Human-enabled edge computing: when mobile crowd-sensing meets mobile edge computing

Foschini L., Girolami M.

Mobile Edge Computing (MEC)  Mobile Crowd-Sensing (MCS)  Human-driven Edge Computing (HEC) 

EC is an architectural model and specification proposal (i.e., by European Telecommunications Standards Institute - ETSI) that aims at evolving the traditional two-layers cloud-device integration model, where mobile nodes directly communicate with a central cloud through the Internet, with the introduction of a third intermediate middleware layer that executes at so-called network edges. This promotes a new three-layer device-edge-cloud hierarchical architecture, which is recognized as very promising for several application domains [1]. In fact, the new MEC model allows moving and hosting computing/storage resources at network edges close to the targeted mobile devices, thus overcoming the typical limitations of direct cloud-device interactions, such as high uncertainty of available resources, limited bandwidth, unreliability of the wireless network trunk, and rapid deployment needs. Although various MEC solutions based on fixed edges enable an increase of the quality and performance of several cloud-assisted device services, currently there are still several non-negligible weaknesses that affect this emerging new model. First, the number of edges is generally limited because edges are deployed statically (usually by telco providers) and their configuration and operation introduce additional costs for the supported services, such as deployment, maintenance, and configuration costs. Second, once deployed, edges are rarely re-deployed (due to the high re-configuration cost) in other positions and this might result in high inefficiency, e.g., as service load conditions might significantly change dynamically. Finally, some geographical areas might become interesting hotspots for a service only during specific time slots, such as a square becoming crowded due to an open market taking place only at a specific timeslot and day of the week. At the same time, the possibility to leverage people roaming though the city with their sensor-rich devices has recently enabled Mobile Crowd-Sensing (MCS). In fact, by installing an MCS application, any smartphone can become part of a (large-scale) mobile sensor network, partially operated by the owners of the phones themselves. However, for some high-demanding MCS applications (e.g., a surveillance service that, for security purposes, monitors an environment with smartphone cameras that capture photos/videos of the surroundings and exploits face recognition to trace suspicious users' movements), regular smartphones often have not enough capabilities to timely perform the requested local tasks, in particular if considering their possible immersion in hostile environments with possible frequent intermittent disconnections from the global cloud. In other words, we claim that there are several practical cases of large and growing relevance where the joint exploitation of MEC and MCS would bring highly significant benefits in terms of efficient resource usage and perceived service quality. However, notwithstanding recent advances in both MEC and MCS, to the best of our knowledge, only a very limited number of seminal works has explored the mutual advantages in the joint use of these two classes of solutions, and they are mostly focused on pure technical communication aspects without considering the crucial importance of having humans as central contributors in the loop [2, 3, 4]. The paper reports some research ideas and findings in a brand new area that we call Human-driven Edge Computing (HEC) defined as a new model to ease the provisioning and deployment of MEC platforms as well as to enable more powerful MEC-enabled MCS applications. First and foremost, HEC eases the planning and deployment of the basic MEC model: it mitigates the potential weaknesses of having only Fixed MEC entities (FMEC) by exploiting MCS to continuously monitor humans and their mobility patterns, as well as to dynamically re-identify hot locations of potential interest for the deployment of new edges. Second, to overcome FMEC limitations, HEC enables the implementation and dynamic activation of impromptu and temporary Mobile MEC entities (M2EC) that leverage resources of locally available mobile devices. Hence, a M2EC is a local middleware proxy dynamically activated in a logical bounded location where people tend to stay for a while with repetitive and predictive mobility patterns [5], thus realizing a mobile, opportunistic, and participatory edge node. Third, given that M2EC, differently from FMEC, does not implement powerful backhaul links toward the core cloud, HEC exploits local one-hop communications and the store-and-forward principle by using humans (moving with their devices) as VM/container couriers to enable migrations between well-connected FMEC and local M2EC.

Source: ISTI Technical reports, 2017