Paper "Querying IoT Services: A Smart Carpark Recommender Use Case" accepted at WF-IoT 2018

posted Mar 13, 2018, 9:27 PM by Prem Prakash Jayaraman

Paper "Context-as-a-Service Platform Exchange and Share Context in an IoT Ecosystem" Accepted at PerCom Workshop

posted Mar 13, 2018, 9:26 PM by Prem Prakash Jayaraman

Alireza Hassani, Alexey Medvedev, Arkady Zaslavsky, Pari Delir Haghighi, Prem Prakash Jayaraman, Maria indrawan-Santiago, Sea Ling, "Context-as-a-Service Platform Exchange and Share Context in an IoT Ecosystem"

CDQL: A Generic Context Representation and Querying Approach for Internet of Things Applications

posted Feb 14, 2017, 5:40 PM by Prem Prakash Jayaraman   [ updated Jun 7, 2017, 7:35 AM ]

Our paper proposing a query language for Context now available online.

Abstract: As the standardization efforts for IoT is fast progressing, we will quickly get to a point where context derived from IoT data and relations will be the underpinning factor to enable interaction between "smart things". Therefore, having a generic approach for describing and querying context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange context. CDQL consists of two main parts: Context Definition Model, which is designed to describe the contextual attributes and context related capabilities of each "thing"; and Context Query Language (CQL), which is a flexible query language to express contextual information requirements without considering details of the underlying data structure. We exemplify the usage of the proposed CDQL, via a smart city use case study that highlight how CDQL can be utilized to deliver context information to IoT applications.

Best Paper Award at HICSS 2017 - "ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications"

posted Jan 19, 2017, 4:37 PM by Prem Prakash Jayaraman   [ updated Jun 7, 2017, 7:35 AM ]

Paper Link: PDF

Abstract: The Internet of Things (IoT) is a new internet evolution that involves connecting billions of sensors and other devices to the Internet. Such IoT devices or IoT things can communicate directly. They also allow Internet users and applications to access and distil their data, control their functions, and harness the information and functionality provided by multiple IoT devices to offer novel smart services. IoT devices collectively generate massive amounts of data with an incredible velocity. Processing IoT device data and distilling high-value information from them presents an Internet-scale computational challenge. Contextualisation of IoT data can help improve the value of information extracted from IoT. However, existing contextualisation techniques can only handle small datasets from a modest number of IoT devices. In this paper, we propose a general-purpose architecture and related techniques for the contextualisation of IoT data. In particular, we introduce a Contextualisation-as-a-Service (ConTaaS) architecture that incorporates scalability improving techniques, as well as a proof-of-concept implementation of all these that utilises elastic cloud-based infrastructure to achieve near real-time contextualisation of IoT data. Experimental evaluations validating the efficiency of ConTaaS are also provided in this paper.

New Paper Published in MDPI Sensors Journal - Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

posted Jan 19, 2017, 4:33 PM by Prem Prakash Jayaraman   [ updated Jun 7, 2017, 7:35 AM ]

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

CFP: Special Issue on Scheduling Algorithms for Cyber-Physical-Social Workflows

posted Jan 19, 2017, 4:30 PM by Prem Prakash Jayaraman   [ updated Jun 7, 2017, 7:37 AM ]

Topics of interest include, but are not limited to:

  • Novel performance optimization heuristics for CPS-DS workflows
  • Novel data flow behavior prediction algorithms across heterogeneous CPS-DS workflow activities
  • Automated network edge device configuration selection and allocation
  • Innovative failure-proof workflow scheduling algorithms for handling run-time issues
  • Novel scheduling middleware for integrating multiple CPS-DS data and workflows
  • Benchmarking kernel for cyber-physical-social elements
  • Novel ontological modelling of new types of cyber devices and data sources
  • Performance modelling and benchmarking techniques for CPS-DS workflow activities on EDC
  • Best practices, success factors, and empirical studiesTimeline

  • Submission Deadline: June 1 2017 July 1st 2017
  • Reviews Completed: September 1 2017
  • Major Revisions Due (if Needed): October 1 2017
  • Reviews of Revisions Completed (if Needed): November 1 2017
  • Minor Revisions Due (if Needed): December 1 2017 Notification of Final Acceptance: February 1 2018
  • Publication Materials for Final Manuscripts Due: March 1 2018
  • Publication date: Second Issue 2018 (June Issue)

IOTSim: A simulator for analysing IoT applications

posted Aug 18, 2016, 4:38 PM by Prem Prakash Jayaraman   [ updated Jun 7, 2017, 7:38 AM ]

Our paper IOTSim has been accepted and now available online with Journal of Systems Architecture.

Abstract: A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.

Internet of things (iot); Big data; Mapreduce; Cloud computing; Programming model; Modelling and simulation

Internet of things: from internet scale sensing to smart services

posted Aug 18, 2016, 4:35 PM by Prem Prakash Jayaraman   [ updated Jun 7, 2017, 7:38 AM ]

Our recent paper in Internet of Things has been accepted and published in Springer Computing Journal.


