In the current era, blockchain has approximately 30 consensus algorithms. This architecturally distributed database stores data in an encrypted form with multiple checks, including elliptical curve cryptography (ECC) and Merkle hash tree. Additionally, many researchers aim to implement a public key infrastructure (PKI) cryptography mechanism to boost the security of blockchain-based data management. However, the issue is that many of these are required for advanced cryptographic protocols. For all consensus protocols, security features are required to be discussed because these consensus algorithms have recently been attacked by address resolution protocols (ARP), distributed denial of service attacks (DDoS), and sharding attacks in a permission-less blockchain. The existence of a byzantine adversary is perilous, and is involved in these ongoing attacks. Considering the above issues, we conducted an informative survey based on the consensus protocol attack on blockchain through the latest published article from IEEE, Springer, Elsevier, ACM, Willy, Hindawi, and other publishers. We incorporate various methods involved in blockchain. Our main intention is to gain clarity from earlier published articles to elaborate numerous key methods in terms of a survey article.

Researchers are interested in Facial Emotion Recognition (FER) because it could be useful in many ways and has promising applications. The main task of FER is to identify and recognize the original facial expressions of users from digital inputs. Feature extraction and emotion recognition make up the majority of the traditional FER. Deep Neural Networks, specifically Convolutional Neural Network (CNN), are popular and highly used in FER due to their inherent image feature extraction process. This work presents a novel method dubbed as EfficientNet-XGBoost that is based on Transfer Learning (TL) technique. EfficientNet-XGBoost is basically a cascading of the EfficientNet and the XGBoost techniques along with certain enhancements by experimentation that reflects the novelty of the work. To ensure faster learning of the network and to overcome the vanishing gradient problem, our model incorporates fully connected layers of global average pooling, dropout and dense. EfficientNet is fine-tuned by replacing the upper dense layer(s) and cascading the XGBoost classifier making it suitable for FER. Feature map visualization is carried out that reveals the reduction in the size of feature vectors. The proposed method is well-validated on benchmark datasets such as CK+, KDEF, JAFFE, and FER2013. To overcome the issue of data imbalance, in some of the datasets namely CK+ and FER2013, we augmented data artificially through geometric transformation techniques. The proposed method is implemented individually on these datasets and corresponding results are recorded for performance analysis. The performance is computed with the help of several metrics like precision, recall and F1 measure. Comparative analysis with competent schemes are carried out on the same sample data sets separately. Irrespective of the nature of the datasets, the proposed scheme outperforms the rest with overall rates of accuracy being 100%, 98% and 98% for the first three datasets respectively. However, for the FER2013 datasets, efficiency is less promisingly observed in support of the proposed work.

The fault is inevitable in case of the wireless sensor network (WSN) because of its remote deployment and constrained architecture. In literature, many algorithms have been proposed to address several types of fault in the WSN life‐cycle. In the context of fault tolerance, the clustering method has been adopted as a proven technique. Heintzelman et al. first considered the clustering technique and proposed the seminal low energy adaptive clustering hierarchy (LEACH) algorithm for fault handling in WSN. In this study, the authors propose a LEACH variant clustering convention called partitioned‐based energy‐efficient – LEACH (PE‐LEACH) protocol which tends to the energy‐based fault‐tolerant technique. They also study and present a taxonomy on LEACH variants. The execution of PE‐LEACH is analysed against its predecessors; hard‐computing based LEACH, energy‐efficient LEACH (E‐LEACH) convention and soft‐computing‐based energy‐swarm‐optimisation LEACH (ESO‐LEACH). They have found through their recreation that PE‐LEACH outperforms than LEACH and E‐LEACH separately whereas, for ESO‐LEACH the PE‐LEACH is a tough competitor.

The smart city concept is a solution to many problems that we are facing in our day-to-day life. Many countries have been started adopting many prototypes to solve daily challenges. Still, the smart city term does not have any universally accepted definition. The smart city is a ubiquitous term whose definition varies from person to person, city to city, and country to country. The lack of a common definition remains the term smart city is in a chaotic state. This paper has presented the socio-technical challenges during the implementation of smart city plans. It has analyzed the smart city execution problems in the context of developed and underdeveloped countries.

The study aims to understand the various types of faults occur in a wireless sensor network (WSN). The primary objective of the fault‐tolerant technique is to enhance the robustness and self‐healing capacity of sensor network. The resource constraint environment, remote deployment and harsh monitoring conditions frequently invite different types of faults in WSN. The fault can be handled in two ways such as fault tolerance and fault prevention method. The novelty of this state‐of‐art is, the authors propose two new algorithms named Uniform Energy Clustering for fault tolerance and Population‐Based Clustering for fault prevention. The result of the simulation is satisfactory in comparison to existing algorithms such as Low Energy Adaptive Clustering Hierarchy, Stable Election Protocol, Virtual Area Partition – Energy and Energy‐Aware Multi‐Hop Multi‐Path Hierarchical protocols. In addition to these new algorithms, they also propose a unique taxonomy on fault which is based on sensitivity issues of specific applications.