A mobility-assisted protocol for supervised learning of link quality estimates in wireless networks

Flushing, Eduardo Feo; Nagi, Jawad; Di, Gianni A.

doi:10.1109/iccnc.2012.6167397

Cited by 18 publications

(31 citation statements)

References 26 publications

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“…RP has been used previously for link quality estimation in wireless sensor networks. Flushing et al have used the packet reception probability to estimate link quality in [42] and refer to it as mobility-assisted proactive probing and learning estimates (MAPPLE). Alizai et al [43] and Becher et al [44] have also used the predicted probability of successful packet transmission to estimate link quality.…”

Section: Link Quality Estimation Using Reception Probabilitymentioning

confidence: 99%

Communication- and position-aware reconfigurable route optimization in large-scale mobile sensor networks

Fida

Iqbal

Ngo

2014

J Wireless Com Network

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We consider the problem of route selection and optimization for a mobile sensor network, which involves two nodes transferring information over a large area using a number of intermediate routers in the presence of noise, path loss, multipath fading, and interference. The communication-and position-aware reconfigurable (COMPARE) route optimization framework is proposed to improve the end-to-end throughput of the routes in the realistic communication environment. Initially, a communication-aware route selection strategy selects the optimal route by integrating the routing decisions with the link quality. The quality of the wireless link is characterized by the reception probability, i.e., the probability of successfully receiving packets over a realistic communication link. The selected route is reconfigured by exploiting the multipath fading, position information, and the mobility of the nodes during the phase of position-aware optimization. The optimal position for a router is heuristically found using a priori information about fading channels and the positions of transmitting and interfering nodes. The router is guided to the optimal position using the feedback mobility control, and only the route selected by the COMPARE framework is used for data transmission between the source and destination node. We illustrate through simulations that the proposed framework provides routes with considerably better performance than conventional route selection metrics, in terms of the end-to-end throughput.

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Section: Link Quality Estimation Using Reception Probabilitymentioning

confidence: 99%

Communication- and position-aware reconfigurable route optimization in large-scale mobile sensor networks

Fida

Iqbal

Ngo

2014

J Wireless Com Network

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“…The use of regression techniques in the form of supervised learning have also been explored in a series of works by the same research center [28][29][30]91]. In [28,29], a distributed protocol was designed to exploit the mobility of nodes for gathering diverse training samples, and afterwards, use the training samples in an offline supervised learning algorithm.…”

Section: Machine Learningmentioning

confidence: 99%

“…For instance, some authors suggest that robots should randomly move around for a period of time in order to collect diverse samples [28,29], but such artificial movement consumes time and energy. To mitigate this issue, other authors have suggested that nodes should exchange labeled samples across multiple links [24,30], but the problem is that the statistical relationship between the inputs and target are link-specific and should not be aggregated.…”

Section: Overview On Existing Approaches and The State Of The Artmentioning

confidence: 99%

“…Using simulation data, attribute selection revealed that RSSI and distance were the most critical to prediction accuracy, but all eight features were retained during the followon evaluation. As for the learning phase, both works (i.e., [28,29]) perform the learning step offline at a distributed node that later disseminates the predication model after training is complete. The learning algorithm used in both papers was based on Support Vector…”

Section: Machine Learningmentioning

confidence: 99%

“…In [28,29], a distributed protocol was designed to exploit the mobility of nodes for gathering diverse training samples, and afterwards, use the training samples in an offline supervised learning algorithm. The primary difference between the two works is that the study by Flushing et al (2012) was simulation-based [28], while the work by Kudelski et al (2014) was a validation study using actual mobile robots [29]. Overall, the framework used in these works can be classified into four basic phases: collect, learn, deploy, and use.…”

Section: Machine Learningmentioning

confidence: 99%

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A mobility-assisted protocol for supervised learning of link quality estimates in wireless networks

Cited by 18 publications

References 26 publications

Communication- and position-aware reconfigurable route optimization in large-scale mobile sensor networks

Communication- and position-aware reconfigurable route optimization in large-scale mobile sensor networks

An adaptable fuzzy-based model for predicting link quality in robot networks.

The Role of Machine Learning and Radio Reconfigurability in the Quest for Wireless Security

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