Internet of Things (IoT) for Creating Smart Cities

Research Team: Prof. Marimuthu Palaniswami, Dr. Sutharshan Rajasegarar, Dr. Jayavardhana Gubbi, Dr. Yee Wei Law, Dr. Slaven Marusic, Prof. Christopher Leckie, Prof. Rajkumar Buyya, Dr. Shanika Karunasekara  

Collaborators: Prof. Ba-Ngu Vo (University of Western Australia), Paul Stanley (ARUP), Andrew Maher (ARUP), Trevor Dohnt (Melbourne Cricket Club), Dr. Subhash Challa (SenSen Pty Ltd), Dr. Vincent Cheng-Siong Lee (Monash University), Dr. Wee-Keong Ng (NTU), Dr. Tie Luo (A*STAR I2R) Dr. Yu Zhang (Northwestern Polytechnical University), Dr. Zheng Gong (South China Normal University), Dr. Gina Kounga (Oxford University), Dr. Anthony Lo (Delft University of Technology), Prof. Danny Ralph (Cambridge University), Dr. Alex Gluhak (University of Surrey), Prof. Rahim Tafazolli (University of Surrey), Mirko Presser (Alexandra Institute)

RHD Students (Current): Mr. Aravinda Rao, Mr. Fu-Chun Hsu, Ms. Sarah Erfani, Mr. Dheeraj Kumar

Summer Students (Current): Ms. Glauci Vieira (visiting student from Brazil)
Industry collaborations in the past 5 years:
iOmniscient Ltd, SenSen Networks, ARUP, Melbourne Cricket Club, City of Melbourne, Australian Institute of  Marine Science (AIMS)

Research Sponsors: Australian Research Council (ARC), European Union, Institute for a Broadband-Enabled Society, City of Melbourne

Grants: ARC Linkage, ARC LIEF, EU FP7, Institute for a Broadband-Enabled Society seed fund

Description:By 2050, 70% of the world's population and over 6 billion people are expected to live in cities and surrounding regions. So, cities will need to be smart, if only to survive as platforms that enable economic, social and environmental well-being. Smartness of a city is technologically enabled by the emerging Internet of Things, which can be seamlessly integrated into the urban infrastructure (transport, health, environment, etc.) and thus forms a digital skin over the city. The city needs smart networking solutions in order to fully utilize the data collected and further define a common operating picture of the city, which is useful in policy decisions. The aims are to create a Smart City capability through seamless urban environment monitoring via large scale sensing, data analytics and information representation. Interconnection of sensing and actuating devices as Internet of Things (IoT) addresses the ability to share information across platforms through a unified framework, developing a common operating picture for city management. The interpretation of events and visualisation of information for end users will ensure sustainability and higher quality of life in the urban environment.

IoT Deployment :

As part of this project, a pilot deployment of IoT devices in the City of Melbourne has been completed. More information about this deployment and live data can be seen from this link "Internet of Things: A pilot deployment in the city of Melbourne, Australia".

IoT Projects

Under the Smart City heading, the ISSNIP group is participating in several ongoing IoT projects, described and grouped according to the technical themes below.

Noise monitoring: The aims include development of low-cost noise monitoring hardware, deployment of energy-efficient noise monitoring sensor network, and development of protocols for efficient collection of high-sample rate noise data from sensors deployed over large geographical regions, and developing methodologies for analysis and visualisation of the noise pollution profile.  This will offer a city council understand dynamic noise pollution profile, assess its impact on health and wellbeing, and better plan for noise reduction and desirable urban sound-scape.  Media coverage: The Age 13-Nov-2010.

Pollution monitoring: Increased level of pollution in the atmosphere contributes to respiratory health problems in the population. Particulate matter (PM), which is a fluid mixture of solids and liquids, is arguably one of the most dangerous of all pollutants to cause a population health hazard. While expensive, top end pollution monitoring equipment that is capable of reliably measuring very low levels of pollutants is used for this purpose, currently monitoring is often done at a low spatial resolution due to the excessive cost of equipment. The aims are to develop and use wireless sensor nodes equipped with low-cost PM sensors to supplement the existing high-accuracy PM devices to improve the estimation at higher spatial and temporal resolutions, and to develop spatiotemporal estimation algorithms and tools to efficiently estimate the data at unobserved locations using the combination of high-cost and low-cost sensors.

Energy-efficient sensing using compressive sensing: We are developing a generalised framework for data collection, that effectively exploits the spatial and temporal characteristics of the data, both in the initial sensing domain as well as the associated transform domains. Current methods provide inadequate, piecemeal solutions focussing only on select elements. Efficient heterogeneous sensing of the urban environment demands the capacity to simultaneously meet competing demands of multiple sensing modalities. The main objectives are

Adaptive learning in visual sensor networks for crowd modelling: The prevalence of camera networks for surveillance, together with the decreasing cost of infrastructure, has produced a significant demand for robust monitoring systems. Current systems offer limited functionality, particularly in their reliance on centralised processing of gathered information. This project addresses end-to-end system challenges of wireless visual sensor networks. Integrating developments across the spatial, spatio-temporal and decision domains, the project will incorporate distributed sensor network technology with intelligent fusion of information, to deliver unique long-term behaviour analysis capabilities for efficient planning in highly crowded environments.

Event Analytics in Big Data: The current Internet is evolving into the Internet of Things (IoT), where networked devices have the ability to compute, sense and interact with their surroundings. IoT deployments will generate a vast amount of data, but the value of the data lies in how we can exploit it. Currently there is little domain expertise to automate this Big data analysis, and traditional supervised machine learning techniques suffer from a lack of labelled training data in this context. The aim is to develop scalable distributed and incremental event detection techniques in addition to privacy-preserving data analytics solutions for the IoT. This will gain public confidence in the privacy aspects, and enable a new generation of applications in IoT applications such as smart cities.

Security and privacy: The IoT consists of islands of sensor networks are meant to operate unattended, potentially in harsh and even hostile environments. Security is thus essential to ensuring the intended operation of the IoT. For businesses and consumers, security enables the IoT to be used with confidence. The ISSNIP group has made significant contributions to various aspects of security, including multicast authentication, secure reprogramming, resilience to smart jamming, cryptographic key management, secure routing, and secure data aggregation. In particular, our secure reprogramming work is being conducted as part of the FP7 project "SmartSantander" ( Also central to the IoT is its ability to capture data actively contributed by human users. For example, the City of Melbourne, as our smart city project partner, is monitoring urban noise by collecting noise samples from noise sensors and smartphones of environmentally conscious citizens; this is the participatory sensing paradigm. Clearly, here the data can contain sensitive information, such as participants' location, voice and other proprietary audio-visual information. As part of the upcoming FP7 project "SOCIOTAL", we are investigating privacy-preserving data mining, that is, we are addressing the important topic of preserving users' privacy to allow a third-party data miner mines user-contributed data for useful information.

Cloud computing for sensor networks: Cloud Computing enables software applications on managed infrastructure with access to these offered as services over the Internet. This provides a scalable model to service providers to deliver applications to end-users without capital and expertise investment to purchase, install, and manage large IT infrastructures. The main objectives are:

 EU FP7 projects (partnered with ISSNIP):

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