Evaluating the cumulative impact problem in spatial planning: a case study of wind turbines in Aberdeenshire, UK

Abstract

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.

Leveraging the Opportunities of Wind for Cities through Urban Planning and Design: A PRISMA Review

“…In terms of social acceptability, it appears that the public's attitude towards the installation of urban wind turbines deviates from the previous assumption of "not in my backyard (NIMBY)" due to nuisance, and includes other layers of concerns, such as the distrust of technology, businesses, and public institutions [119]. Local public opposition and distrust towards wind turbines may result in significant project deficiencies, such as a reduction in the scope of adoption [120].…”

Improving strategic planning for nature: Panacea or pandora’s box for the built and natural environment?

“…The temporal dimension shifts focus away from short-term timeframe such as election cycles towards longer-term perspectives to tackle issues such as cumulative impacts and lag effects (Scott et al 2014b ).…”