HyungJune Lee

Predictive Data Delivery to Mobile Users Through Mobility Learning in Wireless Sensor Networks

We consider applications, such as indoor navigation, evacuation, or targeted advertising, where mobile users equipped with a smartphone-class device require access to sensor network data measured in their proximity. Specifically, we focus on efficient communication protocols between static sensors and users with changing location. Our main contribution is to predict a set of possible future paths for each user and store data at sensor nodes with which the user is likely to associate. We use historical data of radio connectivity between users and static sensor nodes to predict the future user-node associations and propose a network optimization process, i.e., data stashing, which uses the predictions to minimize network and energy overheads of packet transmissions. We show that data stashing significantly decreases routing cost for delivering data from stationary sensor nodes to multiple mobile users compared with routing protocols where sensor nodes immediately deliver data to the last known association nodes of mobile users. We also show that the scheme provides better load balancing, avoiding collisions and consuming energy resources evenly throughout the network, leading to longer overall network lifetime. Finally, we demonstrate that even limited knowledge of the location of future users can lead to significant improvements in routing performance.

Adaptive Path Planning of UAVs for Delivering Delay-Sensitive Information to Ad-Hoc Nodes

We consider the problem of path planning using multiple UAVs as message ferries to deliver delay-sensitive information in a catastrophic disaster scenario. Our main goal is to find the optimal paths of UAVs to maximize the number of nodes that can successfully be serviced within each designated packet deadline. At the same time, we want to reduce total travel time for visiting over a virtual grid topology. We propose a distributed path planning algorithm that determines the next visit grid point based on a weighted sum of travel time and delivery deadline. Together with path planning, we incorporate a task division mechanism that collaboratively distributes the unvisited grid points with other UAVs so that the entire travel time can substantially be reduced. Simulation results demonstrate that our distributed path planning algorithm mixed with task division outperforms all baseline counterpart algorithms in terms of on-time service node rate and total travel time.

DMirNet: Inferring direct microRNA-mRNA association networks

BackgroundMicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles. 1) Inferred correlations from the observed expression profiles using conventional correlation-based methods include numerous erroneous links or over-estimated edge weight due to the transitive information flow among direct associations. 2) Due to the high-dimension-low-sample-size problem on the microarray dataset, it is difficult to obtain an accurate and reliable estimate of the empirical correlations between all pairs of expression profiles. 3) Because the previously proposed computational methods usually suffer from varying performance across different datasets, a more reliable model that guarantees optimal or suboptimal performance across different datasets is highly needed.ResultsIn this paper, we present DMirNet, a new framework for identifying direct miRNA-mRNA association networks. To tackle the aforementioned issues, DMirNet incorporates 1) three direct correlation estimation methods (namely Corpcor, SPACE, Network deconvolution) to infer direct miRNA-mRNA association networks, 2) the bootstrapping method to fully utilize insufficient training expression profiles, and 3) a rank-based Ensemble aggregation to build a reliable and robust model across different datasets.Our empirical experiments on three datasets demonstrate the combinatorial effects of necessary components in DMirNet. Additional performance comparison experiments show that DMirNet outperforms the state-of-the-art Ensemble-based model [1] which has shown the best performance across the same three datasets, with a factor of up to 1.29. Further, we identify 43 putative novel multi-cancer-related miRNA-mRNA association relationships from an inferred Top 1000 direct miRNA-mRNA association network.ConclusionsWe believe that DMirNet is a promising method to identify novel direct miRNA-mRNA relations and to elucidate the direct miRNA-mRNA association networks. Since DMirNet infers direct relationships from the observed data, DMirNet can contribute to reconstructing various direct regulatory pathways, including, but not limited to, the direct miRNA-mRNA association networks.

Greedy Data Transportation Scheme with Hard Packet Deadlines for Wireless Ad Hoc Networks

We present a greedy data transportation scheme with hard packet deadlines in ad hoc sensor networks of stationary nodes and multiple mobile nodes with scheduled trajectory path and arrival time. In the proposed routing strategy, each stationary ad hoc node en route decides whether to relay a shortest-path stationary node toward destination or a passing-by mobile node that will carry closer to destination. We aim to utilize mobile nodes to minimize the total routing cost as far as the selected route can satisfy the end-to-end packet deadline. We evaluate our proposed routing algorithm in terms of routing cost, packet delivery ratio, packet delivery time, and usability of mobile nodes based on network level simulations. Simulation results show that our proposed algorithm fully exploits the remaining time till packet deadline to turn into networking benefits of reducing the overall routing cost and improving packet delivery performance. Also, we demonstrate that the routing scheme guarantees packet delivery with hard deadlines, contributing to QoS improvement in various network services.

