Blockchain technology originated alongside Bitcoin as a novel method of conducting financial transactions. It has garnered significant attention from both industry and academia in recent years, emerging as a prominent area of research. It is a decentralized record-keeping system that holds transactional information. The scale of a blockchain network expands according to the growth in the number of nodes and transactions, resulting in issues related to storage capacity, data processing speed, and time delay. These issues have a direct impact on the scalability of a blockchain network. Currently, scalability is one of the prominent concerns in the field of blockchain technology and an active research area. This study conducts a comprehensive survey of the scalability challenges faced by blockchain technology in several sectors. It also examines potential solutions based on consensus mechanisms, smart contracts and directed acyclic graph (DAG). It is observed that the proposed scalability solutions target enhancing system throughput, reducing costs, and improving blockchain efficiency. Therefore, we examine, compare, and evaluate the literature using these specific criteria. Moreover, a survey of existing blockchain based survey papers is presented. A comparative analysis of these survey papers is presented along with their recency score, which is determined by the number of recent publications reviewed in a survey paper. By "recent," we mean the current year (or the publication year of a survey paper) and the three years prior to it. Additionally, this paper offers an elaborate discussion on the forthcoming open research challenges and applications of blockchain.

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

Modern health care system is one of the most popular Wireless Body Area Sensor Network (WBASN) applications and a hot area of research subject to present work. In this paper, we present Reliability Enhanced-Adaptive Threshold based Thermal-unaware Energy-efficient Multi-hop ProTocol (RE-ATTEMPT) for WBASNs. The proposed routing protocol uses fixed deployment of wireless sensors (nodes) such that these are placed according to energy levels. Moreover, we use direct communication for the delivery of emergency data and multihop communication for the delivery of normal data. RE-ATTEMPT selects route with minimum hop count to deliver data which downplays the delay factor. Furthermore, we conduct a comprehensive analysis supported by MATLAB simulations to provide an estimation of path loss, and problem formulation with its solution via linear programming model for network lifetime maximization is also provided. In simulations, we analyze our protocol in terms of network lifetime, packet drops, and throughput. Results show better performance for the proposed protocol as compared to the existing one.

Regarding energy efficiency in wireless sensor networks (WSNs), routing protocols are engaged in a playful manner suggesting a consciousness of high value. In this paper, we present away cluster heads (CHs) with adaptive clustering habit ((ACH) ² ) scheme for WSNs. Our proposed scheme increases the stability period, network lifetime, and throughput of the WSN. The beauty of our proposed scheme is its away CHs formation, and free association mechanisms. The (ACH) ² controls the CHs' election and selection in such a way that uniform load on CHs is ensured. On the other hand, free association mechanism removes back transmissions. Thus, the scheme operations minimize the over all energy consumption of the network. In subject to throughput maximization, a linear programming-based mathematical formulation is carried out in which the induced subproblem of bandwidth allocation is solved by mixed-bias resource allocation scheme. We implement (ACH) ² scheme, by varying node density and initial energy of nodes in homogeneous, heterogeneous, reactive, and proactive simulation environments. Results justify its applicability.

Nowadays Wireless Sensor Networks WSNs are playing a vital role in several application areas ranging health to battlefield Wireless sensor networks are easy to deploy due to its unique characteristics of size and self-organizing networks. Wireless sensor nodes contain small unchangeable and not chargeable batteries. It is a resource constraint type network Routing in WSN is most expensive task as it utilizes more power resources. This paper is intended to introduce energy efficient routing protocol known as Position Responsive Routing Protocol (PRRP) to enhance energy efficiency of WSN. Position responsive routing protocol differs in several ways than other existing routing techniques. Position response routing protocol approach allows fair distribution of gateway\cluster head selection, maximum possible distance minimization among nodes and gateways\cluster heads to utilize less energy. Position responsive routing protocol shows significant improvement of 45% in energy efficiency of wireless sensor network life time as a whole by increasing battery life of individual nodes. Furthermore PRRP shows drastic increases for data throughput and provide better solution to routing energy hole due to it fair distributed approach of gateway selection.