Himadri Sekhar Paul

ANGELS for distributed analytics in IoT

The current global emphasis on “Internet of Things (IoT)” have highlighted the extreme importance of sensor-based intelligent and ubiquitous systems which are more commonly known as “cyber-physical systems.” The technology has the potential to create a network of smart devices and things to an extent that has never been envisaged before, far outnumbering the number of devices connected in the Internet as we know today. The sheer number of such connected ubiquitous devices is likely to give rise to a hitherto unforeseen volume of data of different types with a demand for execution of analytical algorithms over the data. On the success of these analytic processes will depend the actual “smartness” of the “Intelligent Infrastructures” which now form the crux of the IoT paradigm. We have seen the advent of cloud-based paradigms to analyse the data in a data-parallel fashion within large data centres which now form the basis of the “big-data” problem. But apart from the servers in the data centres, we potentially have a huge pool of compute resources if we think about the smart devices in and around our homes collectively, which remain relatively idle. In this paper, we present a proposal with some emulated experimental results where we claim that in an IoT framework, the smart devices such as mobile phones, home gateways etc. can be utilised for execution of dataparallel analytic jobs. This is effectively a work-in-progress and it is acknowledged that there will be further challenges for real devices. Future research will attempt to consider these challenges.

Challenges of Using Edge Devices in IoT Computation Grids

Internet of Things (IoT) has the potential to become a technology revolution with a vision of creating very large scale network, comprising of unprecedented number of connected devices. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, embedded computers. There has been sustained research work and standardization effort from different IoT perspectives like integration of sensor and RFID devices to the Internet. With the increasing trend of gathering business insights from unstructured data, the high volume of data generated by such devices is also of interest. Cloud based data mining platforms are suitable for analyses of such data and researchers have proposed architectures where personal mobile phones can act as Edge Gateway between the sensor network and cloud analytics platform. It seems that the surge in the volume of data generated by huge number of Smart Items can only be matched if a large percentage of mobile users start sharing the computation capability of their personal devices and work together towards true Participatory Computing in the IoT systems. In this work we try to understand the challenges associated with running computation jobs on the mobile devices using different types of workload often observed in IoT applications. Based on the insights gained from experiments performed by us, we propose a scheme where mobile phones, residential gateways and other edge devices offer free slots to servers in a cloud based data analytics system. Based on the free time slots offered by the mobile phones, if commensurately sized computational jobs can be scheduled, the unpredictability associated with using mobile phones as grid resources can be solved.

Utilising condor for data parallel analytics in an IoT context — An experience report

The current emphasis on sensor-based intelligent and ubiquitous systems, more commonly known as “cyber-physical systems”, has the potential to give rise to a new generation of systems and services encompassing several domains such as e-Governance, healthcare, transportation, waste management, energy & utilities, insurance, etc., resulting in the metamorphosis of the Internet as we see it, into the Internet of Things (IoT). One probable commonality in each of these services will be the abundance of different types of data from different sources with the success of the systems depending on real-time or near real-time analysis of data. Such analyses are normally performed via well-known algorithms with a time-constraint on the execution, thus creating a requirement for parallel execution techniques. Some of these analyses may have a higher frequency of execution on a relatively small set of data, in which case the current big-data frameworks may actually add an overhead. Further, the frameworks like Hadoop demand the algorithms to be mapped onto a particular paradigm, which may not always be a suitable option. This paper, which is a work-in-progress, provides an experience report on the use of Condor, a well known Grid framework, for data-parallel “black-box” style execution of analysis algorithms in the context of Internet of Things. We concentrate on algorithms which are already in use, and can be partitioned into data-parallel subtasks without any modification and use Condor, which has traditionally been used for high-performance or high-throughput computing, as the execution framework.

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.

Offloading work to mobile devices: an availability-aware data partitioning approach

In the context of Internet of Things (IoT), data acquisition, management, and analysis for knowledge extraction has given rise to a new generation of services. The heterogeneity of services being offered today on the enormous expanse of data as available from sensors and smart devices, needs a distributed infrastructure for analysis and computation. We consider a scenario where the computing infrastructure of an IoT framework is distributed and is capable of harnessing the computing power of mobile devices connected to the network in a heterogeneous grid. Edge devices like smart-phones, gateways, etc. can potentially contribute to the infrastructure. However, such devices and communication channels to such devices are intermittently unavailable, which may be due to network failure, planned shut-down, high workload on the devices, etc. In addition to this, we consider a task offloading context where the computing infrastructure inside the IoT invites bids from connected and available devices from the network to offload a part of the computation on part of the input data-set. We expect the overall completion time to improve in this setting. We investigate the data partitioning problem under the scenario where unavailability of communication and computation are advertised a priori and we pose the problem as a scheduling problem where cost of execution of an analysis job is to be minimized. We present a constraint-based model, evaluate the same on various scenarios and present some of the results.

A Semantic Algorithm Repository and Workflow Designer Tool: Signal Processing Use Case

Recently major emphasis is exerted on development of effective tools and techniques for enriching IoT development environment. Typically an IoT application, for example a health monitoring application, not only requires domain knowledge of a programmer, but also similar knowledge from a medical practitioner, a sensor manufacturer, an infrastructure manager, etc. Such involvement of several experts makes the development process complex, resulting in escalation of time and cost of the effort. Model Driven Development (MDD) has been proposed as a development technique where such problem can be mitigated. This paper presents a system based on the MDD paradigm. As a part of the system, we present a work-flow designer framework, a visual drag and drop interface, where a developer can stitch various functional models recommended from a well-organized, annotated and crowd-sourced semantic repository of algorithms (from various domains), named as Algopedia, to quickly build a semantic workflow and in turn an end to end IoT application.

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.