sessions protection without link-sharing, 2) multicast sessions protection with link-sharing and 3) multicast sessions protection using rings. These algorithms provision multiple multicast sessions, and protect them against single primary user interruption at a time. They also take into account that the activities of a primary user may disrupt communication in several groups, of secondary users, which are referred to as Shared Primary User Risk Group (SPURG). The objective of the proposed algorithms is to increase the number of sessions that can be accommodated in the network and minimize the cost of provisioning the sessions. Multicast sessions protection with/without link-sharing algorithms generate a primary tree for each multicast session, and protect each link of it using a backup tree. Multicast sessions protection with link-sharing allows backup trees to share some links of the primary tree within the same session, and share some links within backup trees for any session. In the third algorithm, a ring is generated where it starts and ends at the source node, and passes through all destination nodes. Also, we compare the performances of our three proposed algorithms. Simulation results show that the number of accommodated sessions in the network increases and the cost of multicast sessions decreases when the number of available channels increases or the session size decreases. Also, multicast sessions protection with link-sharing algorithm outperforms the other two algorithms in terms of the number of sessions in the network. On the other hand, multicast sessions protection using rings achieves the lowest cost for multicast sessions compared with the other two proposed algorithms

SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as drones, has generated a demand for reliable detection systems. The inappropriate use of drones presents potential security and privacy hazards, particularly concerning sensitive facilities. To overcome those obstacles, we proposed the concept of MultiFeatureNet (MFNet), a solution that enhances feature representation by capturing the most concentrated feature maps. Additionally, we present MultiFeatureNet-Feature Attention (MFNet-FA), a technique that adaptively weights different channels of the input feature maps. To meet the requirements of multi-scale detection, we presented the versions of MFNet and MFNet-FA, namely the small (S), medium (M), and large (L). The outcomes reveal notable performance enhancements. For optimal bird detection, MFNet-M (Ablation study 2) achieves an impressive precision of 99.8\%, while for UAV detection, MFNet-L (Ablation study 2) achieves a precision score of 97.2\%. Among the options, MFNet-FA-S (Ablation study 3) emerges as the most resource-efficient alternative, considering its small feature map size, computational demands (GFLOPs), and operational efficiency (in frame per second). This makes it particularly suitable for deployment on hardware with limited capabilities. Additionally, MFNet-FA-S (Ablation study 3) stands out for its swift real-time inference and multiple-object detection due to the incorporation of the FA module. The proposed MFNet-L with the focus module (Ablation study 2) demonstrates the most remarkable classification outcomes, boasting an average precision of 98.4\%, average recall of 96.6\%, average mean average precision (mAP) of 98.3\%, and average intersection over union (IoU) of 72.8\%. To encourage reproducible research, the dataset, and code for MFNet are freely available as an open-source project: github.com/ZeeshanKaleem/MultiFeatureNet.Comment: Paper accepted in IEEE TV

Seroprevalence of SARS-CoV-2 (COVID-19) among Healthcare Workers in Saudi Arabia: Comparing Case and Control Hospitals

Healthcare workers (HCWs) stand at the frontline for fighting coronavirus disease 2019 (COVID-19) pandemic. This puts them at higher risk of acquiring the infection than other individuals in the community. Defining immunity status among health care workers is therefore of interest since it helps to mitigate the exposure risk. This study was conducted between May 20th and 30th, 2020. Eighty-five hospitals across Kingdom of Saudi Arabia were divided into 2 groups: COVID-19 referral hospitals are those to which RT-PCR-confirmed COVID-19 patients were admitted or referred for management (Case-hospitals). COVID-19 nonaffected hospitals where no COVID-19 patients had been admitted or managed and no HCW outbreak (Control hospitals). Next, seroprevalence of severe acute respiratory syndrome coronavirus 2 among HCWs was evaluated; there were 12,621 HCWs from the 85 hospitals. There were 61 case-hospitals with 9379 (74.3%) observations, and 24 control-hospitals with 3242 (25.7%) observations. The overall positivity rate by the immunoassay was 299 (2.36%) with a significant difference between the case-hospital (2.9%) and the control-group (0.8%) (P value <0.001). There was a wide variation in the positivity rate between regions and/or cities in Saudi Arabia, ranging from 0% to 6.31%. Of the serology positive samples, 100 samples were further tested using the SAS2pp neutralization assay; 92 (92%) samples showed neutralization activity. The seropositivity rate in Kingdom of Saudi Arabia is low and varies across different regions with higher positivity in case-hospitals than control-hospitals. The lack of neutralizing antibodies (NAb) in 8% of the tested samples could mean that assay is a more sensitive assay or that neutralization assay has a lower detection limits; or possibly that some samples had cross-reaction to spike protein of other coronaviruses in the assay, but these were not specific to neutralize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

UAV-Based Intelligent Transportation System for Emergency Reporting in Coverage Holes of Wireless Networks

