Electrocardiogram data collection under network attacks on the MAC platform

Increasing heart disease among human beings needs more precise treatment, which requires monitoring of electrocardiogram (ECG). In many cases, real time monitoring of ECG is needed via wireless or wireline networks. Use of network-connected computers for monitoring proposes can raise security issues, which can be created by viruses, worms, or external agents such as DoS attack traffic. Any alteration of this biomedical signal can lead to wrong diagnosis and wrong treatment. Furthermore, in healthcare industry, HIPAA rules require health information to be kept secure by providing confidentiality, integrity, and availability. This thesis investigates how integrity and availability of remotely monitored ECG signals can be affected silently due to adverse network conditions, hence raising false alarms. In this thesis, components of monitored ECG signals under adverse network conditions are measured and compared against normal ECG signals for detection of different heart diseases

Introducing reinforcement learning in the Wi-Fi MAC layer to support sustainable communications in e-Health scenarios

The crisis of energy supplies has led to the need for sustainability in technology, especially in the Internet of Things (IoT) paradigm. One solution is the integration of Energy Harvesting (EH) technologies into IoT systems, which reduces the amount of battery replacement. However, integrating EH technologies within IoT systems is challenging, and it requires adaptations at different layers of the IoT protocol stack, especially at Medium Access Control (MAC) layer due to its energy-hungry features. Since Wi-Fi is a widely used wireless technology in IoT systems, in this paper, we perform an extensive set of simulations in a dense solar-based energy-harvesting Wi-Fi network in an e-Health environment. We introduce optimization algorithms, which benefit from the Reinforcement Learning (RL) methods to efficiently adjust to the complexity and dynamic behaviour of the network. We assume the concept of Access Point (AP) coordination to demonstrate the feasibility of the upcoming Wi-Fi amendment IEEE 802.11bn (Wi-Fi 8). This paper shows that the proposed algorithms reduce the network&amp;#x2019;s energy consumption by up to 25% compared to legacy Wi-Fi while maintaining the required Quality of Service (QoS) for e-Health applications. Moreover, by considering the specific adjustment of MAC layer parameters, up to 37% of the energy of the network can be conserved, which illustrates the viability of reducing the dimensions of solar cells, while concurrently augmenting the flexibility of this EH technique for deployment within the IoT devices. We anticipate this research will shed light on new possibilities for IoT energy harvesting integration, particularly in contexts with restricted QoS environments such as e-Healthcare.</p

Enabling Energy Harvesting-Based Wi-Fi System for an e-Health Application: A MAC Layer Perspective

The adverse impacts of using conventional batteries in the Internet of Things (IoT) devices, such as cost-effective maintenance, numerous battery replacements, and environmental hazards, have led to an interest in integrating energy harvesting technology into IoT devices to extend their lifetime and sustainably effectively. However, this requires improvements in different IoT protocol stack layers, especially in the MAC layer, due to its high level of energy consumption. These improvements are essential in critical applications such as IoT medical devices. In this paper, we simulated a dense solar-based energy harvesting Wi-Fi network in an e-Health environment, introducing a new algorithm for energy consumption mitigation while maintaining the required Quality of Service (QoS) for e-Health. In compliance with the upcoming Wi-Fi amendment 802.11be, the Access Point (AP) coordination-based optimization technique is proposed, where an AP can request dynamic resource rescheduling along with its nearby APs, to reduce the network energy consumption through adjustments within the standard MAC protocol. This paper shows that the proposed algorithm, alongside using solar energy harvesting technology, increases the energy efficiency by more than 40% while maintaining the e-Health QoS requirements. We believe this research will open new opportunities in IoT energy harvesting integration, especially in QoS-restricted environments

A Comprehensive Review on Energy Harvesting Integration in IoT Systems from MAC Layer Perspective: Challenges and Opportunities

