Input-dependent edge-cloud mapping of recurrent neural networks inference

6noGiven the computational complexity of Recurrent Neural Networks (RNNs) inference, IoT and mobile devices typically offload this task to the cloud. However, the execution time and energy consumption of RNN inference strongly depends on the length of the processed input. Therefore, considering also communication costs, it may be more convenient to process short input sequences locally and only offload long ones to the cloud. In this paper, we propose a low-overhead runtime tool that performs this choice automatically. Results based on real edge and cloud devices show that our method is able to simultaneously reduce the total execution time and energy consumption of the system compared to solutions that run RNN inference fully locally or fully in the cloud.partially_openopenJahier Pagliari D.; Chiaro R.; Chen Y.; Vinco S.; Macii E.; Poncino M.Jahier Pagliari, D.; Chiaro, R.; Chen, Y.; Vinco, S.; Macii, E.; Poncino, M

Clock Synchronization in Wireless Sensor Networks: An Overview

The development of tiny, low-cost, low-power and multifunctional sensor nodes equipped with sensing, data processing, and communicating components, have been made possible by the recent advances in micro-electro-mechanical systems (MEMS) technology. Wireless sensor networks (WSNs) assume a collection of such tiny sensing devices connected wirelessly and which are used to observe and monitor a variety of phenomena in the real physical world. Many applications based on these WSNs assume local clocks at each sensor node that need to be synchronized to a common notion of time. This paper reviews the existing clock synchronization protocols for WSNs and the methods of estimating clock offset and clock skew in the most representative clock synchronization protocols for WSNs

CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks

The excellent accuracy of Recurrent Neural Networks (RNNs) for time-series and natural language processing comes at the cost of computational complexity. Therefore, the choice between edge and cloud computing for RNN inference, with the goal of minimizing response time or energy consumption, is not trivial. An edge approach must deal with the aforementioned complexity, while a cloud solution pays large time and energy costs for data transmission. Collaborative inference is a technique that tries to obtain the best of both worlds, by splitting the inference task among a network of collaborating devices. While already investigated for other types of neural networks, collaborative inference for RNNs poses completely new challenges, such as the strong influence of input length on processing time and energy, and is greatly unexplored.In this paper, we introduce a Collaborative RNN Inference Mapping Engine(CRIME), which automatically selects the best inference device for each input. CRIME is flexible with respect to the connection topology among collaborating devices, and adapts to changes in the connections statuses and in the devices loads. With experiments on several RNNs and datasets, we show that CRIME can reduce the execution time (or end-node energy) by more than 25% compared to any single-device approach

Packet level measurement over wireless access

PhDPerformance Measurement of the IP packet networks mainly comprise of monitoring the network performance in terms of packet losses and delays. If used appropriately, these network parameters (i.e. delay, loss and bandwidth etc) can indicate the performance status of the network and they can be used in fault and performance monitoring, network provisioning, and traffic engineering. Globally, there is a growing need for accurate network measurement to support the commercial use of IP networks. In wireless networks, transmission losses and communication delays strongly affect the performance of the network. Compared to wired networks, wireless networks experience higher levels of data dropouts, and corruption due to issues of channel fading, noise, interference and mobility. Performance monitoring is a vital element in the commercial future of broadband packet networking and the ability to guarantee quality of service in such networks is implicit in Service Level Agreements. Active measurements are performed by injecting probes, and this is widely used to determine the end to end performance. End to end delay in wired networks has been extensively investigated, and in this thesis we report on the accuracy achieved by probing for end to end delay over a wireless scenario. We have compared two probing techniques i.e. Periodic and Poisson probing, and estimated the absolute error for both. The simulations have been performed for single hop and multi- hop wireless networks. In addition to end to end latency, Active measurements have also been performed for packet loss rate. The simulation based analysis has been tried under different traffic scenarios using Poisson Traffic Models. We have sampled the user traffic using Periodic probing at different rates for single hop and multiple hop wireless scenarios. 5 Active probing becomes critical at higher values of load forcing the network to saturation much earlier. We have evaluated the impact of monitoring overheads on the user traffic, and show that even small amount of probing overhead in a wireless medium can cause large degradation in network performance. Although probing at high rate provides a good estimation of delay distribution of user traffic with large variance yet there is a critical tradeoff between the accuracy of measurement and the packet probing overhead. Our results suggest that active probing is highly affected by probe size, rate, pattern, traffic load, and nature of shared medium, available bandwidth and the burstiness of the traffic

