Offloading Positioning onto Network Edge

Abstract

While satellite or cellular positioning implies dedicated hardware or network infrastructure functions, indoor navigation or novel IoT positioning techniques include flexible storage and computation requirements that can be fulfilled by both end-devices or cloud back-ends. Hybrid positioning systems support the integration of several algorithms and technologies; however, the common trend of delegating position calculation and storage of local geoinformation to mobile devices or centralized servers causes performance degradation in terms of delay, battery usage, and waste of network resources. The strategy followed in this work is offloading this computation effort onto the network edge, following a Mobile Edge Computing (MEC) approach. MEC nodes in the access network of the mobile device are in charge of receiving navigation data coming from both the smart infrastructure and mobile devices, in order to compute the final position following a hybrid approach. With the aim of supporting mobility and the access to multiple networks, an Information Centric Networking (ICN) solution is used to access generic position information resources. The presented system currently supports WiFi, Bluetooth LE, GPS, cellular and NFC technologies, involving both indoor and outdoor positioning, using fingerprinting and proximity for indoor navigation, and the integration of smart infrastructure data sources such as the door opening system within real smart campus deployment. Evaluations carried out reveal latency improvements of 50%, as compared with a regular configuration where position fixes are computed by mobile devices; at the same time the MEC solution offers extra flexibility features to manage positioning databases and algorithms and move extensive computation from constrained devices to the edge.

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

“…Current IPS/ILS use two types of architectures based on MEC, the first one is mobile-MEC (see Figure 3 e) and the second one combines MEC and CC (see Figure 3 i). This paradigm is also involved in the deployment of the new mobile technology 5G by offloading certain process on it [ 73 ].…”

“…In order to estimate the user position by using Wi-Fi technology, researchers and developers use different techniques such as fingerprinting [ 73 , 102 ], which is based on the RSS measurements. Fingerprinting is divided into two phases, the offline phase, where the radio map is formed from the collected RSS values and the online phase, where the incoming fingerprint is compared with the fingerprints in the radio map.…”

“…In the analysed studies, the authors combined cellular, Wi-Fi, BLE, and Radio Frequency Identifier (RFID) in order to provide a more accurate solution. For instance, Santa et al [ 73 ] developed an indoor-outdoor positioning system which supports five technologies Wi-Fi, BLE, cellular, Near-field Communication (NFC), and Global Positioning System (GPS), in this case the position estimation is done in MEC nodes. Despite combining five technologies the positioning error was around .…”

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

“…However, some heavy computing tasks are not adequate to be executed by the device, so they should be offloaded. Great efforts are being devoted to the computation offloading paradigm, especially aligned with the edge computing concept [ 60 , 61 ]. As communication activities should be limited, given their notable energy consumption, the amount of data exchanged with the edge or cloud should be reduced.…”

Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transportation systems

“…Offloading algorithms arise very naturally in a Cloud-to-thing continuum systems [20,21] (see also Sect. 3).…”