Efficient scheduling of video camera sensor networks for IoT systems in smart cities

© 2019 John Wiley & Sons, Ltd. Video camera sensor networks (VCSN) has numerous applications in smart cities, including vehicular networks, environmental monitoring, and smart houses. Scheduling of video camera sensor networks (VCSN) can reduce the computational complexity, increase energy efficiency, and enhance throughput for the Internet of things (IoT) systems. In this paper, we apply the iterative low-complexity probabilistic evolutionary method for scheduling video cameras to maximize throughput in VCSNs for IoT systems. Scheduling of video cameras in VCSNs to maximize throughput is a combinatorial optimization problem whose computational complexity increases exponentially with the increase in the number of video cameras. We propose an iterative probabilistic method named as cross-entropy optimization (CEO), which is an evolutionary algorithm. The combinatorial optimization problems can be solved using the CEO which is a generalized Monte Carlo technique. The proposed method updates its selected population (video cameras) at each iteration based on the Kullback Leibler (KL) distance/divergence. The KL distance/divergence is minimized using the probability distribution obtained from the learned from the group of selected samples of better solutions found in the previous iterations. The effectiveness of the CEO is verified in terms of optimality and simplicity through simulations. In addition, the results of the CEO are better than the suboptimal algorithms (ie, best norm-based algorithm, genetic algorithm, and capacity upper-bound–based greedy algorithm) and maximum of 2%-3% deviation from the exhaustive search (optimal) with less complexity. The trade-off between CEO and optimal is the computational complexity

Impact of Covid-19 Pandemic in Pakistani Population

Abstract Objective: The objective of this study was to assess the impact of COVID-19 pandemic on the economic, social and mental health of families living in Lahore, Pakistan. Methodology: A cross-sectional survey conducted for six months included total 200 participants filled a self-designed questionnaire through convenient sampling. The structured questionnaire collected information on socio-demographic profile and data on impact of COVID-19 on economic, social and mental status of residents of Lahore Results: The mean age of the respondents was 42 + 10.281 in years. Nearly 50% of the participants were affected from stress. Most of the participants were stressed, living in nuclear families. Conclusion: COVID-19 pandemic has strong impact on family income. Stress levels were raised especially among male respondents and discord in the family was highlighted. The participants engaged in private jobs were more stressed. Access to friends and families were restricted in this pandemic. Keywords: COVID-19, stress, mental health

A comparison of some forecasting models to forecast the number of old people in Iraqi retirement homes

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2)

Solving MAX-SAT Problem by Binary Biogeograph-based Optimization Algorithm

© 2019 IEEE. Several sensing problems in wireless sensor networks (WSNs) can be modeled to maximum satisfaction (MAX-SAT) or SAT problems. Also, MAX-SAT is an established framework for computationally expensive problems in other fields. There exist efficient algorithms to solve the MAX-SAT, which is an NP-hard problem. The reason for remodeling various sensing problems to MAX-SAT is to use these algorithms to solve challenging sensing problems. In this paper, we test a binary Biogeography-based (BBBO) algorithm for the MAX-SAT as an optimization problem with a binary search space. The original BBO is a swarm intelligence-based algorithm, which is well-tested for continuous (and nonbinary) integer space optimization problems, but its use for the binary space was limited. Since the exact algorithm to solve the MAX-SAT problem using moderate computing resources is not well-known; therefore, swarm intelligence based evolutionary algorithms (EAs) can be helpful to find better approximate solutions with limited computing resources. Our simulation results demonstrate the experimental exploration of the binary BBO algorithm against binary (enhanced fireworks algorithm) EFWA, discrete ABC (DisABC) and Genetic Algorithm (GA) for several classes of MAX-SAT problem instances

Lady and the Tramp Nextdoor: Online Manifestations of Real-World Inequalities in the Nextdoor Social Network

From health to education, income impacts a huge range of life choices. Many papers have leveraged data from online social networks to study precisely this. In this paper, we ask the opposite question: do different levels of income result in different online behaviors? We demonstrate it does. We present the first large-scale study of Nextdoor, a popular location-based social network. We collect 2.6 Million posts from 64,283 neighborhoods in the United States and 3,325 neighborhoods in the United Kingdom, to examine whether online discourse reflects the income and income inequality of a neighborhood. We show that posts from neighborhoods with different income indeed differ, e.g. richer neighborhoods have a more positive sentiment and discuss crimes more, even though their actual crime rates are much lower. We then show that user-generated content can predict both income and inequality. We train multiple machine learning models and predict both income (R-Square=0.841) and inequality (R-Square=0.77)

Charging infrastructure placement for electric vehicles: An optimization prospective

