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The main aim of this paper is to identify the most important issues and problems that we faced in the UAE in the case of coronavirus pandemic and try to come up with solutions that will enhance the overall response of organizations and individuals to this crisis. Moreover, we want to investigate the importance of knowledge management in spreading information regarding COVID-19 and how knowledge management has been used to manage this pandemic as it continues to manifest itself in several areas and different capacities. In addition, this paper will highlight the significance of effective management of knowledge and information with regards to COVID-19, and the extent to which the knowledge that has been shared widely through different platforms can help in resolving the pandemic. These findings will also enable identification of the efficacy of knowledge management and its achievements in dealing with previous pandemics in a manner that gauges the progressive stages of how it was implemented. The quantitative and qualitative data that was gathered through a survey helped us to identify the problems and uncover hidden issues faced by individuals and organizations. The recommended solution we thought the best in terms of cost and efficiency is deploying crisis management in a professional way using knowledge management tools in organizations in public society. We explained the full process of the solution in this paper and the plan of execution and implementation.

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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.