The use of the Internet is growing in this day and age, so another area has developed to use the Internet, called Internet of Things (IoT). It facilitates the machines and objects to communicate, compute and coordinate with each other. It is an enabler for the intelligence affixed to several essential features of the modern world, such as homes, hospitals, buildings, transports and cities. The security and privacy are some of the critical issues related to the wide application of IoT. Therefore, these issues prevent the wide adoption of the IoT. In this paper, we are presenting an overview about different layered architectures of IoT and attacks regarding security from the perspective of layers. In addition, a review of mechanisms that provide solutions to these issues is presented with their limitations. Furthermore, we have suggested a new secure layered architecture of IoT to overcome these issues.

Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access Control) protocol was not specially designed. Although 802.11ax aims to improve Wi-Fi performance in dense scenarios due to modifications in the physical layer (PHY), however, MAC layer operations remain unchanged, and are not capable enough to provide stable performance in dense scenarios. Potential applications of Deep Learning (DL) to Media Access Control (MAC) layer of WLAN has now been recognized due to their unique features. Deep Reinforcement Learning (DRL) is a technique focused on behavioral sensitivity and control philosophy. In this paper, we have proposed an algorithm for setting optimal contention window (CW) under different network conditions called DRL-based Contention Window Optimization (DCWO). The proposed algorithm operates in three steps. In the initial step, Wi-Fi is being controlled by the 802.11 standards. In the second step, the agent makes the decisions concerning the value of CW after the TRAIN procedure for the proposed algorithm. The final phase begins after the training, defined by a time duration specified by the user. Now, the agent is fully trained, and no updates will be no longer received. Now the CW is updated via the OPTIMIZE process of DCWO. We have selected total network throughput, instantaneous network throughput, fairness index, and cumulative reward, and compared our proposed scheme DCWO with the Centralized Contention window Optimization with DRL (CCOD). Simulation results show that DCWO with Double Deep Q-Networks (DDQN) performs better than CCOD with (i) Deep Deterministic Policy Gradient (DDPG) and (ii) Deep Q-Network (DQN). More specifically, DCWO with DDQN gives on average 28% and 23% higher network throughput than CCOD in static and dynamic scenarios. Whereas in terms of instantaneous network throughput DCWO gives around 10% better results than the CCOD. DCWO achieves almost near to optimal fairness in static scenarios and better than DQN and DDPG with CCOD in dynamic scenarios. Similarly, while the cumulative reward achieved by DCWO is almost the same with CCOD with DDPG, the uptrend of DCWO is still encouraging.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

People with severe mental illness (SMI) have a higher prevalence of obesity as compared with the general population, however, there is mixed evidence about the prevalence of underweight. Thus, the aim of this study is to determine the pooled prevalence of underweight in people with SMI and its association with socio-demographic factors; and to compare the prevalence of underweight between SMI and the general population. MEDLINE, PsycINFO, and EMBASE databases were searched to identify observational studies assessing the prevalence of underweight in adults with SMI (schizophrenia, major depressive disorder with psychotic features, and bipolar disorders). Screening, data extraction, and risk of bias assessments were performed independently by two co-authors, with disagreements resolved by consensus. Random effect estimates for the pooled prevalence of underweight and the pooled odds of underweight in people with SMI compared with the general population were calculated. Subgroup analyses were conducted for the type of SMI, setting, antipsychotic medication, region of the world, World Bank country income classification, data collection, and sex. Forty estimates from 22 countries were included. The pooled prevalence of underweight in people with SMI was 3.8% (95% confidence interval[CI] = 2.9-5.0). People with SMI were less likely to be underweight than the general population (odds ratio [OR] 0.65; 95% CI = 0.4-1.0). The pooled prevalence of underweight in SMI in South Asia was 7.5% (95% CI = 5.8-14.1) followed by Europe and Central Asia at 5.2% (95% CI = 3.2-8.1) and North America at 1.8% (95% CI = 1.2-2.6). People with SMI have lower odds of being underweight compared to the general population. People with schizophrenia had the highest prevalence of underweight compared to other types of SMI. Japan and South Asia have the highest prevalence of underweight in people with SMI.

The collection of rules, values and standards of conduct that organize economic life and establish relations of production in an Islamic society is Islamic economic system. These rules and standards are based on Islamic order recognized in Qurran. Islamic economics based on specific concept of universe and the creation of man is contradictory to the concept adopted and accepted by modern science. Islamic economics postulates although ability and expertise is required for progress and growth but distribution of resources completely dependent on it would be cruel, inhuman and bereft of kindness, and lead to oppression. Islamic economics does not favor making human ability and expertise the fulcrum of resource distribution. It should be kind, considerate and based on justice and fairness. This is because according to Islamic philosophy, ownership is considered to be a trust from Allah which has been bestowed on the rich so that they may utilize it correctly. In Islamic economics the role of the individual, has inclinations and his aims and objectives occupy a central position and are vitally important. He is definitely a rational being but his level of rationality is not confined to the calculations of cost and profit. An individual does not want merely to obtain monetary profit and physical pleasure and leisure but he also wants and aims for something beyond what the material world has to offer. The main aim of the study is to find out the relationship between Islam and economics. In Islamic economics the comprehensive moral training of the individual, his technical and educational ability, his aims and his priorities are of primary importance. According to Islamic economics the means of acquiring wealth has the same importance as wealth itself. Dishonesty, abuse of trust and earning of wealth through fraudulent ways and means may perhaps increase the status of an individual but the society suffers because of it on the whole. This leads to an unjust and oppressive economic system.