Explore the vast range of titles available.

\"Integration of Fog computing with the resource limited IoT network, formulate the concept of Fog-enabled IoT system. Due to large number of deployments of IoT devices, a IoT is a main source of Big data and a very high volume of sensing data is generated by IoT system such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT system is a very fundamental research issue. This book focus on Big data Analytics in Fog-enabled-IoT system and provides a comprehensive collection of chapters that are touches different issues related to Healthcare system, Cyber threat detection, Malware detection, security and privacy of big IoT data and IoT network. This book emphasizes and facilitate a greater understanding of various security and privacy approaches using the advance AI and Big data technologies like machine/deep learning, federated learning, blockchain, edge computing and the countermeasures to overcome the vulnerabilities of the Fog-enabled IoT system\"-- Provided by publisher.

With the development of internet of things (IoT), capabilities of computing, networking infrastructure, storage of data and management have come very close to the edge of networks. This has accelerated the necessity of Fog computing paradigm. Due to availability of Internet, most of our business operations are integrated with IoT platform. Fog computing has enhanced the strategy of collecting and processing, huge amount of data. On the other hand, attacks and malicious activities has adverse consequences on the development of IoT, Fog, and cloud computing. This has led to development of many security models using fog computing to protect IoT network. Therefore, for dynamic and highly scalable IoT environment, a distributed architecture based intrusion detection system (IDS) is required that can distribute the existing centralized computing to local fog nodes and can efficiently detect modern IoT attacks. This paper proposes a novel distributed ensemble design based IDS using Fog computing, which combines k-nearest neighbors, XGBoost, and Gaussian naive Bayes as first-level individual learners. At second-level, the prediction results obtained from first level is used by Random Forest for final classification. Most of the existing proposals are tested using KDD99 or NSL-KDD dataset. However, these datasets are obsolete and lack modern IoT-based attacks. In this paper, UNSW-NB15 and actual IoT-based dataset namely, DS2OS are used for verifying the effectiveness of the proposed system. The experimental result revealed that the proposed distributed IDS with UNSW-NB15 can achieve higher detection rate upto 71.18% for Backdoor, 68.98% for Analysis, 92.25% for Reconnaissance and 85.42% for DoS attacks. Similarly, with DS2OS dataset, detection rate is upto 99.99% for most of the attack vectors.

Fundamental design goal of a typical wireless sensor network is to optimize energy consumption. Recent studies have confirmed that node clustering mechanism efficiently utilizes energy resource of the network by organizing nodes into a set of clusters and helps in extending the network lifetime. Most of the existing node clustering schemes suffers from non-uniform distribution of cluster heads, unbalanced load problem among clusters and left-out node issues. In order to solve these issues, we have focused on to design a load-balanced clustering scheme which also resolves the left-out nodes problem. This study proposes a hybrid meta-heuristic technique where best features of Artificial Bee Colony and Differential Evolution are combined to evaluate the best set of load-balanced cluster heads. For energy efficient and load-balanced clustering, a novel objective function is derived based on average energy, intra-cluster distance and delay parameters. Following this, Artificial Bee Colony based meta-heuristic algorithm is proposed for the dynamic re-localization of the mobile sink within a cluster-based network infrastructure. Performance comparison of the proposed scheme with the existing three well known schemes is evaluated under different network scenarios. Simulation results validate that the proposed scheme performs better in terms of average energy consumption, total energy consumption, residual energy, and network lifetime.

In wireless sensor networks, coverage and connectivity are the fundamental problems for monitoring the targets and guaranteed information dissemination to the far away base station from each node which covers the target. This problem has been proved NP-complete problem, where a set of target points are given, the objective is to find optimal number of suitable positions to organize sensor nodes such that it must satisfy both k-coverage and m-connectivity requirements. In this paper, a biogeography-based optimization (BBO) scheme is used to solve this problem. The proposed BBO-based scheme provides an efficient encoding scheme for the habitat representation and formulates an objective function along with the BBO’s migration and mutation operators. Simulation results show the performance of the proposed scheme to find approximate optimal number of suitable positions under different combinations of k and m. In addition, a comparative study with state-of-art schemes has also been done and its analysis confirms the superiority of the proposed BBO-based scheme over state-of-art schemes.

