Strategies and techniques for node placement in wireless sensor networks: A survey

Younis, Mohamed; Akkaya, Kemal

doi:10.1016/j.adhoc.2007.05.003

Cited by 891 publications

(466 citation statements)

References 62 publications

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“…The optimal placement of wireless devices, such as APs or routers, has been also widely studied in [18]. These studies are interesting for our purposes.…”

Section: Related Workmentioning

confidence: 94%

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

et al. 2017

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Some maintenance tasks in Wi-Fi networks may involve removing an access point due to several reasons. As a result, the new infrastructure registers a different number of roamings in the access points according to the users' behaviour, with a certain energy impact added to the consumption caused by the own operations of the devices. This energy effect should be understood in order to tackle the measures aimed at planning the infrastructure deployment. In this work, we propose a methodology to predict the energy consumption in the access points of a Wi-Fi network when we remove a particular device, based on a twofold support. We predict the number of roamings following a method previously validated; on the other hand, we assess the relationship between roamings and energy in the full infrastructure, using the data collected from a high number of network users during a given time in order to reflect the users' behaviour with the maximum accuracy. From this knowledge, we can infer the energy prediction for a different environment where the roamings are predicted using techniques based on recommender systems and machine learning.

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“…The optimal placement of wireless devices, such as APs or routers, has been also widely studied in [18]. These studies are interesting for our purposes.…”

Section: Related Workmentioning

confidence: 94%

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

et al. 2017

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“…The search for optimal sensor placement is still an open problem [45], so we compare two known strategies to place sensors: random [29] and intersection-based placement. The latter is recommended for the Street model because street intersections have direct paths to nearby APs [44].…”

Section: B Signal Prediction Resultsmentioning

confidence: 99%

Practical Conflict Graphs in the Wild

Zhou

Zhang

Wang

et al. 2015

IEEE/ACM Trans. Networking

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Abstract-Today, most spectrum allocation algorithms use conflict graphs to capture interference conditions. The use of conflict graphs, however, is often questioned by the wireless community for two reasons. First, building accurate conflict graphs requires significant overhead, and hence does not scale to outdoor networks. Second, conflict graphs cannot properly capture accumulative interference. In this paper, we use large-scale measurement data as ground truth to understand how severe these problems are and whether they can be overcome. We build "practical" conflict graphs using measurement-calibrated propagation models, which remove the need for exhaustive signal measurements by interpolating signal strengths using calibrated models. Calibrated models are imperfect, and we study the impact of their errors on multiple steps in the process, from calibrating propagation models, predicting signal strengths, to building conflict graphs.At each step, we analyze the introduction, propagation, and final impact of errors by comparing each intermediate result to its ground-truth counterpart. Our work produces several findings. Calibrated propagation models generate location-dependent prediction errors, ultimately producing conservative conflict graphs. While these "estimated conflict graphs" lower spectrum utilization, their conservative nature improves reliability by reducing the impact of accumulative interference. Finally, we propose a graph augmentation technique to address remaining accumulative interference.

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“…Many algorithms for failure detection in wireless sensor networks have been proposed and implemented with their share of pros and cons [10][11][12]. Fault management architecture and smart detection techniques tend to address failure detection in WSN [13,14]. In our work, a method to detect failed sensor network by selecting paths efficiently, minimizing the time required for the detection process has been proposed.…”

Section: Related Workmentioning

confidence: 99%

Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks

Spoorthi

Saha

Mathur

2016

Cybernetics and Information Technologies

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Strategies and techniques for node placement in wireless sensor networks: A survey

Cited by 891 publications

References 62 publications

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

Energy Prediction of Access Points in Wi-Fi Networks According to Users’ Behaviour

Practical Conflict Graphs in the Wild

Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks

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