ORCID Profile
0000-0001-5112-9849
Current Organisation
Deakin University
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Publisher: IEEE
Date: 05-2011
Publisher: IEEE
Date: 02-2010
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2011
Publisher: IEEE
Date: 12-2009
Publisher: Bentham Science Publishers Ltd.
Date: 2008
Publisher: MDPI AG
Date: 10-11-2022
DOI: 10.20944/PREPRINTS202211.0190.V1
Abstract: Ever-increasing need for improving the livability of a city and improve outcomes for its residents, over the last decade, the adoption of technology to develop urbanised societies around the world has given rise to the need for developing smart cities. The speed at which the world population is growing, the use of Internet of Things in smart cities have really advanced the quality of life. One significant area of concern within the smart city framework is waste management. If the waste within a city is not adequately managed, then it leads to issues in the health of the citizens. Additionally, the waste management has such a high impact on the environmental footprint, hence the need to have a smart way of managing waste is of critical importance. Through our research, we analyse the challenges of waste management within a city to understand the impact of the problem on to the citizens and overall city operations. We then investigate ways in which we can solve these problems using the emerging technologies, such as the Internet of Things, to collect valuable data of large volumes arriving at an astronomical rate, then apply multi-agent deep reinforcement learning algorithms to harness the power of big data to extract meaningful information and actionable insights. We ingest data generated by our Internet of Things into our algorithm for three main purposes including providing the notifications to an external system, for ex le, a map navigation engine out of the scope for this project but a future extension for route optimisation and waste vehicle tracking extracting and reporting the actionable insights from the underlying data and consuming the extracted data for predictive forecasting to draw out the unknown patterns of waste fill levels within various geographical locations and again send out triggers and notification to external systems for ex le a waste collection authority who can efficiently schedule the waste collection vehicles and optimise the route. To achieve the above mentioned outcomes, we propose a framework that is agnostic of the hardware that it connects to and can effectively interface with a wide variety of hardware keeping a level of abstraction in the architecture.
Publisher: Elsevier BV
Date: 10-2012
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 04-2018
Publisher: IEEE
Date: 2010
Publisher: IEEE
Date: 05-2008
Publisher: MDPI AG
Date: 22-10-2014
DOI: 10.3390/MTI4040076
Abstract: The use of technology for social connectivity and achieving engagement goals is increasingly essential to the overall well-being of our rapidly ageing population. While much of the extant literature has focused on home automation and indoor remote health monitoring there is a growing literature that finds personal health and overall well-being improves when physical activities are conducted outdoors. This study presents a review of possible innovative and assistive eHealth technologies suitable for smart therapeutic and rehabilitation outdoor spaces for older persons. The article also presents key performance metrics required of eHealth technologies to ensure robust, timely and reliable biometric data transfer between patients in a therapeutic landscape environment and respective medical centres. A literature review of relevant publications with a primary focus of integrating sensors and eHealth technologies in outdoor spaces to collect and transfer data from the elderly demographic who engage such built landscapes to appropriate stakeholders was conducted. A content analysis was carried out to synthesize outcomes of the literature review. The study finds that research in assistive eHealth technologies and interfaces for outdoor therapeutic spaces is in its nascent stages and has limited generalisability. The level of technology uptake and readiness for smart outdoor spaces is still developing and is currently being outpaced by the growth of elderly fitness zones in public spaces. Further research is needed to explore those eHealth technologies with interactive feedback mechanisms that are suitable for outdoor therapeutic environments.
Publisher: Walter de Gruyter GmbH
Date: 2014
Abstract: Global warming and lack of rain were the main problems that caused increased drought around the world. In New Zealand, according to National Institute of Water and Atmospheric Research (NIWA) the drought in 2012 and 2013 was the worst drought in the last 70 years. Therefore, there is a need for technological intervention to monitor basic information about the weather and soil condition in order to identify and predict drought conditions. Initial experiments have shown that the proposed wireless sensor drought monitoring system is capable of remote real-time monitoring for extended periods. This monitoring can also help identify drought in the early stages and thereby indicate promptly when to take corrective measures. Intelligent sensors in a wireless network monitor the soil condition. These sensors collect various environmental parameters and then send the pre-processed data wirelessly to a base station. From the base station this data uploads every two seconds to the cloud (internet) for further analysis. If a drought condition is identified by the monitoring system then an alert message is sent to the user via text message or email.
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-3682-8.CH007
Abstract: Sensors are increasingly being employed to determine different activities of a person living at home. Numerous sensors can be used to obtain a variety of information. While many sensors may be used to make a system, it is important to look into the availability, cost, installation, mechanism, and performance of sensors. This chapter investigates different sensors and their usefulness in a smart home monitoring system. A smart home monitoring system provides a safe, sound, and secure living environment for elderly people. Statistics show that the population of elderly people is increasing around the world and this trend is not going to change in the near future. The authors have developed a smart home that consists of an optimum number of wireless sensors that includes current flow, water flow, and bed usage sensors. The sensors provide information that can be used for monitoring elderly people by detecting abnormal patterns in their daily activities. The system generates and sends an early warning message to the caregiver when an unforeseen abnormal condition occurs.
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 2011
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 11-2008
Publisher: IEEE
Date: 12-2019
Publisher: MDPI AG
Date: 20-03-2020
DOI: 10.3390/ELECTRONICS9030511
Abstract: The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many erse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of in iduals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and t ering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.
No related grants have been discovered for Anuroop Gaddam.