Optimizing Blockchain Networks with Artificial Intelligence: Towards Efficient and Reliable IoT Applications

2020 ◽  
pp. 299-321 ◽  
Author(s):  
Furqan Jameel ◽  
Uzair Javaid ◽  
Biplab Sikdar ◽  
Imran Khan ◽  
George Mastorakis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Iot Applications

scholarly journals Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2047 ◽  
Author(s):  
Yung-Yao Chen ◽  
Yu-Hsiu Lin ◽  
Chia-Ching Kung ◽  
Ming-Han Chung ◽  
I-Hsuan Yen
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Modern Society ◽  
Smart Homes ◽  
Demand Side Management ◽  
Demand Side ◽  
Smart Meters ◽  
Smart Power ◽  
Iot Applications ◽  
User Centric

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

scholarly journals Guest Editorial Special Issue on Artificial Intelligence-Based Sensors for Next Generation IoT Applications

2021 ◽  
Vol 21 (22) ◽  
pp. 24919-24919
Author(s):  
Sumarga Kumar Sah Tyagi ◽  
Wei Wei ◽  
Vincenzo Piuri ◽  
Subhas Chandra Mukhopadhyay ◽  
Aaron Striegel ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Guest Editorial ◽  
Next Generation ◽  
Special Issue ◽  
Iot Applications ◽  
Editorial Special Issue

scholarly journals DESIGN AND DEVELOPMENT AI-ENABLED EDGE COMPUTING FOR INTELLIGENT-IOT APPLICATIONS

2019 ◽  
Vol 2019 (02) ◽  
pp. 84-94 ◽  
Author(s):  
Dr. Sivaganesan D.
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Edge Computing ◽  
The Internet ◽  
Service Provisioning ◽  
Iot Applications ◽  
The People ◽  
Smart Farming ◽  
The Internet Of Things ◽  
Day By Day

The advancements in the technologies and the increase in the digital miniaturization day by day are causing devices to become smarter and smarter and the emergence of the internet of things and the cloud has made things even better with insightful suggestions for organization as well as the way the people work and lead their life. The limitations in the cloud paradigm in terms of processing complexity, the latency in the service provisioning and improper resource scheduling, remains as a reason leading to shifting of applications from cloud to edge. More over the emergence of the artificial intelligence in the edge computing has turned out to be center of attention as it improves the speed and the range of the IOT applications. The paper also puts forth the design of the AI-enabled Edge computing for developing a Smart Farming.

scholarly journals An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 833 ◽  
Author(s):  
Ingook Jang ◽  
Donghun Lee ◽  
Jinchul Choi ◽  
Youngsung Son
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Power Consumption ◽  
Response Time ◽  
Knowledge Sharing ◽  
Smart Home ◽  
Artificial Intelligence Techniques ◽  
Iot Applications ◽  
Iot Devices

The traditional Internet of Things (IoT) paradigm has evolved towards intelligent IoT applications which exploit knowledge produced by IoT devices using artificial intelligence techniques. Knowledge sharing between IoT devices is a challenging issue in this trend. In this paper, we propose a Knowledge of Things (KoT) framework which enables sharing self-taught knowledge between IoT devices which require similar or identical knowledge without help from the cloud. The proposed KoT framework allows an IoT device to effectively produce, cumulate, and share its self-taught knowledge with other devices at the edge in the vicinity. This framework can alleviate behavioral repetition in users and computational redundancy in systems in intelligent IoT applications. To demonstrate the feasibility of the proposed concept, we examine a smart home case study and build a prototype of the KoT framework-based smart home system. Experimental results show that the proposed KoT framework reduces the response time to use intelligent IoT devices from a user’s perspective and the power consumption for compuation from a system’s perspective.

Modified Backward Chaining Algorithm Using Artificial Intelligence Planning IoT Applications

2019 ◽  
pp. 153-169
Author(s):  
Pradheep Kumar K. ◽  
Srinivasan N.
Keyword(s):  
Artificial Intelligence ◽  
Resource Allocation ◽  
Partial Order ◽  
Automated Planning ◽  
Optimal Plan ◽  
Backward Chaining ◽  
Iot Applications ◽  
Artificial Intelligence Planning ◽  
Planning Algorithm ◽  
Iot Devices

In this chapter, an automated planning algorithm has been proposed for IoT-based applications. A plan is a sequence of activities that leads to a goal or sub-goals. The sequence of sub-goals leads to a particular goal. The plans can be formulated using forward chaining where actions lead to goals or by backward chaining where goals lead to actions. Another method of planning is called partial order planning where all actions and sub-goals are not illustrated in the plan and left incomplete. When many IoT devices are interconnected, based on the tasks and activities involved resource allocation has to be optimized. An optimal plan is one where the total plan length is minimum, and all actions consume similar quantum of resources to achieve a goal. The scheduling cost incurred by way of resource allocation would be minimum. Compared to the existing algorithms L2-Plan (Learn to Plan) and API, the algorithm developed in this work improves optimality of resources by 14% and 36%, respectively.

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