Survival Study on Blockchain Based 6G-Enabled Mobile Edge Computation for IoT Automation

Internet of Things (IoT) and Mobile Edge Computing (MEC) technology acts as a significant part of daily lives to facilitate control and monitoring of objects to revolutionize the ways that human interacts with physical world. IoT system includes large volume of data with network connectivity, power, and storage resources to transform data into meaningful information. Blockchain has decentralized nature to provide useful mechanism for addressing IoT challenges. Blockchain is distributed ledger with fundamental attributes, namely recorded, transparent, and decentralized. Blockchain formed participants in distributed ledger to record the transactions and communicate with other through trustless method. Security is considered as the most valuable features of Blockchain. IoT and Blockchain are emerging ideas for creating the applications to share the intrinsic features. Several existing works has been developed for the integration of blockchain with IoT. But, Blockchain protocols in the state-of-the-art works with IoT failed to consider the computational loads, delays, and bandwidth overhead which lead to new set of problems. The review estimates main challenges in integration of Blockchain and IoT technologies to attain high-level solutions by addressing the shortcomings and limitations of IoT and Blockchain technologies

Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach

© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments

Optimization of routing-based clustering approaches in wireless sensor network: Review and open research issues

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In today’s sensor network research, numerous technologies are used for the enhancement of earlier studies that focused on cost-effectiveness in addition to time-saving and novel approaches. This survey presents complete details about those earlier models and their research gaps. In general, clustering is focused on managing the energy factors in wireless sensor networks (WSNs). In this study, we primarily concentrated on multihop routing in a clustering environment. Our study was classified according to cluster-related parameters and properties and is subdivided into three approach categories: (1) parameter-based, (2) optimization-based, and (3) methodology-based. In the entire category, several techniques were identified, and the concept, parameters, advantages, and disadvantages are elaborated. Based on this attempt, we provide useful information to the audience to be used while they investigate their research ideas and to develop a novel model in order to overcome the drawbacks that are present in the WSN-based clustering models

Linear Weighted Regression and Energy-Aware Greedy Scheduling for Heterogeneous Big Data

Data are presently being produced at an increased speed in different formats, which complicates the design, processing, and evaluation of the data. The MapReduce algorithm is a distributed file system that is used for big data parallel processing. Current implementations of MapReduce assist in data locality along with robustness. In this study, a linear weighted regression and energy-aware greedy scheduling (LWR-EGS) method were combined to handle big data. The LWR-EGS method initially selects tasks for an assignment and then selects the best available machine to identify an optimal solution. With this objective, first, the problem was modeled as an integer linear weighted regression program to choose tasks for the assignment. Then, the best available machines were selected to find the optimal solution. In this manner, the optimization of resources is said to have taken place. Then, an energy efficiency-aware greedy scheduling algorithm was presented to select a position for each task to minimize the total energy consumption of the MapReduce job for big data applications in heterogeneous environments without a significant performance loss. To evaluate the performance, the LWR-EGS method was compared with two related approaches via MapReduce. The experimental results showed that the LWR-EGS method effectively reduced the total energy consumption without producing large scheduling overheads. Moreover, the method also reduced the execution time when compared to state-of-the-art methods. The LWR-EGS method reduced the energy consumption, average processing time, and scheduling overhead by 16%, 20%, and 22%, respectively, compared to existing methods

Measurements, Models, Systems and Design

531 s.

Improving power and resource management in heterogeneous downlink OFDMA networks

© 2020 by the authors. In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques

Quiescience as a mechanism for cyclical hypoxia and acidosis

Tumour tissue characteristically experiences fluctuations in substrate supply. This unstable microenvironment drives constitutive metabolic changes within cellular populations and, ultimately, leads to a more aggressive phenotype. Previously, variations in substrate levels were assumed to occur through oscillations in the hæmodynamics of nearby and distant blood vessels. In this paper we examine an alternative hypothesis, that cycles of metabolite concentrations are also driven by cycles of cellular quiescence and proliferation. Using a mathematical modelling approach, we show that the interdependence between cell cycle and the microenvironment will induce typical cycles with the period of order hours in tumour acidity and oxygenation. As a corollary, this means that the standard assumption of metabolites entering diffusive equilibrium around the tumour is not valid; instead temporal dynamics must be considered

Detection and isolation of black hole attack in mobile ad hoc networks: A review

© 2020 SPIE. Mobile Ad hoc Network or MANET is a wireless network that allows communication between the nodes that are in range of each other and are self-configuring. The distributed administration and dynamic nature of MANET makes it vulnerable to many kind of security attacks. One such attack is Black hole attack which is a well known security threat. A node drops all packets which it should forward, by claiming that it has the shortest path to the destination. Intrusion Detection system identifies the unauthorized users in the system. An IDS collects and analyses audit data to detect unauthorized users of computer systems. This paper aims in identifying Black-Hole attack against AODV with Intrusion Detection System, to analyze the attack and find its countermeasure