Background Copper oxide (CuO) nanoparticles (NPs) are known to trigger cytotoxicity in a variety of cell models, but the mechanism of cell death remains unknown. Here we addressed the mechanism of cytotoxicity in macrophages exposed to CuO NPs versus copper chloride (CuCl 2 ). Methods The mouse macrophage cell line RAW264.7 was used as an in vitro model. Particle uptake and the cellular dose of Cu were investigated by transmission electron microscopy (TEM) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The deposition of Cu in lysosomes isolated from macrophages was also determined by ICP-MS. Cell viability (metabolic activity) was assessed using the Alamar Blue assay, and oxidative stress was monitored by a variety of methods including a luminescence-based assay for cellular glutathione (GSH), and flow cytometry-based detection of mitochondrial superoxide and mitochondrial membrane potential. Protein aggregation was determined by confocal microscopy using an aggresome-specific dye and protein misfolding was determined by circular dichroism (CD) spectroscopy. Lastly, proteasome activity was investigated using a fluorometric assay. Results We observed rapid cellular uptake of CuO NPs in macrophages with deposition in lysosomes. CuO NP-elicited cell death was characterized by mitochondrial swelling with signs of oxidative stress including the production of mitochondrial superoxide and cellular depletion of GSH. We also observed a dose-dependent accumulation of polyubiquitinated proteins and loss of proteasomal function in CuO NP-exposed cells, and we could demonstrate misfolding and mitochondrial translocation of superoxide dismutase 1 (SOD1), a Cu/Zn-dependent enzyme that plays a pivotal role in the defense against oxidative stress. The chelation of copper ions using tetrathiomolybdate (TTM) prevented cell death whereas inhibition of the cellular SOD1 chaperone aggravated toxicity. Moreover, CuO NP-triggered cell death was insensitive to the pan-caspase inhibitor, zVAD-fmk, and to wortmannin, an inhibitor of autophagy, implying that this was a non-apoptotic cell death. ZnO NPs, on the other hand, triggered autophagic cell death. Conclusions CuO NPs undergo dissolution in lysosomes leading to copper-dependent macrophage cell death characterized by protein misfolding and proteasomal insufficiency. Specifically, we present novel evidence for Cu-induced SOD1 misfolding which accords with the pronounced oxidative stress observed in CuO NP-exposed macrophages. These results are relevant for our understanding of the consequences of inadvertent human exposure to CuO NPs.

Localization is an important issue in field of Wireless Sensor Networks. Range-free approach is most promising solution used for networks due to its low cost and less power consumption. The main limitation of range-free approach is low accuracy as it is affected by many factors such as node density, coverage and topology diversity. This work proposes a solution that achieves higher accuracy and gives higher coverage for all kinds of network scenarios, even for sparse one. The approach is mainly developed to improve traditional range-free (DV-Hop) method by using a single mobile anchor and mesh based path planning models. The single mobile anchor identifies different reference points in uniform manner to locate nodes in the network region. The performance of proposed methods is evaluated via simulations to demonstrate its effectiveness and simulation results confirm adequacy of the proposed solutions with higher accuracy and higher coverage as compared to traditional one.

The fabrication of unique taper-ended GaN-Nanotowers structure based highly efficient ultraviolet photodetector is demonstrated. Hexagonally stacked, single crystalline GaN nanocolumnar structure (nanotowers) grown on AlN buffer layer exhibits higher photocurrent generation due to high quality nanotowers morphology and increased surface/volume ratio which significantly enhances its responsivity upon ultraviolet exposure leading to outstanding performance from the developed detection device. The fabricated detector display low dark current (~ 12 nA), high I Light /I Dark ratio (> 10 4 ), fast time-correlated transient response (~ 433 µs) upon ultraviolet (325 nm) illumination. A high photoresponsivity of 2.47 A/W is achieved in self-powered mode of operation. The reason behind such high performance could be attributed to built-in electric field developed from a difference in Schottky barrier heights will be discussed in detail. While in photoconductive mode, the responsivity is observed to be 35.4 A/W @ − 3 V along with very high external quantum efficiency (~ 10 4 %), lower noise equivalent power (~ 10 –13 WHz −1/2 ) and excellent UV–Vis selectivity. Nanotower structure with lower strain and dislocations as well as reduced trap states cumulatively contributed to augmented performance from the device. The utilization of these GaN-Nanotower structures can potentially be useful towards the fabrication of energy-efficient ultraviolet photodetectors.

