Pubali Datta

ANGELS: A framework for mobile grids

The current emphasis on Internet of Things (IoT) across the globe highlights the extreme importance of sensor-based intelligent and ubiquitous systems which are collectively known as cyber-physical systems. It is envisaged that the technology will create a network of smart devices and things to an unprecedented extent, far outnumbering the number of devices connected in the Internet as we know today. This unprecedented number of interconnected smart devices is likely to generate an unforeseen volume of data of different types, ready for analysis. The smartness of the Intelligent Infrastructures will depend on effective execution of various analytical algorithms on this accumulated dataset which forms the crux of the IoT paradigm. As of now, researchers rely on cloud-based paradigms to analyse the data in a data-parallel fashion, where analyses take place in large data centres comprised of large number of servers. Apart from the servers, there is a huge pool of relatively unused pool of computing resources in form of smart devices in and around our homes, such as mobile phones, home energy gateways, play-stations etc., which we collectively call edge devices. It is our belief that this pool of resources, if used in the right manner, can contribute to a large extent to effective and successful analysis of the data-set generated by the ubiquitous sensors, leading to a proper realization of the Intelligent Infrastructure paradigm, and we call this approach ANGELS computing. In this paper, we describe a framework which allows remote execution of programs within mobile devices and thus can act as one of the building blocks for the IoT paradigm. This is effectively a work-in-progress and it is acknowledged that there will be further challenges in such a framework and we will attempt to solve them during our course of research.

Demo: A Smart Framework for IoT Analytic Workflow Development

Developing analytical applications for IoT based on sensor signal processing tends to be complicated as applications are executed as sequence of steps comprising of multiple alternative algorithms, including suitable feature extraction modules depending on the goal of the application. Experience shows that developers spend considerable time and effort in performing feature extraction and dimensionality reduction. In this paper we propose a framework based on a relevant case study which allows developers to drag and drop algorithms to create a workflow chain, automatically select the most relevant signal features for the particular analytic application using a training data set to generate a model and deploy the model for use. The method reduces the effort and cost of development which is deemed highly important for the analytics industry.

A Framework for Speculative Scheduling and Device Selection for Task Execution on a Mobile Cloud

In this paper, we study the problem of opportunistic task scheduling and workload management in a mobile cloud setting considering computation power variation. We gathered mobile usage data for a number of persons and applied supervised clustering to show that a pattern of usage exists and that follows a state-based model. Based on this model, we present a strategy to choose and offload work on a mobile device. We present a framework and experimental results showing the efficacy of our proposed approach.

Compute on the Go: A Case of Mobile-Cloud Collaborative Computing Under Mobility

The objective of mobile cloud computing (MCC) is to augment the computation resources of mobile devices to reduce energy consumption of the device and utilization of high computation resources in the cloud. In an MCC framework a mobile device opportunistically offloads some of its computation tasks to remote cloud infrastructure in order to reduce its energy consumption. The scheme is sensitive to communication bandwidth, since low bandwidth implies longer duration that the network card remains active for and therefore consumes higher energy. To mitigate this problem, systems like MAUI, periodically updates its offloading strategy. But execution of such strategy is also associated with some cost in form of computation or energy. In this paper we present some on-line algorithms, which are computationally less costly, yet perform same as MAUI’s optimizer. We found experimentally that if we augment our proposed offloading algorithms with mobility model of the device in a WiFi covered area, we do not achieve any significant gain in terms of saving energy of the device.

To Run or Not to Run: Predicting Resource Usage Pattern in a Smartphone

Smart mobile phones are vital to the Mobile Cloud Computing (MCC) paradigm where compute jobs can be offloaded to the devices from the Cloud and vice-versa, or the devices can act as peers to collaboratively perform a task. Recent research in IoT context also points to the use of smartphones as sensor gateways highlighting the importance of data processing at the network edge. In either case, when a smart phone is used as a compute resource or a sensor gateway, the corresponding tasks must be executed in addition to the user’s normal activities on the device without affecting the user experience. In this paper, we propose a framework that can act as an enabler of such features by classifying the availability of system resources like CPU, memory, network usage based on applications running on an Android phone. We show that, such app-based classifications are user-specific and app usage varies with different handsets, leading to different classifications. We further show that irrespective of such variation in classification, distinct patterns exist for all users with available opportunity to schedule external tasks, without affecting user experience. Based on the next to-be-used applications, we output a predicted set of system resources. The resource levels along with handset architecture may be used to estimate worst case execution time for external jobs.

An access point to device association technique for optimized data transfer in mobile grids

In a mobile grid computing framework where mobile devices are used as computing resources, minimizing the task offloading time remains an important issue. A task is an independent unit of execution consisting of a input data volume for execution and optionally a target-specific executable. We consider a mobile grid infrastructure where mobile devices are connected via Wi-Fi network and the grid infrastructure has a set of tasks (i.e. a set of data volumes) to be transferred to a subset of the mobile devices. In a Wi-Fi network, mobile devices usually associate themselves to the access points (APs) having the strongest radio signal. In this paper, we address the problem of AP activation (by frequency assignment) and association of AP with devices in the context of minimizing the overall data-transfer completion time. We present a constraint based formulation and also a heuristic as solutions. Simulations results are presented which contrast our proposed methods with some of the earlier works.

Facilitating continued run of sensor data analytics services using user driven proactive memory reclamation scheme

Smartphones are currently being used to develop diverse range of applications (apps) involving sensors. These apps generally acquire and analyze sensor data and are usually implemented as background services. The importance values of Android processes are in a hierarchy of foreground, visible, background etc. in decreasing order of importance. Whenever a new process arrives, it may necessitate removal of old and less important processes for reclaiming memory. Current smartphones do not provide any options through which users idea of priority can override that of the system defaults. In this work we present an implementation that enables the user to obtain alerts on system load and recommendations to proactively kill a set of processes to reclaim system memory. This enables user selected background process to be spared from the standard android policy of process termination, in lieu of foreground apps, relatively unimportant from user perspective, during that period. We show that manual reclaiming of memory based on recommendations from our app, reduces the automatic killing and measurement lag experienced by a sensor analytics app under test. This work is redundant if processing power and main memory of a smartphone is always surplus than required for its normal usage.