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"Embedded systems"
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Smart grid cyber-physical systems: communication technologies, standards and challenges
The recent developments in embedded system design and communication technologies popularized the adaption of the cyber-physical system (CPS) for practical applications. A CPS is an amalgamation of a physical system, a cyber system, and their communication network. The cyber system performs extensive computational operations on the data received from the physical devices, interprets the data, and initiates effective control actions in real-time. One such CPS is the smart grid CPS (SG-CPS) consisting of physical devices with diverse communication requirements, and intermediate communication networks. Thus, reliable communication networks are paramount for the effective operation of the SG-CPS. This paper is an elaborate survey on the communication networks from the perspective of the SG-CPS. This paper presents the state-of-art communication technologies that can meet the communication requirements of the various SG-CPS applications. The communications standards and communication protocols are also comprehensively discussed. A systematic mapping among communication technologies, standards, and protocols for various SG-CPS applications has been presented based on an extensive literature survey in this paper. Furthermore, several challenges, such as security, safety, reliability and resilience, etc., have been addressed from SG-CPS’s perspective. This work also identifies the research gaps in the various domains of the SG-CPS that can be of immense benefit to the research community.
Journal Article
Smart Memory Management (SaMM) For Embedded Systems without MMU
In the wake of extensible usage of IOT (Internet of Things) enabled Embedded Systems, it is of great importance to find ways of using Memory in the most efficient way. Embedded Systems are also space constrained, hence at various places, it may not be possible to deploy a Hardware based-(MMU) Memory Management Units. In MMU less Embedded systems, there are various principles of DMA being applied; however, they are all constrained in one way or the other. With this backdrop, this paper is exploring opportunities to achieve higher performance and efficiency in Memory Management by using Smart and Programmable methods. Proposed methods have achieved an overall improvement in Allocation speeds to the tune of 3-4 times, however there is a marginal drop in Deallocation speed. Overall a better bargain and another very good outcome is 0 defragmented memory. This paper also presents algorithms for Allocation, Deallocation and Defragmentation processes, which can be implemented within available Application Software.
Journal Article
An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem
Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.
Journal Article
LP-OPTIMA: A Framework for Prescriptive Maintenance and Optimization of IoT Resources for Low-Power Embedded Systems
Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.
Journal Article
Implementation of Artificial Intelligence Algorithm In Embedded System
The development of embedded technology directly affects the development of Internet of Things, and the development of artificial intelligence brings great convenience to people's life. Based on this, the implementation method of artificial intelligence algorithm in embedded system is studied. Based on embedded system hardware configuration and the embedded system software running algorithm is optimized, simplifying the operation steps of the embedded system, improve the effect of embedded system, to strengthen the research of embedded technology, and increase investment in manpower and material resources in embedded system research, learning western advanced technology, and the embedded technology is applied in the Internet of things, to improve the effect of artificial intelligence algorithm in the application of the embedded system. The experimental results show that the artificial intelligence algorithm is applied to the embedded system to improve the system performance effectively.
Journal Article