The purpose of this paper is to review the existing literature to draw an understanding of the relationship between indoor environmental quality and occupant productivity in an office environment. The study reviews over 300 papers from 67 journals, conference articles and books focusing on indoor environment, occupant comfort, productivity and green buildings. It limits its focus to the physical aspects of an office environment. The literature outlines eight Indoor Environmental Quality (IEQ) factors that influence occupant productivity in an office environment. It also discusses different physical parameters under each of the IEQ factors. It proposes a conceptual model of different factors affecting occupant productivity. The study also presents a review of the data collection methods utilised by the research studies that aim to investigate the relationship between IEQ and occupant productivity. The study presents a comprehensive discussion and analysis of different IEQ factors that affect occupant productivity. The paper provides a concise starting point for future researchers interested in the area of indoor environmental quality. Keywords: occupant productivity, workplace satisfaction, indoor environment quality, occupant comfortPaper type: Literature review IntroductionHumans spend most of their time indoors, and the majority of the world's population lives in urban areas and work in an office environment (ASHRAE, 1993). There has been a significant global shift in the economy from the manufacturing sector towards the service and knowledge-based sectors which operate in indoor office environments (Haynes, 2008b, World Green Building Council, 2014. Hence, it is becoming important to understand the indoor office environment and the effect it has on occupant productivity. An office environment has a high level of influence on its occupants' productivity (Leaman and Bordass, 1999, Frontczak et al., 2012, Roelofsen, 2002, Mawson, 2002, Van der Voordt, 2004. Past studies on sustainable buildings postulate that green design strategies and technologies enhance the indoor workplace environment. Such strategies/technologies enable the creation of an environment which favours occupants' comfort and performance in both newly built and retrofitted buildings (Romm and Browning, 1994). The majority of the building stock that will exist in 2050 has already been built (2009). Thus, there is a need to investigate the quality of the indoor workplace environment and its relationship to occupant productivity. This paper establishes the state-of-the-art on environmental factors that influence occupant productivity in the office environment. It also highlights and discusses various occupant productivity measurement methods used in indoor environment research studies. This research will help construction industry professionals improve the designs that allow better operation of office buildings along with improving the productivity of the occupants. BackgroundThe research on the direct and indirect effects of indoor environ...

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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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