Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Flexible Deep Learning in Edge Computing for Internet of Things

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT

Mission Control Center (MCC) System Specification for the Shuttle Orbital Flight Test (OFT) Timeframe

System specifications to be used by the mission control center (MCC) for the shuttle orbital flight test (OFT) time frame were described. The three support systems discussed are the communication interface system (CIS), the data computation complex (DCC), and the display and control system (DCS), all of which may interfere with, and share processing facilities with other applications processing supporting current MCC programs. The MCC shall provide centralized control of the space shuttle OFT from launch through orbital flight, entry, and landing until the Orbiter comes to a stop on the runway. This control shall include the functions of vehicle management in the area of hardware configuration (verification), flight planning, communication and instrumentation configuration management, trajectory, software and consumables, payloads management, flight safety, and verification of test conditions/environment

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Incentive-driven QoS in peer-to-peer overlays

A well known problem in peer-to-peer overlays is that no single entity has control over the software, hardware and configuration of peers. Thus, each peer can selfishly adapt its behaviour to maximise its benefit from the overlay. This thesis is concerned with the modelling and design of incentive mechanisms for QoS-overlays: resource allocation protocols that provide strategic peers with participation incentives, while at the same time optimising the performance of the peer-to-peer distribution overlay. The contributions of this thesis are as follows. First, we present PledgeRoute, a novel contribution accounting system that can be used, along with a set of reciprocity policies, as an incentive mechanism to encourage peers to contribute resources even when users are not actively consuming overlay services. This mechanism uses a decentralised credit network, is resilient to sybil attacks, and allows peers to achieve time and space deferred contribution reciprocity. Then, we present a novel, QoS-aware resource allocation model based on Vickrey auctions that uses PledgeRoute as a substrate. It acts as an incentive mechanism by providing efficient overlay construction, while at the same time allocating increasing service quality to those peers that contribute more to the network. The model is then applied to lagsensitive chunk swarming, and some of its properties are explored for different peer delay distributions. When considering QoS overlays deployed over the best-effort Internet, the quality received by a client cannot be adjudicated completely to either its serving peer or the intervening network between them. By drawing parallels between this situation and well-known hidden action situations in microeconomics, we propose a novel scheme to ensure adherence to advertised QoS levels. We then apply it to delay-sensitive chunk distribution overlays and present the optimal contract payments required, along with a method for QoS contract enforcement through reciprocative strategies. We also present a probabilistic model for application-layer delay as a function of the prevailing network conditions. Finally, we address the incentives of managed overlays, and the prediction of their behaviour. We propose two novel models of multihoming managed overlay incentives in which overlays can freely allocate their traffic flows between different ISPs. One is obtained by optimising an overlay utility function with desired properties, while the other is designed for data-driven least-squares fitting of the cross elasticity of demand. This last model is then used to solve for ISP profit maximisation

Social work with airports passengers

Social work at the airport is in to offer to passengers social services. The main methodological position is that people are under stress, which characterized by a particular set of characteristics in appearance and behavior. In such circumstances passenger attracts in his actions some attention. Only person whom he trusts can help him with the documents or psychologically

A Self-Organizing Wireless Sensor Network and Distributed Computing Engine for Commodity and Future Palmtop Computers

The embedded class processors found in commodity palmtop computers continue to become increasingly capable while retaining an energy-efficient footprint. Palmtop computers themselves, including smartphones and tablets, provide a small form factor system integrating wireless communication and non-volatile storage with these energy-efficient processors. Also, various wireless connectivity functions on mobile devices provide new opportunities in designing more flexible, smarter wireless sensor networks (WSNs), and utilizing the computation power in a way we could never imagine before. In this dissertation, I present a WSN concept for current and future generation tablet devices. My contributions include developments at the system level, architecture level, and collaborative design between different layers of the system. At the system level, I developed Ocelot, a grid-like computing environment for palmtop computers in place of traditional workstation or server class machines to compute highly parallel light to medium-weight tasks in an energy efficient manner. Additionally, I developed Lynx, a self-organizing wireless sensor network, which is a further step taken in exploiting the potential of palmtop computers. At the architecture level, to increase the storage capacity of future palmtop computers, I explore the use of a new storage class magnetic memory, Racetrack Memory (RM), throughout the memory hierarchy. Thus, I developed FusedCache, a naturally inclusive, dual-level private cache design for RM that provides fast uniform access at one level, and non-uniform access at the next, which allows RM to be effective as close to the processor as an L1 cache. For higher levels of the memory hierarchy such as the last level cache, I propose a Multilane Racetrack Cache (MRC), an RM last level cache design utilizing lightweight compression combined with independent shifting. MRCs allow cache lines mapped to the same Racetrack structure to be accessed in parallel when compressed, mitigating potential shifting stalls in an RM cache. Finally, leveraging the lightweight compression from MRC and the need for efficient communication in Lynx, I present a cross-level design combining memory-level lightweight compression with network-level packet transfer, together with a technique called Source-Aware Layout Reorganization (SALR) to increase the compressibility of sensor data