Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks

Meseguer, Roc; Molina, Carlos; Ochoa, Sergio F.; Santos, Rodrigo

doi:10.3390/s140202619

Cited by 12 publications

(12 citation statements)

References 44 publications

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“…Prediction techniques have been applied in several ways such as routing traffic reduction [24][25][26], energy efficient routing [27][28][29], or LQ estimation.…”

Section: Link and End-to-end Quality Prediction In Heterogeneous Netwmentioning

confidence: 99%

Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

et al. 2019

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Community Networks have been around us for decades being initially deployed in the USA and Europe. They were designed by individuals to provide open and free “do it yourself” Internet access to other individuals in the same community and geographic area. In recent years, they have evolved as a viable solution to provide Internet access in developing countries and rural areas. Their social impact is measurable, as the community is provided with the right and opportunity of communication. Community networks combine wired and wireless links, and the nature of the wireless medium is unreliable. This poses several challenges to the routing protocol. For instance, Link-State routing protocols deal with End-to-End Quality tracking to select paths that maximize the delivery rate and minimize traffic congestion. In this work, we focused on End-to-End Quality prediction by means of time-series analysis to foresee which paths are more likely to change their quality. We show that it is possible to accurately predict End-to-End Quality with a small Mean Absolute Error in the routing layer of large-scale, distributed, and decentralized networks. In particular, we analyzed the path ETX behavior and properties to better identify the best prediction algorithm. We also analyzed the End-to-End Quality prediction accuracy some steps ahead in the future, as well as its dependency on the hour of the day. Besides, we quantified the computational cost of the prediction. Finally, we evaluated the impact of the usage for routing of our approach versus a simplified OLSR (ETX + Dijkstra) on an overloaded network.

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“…Prediction techniques have been applied in several ways such as routing traffic reduction [24][25][26], energy efficient routing [27][28][29], or LQ estimation.…”

Section: Link and End-to-end Quality Prediction In Heterogeneous Netwmentioning

confidence: 99%

Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

et al. 2019

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“…These communication limitations have frequently been addressed using mobile ad hoc or opportunistic networks, as well as routing protocols on these networks [4][5][6]. These networks have low bandwidth and their topology is highly dynamic, and as such, they require the exchange of an goals, e.g., to support message delivery in mobile ad hoc networks (MANETs) [25][26][27][28], reduce the traffic [29][30][31] and keep the energy consumption in these infrastructures under control [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…These messages not only overload the communication links, but also increase the energy consumption of the nodes. The authors have also shown that the problem of delivering so much control information through the network can be addressed using predictions [29,31]. Particularly, the OLSRp protocol was proposed to eliminate redundant control information, and thus, reduce CPU and energy consumption in MANETs.…”

Section: Introductionmentioning

confidence: 99%

Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History

Millan

Aliagas

Molina

et al. 2019

Sensors

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The mobile ad hoc communication in highly dynamic scenarios, like urban evacuations or search-and-rescue processes, plays a key role in coordinating the activities performed by the participants. Particularly, counting on message routing enhances the communication capability among these actors. Given the high dynamism of these networks and their low bandwidth, having mechanisms to predict the network topology offers several potential advantages; e.g., to reduce the number of topology propagation messages delivered through the network, the consumption of resources in the nodes and the amount of redundant retransmissions. Most strategies reported in the literature to perform these predictions are limited to support high mobility, consume a large amount of resources or require training. In order to contribute towards addressing that challenge, this paper presents a history-based predictor (HBP), which is a prediction strategy based on the assumption that some topological changes in these networks have happened before in the past, therefore, the predictor can take advantage of these patterns following a simple and low-cost approach. The article extends a previous proposal of the authors and evaluates its impact in highly mobile scenarios through the implementation of a real predictor for the optimized link state routing (OLSR) protocol. The use of this predictor, named OLSR-HBP, shows a reduction of 40-55% of topology propagation messages compared to the regular OLSR protocol. Moreover, the use of this predictor has a low cost in terms of CPU and memory consumption, and it can also be used with other routing protocols.Sensors 2020, 20, 24 2 of 24 important number of topology propagation (TP) messages to keep the delivery of regular messages under control. The exchange of TP messages helps reduce the probability of overflowing the network with regular messages that do not reach the destination, rendering the communication system useless at supporting the coordination of the emergency response activities. Provided that many of these communication systems involve mobile ad hoc networks, the routing protocols used to support interactions among nodes (i.e., the first responders) must be simple, efficient and reliable and have the capability of quickly adapting themselves to changes in the network topology [7-10]. Thus, these protocols intend to maximize the reachability of the target nodes, by consuming as little energy from the network as possible.Regardless of the extensive research done in the area, addressing this communication challenge, considering the previous constraints, is still an open issue. Some recent proposals try to deal with this challenge using deep learning in static or quasi-static networks [11]. Although deep learning techniques have shown positive results, these approaches are quite limited when used in mobile scenarios, like emergency responses. Moreover, they require a previous training stage to generate the network model. Novel solutions are therefore required to reduce the number of TP messages ex...

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“…Prediction techniques have been applied in several ways such as routing traffic reduction [16], [17], [18], energy efficient routing [19], [20], [21] or LQ estimation.…”

Section: Link Quality Prediction In Community Networkmentioning

confidence: 99%

Tracking and Predicting End-to-End Quality in Wireless Community Networks

Millan

Molina

Dimogerontakis

et al. 2015

2015 3rd International Conference on Future Internet of Things and Cloud

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Abstract-Community networks are an emergent model with mottos like "a free net for everyone is possible" or "don't buy the network, be the network". Their social impact is measurable, as the community is provided with the right and opportunity of communication. The combination of wired and wireless links in these networks, and the unreliable nature of the wireless medium, poses several challenges to the routing protocol. End-toEnd quality tracking helps the routing layer to select paths that maximize the delivery rate and minimize traffic congestion. We believe that End-to-End quality prediction can be a technique that surpasses End-to-End quality tracking by foreseeing which paths are more likely to change quality. In this work, we focus on End-to-End quality prediction by means of time-series analysis. We apply this prediction technique in the routing layer of largescale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict End-to-End Quality with an average Mean Absolute Error of just 2.4%. Particularly, we analyze the path properties and path ETX behavior to identify the best prediction algorithm. Moreover, we analyze the EtEQ prediction accuracy some steps ahead in the future and also its dependency of the time of the day.

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Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks

Cited by 12 publications

References 44 publications

Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

Time Series Analysis to Predict End-to-End Quality of Wireless Community Networks

Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History

Tracking and Predicting End-to-End Quality in Wireless Community Networks

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