Sensors connected to the cloud services equipped with data analytics has created a plethora of new type of applications ranging from personal to an industrial level forming to what is known today asInternet of Things (IoT). IoT-based system follows a pattern of data collection, data analytics, automation, and system improvement recommendations. However, most applications would have its own unique requirements in terms of the type of the smart devices, communication technologies as well as its application provisioning service. In order to enable an IoT-based system, various services are commercially available that provide services such as backend-as-a-service (BaaS) and software-as-a-service (SaaS) hosted in the cloud. This, in turn, raises the issues of security and privacy. However there is no plug-and-play IoT middleware framework that could be deployed out of the box for on-premise server. This paper aims at providing a lightweight IoT middleware that can be used to enable IoT applications owned by the individuals or organizations that effectively securing the data on-premise or in remote server. Specifically, the middleware with a standardized application programming interface (API) that could adapt to the application requirements through high level abstraction and interacts with the application service provider is proposed. Each API endpoint would be secured using Access Control List (ACL) and easily integratable with any other modules to ensure the scalability of the system as well as easing system deployment. In addition, this middleware could be deployed in a distributed manner and coordinate among themselves to fulfil the application requirements. A middleware is presented in this paper with GET and POST requests that are lightweight in size with a footprint of less than 1 KB and a round trip time of less than 1 second to facilitate rapid application development by individuals or organizations for securing IoT resources.

Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

Malaysia is a country which has one of the biggest Muslim societies in the world, followed by a large number of mosques scattered in various places and these mosques have maintenance problems especially in energy consumption. Mosques normally experience sudden influx of users at five specified times throughout the day, and the use of the fans in the mosques is very inefficient and wasteful, corresponding with the five daily Islamic prayers. Regarding this matter, this project is conducted in order to reduce mosque energy consumption by developing smart mosque temperature control. The temperature control aims to focus on reading the temperature of mosque and then smartly controls fan according to the temperature reading by applying Internet-of-Things (IoT). The Arduino Uno controller board will read and process the sensors to trigger the fan switch. The act of controlling the fan according to the ambient temperature of the mosques will help to reduce power consumption. The smart mosque temperature control can be applied widely in Malaysia to make mosques smarter and efficient in energy consumption.

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The river plays an important role in the source of water in Malaysia as it is a part of the water cycle while acting as a drainage channel for surface water. One of the major threats to its sustainability is water pollution. The conventional methods for monitoring the water quality in the river are manual and continuous monitoring that will be costly and less efficient. Hence, this paper proposes the Internet of Things (IoT) as a promising technique that can provide real-time monitoring and enhances the efficiency of data collection. To make the data secure and trustworthy, blockchain technology will be used as a platform for all the data transaction in real time. The proposed system will provide a better solution to monitor the quality of the river and for the user to interact, retrieve and analyse real and historical data.