{'fraction_non_alphanumeric': 0.0304601425793908, 'fraction_numerical': 0.0038885288399222295, 'mean_word_length': 5.2008032128514055, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
LAMEN: leveraging resources on anonymous mobile edge nodes

The increased intelligence on network devices opened the horizon for a new computation approach, i.e., Edge Computing, to push computations away from the network's core. In this work, we present LAMEN, an initial attempt to use end devices at the edge to execute complex applications instead of offloading to the cloud, e.g., Augmented Reality. Unlike crowd-sensing where end devices are used as data collectors wasting their bandwidth in raw data transmission for cloud processing. LAMEN proposes a hierarchical architecture to group devices by location. Each group elects a head node, i.e., mediator, to control service delivery and execution. Within a group, mediators name and organize devices in terms of resources they hold. This enables efficiently locating suitable resources per application. We envision pushing application's execution to end devices to hold the following benefits: (a) avoid sharing user's data with cloud to maintain their privacy (b) minimize bandwidth misuse through local processing instead of raw data transmission to cloud and (c) introduce mediators as local cloud for composing homogenous large resources out of heterogeneous end devices and anonymizing user's identity. Our evaluation shows LAMEN to enhance resource lookup by 70% compared to similar approaches, i.e., Chord, and speeds up application distribution 500x than regular application download per device. In addition to, scalability in handling large networks up to 8k device per group.