A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

Abstract

Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization

“…In addition, the household robot cleaners are providing reliable services for families, which also facilitate the gradually increasing LBS market. Moreover, indoor localization technology also plays an important role in elderly care, disaster management, and assistance [ 5 ]. COVID-19 has been spreading globally since December 2019.…”

Cloud-based Indoor Positioning Platform for Context-adaptivity in GNSS-denied Scenarios

“…Section 2.4.1 featured a full review of the computing paradigms used in the selected research studies. The review concluded that CC ( articles [62, 35, [56,3,76,133]), FC ( articles [48,49,64,16,134,135,136]), and EC ( articles [53,18,31,110,54,52,51,142,143,137,138,139,140,141]).…”

“…In general, CC and MCC is used to provide a better experience to the end-user ( articles [103,62,77,2,57,105,80,89,104,59,61,60,86,73,118,119,36,123,124,129] focus on usability). EC is used to diminish the computational intensity in the user device ( articles energy eciency [31,94,51,142,138,139]). The combination of CC-EC, FC-CC and MEC-CC is used to improve the localisation accuracy.…”

“…place is computational eciency with articles[48,75,80,89,45,56,42,38,6,64,3,70,31,94,134,51,142,138,139]. And third, we have localisation with articles[101…”