The Internet of Things (IoT) is revolutionizing technology in a wide variety of areas, from smart healthcare to smart transportation. Due to the increasing trend in the number of IoT devices and their different levels of energy requirements, one of the significant concerns in IoT implementations is powering up the IoT devices with conventional limited lifetime batteries. One efficient solution to prolong the lifespan of these implementations is to integrate energy harvesting technologies into IoT systems. However, due to the characteristics of the energy harvesting technologies and the different energy requirements of the IoT systems, this integration is a challenging issue. Since Medium Access Control (MAC) layer operations are the most energy-consuming processes in wireless communications, they have undergone different modifications and enhancements in the literature to address this issue. Despite the essential role of the MAC layer to efficiently optimize the energy consumption in IoT systems, there is a gap in the literature to systematically understand the possible MAC layer improvements allowing energy harvesting integration. In this survey paper, we provide a unified framework for different wireless technologies to measure their energy consumption from a MAC operation-based perspective, returning the essential information to select the suitable energy harvesters for different communication technologies within IoT systems. Our analyses show that only 23% of the presented protocols in the literature fulfill Energy Neutral Operation (ENO) condition. Moreover, 48% of them are based on the hybrid approaches, which shows its capability to be adapted to energy harvesting. We expect this survey paper to lead researchers in academia and industry to understand the current state-of-the-art of energy harvesting MAC protocols for IoT and improve the early adoption of these protocols in IoT systems

Assessment of Improvement in Oxidative Stress Indices with Resocialization in Memory Retrieval in Y-Maze in Male Rats

Introduction: Memory deficit is an important issue in some psychiatric diseases either as a primary symptom or as a comorbid symptom. Factors that determine the decline or improvement of memory are an important subject to reduce the severity of these diseases. Methods and materials: In this study, 32 male Sprague-Dawley rats were randomly divided into 4 experimental groups: social (control), isolation, resocialization for 3 days, and resocialization for 7 days. Isolation occurred for 14 days. Resocialization groups were resocialized for 3 or 7 days after isolation. In the social group, there was no intervention with normal socializing among the rats. In the isolation group, rats were isolated with no resocialization. In all 4 groups, after performing the Y-maze, the rats’ brains were removed to assess oxidative stress status in the hippocampus and prefrontal cortex. Results: Y-maze performance improved after 3 and 7 days of resocialization. However, oxidative stress status for malondialdehyde, glutathione and nitrite/nitrate returned to normal levels except in 2 experiments after 7 days of resocialization. In addition, in 2 experiments, just glutathione in the prefrontal cortex and nitrite/nitrate in the hippocampus after 3 days of resocialization improved. Conclusions: A return to normal levels in all types of antioxidant markers in the resocialization groups is not the only factor for improving memory deficits resulting from isolation. Resocialization may also be activating other regulatory mechanisms besides an antioxidant defense

Achieving proportional fairness in WiFi networks via convex bandit optimization

In the last years, proportional fairness has attracted attention in the literature on multi-rate IEEE 802.11 WLANs. One way to improve the performance of wireless networks is contention window tuning based on proportional fairness. In this thesis, we investigate how to apply a bandit convex optimization algorithm - a powerful framework for wireless network optimization - to proportional fair resource allocation in wireless networks. We propose an algorithm which is able to learn the optimal slot transmission probability only by monitoring the throughput of the network. We have evaluated the Online Gradient Descent with Sequential Multi-Point Gradient Estimates algorithm both by using the true value of the function to optimize, as well as adding estimation errors by using a network simulator. By means of the proposed algorithm, we provide extensive experimental results which illustrate the sensitivity of the algorithm to different exploration schedules, exploration parameters and gradient descent step size. We also show the sensitivity of the algorithm to noisy gradient estimates. We believe this research can be considered as a practical solution in order to improve the performance of wireless networks, in particular, in commercial WiFi cards

Overview of withdrawal syndrome mechanisms in different substance abuse addictions: Neuronal circuits and transmitters

Substance abuse is one of the major concerns of human societies. Many problems exist for controlling drug abuse that profoundly influences treatment. One of the most important and prevalent problems is Withdrawal Syndrome (WS). The WS describes a host of unpleasant symptoms that develop in the withdrawal period. WS should be suppressed in the withdrawal period because in the presence of WS dependence on drugs will continue. Using the drug to which one is addicted is the most effective way to suppress the withdrawal syndrome, which is why many users relapse after a period of abstinence. The purpose of this review article is to describe the basic brain mechanisms that are responsible for the emergence of WS that cannot be tolerated in the abstinence period. This may be helpful for a better understanding of the nature of WS in the withdrawal period for implementing the best approach with new insights for highlighting new horizons for future goal-directed studies. The important topics in this regard are non-related brain areas based on recent studies that have been proposed. They have included the rewarding center, endogenous cannabinoid system, corticotropin-releasing factor, locus coeruleus, and orexin system. According to the above facts, this syndrome arises with different mechanisms, and a multi-approach treatment toward this syndrome is required.&nbsp;</p