Multi-scale movement of demersal fishes in Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2019Information on the movement of migratory demersal fishes such as Pacific halibut, Pacific cod, and sablefish is needed for management of these valuable fisheries in Alaska, yet available methods such as conventional tagging are too coarse to provide detailed information on migration characteristics. In this dissertation, I present methods for characterizing seasonal and annual demersal fish movement at multiple scales in space and time using electronic archival and acoustic tags. In Chapter 1, acoustic telemetry and the Net Squared Displacement statistic were used to identify and characterize small-scale movement of adult female Pacific halibut during summer foraging in a Marine Protected Area (MPA). The dominant movement pattern was home range behavior at spatial scales of less than 1 km, but a more dispersive behavioral state was also observed. In Chapter 2, Pop-up Satellite Archival Tags (PSATs) and acoustic tags were deployed on adult female Pacific halibut to determine annual movement patterns relative to MPA boundaries. Based on observations of summer home range behavior, high rates of year-round MPA residency, migration timing that largely coincided with winter commercial fisheries closures, and the demonstrated ability of migratory fish to return to previously occupied summer foraging areas, the MPA is likely to be effective for protecting both resident and migrant Pacific halibut brood stock year-round. In Chapter 3, I adapted a Hidden Markov Model (HMM) originally developed for geolocation of Atlantic cod in the North Sea for use on demersal fishes in Alaska, where maximum daily depth is the most informative and reliable geolocation variable. Because depth is considerably more heterogeneous in many regions of Alaska compared to the North Sea, I used simulated trajectories to determine that the degree of bathymetry heterogeneity affected model performance for different combinations of likelihood specification methods and model grid sizes. In Chapter 4, I added a new geolocation variable, geomagnetic data, to the HMM in a small-scale case study. The results suggest that the addition of geomagnetic data could increase model performance over depth alone, but more research is needed to continue validation of the method over larger areas in Alaska. In general, the HMM is a flexible tool for characterizing movement at multiple spatial scales and its use is likely to enrich our knowledge about migratory demersal fish movement in Alaska. The methods developed in this dissertation can provide valuable insights into demersal fish spatial dynamics that will benefit fisheries management activities such as stock delineation, stock assessment, and design of space-time closures.Rasmuson Fisheries Research Center and the Pollock Conservation Cooperative Research CenterChapter 1: Characterizing Pacific halibut movement and habitat in a Marine Protected Area using net squared displacement analysis methods -- Chapter 2: Interannual site fidelity of Pacific halibut: potential utility of protected areas for management of a migratory demersal fish -- Chapter 3 Effect of study area bathymetric heterogeneity on parameterization and performance of a depth depth-based geolocation model for demersal fishes -- Chapter 4 Potential utility of geomagnetic data for geolocation of demersal fish in the North Pacific Ocean -- General conclusion -- References -- Appendix A: Geolocation of demersal fishes in the North Pacific Ocean: Hidden Markov model framework and data likelihood models

Full waveform analysis for long-range 3D imaging laser radar

The new generation of 3D imaging systems based on laser radar (ladar) offers significant advantages in defense and security applications. In particular, it is possible to retrieve 3D shape information directly from the scene and separate a target from background or foreground clutter by extracting a narrow depth range from the field of view by range gating, either in the sensor or by postprocessing. We discuss and demonstrate the applicability of full-waveform ladar to produce multilayer 3D imagery, in which each pixel produces a complex temporal response that describes the scene structure. Such complexity caused by multiple and distributed reflection arises in many relevant scenarios, for example in viewing partially occluded targets, through semitransparent materials (e.g., windows) and through distributed reflective media such as foliage. We demonstrate our methodology on 3D image data acquired by a scanning time-of-flight system, developed in our own laboratories, which uses the time-correlated single-photon counting technique

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study