PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities

Santos, Pedro M.; Rodrigues, João G. P.; Cruz, Susana B.; Lourenço, Tiago; d’Orey, Pedro M.; Luis, Yunior; Rocha, Cecília; Sousa, Sofia; Crisóstomo, Sérgio; Queirós, Cristina; Sargento, Susana; Aguiar, Ana; Barros, João

doi:10.1109/jiot.2018.2791522

Cited by 159 publications

(77 citation statements)

References 31 publications

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“…They showed that, unlike the received signal strength indication, bus speed may be a good indicator to whether a connection is established inside or outside the bus. Santos et al [41] presented a multisource sensing infrastructure (i.e., Porto Living Lab) based on the IoT technology. It is conceived to detect on city-scale four phenomena, i.e., weather, environment, public transportation, and people flows.…”

Section: Emerging Automatic Passenger Counting Systemsmentioning

confidence: 99%

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

et al. 2020

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Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other.

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Section: Emerging Automatic Passenger Counting Systemsmentioning

confidence: 99%

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

et al. 2020

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“…Without a proper data managing design for the end devices and the fog nodes, data traffic will be an issue for the server that will store all the results. Realtime applications such as urban sound classification will have data that will continuously be streamed to the server by multiple nodes [13,27]. If the receiving end of the server cannot handle the volume of data, then the framework will not work.…”

Section: Open Issues For Iot-fog Frameworkmentioning

confidence: 99%

A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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“…This motivates us to conduct the research of designing an effective DM‐based collection scheme for urban environment while focusing on the improvement of the data delivery delay. Unlike previous works that require the collection process to follow planed paths or those relying on specific class of vehicles such as taxis or buses; our scheme involves all kinds of vehicles to provide each road of an interested area with a DM for collecting the sensor data without paths plan. To summarize, Table gives key differences between the proposed scheme and some of the above mentioned works.…”

Section: Related Workmentioning

confidence: 99%

“…One effective, economical, and suitable way to deliver sensor data to the central controller is to exploit mobile elements (MEs) that may be robots or vehicles. In fact, while moving, MEs can download sensors data once they are within their communication range . This approach, well known as data mule (DM) approach, is proved to be effective in energy economy as well as energy consumption balancing, which are considered as the main issues in traditional WSN multi‐hop forwarding strategies.…”

Section: Introductionmentioning

confidence: 99%

OMS: Opportunistic mules for short latency data collection in smart cities

Chehbour

Doukha

Moussaoui

et al. 2019

Int J Communication

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In smart city scenarios, data collected by sensors are required to be conveyed to central controllers for processing. Thus, efficient data collection mechanisms considering the urban environment characteristics should take place. To that end, the data mules approach is proved to be efficient. However, being applied to urban environment leads to high data delivery latency due to the characteristics of this environment. Motivated by the need to reduce the collection latency of such an approach, we propose to provide each road segment with a data mule to create a process of distributed data collection. Our scheme leverages opportunistic inter-vehicular communications for assigning the mule role to adequate vehicles. The simulation in realistic mobility settings reveals that our scheme can provide more than 80% of the area of interest with data mules in less than 1 minute. In addition, the data collection latency can be reduced from 24 hours to few minutes. VANETs INTRODUCTIONOne goal of Internet of Things (IoT) paradigm is to provide cities with smart services in order to enhance their sustainability. Smart City projects rely on advanced technologies such as wireless sensing and communication technologies to deal with air pollution, poor waste management, traffic congestion, and energy misuse. 1 Wireless sensor networks (WSNs) are core components for data acquisition in smart city applications. Indeed, due to their low cost and small size, sensing devices can be easily deployed at a large variety of locations allowing coupling the physical infrastructure to information and communication technologies. However, the issue of collecting the data from the huge amount of sensors and actuators spread across streets is still considered as the main challenge for smart city realization. In fact, the complexity of the urban environment can cause network fragmentation due to forcefully sparse node deployments or unpredictable dynamics in wireless link availability. 2 Even if cellular communications have been suggested as an alternative solution, it could be very expensive because of the need of a permanent infrastructure to be built to cover all the nodes. Moreover, it is difficult from economic and administrative points of view to equip each sensor device with a SIM card. 3 Furthermore, the energy consumed for the data collection process could be critical and highly dependent on the quality of the required communication.One effective, economical, and suitable way to deliver sensor data to the central controller is to exploit mobile elements (MEs) that may be robots or vehicles. In fact, while moving, MEs can download sensors data once they are within their communication range. [2][3][4][5][6][7][8] This approach, well known as data mule (DM) approach, is proved to be effective in energy Int J Commun Syst. 2020;33:e4207.wileyonlinelibrary.com/journal/dac

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PortoLivingLab: An IoT-Based Sensing Platform for Smart Cities

Cited by 159 publications

References 31 publications

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

A scalable IoT-fog framework for urban sound sensing

OMS: Opportunistic mules for short latency data collection in smart cities

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