To enhance the universal adaptability of the Real-Time deployment of Industry 5.0, various machine learning-based cyber threat detection models are given in the literature. Most of the existing threat detection models may not be able to detect zero-day cyber threats and are prone to producing a high False Positive Rate (F PR ) due to irrelevant features and imbalanced class samples. Furthermore, its predictive decisions are also difficult to comprehend even by security experts. Consequently, an intelligent and more robust model is needed to mitigate zero-day cyber threats. This study proposes an explainable model named BRL-ETDM for detecting cyber threats in Industry 5.0. In this model, features are optimized by Bayesian Reinforcement Learning ( BRL )-based Bee Swarm Optimization ( BSO ) technique in which the exploitation phase of BSO is improved by the BRL technique. Then, an improved weighted majority voting-based ensemble technique is designed to enhance threat detection performance. Additionally, an explainable AI technique is employed to explain the threat predictions. This model is tested and validated using two realistic datasets named Edge-IIoTset and ToN-IoT. Experimental results show that the proposed model achieved a maximum accuracy of 96.15% with a minimum number of features and F PR of 0.27% as compared to existing techniques.

Plasmonic coatings can absorb electromagnetic radiation from visible to far-infrared spectrum for the better performance of solar panels and energy saving smart windows. For these applications, it is important for these coatings to be as thin as possible and grown at lower temperatures on arbitrary substrates like glass, silicon, or flexible polymers. Here, we tune and investigate the plasmonic resonance of titanium nitride thin films in lower thicknesses regime varying from ~ 20 to 60 nm. High-quality crystalline thin films of route-mean-square roughness less than ~ 0.5 nm were grown on a glass substrate at temperature of ~ 200 °C with bias voltage of − 60 V using cathodic vacuum arc deposition. A local surface-enhanced-plasmonic-resonance was observed between 400 and 500 nm, which further shows a blueshift in plasmonic frequency in thicker films due to the increase in the carrier mobility. These results were combined with finite-difference-time-domain numerical analysis to understand the role of thicknesses and stoichiometry on the broadening of electromagnetic absorption.

In the Autonomous Underwater Vehicle ( AUV ) based Underwater Acoustic Sensor Network ( UASN ), efficient data collection with minimum delay and high throughput is a fundamental research challenge. Most of the existing data collection schemes using AUVs are suffered from unbalanced energy consumption, long delay, partial coverage, and incomplete data collection problems. To overcome these problems, this paper proposed an optimized coverage-aware target node selection and trajectory planning scheme for AUVs for fast and efficient data collection in the Underwater Sensor Networks. Optimal selection of coverage-aware target nodes and trajectory planning of the multiple AUVs are proposed using Backtracking Search Optimization ( BSO ) technique. After deployment of the underwater sensor nodes, first, network is partitioned into a set of load balanced cluster-region. After that, optimized coverage-aware target node is selected from each cluster-region for collection of the sensed data using AUVs . For optimizing the trajectory of the AUVs , a BSO -based trajectory planning scheme is proposed with novel fitness function. The proposed scheme dispatches multiple AUVs concurrently for high availability and low delay in the data collected from the cluster-regions. Performance of the proposed scheme is evaluated and compared with some latest state-of-art existing schemes in terms of coverage ratio, total travel distance, maximum travel distance, delay, and average energy consumption. Simulation results confirm that the proposed scheme performs well and very capable in providing the fast and high availability of the sensed data collection from UASN .