Abstract: Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents…using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.
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Keywords: Cloud storage, data privacy, ranked retrieval, index tree, authorized retrieval
Abstract: Edge computing offers potential benefits to applications working in IoT (Internet of Things) and CPS (Cyber Physical Systems) environments by bringing the power of computing proximate to the devices, which demand high computational resources. As computational capabilities are currently untapped in edge devices like the IoT gateway, the computational intensive part of an application like a thread, a module or a task can be offloaded to the edge devices rather than to the cloud by the end devices. In this paper, an approach that employs Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is used to determine the near optimal…solution for scheduling offloadable components in an application, with the intent of significantly reducing the execution time of an application and energy consumption of the smart devices. With a new inertial weight equation, an Adaptive Genetic Algorithm – Particle Swarm Optimization (AGA-PSO) algorithm is proposed which uses GA’s ability in exploration and PSO’s ability in exploitation to make the offloading optimized without violating the deadline constraint of an application.
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Keywords: Internet of things (IoT), edge computing, computation offloading, application partitioning, particle swarmoptimization, genetic algorithm
Abstract: Generally, several IoT (Internet of Things) applications employ cloud data centre for processing the data generated by edge devices like smartphones and tablets. Due to the increasing use of the IoT devices, the demand for higher computational and communication capabilities are also increasing. With the advent of Edge Computing and given the fact that computational capabilities are currently untapped, a part of the computational load can be offloaded to the edge nodes. In this paper, a Grefenstette bias based Genetic Algorithm for MultiSite Offloading (GGA-MSO) is proposed. This algorithm decides the schedule of the application that could be offloaded. The proposed…algorithm provides a solution which has convergence in lesser time by employing diversification of initial population using the Grefenstette’s Bias method. Besides, the container based lightweight virtualization is analyzed for offloading code and data to the nearby devices. The evaluation of the proposed work on random graphs shows that the proposed method starts to converge with significantly lesser iterations than its counterpart with undiversified population. The test bed results on Single Board Computers (SBC) like Raspberry Pi setup indicates that by adapting container virtualization in the edge environment, the performance of the IoT devices is improved and the communication overhead is reduced.
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Keywords: Internet of things (IoT), edge computing, computation offloading, application partitioning, Docker container, raspberry Pi
Abstract: Internet of Things (IoT) devices are major stakeholders of contemporary network bandwidth. The proliferation of IoT devices and the demand for latency-free communication in time-critical applications has proven the drawback of cloud-based solutions. Edge computing is an paradigm that reduces the application’s response time by utilizing computation and storage proximate to each devices. Privacy in cloud computing is attained by system virtualization, containerization, among other evolved technologies. As privacy remains a primary concern, there is a need to test the feasibility of resource-constrained edge devices. Hence, this work aimed to examine the usability of such devices in edge computing by…benchmarking on different runtime environments. The results reveal that a standard mechanism was achieved for defining the criteria to identify the suitable edge devices for computation offloading, particularly for a set of smart traffic surveillance use cases. Further, an optimization algorithm was designed to generate an optimum schedule that decides the best device to execute a particular task from the set of suitable edge devices to enhance energy and execution time in a global view. Based on the feasibility study and optimal schedule, a makespan that is nearly 11 times better than local execution for the considered traffic surveillance workflow was achieved.
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