This paper describes an analysis of Product Data and Information Technologies (PDIT) which are available to support processes in Large Scale Engineering (LSE), particularly those which are construction related. Three main areas are addressed: supporting environment; systems and technologies; and application software. On‐going and future developments in these areas are considered. The findings from each of the PDIT areas examined are presented, together with their potential opportunities for exploitation within LSE in construction. The perceived barriers to the adoption of such technologies are also addressed. Considerations are given to the most significant emerging technologies within the IT industry and the potential impact these may have on the business needs within LSE. The work was undertaken within the User Reference Project ESPRIT 20876—eLSEwise—European Large Scale Engineering Wide Integration Support Effort.
This paper describes an analysis of Product Data and Information Technologies (PDIT) which are available to support processes in Large Scale Engineering (LSE), particularly those which are construction related. Three main areas are addressed: supporting environment; systems and technologies; and application software. On‐going and future developments in these areas are considered. The findings from each of the PDIT areas examined are presented, together with their potential opportunities for exploitation within LSE in construction. The perceived barriers to the adoption of such technologies are also addressed. Considerations are given to the most significant emerging technologies within the IT industry and the potential impact these may have on the business needs within LSE. The work was undertaken within the User Reference Project ESPRIT 20876—eLSEwise—European Large Scale Engineering Wide Integration Support Effort.
Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.
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