Performance Analysis of a Novel GPU Computation-to-core Mapping Scheme for Robust Facet Image Modeling

Though the GPGPU concept is well-known in image processing, much more work remains to be done to fully exploit GPUs as an alternative computation engine. This paper investigates the computation-to-core mapping strategies to probe the efficiency and scalability of the robust facet image modeling algorithm on GPUs. Our fine-grained computation-to-core mapping scheme shows a significant performance gain over the standard pixel-wise mapping scheme. With in-depth performance comparisons across the two different mapping schemes, we analyze the impact of the level of parallelism on the GPU computation and suggest two principles for optimizing future image processing applications on the GPU platform

IoT Load Classification and Anomaly Warning in ELV DC Pico-grids using Hierarchical Extended k-Nearest Neighbors

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc pico-grids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended k-nearest neighbors (HE-kNN) technique that exploits the use of distance in kNN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This paper presents the successful implementation of HE-kNN technique in three different circuits: lightings, air-conditioning and multiple load dc pico-grids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data

Fundamental Limits of Low-Density Spreading NOMA with Fading

Spectral efficiency of low-density spreading non-orthogonal multiple access channels in the presence of fading is derived for linear detection with independent decoding as well as optimum decoding. The large system limit, where both the number of users and number of signal dimensions grow with fixed ratio, called load, is considered. In the case of optimum decoding, it is found that low-density spreading underperforms dense spreading for all loads. Conversely, linear detection is characterized by different behaviors in the underloaded vs. overloaded regimes. In particular, it is shown that spectral efficiency changes smoothly as load increases. However, in the overloaded regime, the spectral efficiency of low- density spreading is higher than that of dense spreading

Energy Efficiency of Distributed Signal Processing in Wireless Networks: A Cross-Layer Analysis

In order to meet the growing mobile data demand, future wireless networks will be equipped with a mulitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: 1) hybrid, where the signal processing operations are shared between nodes and APs; 2) centralized, where signal processing is entirely implemented at the APs; and 3) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches

PV cell angle optimization for energy generation-consumption matching in a solar powered cellular network

An inherent problem of solar-energy-powered-smallcell base stations (SBSs) is that the energy generation of the photovoltaic (PV) cell does not match the energy consumption of the SBS in time. In this paper, we propose to optimize the PV cell orientation angle to achieve a good match between the energy generation and consumption profiles on a daily time scale. The optimization is formulated as an integer linear programming problem. We also derive an expression for the correlation between the energy generation and consumption profiles to evaluate their general interaction independent of the exact PV cell or SBS deployment setup. The numerical evaluation of the proposed angle optimization in a business area in London in summer/winter shows that the optimal PV cell orientation in summer contradicts the conventional assumption of south facing being optimal in the northern hemisphere. Instead, a southwest orientation should be chosen in summer due to its ability to shift the energy generation peak towards the energy consumption peak in the afternoon at a SBS in central London. This is in accordance with the prediction given by our derived correlation between the solar energy generation and consumption profiles

Coordinated Container Migration and Base Station Handover in Mobile Edge Computing

Offloading computationally intensive tasks from mobile users (MUs) to a virtualized environment such as containers on a nearby edge server, can significantly reduce processing time and hence end-to-end (E2E) delay. However, when users are mobile, such containers need to be migrated to other edge servers located closer to the MUs to keep the E2E delay low. Meanwhile, the mobility of MUs necessitates handover among base stations in order to keep the wireless connections between MUs and base stations uninterrupted. In this paper, we address the joint problem of container migration and base-station handover by proposing a coordinated migration-handover mechanism, with the objective of achieving low E2E delay and minimizing service interruption. The mechanism determines the optimal destinations and time for migration and handover in a coordinated manner, along with a delta checkpoint technique that we propose. We implement a testbed edge computing system with our proposed coordinated migration-handover mechanism, and evaluate the performance using real-world applications implemented with Docker container (an industry-standard). The results demonstrate that our mechanism achieves 30%-40% lower service downtime and 13%-22% lower E2E delay as compared to other mechanisms. Our work is instrumental in offering smooth user experience in mobile edge computing.Comment: 6 pages. Accepted for presentation at the IEEE Global Communications Conference (Globecom), Taipei, Taiwan, Dec. 202