Smart City Wireless Connectivity Considerations and Cost Analysis: Lessons Learnt From Smart Water Case Studies

Kulkarni, Parag; Farnham, Tim

doi:10.1109/access.2016.2525041

Cited by 54 publications

(21 citation statements)

References 4 publications

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“…Thus, the SWG system is evaluated here based on cost. There are two types of SWG systems [14], 1) field devices that interface with the existing SCADA systems of the utility providers, and 2) field devices (with or without a gateway) and a backend cloud server. In Pakistan, there are currently no SCADA or SWG systems for tube wells.…”

Section: Smart Water Gridsmentioning

confidence: 99%

Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries

Khattak

Jawwad

Khan

et al. 2021

emitter

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According to a United Nations report, the world population will increase from 7 billion to 9 billion by 2050. Further, the water stress level is more than 70% in 22 countries while in another 31 countries it is between 25% and 70%. More than 2 billion people live in these 53 countries which are all underdeveloped. Water use has increased by 1% per year since the 1980s, so global demand is expected to rise by 30% by 2050. Thus, efficient water grid management is imperative to ensure there is sufficient water for the future. Information and Communication Technology (ICT) can be used to create smart water grids to optimize water distribution, reduce waste and leakage, and resolve quality and overuse issues. In this work, a low cost, real-time, reliable and sustainable IoT based solution called SmartTubewell is proposed for smart water grid management. It is composed of two components, a sensor node installed at tube wells and an application layer on Amazon Web Services (AWS) for data analysis, storage and processing. The sensor node is based on a Raspberry Pi with integrated current and voltage sensors and a local database. The sensor data is transmitted to AWS using a cellular (GPRS) network. A comparison between the proposed system and SCADA is presented which shows that SmartTubewell has a much lower cost. A field test with multiple tube wells in Peshawar, Pakistan indicates that this is a suitable solution for developing countries.

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Section: Smart Water Gridsmentioning

confidence: 99%

Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries

Khattak

Jawwad

Khan

et al. 2021

emitter

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“…The Harvard Business Bureau states that connection to external devices gives these objects new functionality, such as the ability to monitor their environment, be remotely controlled, automate their functions, and provide data for optimization [14]. However, these functions are better suited to fog nodes, as the distance between an end device to a cloud data-center may introduce too much latency for the ''seamless'' integration of objects into the network [23]. Fog nodes are a type of edge device that can provide the services of cloud servers, but at a ''grounded'' level [37].…”

Section: A Internet Of Things Hierarchymentioning

confidence: 99%

A Layer-Partitioning Approach for Faster Execution of Neural Network-Based Embedded Applications in Edge Networks

Saguil

Azim

2020

IEEE Access

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As embedded systems become more prominent in society, the technologies that run on them must be used efficiently. One such technology is the Neural Network (NN). NN's, combined with the Internet of Things (IoT), can utilize the massive amounts of data produced to optimize, control, and automate embedded systems, giving them more functionality than ever before. However, the status quo of offloading all NN functionality onto external devices has many flaws. It forces the embedded system to entirely rely on networks that may have high latency or connection issues. Networks may also expose them to security risks. To reduce the reliance of IoT devices on networks, we examined several solutions, such as delegating some NN's to run solely on the IoT device or splitting the NN and distributing the subnetworks into different devices. It was found that, for shallow NN's, the IoT device itself could run the NN at a rate faster than offloading it to an external device, but the IoT device needed to offload its inputs once the NN's started to increase in layers and complexity. When splitting the NN, it was found that the number of messages sent between devices could be reduced by up to 97% while only reducing the accuracy of the NN by 3%. INDEX TERMS Layer-partitioning, edge networks, neural network models.

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“…The proposed system will save water and provide good quality of water to people. Kulkarni and Farnham (2016) his article provided an overview of issues involved in water monitoring and management and highlighted the wire-less connectivity considerations to enable smart water man-agement. Challenges and design considerations involved in choosing from a variety of architectural approaches to realise communications were highlighted.…”

Section: Introductionmentioning

confidence: 99%

IoT Based Water Monitoring System for Agriculture

Nankani¹,

Gupta²,

Mondal³

et al. 2020

Agric. J.

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Water being the utmost need for everyone in today's world needs to be pure and safe for everyone. This study presents a model on smart water monitoring which holds quantity and quality of water for agriculture, irrigation or drinking purposes. Thus, a model containing Arduino and some sensors for detection of contamination, turbidity, temperature, water level and water flow. The micro-controller Arduino is connected with the sensors which collect the data and then displays the data on the screen. The WI-FI module is used to make a connection with the Arduino which collect all the datas which are sensed by the sensors and then is stored in the cloud server. This model is highly useful for the future safety of the water for the next generation.

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Smart City Wireless Connectivity Considerations and Cost Analysis: Lessons Learnt From Smart Water Case Studies

Cited by 54 publications

References 4 publications

Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries

Sustainable and Resilient Smart Water Grids: A Solution for Developing Countries

A Layer-Partitioning Approach for Faster Execution of Neural Network-Based Embedded Applications in Edge Networks

IoT Based Water Monitoring System for Agriculture

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