© 2017 IEEE. Electric Vehicles (EVs) can be considered as a step forward towards the green environment and economical transportation. Moreover, EVs offer fuel economy, clean environment, and less cost of vehicle charging as compared to gasoline refilling. These are the main motivations towards the adaptation of EVs by the users. In order to increase the penetration of EVs into the transportation system, the EV charging stations become necessary to fulfill the charging needs. The charging stations can be placed considering different scenarios and objectives. Placement of charging stations in the service area requires a huge amount of budget and their locations are critical to select. In this paper, we formulate an optimization problem with an objective to minimize the overall cost of the charging infrastructure placement subject to the constraint on charging requirements in the service area. The proposed problem is solved using the branch and bound algorithm. Simulations results show the effectiveness of proposed placement strategy to minimize overall placement cost

Unmanned aerial vehicles enabled IoT platform for disaster management

© 2019 by the authors. Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field

A Rare Case of X-Linked Bulbo-Spinal Muscular Atrophy with Sensory Neuropathy and Tremors

Kennedy disease (KD) is also known as spino bulbar muscular atrophy caused by a tandem C-A-G tri-nucleotide repeat. It is an adult-onset X-linked recessive inherited neurodegenerative disease involving lower motor neuron damage with predominance of facial muscles. It is often accompanied with androgen sensitivity, sensory nerve damage and endocrinal involvement. It has similar confusing symptoms and is often mis-diagnosed with most of the neuromuscular diseases like POEMS syndrome, myasthenia gravis, mitochondrial myopathy and amyotrophic lateral sclerosis. Hence clinical differentiation is important to prevent adverse outcomes and un-necessary treatment. Here we describe a rare case of a 46 year old Pakistani male who presented to us with progressive weakness and tingling of the limbs, bulbar symptoms, postural tremors and painful recurrent ulceration of the feet. Based on family history, clinical and electro diagnostic study he was diagnosed to have Kennedy disease. To the best of our knowledge, it is the first case report of Kennedy disease from Pakistan

Maximizing Growth and Yield Of Black Seed (Nigella Sativa L) through Optimized Phosphorus and Boron Levels

Improper nutrient management cause yield reduction in black seed yield and quality. Therefore, a field trial was executed in the growing season 2022-23 at The University of Agriculture Peshawar to inspect the effects of different phosphorus (P) and boron (B) levels on black seed (Nigella sativa L.). The experiment used a randomized complete block design with a split-plot arrangement, replicated thrice. The study examined four P levels (0, 15, 30, and 45 kg/ha) and four B levels (0, 1, 2, and 3 mg/L) on various growth and yield parameters of black seed. Plots treated with 45 kg/ha of P exhibited the taller plants (37.00 cm), more branches (16.60), larger leaf area (128.30 cm²), longest roots (10.00 cm), highest levels of chlorophyll a, b and carotenoids (1.80, 1.30 and 1.10 mg/g) compared to control. 45 kg/ha P also produced maximum flowers/plant (23.10), capsules/plant (22.80), seeds/plant (1325.50), thousand-seed weight (2.80 g), and the maximum seed yield (255.30 kg/ha). Similarly, plants treated with 3 mg/L of B had the tallest plants (33.60 cm), more branches (15.90), leaf area (128.20 cm²), longest roots (8.70 cm), chlorophyll a, b and carotenoids (1.40, 1.20 and 1.00 mg/g), more flowers/plant (21.30), capsules/plant (20.70), seeds/capsule (58.70), seeds/plant (1193.60), the heaviest thousand-seed weight (2.70 g) and the maximum seed yield (255.30 kg/ha). Moreover, significant association was prominent between growth and yield components with seed yield of black seed under different treatments. Therefore, it was concluded that the best growth and yield for black seed were observed with fertilization of 45 kg/ha of P and 3 mg/L of B

Selective harmonic mitigation based two-scale frequency control of cascaded modified packed U-cell inverters

A new Modified Packed U-Cell (MPUC) converter architecture with cascading is proposed in this paper. To provide an output voltage of 25 levels, the proposed cascaded MPUC needs only twelve power switches and four power sources. The converter comprises two cascaded MPUCs with DC supply in a ratio of 5 : 1. One converter is operating at low frequency (LF) and the other at high frequency (HF) that leads to lower power losses and higher levels. Besides, a variable frequency method is anticipated to produce a 25-level output voltage which has low harmonic content (THD) with the help of Selective Harmonic Mitigation (SHM). The optimum switching angles for SHM are obtained through solving the SHM equations using the Genetic Algorithm (GA). The designed controller is efficient and suitable for applications that require low-frequency operation either in stand-alone or grid-tied. The proposed inverter and its operation procedure have been investigated using MATLAB®/Simulink software, and the findings demonstrate that the proposed inverter output voltage has reduced THD significantly. The simulation results are verified using the typhoon HIL-402 emulator. Also, the power loss analysis is done using PLECS. The maximum efficiency of the converter is found to be around 98.34 %. The simulation results justified the efficiency and viability of low 25-level THD voltages