IPAWL: An integrated power aware Wireless sensor network and Location-Based social network for incidence reporting

Honarparvar, Sepehr; Malek, Mohammad Reza; Saeedi, Sara; Liang, Steve H. L.

doi:10.1016/j.jag.2021.102540

Cited by 4 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is highly critical to transfer data effectively and efficiently during a disaster, considering the limited bandwidth and intermittent connectivity [ 54 ]. Honarparvar et al [ 55 ] address the energy consumption problem in wireless sensor networks where sensor nodes are located far from the base stations. The authors propose an integrated location based social network that can reduce energy consumption up to 42% through reduced routes to BSs.…”

Section: Resultsmentioning

confidence: 99%

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

Cicek

Kantarcı

2023

Sensors

View full text Add to dashboard Cite

With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big “sensed” data leads to an effective and efficient management of disaster situations so as to prevent human and economic losses. The advancement of built-in sensing technologies in smart mobile devices enables crowdsourcing of sensed data, which is known as mobile crowdsensing (MCS). This systematic literature review investigates the use of mobile crowdsensing in disaster management on the basis of the built-in sensor types in smart mobile devices, disaster management categories, and the disaster management cycle phases (i.e., mitigation, preparedness, response, and recovery activities). Additionally, this work seeks to unveil the frameworks or models that can potentially guide disaster management authorities towards integrating crowd-sensed data with their existing decision-support systems. The vast majority of the existing studies are conceptual as they highlight a challenge in experimental testing of the disaster management solutions in real-life settings, and there is little emphasis on the use cases of crowdsensing through smartphone sensors in disaster incidents. In light of a thorough review, we provide and discuss future directions and open issues for mobile crowdsensing-aided disaster management.

show abstract

Section: Resultsmentioning

confidence: 99%

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

Cicek

Kantarcı

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…This study introduced the LSCP model based on the spatial distribution of power loads and characteristics of distribution networks (transformer capacity, transformer units, power factor, supply radius, maximum load factor). Considering the actual substation power supply range division, we aimed to maximize urban grid coverage and utilization of grid equipment (Honarparvar et al, 2021; Ye et al, 2018), thus establishing the LSCP‐based urban substation site selection problem. The problem can be formulated as follows: within the urban area, the minimum number of terminal substations was used to cover all power demand points, and the objective was to maximize the grid coverage and equipment utilization.…”

Section: Methodsmentioning

confidence: 99%

A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

Yao,

Feng,

Xie

et al. 2023

Transactions in GIS

View full text Add to dashboard Cite

The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unreasonable energy transfer and waste, leading to power outages in some areas. Aiming to maximize the grid coverage and transformer utilization, we propose a multi‐scene micro‐scale urban substation siting framework (UrbanPS): (1) The framework uses multi‐source big data and the machine learning model to estimate fine‐scale power consumption for different scenarios; (2) the region growing algorithm is used to divide the power supply area of substations; and the (3) location set coverage problem and genetic algorithm are introduced to optimize the substation location. The UrbanPS was used to perform siting optimization of 110 kV terminal substations in Pingxiang City, Jiangxi Province. Results show that the coverage and utilization rate of the optimization results under different power consumption scenarios are close to 99%. We also found that the power can be saved by dynamic regulation of substation operation.

show abstract

Strategic Planning for large-scale Deployment of a Geo-positioned Smart Metering Network

Inga,

Campaña

2023

2023 IEEE/CIC International Conference on Communications in China (ICCC)

View full text Add to dashboard Cite

IPAWL: An integrated power aware Wireless sensor network and Location-Based social network for incidence reporting

Cited by 4 publications

References 31 publications

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

Strategic Planning for large-scale Deployment of a Geo-positioned Smart Metering Network

Contact Info

Product

Resources

About