The advancements in Industry 4.0 have opened up new ways for the structural deployment of Smart Grids (SGs) to face the endlessly rising challenges of the 21st century. SGs for Industry 4.0 can be better managed by optimized routing techniques. In Mobile Ad hoc Networks (MANETs), the topology is not fixed and can be encountered by interference, mobility of nodes, propagation of multi-paths, and path loss. To extenuate these concerns for SGs, in this paper, we have presented a new version of the standard Optimized Link State Routing (OLSR) protocol for SGs to improve the management of control intervals that enhance the efficiency of the standard OLSR protocol without affecting its reliability. The adapted fault tolerant approach makes the proposed protocol more reliable for industrial applications. The process of grouping of nodes supports managing the total network cost by reducing severe flooding and evaluating an optimized head of clusters. The head of the unit is nominated according to the first defined expectation factor. With a sequence of rigorous performance evaluations under simulation parameters, the simulation results show that the proposed version of OLSR has proliferated Quality of Service (QoS) metrics when it is compared against the state-of-the-art-based conventional protocols, namely, standard OLSR, DSDV, AOMDV and hybrid routing technique.

The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this IoT is being served in many application domains including healthcare, surveillance, manufacturing, industrial automation, smart homes, the military, etc. Medical Internet of Things (MIoT), or the use of IoT in healthcare, is becoming a booming trend towards improving the health and wellbeing of billions of people by offering smooth and seamless medical facilities and by enhancing the services provided by medical practitioners, nurses, pharmaceutical companies, and other related government and non-government organizations. In recent times, this MIoT has gained higher attention for its potential to alleviate the massive burden on global healthcare, which has been caused by the rise of chronic diseases, the aging population, and emergency situations such as the recent COVID-19 global pandemic, where many government and non-government medical resources were challenged, owing to the rising demand for medical resources. It is evident that with this recent growing demand for MIoT, the associated technologies and its interconnected, heterogeneous nature adds new concerns as it becomes accessible to confidential patient data, often without patient or the medical staff consciousness, as the security and privacy of MIoT devices and technologies are often overlooked and undermined by relevant stakeholders. Hence, the growing security breaches that target the MIoT in healthcare are making the security and privacy of Medical IoT a crucial topic that is worth scrutinizing. In this study, we examined the current state of security and privacy of the MIoT, which has become of utmost concern among many security experts and researchers due to its rapid demand in recent times. Nevertheless, pertaining to the current state of security and privacy, we also examine and discuss a number of attack use cases, countermeasures and solutions, recent challenges, and anticipated future directions where further attention is required through this study.

We first introduce the concept of b-metric-like space which generalizes the notions of partial metric space, metric-like space and b-metric space. Then we establish the existence and uniqueness of fixed points in a b-metric-like space as well as in a partially ordered b-metric-like space. As an application, we derive some new fixed point and coupled fixed point results in partial metric spaces, metric-like spaces and b-metric spaces. Moreover, some examples and an application to integral equations are provided to illustrate the usability of the obtained results. MSC:47H10, 54H25, 55M20.

As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.