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The Internet of Things (IoT) is growing rapidly in various domains, including smart city applications. In many cases, IoT data in smart city applications have time constraints in which they are relevant and acceptable to the task at hand—a window of validity (WoV). Existing algorithms, such as ex post facto adjustment, data offloading, fog computing, and blockchain applications, generally focus on managing the time-validity of data. In this paper, we consider that the functional components of the IoT devices' decision-making strategies themselves may also be defined in terms of a WoV. We propose an aspect-oriented mechanism to supervise the execution of the IoT device's strategy, manage the WoV constraints, and resolve invalidated functional components through communication in the multi-agent system. The applicability of our proposed approach is considered with respect to the improved cost, service life, and environmental outcomes for IoT devices in a smart cities context. [ABSTRACT FROM AUTHOR]

Cloud computing has become ubiquitous in the enterprise environment as its on-demand model realizes technical and economic benefits for users. Cloud users demand a level of reliability, availability, and quality of service. Improvements to reliability generally come at the cost of additional replication. Existing approaches have focused on the replication of virtual environments as a method of improving the reliability of cloud services. As cloud systems move towards microservices-based architectures, a more granular approach to replication is now possible. In this paper, we propose a cloud orchestration approach that balances the potential cost of failure with the spot market running cost, optimizing the resource usage of the cloud system. We present the results of empirical testing we carried out using a simulator to compare the outcome of our proposed approach to a control algorithm based on a static reliability requirement. Our empirical testing showed an improvement of between 37% and 72% in total cost over the control, depending on the specific characteristics of the cloud models tested. We thus propose that in clouds where the cost of failure can be reasonably approximated, our approach may be used to optimize the cloud redundancy configuration to achieve a lower total cost. [ABSTRACT FROM AUTHOR]

Driven by the ever-growing diversity of software and hardware agents available on the market, Internet-of-Things (IoT) systems, functioning as heterogeneous multi-agent systems (MASs), are increasingly required to provide a level of reliability and fault tolerance. In this paper, we develop an approach to generalized quantifiable modeling of fault-tolerant and reliable MAS. We propose a novel software architectural model, the Intelligence Transfer Model (ITM), by which intelligence can be transferred between agents in a heterogeneous MAS. In the ITM, we propose a novel mechanism, the latent acceptable state, which enables it to achieve improved levels of fault tolerance and reliability in task-based redundancy systems, as used in the ITM, in comparison with existing agent-based redundancy approaches. We demonstrate these improvements through experimental testing of the ITM using an open-source candidate implementation of the model, developed in Python, and through an open-source simulator that tested the behavior of ITM-based MASs at scale. The results of these experiments demonstrated improvements in fault tolerance and reliability across all MAS configurations we tested. Fault tolerance was observed to improve by a factor of between 1.27 and 6.34 in comparison with the control group, depending on the ITM configuration tested. Similarly, reliability was observed to improve by a factor of between 1.00 and 4.73. Our proposed model has broad applicability to various IoT applications and generally in MASs that have fault tolerance or reliability requirements, such as in cloud computing and autonomous vehicles. [ABSTRACT FROM AUTHOR]