Abstract: The internet of things (IoT) is the latest web evolution that incorporates billions of devices (such as cameras, sensors, RFIDs, smart phones, and wearables), that are owned by different organizations and people who are deploying and using them for their own purposes. Federations of such IoT devices (we refer to as IoT things) can deliver the information needed to solve internet-scale problems that have been too difficult to obtain and harness before. To realize this unprecedented IoT potential, we need to develop IoT solutions for discovering the IoT devices each application needs, collecting and integrating their data, and distilling the high value information each application needs. We also need to provide solutions that permit doing these tasks in real-time, on the move, in the cloud, and securely. In this paper we present an overview of a collection of IoT solutions (which we have developed in partnerships with other prominent IoT innovators and refer to them collectively as IoT platform) for addressing these technical challenges and help springboard IoT to its potential. We also describe a variety of IoT applications that have utilized the proposed IoT platform to provide smart IoT services in the areas of smart farming, smart grids, and smart manufacturing. Finally, we discuss future research and a vision of the next generation IoT infrastructure.

KeywordsInternet of thingsSensor discoverySensor integrationReal-time data analysisIoT applications

PhD Scholarship at RMIT - Position Filled

posted Dec 6, 2015, 9:13 PM by Prem Prakash Jayaraman   [ updated May 28, 2016, 8:36 AM ]

***Title: PhD Scholarship on context-aware data analysis for micro-scale air pollution monitoring, predicting and alerting


RMITs School of Computer Science and Information Technology in Melbourne, Australia, invites outstanding candidates to apply for 3 years (2016 – 2019) PhD Scholarship that is funded by Data61 at CSIRO.


***Open Date: Applications are NOW open!


***Closing Date: 30 January, 2016


***Commencement Date: Successful applicants for this PhD position must commence by March 2016.


***Value and duration:

The scholarship is valued at AU$26,000 per annum for three years (with a possibility of a six months extension).

The selected candidate will also receive a tuition-fee waiver.




To be eligible for this scholarship you must:

  • Meet RMIT’s PhD entry requirements.

  • Have highly competitive grades.

  • Show evidence of research ability, i.e., have an honours degree, a master thesis, publications, or research training.

  • Have strong research interests in the Internet of things, Context-aware computing, Mobile Computing, and/or Data Mining, and submit a PhD research proposal in a relevant topic.

  • Have interest to perform cross-disciplinary research and work in a team.

  • Possess strong programming skills (C/C++, Java, Python, Ruby).


***Research Project Background:


Air pollution is a significant threat in urban environments since it is known to cause respiratory problems as well as various lung diseases. Therefore, continuous air quality monitoring, visualisation, dissemination to customers is very important, especially in (Smart) Australian state capitals. For example, Melbourne's city centre has almost as many days of poor air quality as the coal-burning Latrobe Valley and experts warn it could get worse as our population grows. The fixed air monitoring stations (network consists of 11 stations for the whole city of 4 million people) of EPA (Environment Protection Authority Victoria) update air quality data hourly at a macro scale and only cover a few suburbs. Since the air contamination is usually location-dependent (e.g., transport junctions and industrial areas increase air pollution), the air quality should be monitored in city areas at finer granularity, both in space and time (micro-scale). This is currently not feasible by static measurement stations, but can be achieved by involving citizens in the air quality monitoring process (crowd-sensing or crowd-sourcing) so that they carry wearable sensors measuring various air pollutant gases while moving through the city.


In this project, we aim to investigate the use of a number of mobile air pollution detectors (e.g., 100) connected to mobile smart phones, each mounted to a moving object such as a person or a vehicle combined with static air monitoring station, to capture and predict air quality and draw real-time air pollution map of the city at a micro scale. In order to deliver high quality micro scale air pollution map of the city, the pollution model has to consider the spatial and temporal dimensions of the data collected, capture the local context of the region to detect air pollution problems, and consider the relationship between the density of the monitoring data and the prediction accuracy (e.g. solving the hard problem of the minimal set of mobile stations required to reach acceptable level of accuracy depending on application requirements).


The PhD student may have an opportunity to contribute to the Data61, CSIRO EU Horizon 2020 project "bIoTope H2020 project (2016-2019)" (


***Research project Team:


RMIT University


Professor Dimitrios Georgakopoulos


Dr.Ke Deng (Lecturer)


Dr. Prem Prakash Jayaraman (Research Fellow) (contact for further enquires and expression of interest)



Professor Arkady Zaslavsky (Senior Principal Research Scientist)


***How to Apply


To apply for the scholarship, you must complete an application consisting of:


  • A detailed curriculum vitae with publications (if any)

  • Undergraduate and honours/master transcripts

  • For international students from non-English speaking countries, when submitting the application to RMIT system, a proof of English proficiency may also be required (IELTS or TOEFL)

  • Contact details of at least two referees.


Please first submit these documents to for approval before you submit your PhD application to RMIT University.

Winner - Unearthed Hackathon

posted Dec 6, 2015, 9:07 PM by Prem Prakash Jayaraman   [ updated Dec 6, 2015, 9:14 PM ]

The team from RMIT University and CSIRO have won the Unearthed Hackathon Challenge.


We developed an innovative solution to increase safety and security of personell working in hazardeous areas. Our solution PPEofThings is an Internet of Things-based global awareness system that uses low cost, energy efficient sensing technologies (e.g., iBeacon and TI SensorTag) attached to regular PPE equipment and clothing, such as helmet, safety glasses, etc. PPEofThings makes normal PPE equipment and clothing smart objects capable of detecting and informing mine staff of potential safety hazards such as forgetting to use safety glasses and crossing geo-fences to potentially dangerous areas.


A media article from the Minister for Energy and Resources, Victoria, Lily D’Ambrosio


More infromation about what we developed



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