Measuring Cities with Software-Defined Sensors

Catlett, Charlie; Beckman, Pete; Ferrier, Nicola; Nusbaum, Howard C.; Papka, Michael E.; Berman, Marc G.; Sankaran, Rajesh

doi:10.23919/jsc.2020.0003

Cited by 35 publications

(27 citation statements)

References 38 publications

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“…Right now, in cities where such networks are implemented, the numbers of nodes usually range from 500-5000. For these modest numbers of devices, operators predict lifetimes of 2-7 years until the system is upgraded [4,6]. This limits systems to settings that are comparatively easy to access and replace.…”

Section: What Is Smart Infrastructure Who Wants It and Why?mentioning

confidence: 99%

“…Raspberry Pi-class device. 6 The initial application supported by the gateway is transmit-only, which would allow it to be aggressively firewalled and limit the security risk of not attending to updates. Unidirectional gateways limit the utility of our deployed infrastructure, however.…”

Section: Managementmentioning

confidence: 99%

“…In at least one case, a non-networked Raspberry Pi has operated unattended for nearly eight years and counting[5] 6. …”

mentioning

confidence: 99%

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Century-scale smart infrastructure

Jagtap

Bhaskar

Pannuto

2021

Proceedings of the Workshop on Hot Topics in Operating Systems

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On average, wireless electronics devices are replaced every 50 months. On average, a bridge is replaced every 50 years. As we begin to imagine integrating electronics and intelligence into the built environment, we need to to begin to think about electronic devices and systems on infrastructure timelines. This is not to say that every individual electronic device can, will, or should last for decades, but much like the ship of Theseus, the system that defines emerging Smart Cities will have a lifetime reaching into the century-scale. In this paper, we contemplate what the devices, gateways, network architectures, and their management might look like for a system designed to operate for decades. The result is a mixture of actionable insights for today and research questions for tomorrow, which culminates in the commencement of a 50-year experiment designed to see how long energyharvesting sensors, without the implicit lifetime of batteries, can remain viable without human attention or intervention.

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Section: What Is Smart Infrastructure Who Wants It and Why?mentioning

confidence: 99%

Section: Managementmentioning

confidence: 99%

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Century-scale smart infrastructure

Jagtap

Bhaskar

Pannuto

2021

Proceedings of the Workshop on Hot Topics in Operating Systems

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“…e developmental trends of arti cial intelligence in recent years are re ected not only in the iterative innovation of technology but also in the expansion of application elds. For example, these trends are evident in the intelligent analysis of education evaluation in the education industry [16], intelligent risk monitoring and investment prediction in the nancial industry [17,18], intelligent monitoring and control of UAVs and vehicles [19,20], the e cient scheduling and management of dynamic resources [21][22][23][24], and other elds closely related to the economy and people's livelihood, such as intelligent cities [25,26], intelligent medical treatment [27,28], intelligent communication [29], and intelligent infrastructure [30,31]. All these trends re ect the intelligent upgrading of traditional industries.…”

Section: Introductionmentioning

confidence: 99%

Research on an Artificial Intelligence-Based Professional Ability Evaluation System from the Perspective of Industry-Education Integration

Bian

2022

Scientific Programming

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The rapid development of artificial intelligence technology demands higher requirements for employment and talent training. The integration of industry and education is an important way to solve the mismatch between industrial demand and talent supply. Therefore, this study starts from the perspective of the integration of industry and education. We collect recruitment texts from the perspective of “industry” and mine the specific requirements of the artificial intelligence post system through the LDA topic model and the combination of Word2Vec and K-means. We then conduct expert consultations and adjust the selected indicators from the perspective of “education.” Finally, we construct a four-dimensional vocational ability grade evaluation index system, including basic vocational skills of artificial intelligence, database, network skills, algorithm and design skills, and research and practice skills. The intuitionistic fuzzy analytic hierarchy process, which can eliminate the subjective uncertainty of experts in the scoring process, is applied to calculate the index weights. We find that the weight of algorithm and design skill is the highest, which is an important criterion for artificial intelligence professional ability evaluation. Among the second-level indicators, practical indicators such as team spirit, innovation ability, and communication ability are the focus of investigation from the perspective of industry, while in education, the cultivation of knowledge and skills such as programming ability, applied mathematics ability, data structures, and algorithms are more important.

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“…erefore, sensor power supply capability is the main obstacle that limits the application of sensor network technology [8]. However, the power of sensor nodes is limited and di cult to replace [9][10][11], how to reduce the energy consumption of sensor nodes becomes the core problem of WSNs [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Two FCA-Based Methods for Reducing Energy Consumption of Sensor Nodes in Wireless Sensor Networks

Lei

Chen

et al. 2022

Scientific Programming

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In the design of wireless sensor networks (WSNs), it is important to reduce energy consumption and extend the service life of the sensors. Selecting one of the minimum sensor combinations (MSCs) that can monitor all areas, while the other MSCs are asleep, can effectively extend the lifetime of WSNs. This paper proposes two algorithms based on Formal Concept Analysis (FCA) for extracting some MSCs, to minimize the energy consumption and meet the coverage requirement. These two methods firstly extract the concept lattice from a monitor-areas context, and then it is simple to extract sensor nodes monitoring the overlapping area from the concept lattice. The algorithms consist of three steps as follows: the first step is to transform sensors and monitoring areas into a context, the second is to extract the concept lattice and implications among areas, and the third is to extract some different MSCs that can monitor all areas. Thus, some strategies are designed to awaken different MSCs to achieve the purpose of reducing energy consumption. Experimental results show that these methods have played a positive effect on extracting different MSCs and extending the lifetime of sensor networks.

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Measuring Cities with Software-Defined Sensors

Cited by 35 publications

References 38 publications

Century-scale smart infrastructure

Century-scale smart infrastructure

Research on an Artificial Intelligence-Based Professional Ability Evaluation System from the Perspective of Industry-Education Integration

Two FCA-Based Methods for Reducing Energy Consumption of Sensor Nodes in Wireless Sensor Networks

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