With the advent of cloud computing and wireless sensor networks, the number of cyberattacks has rapidly increased. Therefore, the proportionate security of networks has become a challenge for organizations. Information security advisors of organizations face difficult and complex decisions in the evaluation and selection of information security controls that permit the defense of their resources and assets. Information security controls must be selected based on an appropriate level of security. However, their selection needs intensive investigation regarding vulnerabilities, risks, and threats prevailing in the organization as well as consideration of the implementation, mitigation, and budgetary constraints of the organization. The goal of this paper was to improve the information security control analysis method by proposing a formalized approach, i.e., fuzzy Analytical Hierarchy Process (AHP). This approach was used to prioritize and select the most relevant set of information security controls to satisfy the information security requirements of an organization. We argue that the prioritization of the information security controls using fuzzy AHP leads to an efficient and cost-effective assessment and evaluation of information security controls for an organization in order to select the most appropriate ones. The proposed formalized approach and prioritization processes are based on International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001:2013. But in practice, organizations may apply this approach to any information security baseline manual.

The demand of devices for safe mobility of blind people is increasing with advancement in wireless communication. Artificial intelligent devices with multiple input and output methods are used for reliable data estimation based on maximum probability. A model of a smart home for safe and robust mobility of blind people has been proposed. Fuzzy logic has been used for simulation. Outputs from the internet of things (IoT) devices comprising sensors and bluetooth are taken as input of the fuzzy controller. Rules have been developed based on the conditions and requirements of the blind person to generate decisions as output. These outputs are communicated through IoT devices to assist the blind person or user for safe movement. The proposed system provides the user with easy navigation and obstacle avoidance.

The internet of medical things (IoMT) is used for the acquisition, processing, transmission, and storage of medical data of patients. The medical information of each patient can be monitored by hospitals, family members, or medical centers, providing real-time data on the health condition of patients. However, the IoMT requires monitoring healthcare devices with features such as being lightweight, having a long lifetime, wearability, flexibility, safe behavior, and a stable electrical performance. For the continuous monitoring of the medical signals of patients, these devices need energy sources with a long lifetime and stable response. For this challenge, conventional batteries have disadvantages due to their limited-service time, considerable weight, and toxic materials. A replacement alternative to conventional batteries can be achieved for piezoelectric and triboelectric nanogenerators. These nanogenerators can convert green energy from various environmental sources (e.g., biomechanical energy, wind, and mechanical vibrations) into electrical energy. Generally, these nanogenerators have simple transduction mechanisms, uncomplicated manufacturing processes, are lightweight, have a long lifetime, and provide high output electrical performance. Thus, the piezoelectric and triboelectric nanogenerators could power future medical devices that monitor and process vital signs of patients. Herein, we review the working principle, materials, fabrication processes, and signal processing components of piezoelectric and triboelectric nanogenerators with potential medical applications. In addition, we discuss the main components and output electrical performance of various nanogenerators applied to the medical sector. Finally, the challenges and perspectives of the design, materials and fabrication process, signal processing, and reliability of nanogenerators are included.

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO ${}_2$ ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

Fuzzy logic-based control systems are widely used in various fields like home appliances, medical instruments, automobiles, textile machinery, agriculture equipment and aviation for process control and data analysis. Fuzzy logic technique has shown great potential to solve the complex problems of physical world due to similarity with human understanding. Its advancements have gained widespread attention in different research areas. In several cases, it is very suitable for electronic devices which need to be precisely self-powered. In this work, an ANSYS-based simulation, fuzzy analysis, development and testing of a microelectromechanical system (MEMS)-based energy harvester have been presented. Zinc oxide (ZnO) nano rods were synthesized on an anodic aluminum oxide (AAO) template to form the MEMS energy harvester and study the effect of energy generation by applying force. The power of 5.16 nano Watts has been obtained by taking the numerical value of voltage (V_oc) and current (I_sc) as 3.16 mV and 0.985 µA respectively using fuzzy logic tool. Experimental testing of the harvester shows that the range of V_oc is 3–6.4 mV and I_sc is 0.45–1.5 μA. The results depict that this device can be used for touch screens to generate energy that can be further utilized for charging smart devices.