Designing an efficient, reliable, and stable algorithm for underwater acoustic wireless sensor networks (UA‐WSNs) needs immense attention. It is due to their notable and distinctive challenges. To address the difficulties and challenges, the article introduces two algorithms: the multilayer sink (MuLSi) algorithm and its reliable version MuLSi‐Co using the cooperation technique. The first algorithm proposes a multilayered network structure instead of a solid single structure and sinks placement at the optimal position, which reduces multiple hops communication. Moreover, the best forwarder selection amongst the nodes based on nodes’ closeness to the sink is a good choice. As a result, it makes the network perform better. Unlike the traditional algorithms, the proposed scheme does not need location information about nodes. However, the MuLSi algorithm does not fulfill the requirement of reliable operation due to a single link. Therefore, the MuLSi‐Co algorithm utilizes nodes’collaborative behavior for reliable information. In cooperation, the receiver has multiple copies of the same data. Then, it combines these packets for the purpose of correct data reception. The data forwarding by the relay without any latency eliminates the synchronization problem. Moreover, the overhearing of the data gets rid of duplicate transmissions. The proposed schemes are superior in energy cost and reliable exchanging of data and have more alive and less dead nodes.

The global impact of social values, norms, and cultures set the growth and future dimensions of most businesses. In global business governess, the sustainability of social entrepreneurship is heavily dependent on peoples' opinions and their social interactions. Nowadays, social media platforms represent the big global repositories of publically available information that can be exploited by social entrepreneurs to measure and assess the social impact of their business. There is still inadequate research that focuses on assessing social entrepreneurship impact in the area of big data. This paper aims to investigate the potential of big data in global social entrepreneurship. It examines the possibility of global impact of big data in social entrepreneurship. As an outcome, this paper highlight the challenges of social entrepreneurship dealing with, how they tackle globally, big data in social innovation, and how big data analytics needs for social entrepreneurship towards achieving social goods and sustainable change.

Summary DNA signals mainly differ from standard digital signals due to their biological data contents. Owing to unique properties of DNA signals the conventional signal processing techniques, such as digital filters, suffers with spectral leakage and results in insignificant noise suppression in DNA sequence analysis. This article presents an intelligent noise suppression window filter (INSWF) for DNA signal analysis. The filter demises the signal by separating high‐level frequency contents and by identifying nucleotides with high fuzzy membership contribution at particular locations. The nucleotide contents of signals are later filtered by application of median filtering employing a combination of s‐shaped and z‐shaped filters. The fundamental characteristic of codons usage that causes uneven nucleotides segmentation has been tackled by finding the best fit of the curve in biological contents of filter. One of the fuzzy correlations existing between codons and median that nucleotides incorporated to reduce the signal noise to a larger magnitude. TheINSWFfilter outperformed the existing fixed‐length digital filters tested over 250 benchmarked and random datasets of various species. A notable enhancement of 45% to 130% was achieved by significantly suppressing signal noise as compared with conventional digital filters in DNA sequence analysis.

The development of vehicular Internet of Things (IoT) applications, such as E-Transport, Augmented Reality, and Virtual Reality are growing progressively. The mobility aware services and network-based security are fundamental requirements of these applications. However, multi-side offloading enabling blockchain and cost-efficient scheduling in heterogeneous vehicular fog cloud nodes network become a challenging task. The study formulates this problem as a convex optimization problem, where all constraints are the convex set. The goal of the study is to minimize communication cost and computation cost of applications under mobility, security, deadline, and resource constraints. Initially, we propose a novel vehicular fog cloud network (VFCN) which consists of different components and heterogeneous computing nodes. The ensure mobility privacy, the study devises Mobility Aware Blockchain-Enabled offloading scheme (MABOS). It extends blockchain enable multi-side offloading (e.g., offline offloading and online offloading) with proof of work (PoW), proof of creditability (PoC) and fault-tolerant techniques. The purpose is to offload all tasks under the secure network without any violation. Furthermore, to ensure Quality of Service (QoS) of applications, this work suggests linear search based task scheduling (LSBTS) method, which maps all tasks onto appropriate computing nodes. The experimental results show that devise schemes outperform all existing baseline approaches to the considered problem.

Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

Internet of Things (IoT) is considered one of the world’s ruling technologies. Billions of IoT devices connected together through IoT forming smart cities. As the concept grows, it is very challenging to design an infrastructure that is capable of handling large number of devices and process data effectively in a smart city paradigm. This paper proposed a structure for smart cities. It is implemented using a lightweight easy to implement network design and a simpler data format for information exchange that is suitable for developing countries like Pakistan. Using MQTT as network protocol, different sensor nodes were deployed for collecting data from the environment. Environmental factors like temperature, moisture, humidity, and percentage of CO 2 and methane gas were recorded and transferred to sink node for information sharing over the IoT cloud using an MQTT broker that can be accessed any time using Mosquitto client. The experiment results provide the performance analysis of the proposed network at different QoS levels for the MQTT protocol for IoT‐based smart cities. JSON structure is used to formulate the communication data structure for the proposed system.

The farmers of agricultural farms manage and monitor different types of livestock. The manual inspection and monitoring of livestock are tedious since the cattle do not stay at fixed locations. Fencing many cattle requires a considerable cost and involves farmers’ physical intervention to keep an eye to stop them from crossing beyond the access points. Visual tracking of livestock and fencing is a time-consuming and challenging job. This research proposes a smart solution for livestock tracking and geofencing using state-of-the-art IoT technology. The study creates a geographical safe zone for cattle based on IoT and GPRS, where the cattle are assigned dedicated IoT sensors. The cattle can be easily remotely monitored and controlled without having any need for farmers to intervene for livestock management physically. The smart system collects the data regarding the location, well-being, and health of the livestock. This kind of livestock management may help prevent the spread of COVID-19, lower the farming costs, and enable remote monitoring.