Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our "true love" and the "significant other". While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is "distributed" because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

https://www.mdpi.com/1424-8220/20/20/5796/pdf

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments.

Load balancing (LB) is nothing but the systematic distribution of load over different servers. The fog server is handling the maximum data of the cloud server to enhance the advancement of users' requests. The growth in data requests is escalating, and fog computing has intensified the accessibility of the data. Fog computing achieves many challenges according to the demands of the users, but even so, some challenges require more progress. The problem faced by fog computing is LB due to an increase in traffic on the network layer. Various LB techniques have already been proposed in the cloud layer, but until now they have only been in progress in the fog layer. Inefficient LB may cause a decrease in service quality, like delays in response time, processing time, security, and many more. In this survey, several algorithms have been discussed that are based on LB, which works out the issue of overloaded data on the network. Some parameters that authors have focused in LB are latency, bandwidth, deadlines, cost, security, execution time, and response time. Other parameters based on fault tolerance are also discussed with their quality parameter table and algorithm. In addition to this some of the limitations of the author's work, that is discussed in this article.

Load balancing techniques for fog computing environment: Comparison, taxonomy, open issues, and challenges

Gas sensor devices usage have found prominence in fields like artificial olfactory systems and is often used to keep in check pollution and other toxic gas hazards. Graphene mono-layered sensing material along with Gold nano-particles have been previously used to formulate a sensor device. In recent times, internet of things (IoT) capable gas sensing device is a subject of great interest. Previously, it has been implemented through various wired and wireless communications like Zigbee Protocol, IBM Bluemix and sensor nodes but they have their own limitations. In this work nano gas sensor device used for detecting perilous and toxic gas has been fabricated and enhanced to increase its selectivity and sensitivity. Fabrication of Au based TiO2, CuO, ZnO, and WO3 resistive type gas sensor array has been done using sol gel method and analyzed to ensure better conduction. Application of the proposed sensing device can prevent accident in mines by precise and prompt checking of hazardous gases present inside. An IoT frame work has been proposed in this study where Node MCU (ESP8266) is used as a WiFi module along with Message Queuing Telemetry Transport protocol, which resulted in better speed and efficiency. Increased sensitivity and selectivity of the sensor nodes with better efficiency and speed of communication ensures a productive mine hazard management.

Performance analysis of gas sensing device and corresponding IoT framework in mines

One of the basic parts of life is Healthcare that the provision several services like diagnosing, treatment and preventing mental trouble, diseases, illnesses, injuries. Applications of Healthcare are the most crucial tasks since they infect the life of a patient directly. One of the key technologies is Computing of Fog which is able to be performed in applications of healthcare. Fog computing bas...

Survey on Load balancing in fog computing in smart healthcare system

The significance of approaches for improvement of systems/products has been increased. In the article, a modular technology challenge system is proposed as a basis for the system improvement process. The combinatorial framework for designing a modular technology challenge system is described: (1) collection of information items (literature sources on technology challenges/key technologies); (2) designing a hierarchy over the set of information items; (3) selection of the sub-hierarchy while taking into account the specified topic(s); (4) composition of a required information item configuration as technology challenge system. The composition stage is based on morphological design. An applied realistic numerical example illustrates the usage of the proposed framework to design a 5G technology challenge system as a set of the selected related optimization actions.

https://link.springer.com/content/pdf/10.1007/s42979-020-00235-7.pdf

Composite Technology Challenge System for Optimization in 5G Communications

The modern society, with the advances in wireless sensor network (WSN) technology, are connected in ways more than one. Since the evolution of evolutionary computing and the Internet of Everything (IoE), the term connected has faced a more significant meaning per se. But as the IoE grows, it becomes more complicated and tackling its complications in various aspects of its architecture becomes all the more paramount. This paper aims to resolve some of the issues so faced by the IoE paradigm, with the help of meta-heuristics and to incorporate proper swarm intelligence based routing algorithms to optimize connection issues such as real time delay, network congestion. Fog Computing is used to distribute the workload and to optimize the utilization of bandwidth, which maintains a clean and efficient channel of communication between the IoE clusters and the primary cloud storage. In this approach a new algorithm based on Directed Artificial Bat Algorithm (DABA) is deployed and Particle Swarm Optimization (PSO) meta-heuristics is used to optimize the capabilities of the IoE cluster and maintain its density. The Fog servers, so implemented, grapple with the increased mobility and network usage using the Dynamic Graph Partitioning algorithm.

Optimization Using Swarm Intelligence and Dynamic Graph Partitioning in IoE Infrastructure: Fog Computing and Cloud Computing

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Optimization Using Swarm Intelligence and Dynamic Graph Partitioning in IoE Infrastructure: Fog Computing and Cloud Computing