Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms

Spectrum sensing is an essential step in cognitive radio-based dynamic spectrum management. Spectrum sensing to detect the presence of the licensed signals in a particular frequency band is one of the most important research topics in cognitive radio. To identify primary user (PU) presence, we propose a low cost and low power consumption implementation of spectrum sensing operation based on real signals. These signals are generated by smart embedded devices at 433 MHz wireless transmitter using ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type. The reception interface is constructed using an RTL-SDR dongle connected to MATLAB software. The signal detection is done by using four techniques: the artificial neural network (ANN), support vector machine (SVM), Decision Trees (TREE), and k-nearest neighbors (KNN). This article comparatively analyzed the performance of the classifiers to identify the best method for spectrum sensing between the three techniques. The performance evaluation of our proposed model is the probability of detection (Pd) and the false alarm probability (Pfa). Results show also that the sensing is susceptible to signal to noise ratio value. This comparative study has been demonstrated that the spectrum sensing operation by ANN and SVM can be more accurate than KNN, TREE, and some other classical detectors

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided

Wireless Body Area Network (WBAN): A Survey on Architecture, Technologies, Energy Consumption, and Security Challenges

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- or nano-sensors capable of processing and sending information to the base station (BS). Sensors embedded in the bodies of individuals can enable vital information exchange over wireless communication. Network forming of these sensors envisages long-term medical care without restricting patients’ normal daily activities as part of diagnosing or caring for a patient with a chronic illness or monitoring the patient after surgery to manage emergencies. This paper reviews WBAN, its security challenges, body sensor network architecture and functions, and communication technologies. The work reported in this paper investigates a significant security-level challenge existing in WBAN. Lastly, it highlights various mechanisms for increasing security and decreasing energy consumption

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources