Location-based Discovery and Vertical Handover in Heterogeneous Low-Power Wide-Area Networks


F. Lemic, A. Behboodi, J. Famaey, R. Mathar,


        Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices promise enabling IoT use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a Mobile Terminal (MT) and a Base Station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs.We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.

BibTEX Reference Entry 

	author = {Filip Lemic and Arash Behboodi and Jeroen Famaey and Rudolf Mathar},
	title = "Location-based Discovery and Vertical Handover in Heterogeneous Low-Power Wide-Area Networks",
	pages = "1-15",
	journal = "{IEEE} Internet of Things Journal",
	doi = 10.1109/JIOT.2019.2935804,
	month = Aug,
	year = 2019,
	hsb = RWTH-2019-09686,


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