DotSlash: A Scalable and Efficient Rescue System for Handling Web Hotspots

This paper describes DotSlash, a scalable and efficient rescue system for handling web hotspots. DotSlash allows different web sites to form a mutual-aid community, and use spare capacity in the community to relieve web hotspots experienced by any individual site. As a rescue system, DotSlash intervenes when a web site becomes heavily loaded, and is phased out once the workload returns to normal. It aims to complement existing web server infrastructure such as CDNs to handle short-term load spikes effectively, but is not intended to support a request load constantly higher than a web site's planned capacity. DotSlash is scalable, cost-effective, easy to use, self-configuring, and transparent to clients. It targets small web sites, although large web site can also benefit from it. We have implemented a prototype of DotSlash on top of Apache. Experiments show that DotSlash can provide an order of magnitude improvement for a web server in terms of the request rate supported and the data rate delivered to clients even if only HTTP redirect is used. Parts of this work may be applicable to other services such as the Grid computational services and media streaming

Integrated mobility and resource management for cross-network resource sharing in heterogeneous wireless networks using traffic offload policies

The problem of efficient use of resources in wireless access networks becomes critical today with users expecting continuous high-speed network access. While access network capacity continues to increase, simultaneous operation of multiple wireless access networks presents an opportunity to increase the data rates available to end-users even further using intelligent cross-network resource sharing. This paper introduces a new integrated mobility and resource management (IMRM) framework for automatic execution of policies for cross-network resource sharing using traffic offload and pre-emptive resource reservation algorithms. The presented framework enables both mobile-initiated and network-initiated resource sharing policies to be executed. This paper presents the framework in detail and analyses its performance using extensive ns-2 simulations of the operation of a set of static policies based on measured signal strength, and dynamic pre-emptive network-initiated policies in a WiFi/WiMAX scenario. The detailed evaluation of the static policies clearly shows that the quality of voice applications shows large deviation, mostly due to very different levels of delay in access networks. Based on these conclusions, this paper presents a design of two new dynamic policies and shows that such policies, when efficiently implemented using the new IMRM framework can greatly improve the capacity of the network to serve voice traffic with a minimal impact on the data traffic and with a very low signalling overhead

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

Situation-aware Edge Computing

Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges. In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications. To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner

DroneTrack: Cloud-Based Real-Time Object Tracking Using Unmanned Aerial Vehicles Over the Internet

Low-cost drones represent an emerging technology that opens the horizon for new smart Internet-of-Things (IoT) applications. Recent research efforts in cloud robotics are pushing for the integration of low-cost robots and drones with the cloud and the IoT. However, the performance of real-time cloud robotics systems remains a fundamental challenge that demands further investigation. In this paper, we present DroneTrack, a real-time object tracking system using a drone that follows a moving object over the Internet. The DroneTrack leverages the use of Dronemap planner (DP), a cloud-based system, for the control, communication, and management of drones over the Internet. The main contributions of this paper consist in: (1) the development and deployment of the DroneTrack, a real-time object tracking application through the DP cloud platform and (2) a comprehensive experimental study of the real-time performance of the tracking application. We note that the tracking does not imply computer vision techniques but it is rather based on the exchange of GPS locations through the cloud. Three scenarios are used for conducting various experiments with real and simulated drones. The experimental study demonstrates the effectiveness of the DroneTrack system, and a tracking accuracy of 3.5 meters in average is achieved with slow-speed moving targets.info:eu-repo/semantics/publishedVersio

OptoCOMM and SUNSET to enable large data offloading in Underwater Wireless Sensor Networks

In this paper we present the initial implementation of an integrated optical and acoustic system that can enable large data transfer between mobile and static nodes in Underwater Wireless Sensor Networks (UWSNs). The proposed system is based on the OptoCOMM optical modem and on the SUNSET Software Defined Communication Stack (S-SDCS) framework. The OptoCOMM modem allows to overcome the limits of maximum data rate and bandwidth imposed by the use of acoustic communication by providing a data rate of 10Mbps. SUNSET SDCS instead has been used to provide networking and fragmentation capabilities to efficiently offload large data in UWSNs. The performance of the proposed approach has been evaluated through in lab experiments where large files with arbitrary sizes have been optically transferred. The results achieved show that our system is able to transfer up to 1.5 GBytes of data in short time

A2^2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems

To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A$^2$-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel pplication-Aware Task Planning Problem (A$^2$-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A$^2$-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A$^2$-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet dataset. Results show that A$^2$-UAV attains on average around 38% more accomplished tasks than the state-of-the-art, with 400% more accomplished tasks when the number of targets increases significantly. To allow full reproducibility, we pledge to share datasets and code with the research community.Comment: Accepted to INFOCOM 202