Algorithmic Approach for Strategic Cell Tower Placement

Kashyap, Rakesh; Bhuvan, Malladihalli S; Chamarti, Srinath; Bhat, P. Devadas; Jothish, Mintu; Annappa, Karker

doi:10.1109/isms.2014.112

Cited by 8 publications

(5 citation statements)

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“…Articles such as [110, 111, 135] may be an initial starting point for research in this area. The OR community has contributed to several related topics including the location/operation of cell towers [27, 71] and wireless sensor networks [40, 108] where spatially distributed nodes with limited broadcast power establish a communication path to a central location (e.g., a cell tower). See also [39, 116].…”

Section: Access Networkmentioning

confidence: 99%

Telecommunications network design: Technology impacts and future directions

Wong

2020

Networks

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During the past 50 years telecommunication network technology has raced ahead powered by exponential advances in computer chip and fiber optic technology. The introduction of packet‐switched networks and the Internet has increased network traffic demands exponentially and has nurtured a variety of new telecommunication services (e.g., email, web‐surfing, social media, streaming services, e‐commerce, the Internet of Things, etc.) over the past 30 years. This rapidly changing environment (along with certain technical considerations) has made it very difficult to give accurate forecasts of traffic demands for both the short‐term (intraday) and for the longer term (year‐to‐year). Telecommunications network design has evolved from the voice‐only networks of the 1970s where operations research (OR) methodology was indispensable to current multiservice packet‐switched data networks whose uncertainty and rapid evolution has reduced the utility of classical optimization models that rely on accurate traffic demand forecasts. After summarizing the major developments in telecommunications network design technologies/services, we describe some promising future research directions to address an environment where technology is ever‐changing and traffic forecasting is difficult. In particular, we focus on the public Internet and emphasize possible work where OR/optimization methodology may be useful in understanding and solving telecommunications network routing and design problems.

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Section: Access Networkmentioning

confidence: 99%

Telecommunications network design: Technology impacts and future directions

Wong

2020

Networks

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“…It clusters the subscribers (nodes) by using a traditional clustering technique to find minimum appropriate number of cell towers and optimizing their distribution. Besides, an algorithmic approach, suggested in [2], leverages satellite imagery and GIS (Geographic Information System) data to identify strategic cell tower placements, highlighting the importance of topographical considerations in minimizing infrastructure costs. This research aims to find a simple implementable algorithm which effectively determines the strategic positions of the cell towers.…”

Section: Introductionmentioning

confidence: 99%

Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model

Chaabane,

Réjichi,

Ben Salem

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy.

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“…A convergence of technological advances and infrastructure developments have brought us to a new phase of human history-the era of the crowded sky. While the sky was once seen as a limitless expanse, we are continuing to see conflicts arising from concurrent human use of the lower atmosphere for transportation (Kopardekar et al, 2008), communication (Kashyap et al, 2014), solar energy (Sawyer et al, 2022), wind energy (Klaeboe and Sundfør, 2016), hunting (Rowland et al, 2021) and drone use (Rule, 2015). Meanwhile, a variety of wildlife requires this airspace as habitat for migration (Peterson et al, 2015), foraging (Remsen and Robinson, 1990), and reproduction (Clark, 2009).…”

Section: Introductionmentioning

confidence: 99%

Perceptions of the crowded sky as assessed through response to aerial infrastructure

Kolbmann,

Leslie,

Anderson

et al. 2023

Front. Built Environ.

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Ever increasing numbers of wind turbines, communication towers, power lines, and aerial vehicles are clear evidence of our growing reliance on infrastructure in the lower aerosphere. As this infrastructure expands, it is important to understand public perceptions of an increasingly crowded sky. To gauge tolerance for aerial crowding, 251 participants from across the US completed a survey where they rated tolerance for a series of aerial infrastructure images (i.e., towers, turbines, and airborne vehicles) in four landscapes with varying degrees of pre-existing ground-level infrastructure that approximated rural, suburban, and urban settings. We predicted lower tolerance for aerial infrastructure 1) in more natural scenes and 2) among rural residents. In general, participants preferred an open aesthetic with relatively little aerial infrastructure across all landscape types. No clear association was found between infrastructure tolerance and natural scenes nor rural residency, with participants slightly less tolerant of infrastructure in the suburban scene. Tolerance scores were generally similar across age, income levels, and political affiliations. Women indicated less crowding tolerance than men, with this effect driven by a disproportionate number of women with zero tolerance for aerial infrastructure. African Americans and Asians had higher tolerance scores than other racial/ethnic groups, but these trends may have been affected by low sample sizes of non-white participants. Our survey revealed fewer differences in crowding tolerance across demographic groups than might be expected given widely reported political and geographic polarization in the U.S. Attitudes toward aerial infrastructure were varied with few associations with demographic parameters suggesting that public opinion has not yet solidified with regard to this issue, making possible opportunities for consensus building with regard to responsible development of aerial infrastructure.

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Algorithmic Approach for Strategic Cell Tower Placement

Cited by 8 publications

References 1 publication

Telecommunications network design: Technology impacts and future directions

Telecommunications network design: Technology impacts and future directions

Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model

Perceptions of the crowded sky as assessed through response to aerial infrastructure

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