Internet of Things (IoT) has become an integral part of a smart community that connects computing devices, smart objects, and mechanical and digital machines, having unique identifiers, to communicate with each, without human intervention. This smart connectivity within a building assists the users in real time to provide an enormous range of connected applications and facilitate the optimized use of multiple resources. These smart building applications can make our lives more comfortable and provide a more sustainable, healthy, and safe workplace. A key challenge for IoT toward smart buildings is to ensure that the service has been taken from a trustworthy service provider. It requires a reliable, robust, and adaptive context-oriented trust mechanism that conforms to the specific requirements of the end users. To enhance the security of smart buildings in IoT, this research proposes an adaptive context-based trust evaluation system for smart building (CTES-SB) applications. The trust score for service is calculated based on the client's previous interaction and recommendation from context-similar clients. Using CTES-SB, the client selects the best service provider based on the previous and current trust scores for the next interaction. The model also helps to filter out malicious nodes through an indirect trust calculation process. This process dynamically assigns weights based on direct interactions and trustworthy recommendations for detecting and avoiding malicious interactions. We have demonstrated the effectiveness of CTES-SB by simulating multiple smart building scenarios under malicious attacks. The proposed architecture of CTES-SB has been experimentally evaluated to benchmark its performance for best service selection and resiliency against malicious nodes. The CTES-SB is proved to be efficient by having a comparison with the state-of-the-art algorithms. The comparison is in terms of filtering the malicious nodes from the network and the result shows that the trust converges quickly toward the ground-truth value.

As clouds increase in size and as machines of different types are added to the infrastructure in order to\nmaximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting\ndifferent architectures poses significant challenges. To efficiently access heterogeneous resources and, at the\nsame time, to exploit these resources to reduce application development effort, to make optimisations easier\nand to simplify service deployment, requires a re-evaluation of our approach to service delivery. We\npropose a novel cloud management and delivery architecture based on the principles of self-organisation\nand self-management that shifts the deployment and optimisation effort from the consumer to the software\nstack running on the cloud infrastructure. Our goal is to address inefficient use of resources and\nconsequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption\nand improved service delivery, with hyperscale systems particularly in mind. The framework is general but\nalso endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service\nprovision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing\nare three downstream use cases that will benefit from the proposed architecture.

This article presents a novel compiler framework for CUDA code generation. The compiler structure is designed to support autotuning , which employs empirical techniques to evaluate a set of alternative mappings of computation kernels and select the mapping that obtains the best performance. This article introduces a Transformation Strategy Generator , a meta-optimizer that generates a set of transformation recipes , which are descriptions of the mapping of the sequential code to parallel CUDA code. These recipes comprise a search space of possible implementations. This system achieves performance comparable and sometimes better than manually tuned libraries and exceeds the performance of a state-of-the-art GPU compiler.