Resource allocation for machine-type communication (MTC) devices is one of the keys challenges in the 5G network as it affects the lifetime of battery powered devices and also the quality of service of the applications. MTC devices are battery restrained and cannot afford a lot of power consumption due to spectrum usage. In this paper, we propose a novel resource allocation algorithm termed threshold controlled access (TCA) protocol. We propose a novel technique of uplink resource allocation in which the devices make a decision of resource allocation blocks based on their battery status and related application’s power profile that eventually leads to required quality of service (QoS) metric. The first phase of the TCA algorithm selects the number of carriers to be allocated to a certain device for the better lifetime of low power MTC devices. In the second phase, the efficient solution is implemented through inducing a threshold value. A certain value of the threshold is selected through a mapping based on a QoS metric. The threshold enhances the selection of subcarriers for less powered devices, such as small e-health sensors. The algorithm is simulated for the physical layer of the 5G network. Simulation results show that the proposed algorithm is less complex and achieves better performance when compared to existing solutions in the literature.

To enable connectivity between massive number of devices in Internet of Things (IoT) paradigm, Machine-to- Machine (M2M) communication is implied. The key feature of emerging and adhoc networks like machine-to-machine communication is a low complexity medium access protocol. In this paper, an uplink centric simple approach is proposed that aims to improve the energy efficiency of devices of M2M communication. To facilitate the requirements of these networks with a massive number of ubiquitous devices, current version of Slotted Aloha is optimized specifically for M2M architecture. To reduce complexity, the paper does not change the architecture but additionally add a simplified approach to gain efficiency. Previous Slotted Aloha protocols works well under less traffic but in case of M2M architecture, it may become a bottleneck. The proposed power adaptive technique adjusts the delay time for transmission slots mainly for small sensor devices used in M2M applications such as health care, weather trackers and other delay sensitive M2M applications. An improvement in energy efficiency was achieved by increasing the energy efficiency at 60% rate as compared with other techniques, and life time to 45% while keeping the throughput steady and reducing the time and space complexity. Results are simulated in Matlab. A better message delivery was achieved by considering power profiles of M2M devices.

In this paper, a reference time synchronization method [1] is adopted with introduced changed and improved fine and refined time estimation step. The communication system on which it is tested is MIMO OFDM based wireless local area network system. The WLAN standard adopted is of IEEE 802.11n, each data packet starts with a preamble, which consists of short as well as long training symbols. Simulation is implemented with C++ eclipse and tested for AWGN, Exponential Rayleigh Decaying and TGN fading channels. The plots generated by the simulation clearly show the improved performance of the introduced scheme

The rapid evolution of artificial intelligence has introduced sophisticated cyber security threats, particularly in the form of AI-driven social engineering. Social workers, who handle highly sensitive and confidential client data, are increasingly targeted by these advanced attacks despite often lacking extensive technical training. This paper explores the intersection of human behavior, survey data analytics, and cyber security to propose a comprehensive framework designed specifically for the social work sector. By leveraging hypothetical survey data to identify psychological vulnerabilities and utilizing AI-simulated phishing campaigns for targeted education, this study outlines a method to transition cyber security awareness from passive compliance to active behavioral change. Ultimately, this research demonstrates that mitigating modern cyber threats in non-technical domains requires a multidisciplinary approach that prioritizes human-centric training alongside technological safeguards.

This study employs advanced AI-driven statistical analyses to compare organic and processed foods across multiple dimensions, including nutritional content, health outcomes, consumer behavior, and environmental impact. AI models reveal significant nutritional differences, with organic foods exhibiting higher levels of vitamins and antioxidants and lower pesticide residues, while processed foods contain more additives. Predictive machine learning models associate organic food consumption with reduced risks of chronic diseases, contrasting with the elevated health risks linked to processed food intake. Consumer sentiment analysis and clustering algorithms highlight distinct preference patterns favoring organic products for health and environmental reasons. Environmental assessments demonstrate lower carbon footprints and water usage in organic food production. Robustness checks, including cross-validation, sensitivity analyses, and confounder controls, ensure model reliability and fairness. Explainability techniques such as SHAP and LIME enhance transparency and bias detection. This comprehensive AI statistical framework advances understanding of the complex differences between organic and processed foods, supporting informed consumer and policy decisions.