Improving Robustness of Next-Hop Routing

A weakness of next-hop routing is that following a link or router failure there may be no routes between some source-destination pairs, or packets may get stuck in a routing loop as the protocol operates to establish new routes. In this article, we address these weaknesses by describing mechanisms to choose alternate next hops. Our first contribution is to model the scenario as the following {\sc tree augmentation} problem. Consider a mixed graph where some edges are directed and some undirected. The directed edges form a spanning tree pointing towards the common destination node. Each directed edge represents the unique next hop in the routing protocol. Our goal is to direct the undirected edges so that the resulting graph remains acyclic and the number of nodes with outdegree two or more is maximized. These nodes represent those with alternative next hops in their routing paths. We show that {\sc tree augmentation} is NP-hard in general and present a simple $\frac{1}{2}$-approximation algorithm. We also study 3 special cases. We give exact polynomial-time algorithms for when the input spanning tree consists of exactly 2 directed paths or when the input graph has bounded treewidth. For planar graphs, we present a polynomial-time approximation scheme when the input tree is a breadth-first search tree. To the best of our knowledge, {\sc tree augmentation} has not been previously studied

Heterogeneous GNN-RL Based Task Offloading for UAV-aided Smart Agriculture

Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this work, we propose a graph neural network-based reinforcement learning solution to optimize the task offloading from these IoT devices to the UAVs. We conduct evaluations to show that our approach reduces task deadline violations while also increasing the mission time of the UAVs by optimizing their battery usage. Moreover, the proposed solution has increased robustness to network topology changes and is able to adapt to extreme cases, such as the failure of a UAV

To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs

A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT) environment. The task offloading technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm. Such consequences include an undetected fire, pest infestation, or a UAV being unusable. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rule-based techniques. The simulation results show that the CVaR-based risk quantifying method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task. As a result, it reduced the total number of deadline violations with a negligible increase in energy consumption.Comment: Accepted for ICC202

Overlaying Circuit Clauses for Secure Computation

Given a set S = {C_1,...,C_k } of Boolean circuits, we show how to construct a universal for S circuit C_0, which is much smaller than Valiant’s universal circuit or a circuit incorporating all C_1,...,C_k. Namely, given C_1,...,C_k and viewing them as directed acyclic graphs (DAGs) D_1,...,D_k, we embed them in a new graph D_0. The embedding is such that a GC garbling of any of C_1,...,C_k could be implemented by a corresponding garbling of a circuit corresponding to D_0. We show how to improve Garbled Circuit (GC) and GMW-based secure function evaluation (SFE) of circuits with if/switch clauses using such S-universal circuit. The most interesting case here is the application to the GMW approach. We provide a novel observation that in GMW the cost of processing a gate is almost the same for 5 (or more) Boolean inputs, as it is for the usual case of 2 Boolean inputs. While we expect this observation to greatly improve general GMW-based computation, in our context this means that GMW gates can be programmed almost for free, based on the secret-shared programming of the clause. Our approach naturally and cheaply supports nested clauses. Our algorithm is a heuristic; we show that solving the circuit embedding problem is NP-hard. Our algorithms are in the semi-honest model and are compatible with Free-XOR. We report on experimental evaluations and discuss achieved performance in detail. For 32 diverse circuits in our experiment, our construction results 6.1x smaller circuit than prior techniques

Effects of Perfluoroalkyl Compounds on mRNA Expression Levels of Thyroid Hormone-Responsive Genes in Primary Cultures of Avian Neuronal Cells

There is growing interest in assessing the neurotoxic and endocrine disrupting potential of perfluoroalkyl compounds (PFCs). Several studies have reported in vitro and in vivo effects related to neuronal development, neural cell differentiation, prenatal and postnatal development and behavior. PFC exposure altered hormone levels and the expression of hormone-responsive genes in mammalian and aquatic species. This study is the first to assess the effects of PFCs on messenger RNA (mRNA) expression in primary cultures of neuronal cells in two avian species: the domestic chicken (Gallus domesticus) and herring gull (Larus argentatus). The following thyroid hormone (TH)–responsive genes were examined using real-time reverse transcription-PCR: type II iodothyronine 5′-deiodinase (D2), D3, transthyretin (TTR), neurogranin (RC3), octamer motif–binding factor (Oct-1), and myelin basic protein. Several PFCs altered the mRNA expression levels of genes associated with the TH pathway in avian neuronal cells. Short-chained PFCs (less than eight carbons) altered the expression of TH-responsive genes (D2, D3, TTR, and RC3) in chicken embryonic neuronal cells to a greater extent than long-chained PFCs (more than or equal to eight carbons). Variable transcriptional changes were observed in herring gull embryonic neuronal cells exposed to short-chained PFCs; mRNA levels of Oct-1 and RC3 were upregulated. This is the first study to report that PFC exposure alters mRNA expression in primary cultures of avian neuronal cells and may provide insight into the possible mechanisms of action of PFCs in the avian brain