An Analytical Framework to Design Efficient and Trustworthy Service Allocation in LBS-enabled IoT

Next-generation networks will envision innovative applications and services empowered by the interoperability of several technologies and the use of context-aware information. In this realm, the Internet of Things (IoT) will play a key role in the successful advent of these services. One of the most relevant contextual data is provided by object location information, thus leading to the so-called Location-Based Services (LBS), which will enrich IoT applications. In this paper, a simple yet insightful analytical framework to predict the performance of LBS-enabled IoT applications is presented. The framework includes a quality-cost trade-off to characterize the system efficiency in the presence of unreliable data, and the possibility of offloading part of the requested application to the Fog-Edge-Cloud (FEC) continuum. Moreover, node trustworthiness is also considered by tackling the presence of malicious nodes aimed at reducing the provided quality of the application until its denial. Numerical results are provided to discuss the relative impact of several parameters on the system efficiency, i.e., malicious attack intensity, offloading percentage, and localization error.