A Reinforcement Learning Quality of Service Negotiation Framework For IoT Middleware

The Internet of Things (IoT) ecosystem is characterised by heterogeneous devices dynamically interacting with each other to perform a specific task, often without human intervention. This interaction typically occurs in a service-oriented manner and is facilitated by an IoT middleware. The service provision paradigm enables the functionalities of IoT devices to be provided as IoT services to perform actuation tasks in critical-safety systems such as autonomous, connected vehicle system and industrial control systems. As IoT systems are increasingly deployed into an environment characterised by continuous changes and uncertainties, there have been growing concerns on how to resolve the Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences to guarantee the execution of mission-critical actuation tasks. With IoT devices with different QoS constraints as IoT service providers spontaneously interacts with IoT service consumers with varied QoS requirements, it becomes essential to find the best way to establish and manage the QoS agreement in the middleware as a compromise in the QoS could lead to negative consequences. This thesis presents a QoS negotiation framework, IoTQoSystem, for IoT service-oriented middleware. The QoS framework is underpinned by a negotiation process that is modelled as a Markov Decision Process (MDP). A model-based Reinforcement Learning negotiation strategy is proposed for generating an acceptable QoS solution in a dynamic, multilateral and multi-parameter scenarios. A microservice-oriented negotiation architecture is developed that combines negotiation, monitoring and forecasting to provide a self-managing mechanism for ensuring the successful execution of actuation tasks in an IoT environment. Using a case study, the developed QoS negotiation framework was evaluated using real-world data sets with different negotiation scenarios to illustrate its scalability, reliability and performance

Developing IoT Applications:Challenges and Frameworks

Internet of things (IoT) is creating new opportunities for developing innovative applications by leveraging on existing and new technologies. In recent years, a variety of consumer and industrial IoT applications have been developed and deployed. Despite much progress, developing IoT applications is still a complex, time-consuming and challenging activity. This is because IoT systems involve a wide range of hardware and software components, depending on a variety of communication and distributed system technologies. Many IoT application frameworks of varying approaches have been developed to manage the complexities of developing IoT applications. However, there remains a paucity of surveys on these IoT application development frameworks. This study presents a comprehensive review and a comparative analysis of existing IoT application development frameworks and toolkits, illustrating their strengths and weaknesses. This study will assist in finding the most appropriate IoT application development paradigm for the desired IoT application. Finally, future research directions are highlighted to improve existing and future frameworks and toolkits for IoT applications

Carotenoids in Cassava (<em>Manihot esculenta</em> Crantz)

Cassava is produced globally and consumed as an important staple in Africa for its calories, but the crop is deficient in micronutrients such as vitamin A. Pro-vitamin A carotenoids including β-carotene are precursors of vitamin A in the human body. Carotenoids are generally associated with colors of fruits and vegetables. Although most cassava varieties have white tuberous roots and generally accepted, naturally; some cassava roots are colored yellow and contain negligible amounts of vitamin A. Several genes have been identified in the carotenoids biosynthesis pathway of plants, but studies show that Phytoene synthase 2 (PSY2), lycopene epsilon cyclase, and β-carotene hydroxylase genes have higher expression levels in yellow cassava roots. So far, the PSY2 gene has been identified as the key gene associated with carotenoids in cassava. Some initiatives are implementing conventional breeding to increase pro-vitamin A carotenoids in cassava roots, and much success has been achieved in this regard. This chapter highlights various prediction tools employed for carotenoid content in fresh cassava roots, including molecular marker-assisted strategies developed to fast-track the conventional breeding for increased carotenoids in cassava