In this paper, we consider a network reconstruction problem using Unmanned Aerial Vehicles (UAVs) where stationary ad-hoc networks are severely damaged in a post-disaster scenario. The main objective of this paper is to repair the network by supplementing aerial wireless links into the isolated ground network using UAVs. Our scheme performs network probing from the air and finds out crucial spots where both local and global routing performance can significantly be recovered if deployed. First, we propose novel distributed coverage path planning algorithms with independent and computationally lightweight navigation based on adaptive "zigzag" patterns. Second, we present route topology discovery schemes that capture both local and non-local network connectivity by extracting inherent route skeletons via stitching partial local paths obtained from simple packet probing by UAVs. Third, we find the optimal UAV relay deployment positions that can improve network-wide data delivery most effectively based on three novel approaches of an optimization technique, an iterative heuristic algorithm, and a topology partitioning of "strongly connected component." Simulation results demonstrate that our distributed traversing algorithms reduce the complete coverage time, the travel distance, and the duplicate coverage compared to other counterpart algorithms. Our deployment algorithms recover severely impaired routes, incurring reasonable computational overhead.

Due to the outbreak of recent network attacks, it is necessary to develop a robust network intrusion detection system (NIDS) that can quickly and effectively identify the network attack. Although the state-of-the-art detection algorithms have shown quite promising detection performance, they suffer from computationally intensive operations and large memory footprint, making themselves infeasible to applications at the resourceconstrained edge devices. We propose a lightweight yet effective NIDS scheme that incorporates a stacked autoencoder with a network pruning technique. By removing a set of ineffective neurons across layers in the autoencoder network with a certain probability based on their importance, a considerably large portion of relatively nominal training parameters are reduced. Then, the pruned and pretrained encoder network is used as-is and is connected with a separate classifier network for attack type inference, avoiding a full retraining from scratch. Experimental results indicate that our stacked autoencoder-based classification network with probabilistic feature extraction has outperformed the state-of-the-art NIDSs in terms of attack detection rate. Further, we have shown that our lightweight NIDS scheme has significantly reduced the computational complexity throughout the architecture, making it feasible to the edge, while maintaining a similar attack type detection quality compared with its original fully connected neural network.

Reliable and timely delivery of data to mobile targets is a challenging problem in mobile ad-hoc networks, because of the opportunistic and unpredictable nature of the problem. Finding a complete series of mobile-to-mobile contacts and forwarding data toward targets within a designated deadline is even more difficult. We leverage unmanned aerial vehicles (UAVs) as message ferries to travel over highly probable rendezvous points for over-the-air delivery to target users. We propose "Rendezvous," an opportunistic yet disciplined data delivery scheme based on trajectory prediction of users and UAV path planning. During the offline learning phase, we identify both temporal and spatial regularities of mobile users from real-world trajectories using sequence-wise clustering, and construct a compact yet well-summarized cluster signature that enables efficient search. In the data delivery phase, given recent movement history of users, UAVs perform their own distributed path planning collaboratively over time. Each UAV finds a chronological sequence of future visiting points where it can make consecutive data delivery efforts to users to overcome the uncertainty in mobility and ensure timely delivery. Real-world trace-driven simulation experiments demonstrate that "Rendezvous" achieves reliable and punctual data delivery using only few UAVs compared with existing algorithms.