Assessment of ITU-R predictions for ku-band rain attenuation in Malaysia

This paper presents findings on the assessments carried out pertaining to ITU-R’s predicted rain attenuations. The predictions are put against measurements acquired from a campaign in Kuala Lumpur, Malaysia. The investigation tasks involve generation of annual cumulative distributions using assorted ITU-R recommendations as well as from measured data collected for a period of 20 months. Predicted values generated using established ITU-R rain attenuation prediction models are then compared with measurements values, in order to validate the applicability and effectiveness of each model. Based on the evaluation, it can be suggested that the ITU-R P.618-5 recommendation seems to be a befitting prediction model and capable of generating satisfactory prediction for Malaysia

A multihoming-based mobility management scheme to reduce registration delay on proxy MIPv6 domain in NEMO

Registration delay is a significant issue for mobile network due to increase traffic load with handoff latency at the time of frequent movement from one subnet to another of Mobile Router (MR) in NEMO Basic Support protocol (NEMO BSP). Hence, a network-based localized protocol (i.e. PMIPv6) is integrated with NEMO in order to solve these matters. Yet, combining this network-based localized protocol for inter mobility handoff (i.e. movement among different access technology) in NEMO environment is a challenging issue as both MR and its MNNs must be taken into consideration. Therefore, this paper proposes a multihoming-based Early Proxy Binding Update scheme in NEMO (EPBU-NEMO) which is based on FPMIPv6 with predictive mode to reduce registration delay during inter mobility handoff. Moreover, numerical framework is formulated to evaluate the outcomes of the EPBU-NEMO scheme. Lastly, it determines that EPBU-NEMO scheme outperforms the standard NEMO BSP related to signaling cost regardless of increasing the number of MRs as well as cell residence time

Machine learning based lightweight interference mitigation scheme for wireless sensor network

The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference

A Microwave Imaging Procedure for Lung Lesion Detection: Preliminary Results on Multilayer Phantoms

In this work, a feasibility study for lung lesion detection through microwave imaging based on Huygens’ principle (HP) has been performed using multilayer oval shaped phantoms mimicking human torso having a cylindrically shaped inclusion simulating lung lesion. First, validation of the proposed imaging method has been performed through phantom experiments using a dedicated realistic human torso model inside an anechoic chamber, employing a frequency range of 1–5 GHz. Subsequently, the miniaturized torso phantom validation (using both single and double inclusion scenarios) has been accomplished using a microwave imaging (MWI) device, which operates in free space using two antennas in multi-bistatic configuration. The identification of the target’s presence in the lung layer has been achieved on the obtained images after applying both of the following artifact removal procedures: (i) the “rotation subtraction” method using two adjacent transmitting antenna positions, and (ii) the “ideal” artifact removal procedure utilizing the difference between received signals from unhealthy and healthy scenarios. In addition, a quantitative analysis of the obtained images was executed based on the definition of signal to clutter ratio (SCR). The obtained results verify that HP can be utilized successfully to discover the presence and location of the inclusion in the lung-mimicking phantom, achieving an SCR of 9.88 dB

Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed

Fuzzy Decision Making and Soft Computing Applications

This Special Issue collects original research articles discussing cutting-edge work as well as perspectives on future directions in the whole range of theoretical and practical aspects in these research areas: i) Theory of fuzzy systems and soft computing; ii) Learning procedures; iii) Decision-making applications employing fuzzy logic and soft computing

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field