Design and implementation of a broker for cloud additive manufacturing services

The growing number of cloud Additive Manufacturing (AM) services, offered by different providers over the Internet, makes it challenging for consumers to compare these cloud AM services to select a service of their choice. In addition, it is even more challenging for consumers to compare these cloud AM services against their personal preferences. This is because, consumers personal preferences on multiple service attributes such as price, material, accuracy, and schedule, should be considered for cloud AM service selection. The decentralized nature of these cloud AM services coupled by the need to consider consumers personal preferences during cloud AM service selection, requires a system that will serve as a broker between cloud AM services and consumers. But, existing frameworks of cloud manufacturing either do not have brokers between cloud manufacturing service providers and consumers or do not support personalized preference and tradeoff based brokerage. To address these issues, we propose a cloud additive manufacturing framework which consists of a service broker system for cloud AM services that provides consumers with a single point of access to a large number of cloud AM services from many additive manufacturing service providers. This broker system also incorporates the first real application of service selection with fuzzy logic based personalized preferences and tradeoff. We also develop a method to generate fuzzy membership functions for each service attribute. This makes it easy for consumers to specify their fuzzy membership functions. We present an application case study to demonstrate the feasibility of brokerage in cloud AM services and finally evaluate our method in terms of performance --Abstract, page iii

Real-Time Control Over Wireless Networks

Industrial internet of Things (IIoT) are gaining popularity for use in large-scale applications such as oil-field management (e.g., 74×8km2 East Texas Oil-field), smart farming, smart manufac- turing, smart grid, and data center power management. These applications require the wireless stack to provide a scalable, reliable, low-power and low-latency communication. To realize a predictable and reliable communication in a highly unreliable wireless environment, industrial wireless standards use a centralized wireless stack design. In a centralized wireless stack design, a central manager generates routes and a communication schedule for a multi-channel time divi- sion multiple access communication (TDMA) based medium access control (MAC). However, a centralized wireless stack design is highly energy consuming, not scalable, and does not support frequent changes to networks or workloads. To address these challenges, the following contribu- tions are made in this dissertation: (1) A scalable and distributed routing algorithm for industrial IoT which generates graph routes, which offer a high degree of redundancy, (2) A local and online scheduling algorithm that is scalable, energy-efficient, and supports network/workload dynamics while ensuring reliability and real-time performance, (3) An approach to minimize latency for in-band integration of multiple low-power networks, (4) A fast and efficient test of schedulability that determines if an application meets the real-time performance requirement for given net- work topology, and (5) A distributed scheduling and control co-design that balances the control performance requirement and real-time performance for industrial IoT

A Sensor Cloud Test-Bed for Multi-Model and Multi-User Sensor Applications

Wireless Sensor Networks (WSNs) are popular for their usage in various application like environmental monitoring because of their small size and ease of deployment. However, there is a considerable cost of owning and maintaining involved which might impede small to medium scale industries from availing their services. Even for large-scale industries, WSNs deployed and maintained with so much financial investment, when remains under utilized affects their profitability. This challenge with WSNs needs to be addressed in such a manner that small and medium scale industries could use a hassle free on-demand provisioned and de-provisioned service by paying usage fee only. On the other hand, this should result in a better utilization of the installed capacity of WSNs, pruning down their under-utilization to the barest minimum. In order to address this challenge such that everybody benefits by using the services offered by WSNs and as well as building new services, we have proposed Sensor Cloud infrastructure. The proposed Sensor Cloud infrastructure is a cloud of heterogeneous WSNs, where owners of WSNs collaborate to offer sensing-as-a-service. In this paper, we discuss the implementation of our proposed Sensor Cloud infrastructure by building middleware, client centric-layer which creates virtual sensors on top of physical WSNs, and present the results of its usage and operation