Deadline-aware packet routing based on optimal charging schedule in electric vehicular networks

We present a deadline-aware packet routing based on optimal charging schedule in Electric Vehicular Ad Hoc Networks. It aims to propose an optimal charging schedule by incorporating EVs as packet carrier and its regular trajectory. Also, we aim to find out an energy efficient routing path by utilizing the EVs for delivering data packets from stationary nodes scattered over the network. We formulate the charging schedule problem into a binary integer program considering a packet deadline constraint. Along with this, we propose a routing protocol to forward data packets over a cost-effective route with the lowest packet transmission cost by fully exploiting EVs. We validate our optimal charging schedule in terms of acceptance ratio and service waiting time compared to a baseline counterpart. We also evaluate our routing protocol in terms of routing cost and on-time packet delivery ratio. We demonstrate that our algorithm increases the acceptance ratio of EVs in an overloaded situation, while decreasing the overall routing cost for forwarding data over the EV network.

DroneNet: Network reconstruction through sparse connectivity probing using distributed UAVs

In this paper, we consider a network reconstruction problem using UAVs where stationary ad-hoc networks are severely damaged in a post-disaster scenario. The main objective of this paper is to repair network by supplementing aerial wireless links into the stationary network to reconnect isolated ground networks each other with a limited number of UAVs. We propose a distributed motion planning that guarantees complete coverage to probe network connectivity from the air over stationary networks, while reducing duplicate coverage with other UAVs. Given the collected local connectivity information over region of interest, we deploy UAVs as relays into the locations of network holes to repair network-wide data delivery most effectively by formulating the problem into a binary integer program. Simulation results show that our network traversing algorithm outperforms a multi-agent exploration algorithm Ants in terms of complete coverage time, travel distance, and duplicate coverage. Also, our deployment optimization enhances network-wide routing performance compared to a practical baseline counterpart.

PUFSec: Device fingerprint-based security architecture for Internet of Things

A low-end embedded platform for Internet of Things (IoT) often suffers from a critical trade-off dilemma between security enhancement and computation overhead. We propose PUFSec, a new device fingerprint-based security architecture for IoT devices. By leveraging intrinsic hardware characteristics, we aim to design a computationally lightweight security software system architecture so that complex cryptography computation can dramatically be prohibited. We exploit the innovative idea of Public Physical Unclonable Functions (PPUFs) that fundamentally protects attackers from recovering the secret key from public gate delay information. We implement its hardware logic in a real-world FPGA board. On top of the PPUF fingerprint hardware, we present an adaptive security control mechanism consisting of adaptive key generation and key exchange protocol, which adjusts security strength depending on system load dynamics. We demonstrate that our PPUF FPGA implementation embeds distinctive variability enough to distinguish between two different PPUFs with high fidelity. We validate our PUFSec architecture by implementing necessary algorithms and protocols in a real-world IoT platform, and performing empirical evaluation in terms of computation and memory usages, proving its practical feasibility.

Social-aware data forwarding through scattered caching in disruption tolerant networks

We present a social-aware data forwarding scheme from a mobile source to a mobile destination in disruption tolerant networks. By incorporating any scattered network devices as temporal data storage for forwarding into parts of forwarding path, we aim to find the most effective relay path consisting of mobile-to-stationary and stationary-to-mobile relays. We combine the “carry-and-forward” scheme for stationary-to-mobile transmission with the “store-and-forward” for mobile-to-stationary transmission for improving both delivery rate and packet delay performance. We formulate this relay selection problem into a mixed-integer linear program, considering two crucial QoS constraints of packet delivery rate and deadline. We find the optimal forwarding path of all relevant mobile relays as well as their corresponding stationary nodes as bridges between mobile relays. We validate our algorithm based on a real-world dataset in terms of routing cost and packet transmission time compared to a baseline counterpart.