During critical moments, disaster and accident victims may not be able to request help from the emergency response center. This may happen when the victim’s vehicle is located within a coverage hole in a wireless network. In this paper, we adopt an unmanned aerial vehicle (UAV) to work as an automatic emergency dispatcher for a user in a vehicle facing a critical condition. Given that the UAV is located within a coverage hole and a predetermined critical condition is detected, the UAV becomes airborne and dispatches distress messages to a communication network. We propose to use a path loss map for UAV trajectory design, and we formulate our problem mathematically as an Integer Linear Program (ILP). Our goals are to minimize the distress messages delivery time and the UAV’s mission completion time. Due to the difficulty of obtaining the optimal solution when the scale of the problem is large, we proposed an efficient algorithm that reduces the computational time significantly. We simulate our problem under different scenarios and settings, and study the performance of our proposed algorithm

An Energy-Efficient Internet of Things Relaying System for Delay-Constrained Applications

The emerging Internet-of-things (IoT) systems contain a large number of small wireless devices with limited energy, communication, and computational capabilities. In such systems, a helping station located between the IoT devices and backhaul servers can be deployed to broadcast the IoT devices to the backhaul networks. This paper investigates a hybrid energy-efficient framework using multiple energy harvested relays with data buffering capabilities. The relays are powered by a hybrid energy supply consisting of a traditional electric grid and renewable energy grid. We propose an energy efficient novel approach aiming to support the wireless uplink transmission from IoT devices to backhaul servers with an acceptable delay threshold or transmission deadline. A mathematical mixed-integer linear programming (MILP) optimization problem is formulated to optimize the relays\u27 placement and energy consumption considering the association between relays and devices, instantaneous relays\u27 battery level, and transmit power budget. Due to the non-convex nature of the formulated optimization problem, we propose two heuristic low-complexity solutions to solve this problem. Finally, we compare the performance of the proposed algorithms with exhaustive search solutions as a benchmark

Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques

Gastrointestinal Cancer Detection and Classification Using African Vulture Optimization Algorithm With Transfer Learning

Gastrointestinal (GI) cancer comprises esophageal, gastric, colon and rectal tumors. The diagnosis of GI cancer often relies on medical imaging modalities namely magnetic resonance imaging (MRI), histopathological slides, endoscopy, and computed tomography (CT) scans. This provides particular details about the size, location, and characteristics of tumors. The high death rate for GI cancer patients shows that it is possible to increase analysis for a more personalized therapy strategy which leads to improved prognosis and few side effects although many extrapolative and predictive biomarkers exist. Gastrointestinal cancer classification is a challenging but vital area of research and application within medical imaging and machine learning. Artificial intelligence (AI) based diagnostic support system, especially convolution neural network (CNN) based image examination tool, has enormous potential in medical computer vision. The study presents a GI Cancer Detection and Classification utilizing the African Vulture Optimization Algorithm with Transfer Learning (GICDC-AVOADL) methodology. The major aim of the GICDC-AVOADL model is to examine GI images for the identification of cancer. To achieve this, the GICDC-AVOADL method makes use of an improved EfficientNet-B5 method to learn features from input images. Furthermore, AVOA is exploited for optimum hyperparameter selection of the improved EfficientNet-B5 method. The GICDC-AVOADL technique applies dilated convolutional autoencoder (DCAE) For GI cancer detection and classification. A complete set of simulations was conducted to examine the enhanced GI cancer detection performance of the GICDC-AVOADL technique. The extensive results inferred superior performance of the GICDC-AVOADL algorithm over current models

The prevalence of sedentary behavior among university students in Saudi Arabia

Abstract Background A considerable body of research has demonstrated that reducing sitting time benefits health. Therefore, the current study aimed to explore the prevalence of sedentary behavior (SB) and its patterns. Methods A total of 6975 university students (49.1% female) were chosen randomly to participate in a face-to-face interview. The original English version of the sedentary behavior questionnaire (SBQ) was previously translated into Arabic. Then, the validated Arabic version of the SBQ was used to assess SB. The Arabic SBQ included 9 types of SB (watching television, playing computer/video games, sitting while listening to music, sitting and talking on the phone, doing paperwork or office work, sitting and reading, playing a musical instrument, doing arts and crafts, and sitting and driving/riding in a car, bus or train) on weekdays and weekends. Results SBQ indicated that the total time of SB was considerably high (478.75 ± 256.60 and 535.86 ± 316.53 (min/day) during weekdays and weekends, respectively). On average, participants spent the most time during the day doing office/paperwork (item number 4) during weekdays (112.47 ± 111.11 min/day) and weekends (122.05 ± 113.49 min/day), followed by sitting time in transportation (item number 9) during weekdays (78.95 ± 83.25 min/day) and weekends (92.84 ± 100.19 min/day). The average total sitting time of the SBQ was 495.09 ± 247.38 (min/day) and 58.4% of the participants reported a high amount of sitting time (≥ 7 hours/day). Independent t-test showed significant differences (P ≤ 0.05) between males and females in all types of SB except with doing office/paperwork (item number 4). The results also showed that male students have a longer daily sitting time (521.73 ± 236.53 min/day) than females (467.38 ± 255.28 min/day). Finally, 64.1% of the males reported a high amount of sitting time (≥ 7 hours/day) compared to females (52.3%). Conclusion In conclusion, the total mean length of SB in minutes per day for male and female university students was considerably high. About 58% of the population appeared to spend ≥7 h/day sedentary. Male university students are likelier to sit longer than female students. Our findings also indicated that SB and physical activity interventions are needed to raise awareness of the importance of adopting an active lifestyle and reducing sitting time