In the modern era business intelligence (BI) has a pivotal role in articulating a strategy and taking correct measures based on data. Business intelligence plays a pivotal role in an inevitable decision support system that enables the enterprise to perform analysis on data and throughout the process of business. Machine learning predicts the forecasting of future demands of the enterprises. Demand forecasting is one of the main decision-making tasks of enterprise. For demand forecasting first raw sales data is collected from the market, then according to data, the future sale/product demands are forecasted. This prediction is based on collected data that compiles through different sources. The machine learning engine executes data from different modules and determines the weekly, monthly, and quarterly demands of goods/commodities. In demand forecasting, its perfect accuracy is non-compromising, the more accurate system model is more efficient. Furthermore, we test the efficiency by comparing the predicted data with actual data and determine the percentage error. Simulation results show that after applying the purposed solution on real-time organization data, we get up to 92.38 % accuracies for the store in terms of intelligent demand forecasting.

A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant's hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6% with 70% of training (33488 samples), 30% of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

Underwater Wireless Sensor Networks (UWSN) have gained more attention from researchers in recent years due to their advancement in marine monitoring, deployment of various applications, and ocean surveillance. The UWSN is an attractive field for both researchers and the industrial side. Due to the harsh underwater environment, own capabilities, and open acoustic channel, it is also vulnerable to malicious attacks and threats. Attackers can easily take advantage of these characteristics to steal the data between the source and destination. Many review articles are addressed some of the security attacks and taxonomy of the Underwater Wireless Sensor Networks. In this study, we have briefly addressed the taxonomy of the UWSNs from the most recent research articles related to the well‐known research databases. This paper also discussed the security threats on each layer of the Underwater Wireless sensor networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues.

Underwater wireless sensor networks (UWSNs) contain sensor nodes that sense the data and then transfer them to the sink node or base station. Sensor nodes are operationalized through limited-power batteries. Therefore, improvement in energy consumption becomes critical in UWSNs. Data forwarding through the nearest sensor node to the sink or base station reduces the network’s reliability and stability because it creates a hotspot and drains the energy early. In this paper, we propose the cooperative energy-efficient routing (CEER) protocol to increase the network lifetime and acquire a reliable network. We use the sink mobility scheme to reduce energy consumption by eliminating the hotspot issue. We have divided the area into multiple sections for better deployment and deployed the sink nodes in each area. Sensor nodes generate the data and send it to the sink nodes to reduce energy consumption. We have also used the cooperative technique to achieve reliability in the network. Based on simulation results, the proposed scheme performed better than existing routing protocols in terms of packet delivery ratio (PDR), energy consumption, transmission loss, and end-to-end delay.

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

The communications in the millimeter wave (mmWave) band, e.g., WiGig (60 GHz), is considered as one of the main components of 5G and beyond 5G (B5G) networks. However, it is characterized by short range transmissions along with high susceptibility to path blockage, e.g., human shadowing. Thus, mmWave multi-hop relaying using device to device (D2D) connections turns to be an efficient solution to extend its communication range and to route around blockages. In this context, relay probing is essential to discover/explore the candidate multi-hop routes from source-to-destination and then select the best route among them. However, a trade-off exists between relay probing and the required overhead coming from mmWave beamforming training. In this paper, taking advantage of the multi-band μW/mmWave relay nodes, an efficient multi-hop relay probing scheme is proposed for mmWave D2D routing. In this scheme, the collected μW received signal strengths (RSSs) among the distributed relay nodes are used to estimate the probability of their mmWave signal-to-noise power ratios (SNRs) while considering line-of-sight (LOS) and non-LOS (NLOS) path availabilities. Then, based on a proposed probabilistic metric, a hierarchical search algorithm is proposed to jointly discover the relay nodes along the candidate multi-hop routes and enumerate the number of routes expected to maximize the spectral efficiency of the whole path from source-todestination. This is done in an offline phase, and only the relay nodes located within the pre-selected multihop routes are requested to do online relay probing. Numerical analysis confirms the superiority of the proposed mmWave multi-hop relay probing scheme over the candidate techniques.