No abstract

Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable.

Due to COVID-19, people have to adapt to the new lifestyle until scientists develop a permanent solution for this pandemic. Monitoring the respiration rate is very important for a COVID-infected person because the Coronavirus infects the pulmonary system of the person. Two problems that arise while monitoring the breath rate are: sensors are contact based and expensive for mass deployment. A conventional wearable breath rate monitoring system burdens the COVID-affected patient and exposes the caregivers to possible transmission. A contactless low-cost breath monitoring system is required, which monitors and records the breath rate continuously. This paper proposes a breath rate monitoring system called COVID-Beat, a wireless, low-cost, and contactless Wi-Fi-based continuous breath monitoring system. This sensor is developed using off-the-shelf commonly available embedded Internet of Thing device ESP32, and the performance is validated by conducting extensive experimentation. The breath rate is estimated by extracting the channel state information of the subcarriers. The system estimates the breath rate with a maximum accuracy of 99% and a minimum accuracy of 91%, achieved by advanced subcarrier selection and fusion method. The experimental results show superior performance over the existing breath rate monitoring technologies.

Multi-radio Multi-channel (MRMC) Wireless Mesh Networks (WMNs) have made quick progress in current years to become the best option for end users due to its reliability and low cost. Although WMNs have already been used still the capacity of WMNs is limited due to information asymmetry and near hidden interference among frequency channels. In the past, various research studies have been done to investigate both these issues. To minimise both these interference types and maximise network capacity; channel assignment has always been a critical area of research in WMNs. In this research, a comparative analysis is done between NH and IA interference based on their impact on network capacity. This comparison is made using the existing Optimal Channel Assignment Model (OCAM). After extensive simulations, it is figured out that NH interference performs a major role in degrading overall network capacity up to 4% in comparison to IA interference. Further, in this research an optimal channel assignment model Information Asymmetry and Near Hidden Minimization (INM) model is proposed that collectively minimises both NH and IA interference problems. The proposed model considers three non-overlapping channels 1, 6 and 11 from IEEE802.11b standard. For simulations, four different Multi-radio Multi-channel Wireless mesh topologies have been considered to find out the average network capacity. An extensive simulation in OPNET shows that the proposed INM model performs 7% better than the existing OCAM model in maximising the WMN net capacity.

Combinatorial Problems (NP hard Problem) have always been a hard task to be solved to optimal level but for the efficiency and finding the best possible solution in a certain span of time it has been solved to suboptimal level. During the study for solving the combinatorial problems to suboptimal level different heuristic algorithms has been used for acquiring results from the TSPLIB Instances. Different Suboptimal level has been achieved through different heuristics like Ant Colony Algorithm, Genetic Algorithm and Simulated Annealing Algorithm. The perimeters were tuned to different levels of all heuristics to find suboptimal level of the instances of TSPLIB. The paper will also present the effects of perimeters tuning to achieve the suboptimal results. distance between two nodes remains constant from i to j and j to i presented d ij  d ji while in asymmetric the i to j and j to i is never same which can be interpreted as