Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments.

Federated learning (FL) is an emerging distributed learning technique through which models can be trained using the data collected by user devices in resource-constrained situations while protecting user privacy. However, FL has three main limitations: First, the parameter server (PS), which aggregates the local models that are trained using local user data, is typically far from users. The large distance may burden the path links between the PS and local nodes, thereby increasing the consumption of the network and computing resources. Second, user device resources are limited, but this aspect is not considered in the training of the local model and transmission of the model parameters. Third, the PS-side links tend to become highly loaded as the number of participating clients increases. The links become congested owing to the large size of model parameters. In this study, we propose a resource-efficient FL scheme. We follow the Pareto optimality concept with the biased client selection to limit client participation, thereby ensuring efficient resource consumption and rapid model convergence. In addition, we propose a hierarchical structure with location-based clustering for device-to-device communication using k-means clustering. Simulation results show that with prate at 0.75, the proposed scheme effectively reduced transmitted and received network traffic by 75.89% and 78.77%, respectively, compared to the FedAvg method. It also achieves faster model convergence compared to other FL mechanisms, such as FedAvg and D2D-FedAvg.

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks.

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms.

Trust Based Multipath QoS Routing Protocol for Mission-Critical Data Transmission in Tactical Ad-Hoc Networks.

In tactical ad-hoc networks, the importance of various tactical sensors and mission-critical data is increasing owing to their role in determining a tactical situation and ensuring the viability of soldiers. In particular, the reliability of mission-critical data has to be ensured for accurate situation determination and decision making. However, managing the network and trustworthiness in an environment where malicious nodes exist and a large amount of mission-critical data occur is a challenging issue. To solve these issues, a routing protocol is needed that can effectively detect malicious nodes and ensure the reliability and quality of service (QoS) of mission-critical data. In this paper, we propose a trust-based multipath QoS routing protocol (called MC_TQR) for tactical ad-hoc networks that can detect malicious nodes and satisfy the requirements of mission-critical data. The proposed scheme is verified using an OPNET simulator, and the results confirm the improved network performance when compared with existing schemes.

Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks.

As a trending and interesting research topic, in recent years, researchers have been adopting the blockchain in the wireless ad-hoc environment. Owing to its strong characteristics, such as consensus, immutability, finality, and provenance, the blockchain is utilized not only as a secure data storage for critical data but also as a platform that facilitates the trustless exchange of data between independent parties. However, the main challenge of blockchain application in an ad-hoc network is which kind of nodes should be involved in the validation process and how to adopt the heavy computational complexity of block validation appropriately while maintaining the genuine characteristics of a blockchain. In this paper, we propose the blockchain-based trust management system with a lightweight consensus algorithm in a mobile ad-hoc network (MANET). The proposed scheme provides the distributed trust framework for routing nodes in MANETs that is tamper-proof via blockchain. The optimized link state routing protocol (OLSR) is exploited as a representative protocol to embed the blockchain concept in MANETs. As a securely distributed and trusted platform, blockchain solves most of the security issues in the OLSR, in which every node is performing the security operation individually and in a repetitive manner. Additionally, using predefined principles, the routing nodes in the proposed scheme can collaborate to defend themselves from the attackers in the network. The experimental results show that the proposed consensus algorithm is suitable to be used in the resource-hungry MANET with reduced validation time and less overhead. Meanwhile, the attack detection overhead and time also decrease because the repetitivity of the process is reduced while providing a scalable and distributed trust among the routing nodes.

A Stepwise and Hybrid Trust Evaluation Scheme for Tactical Wireless Sensor Networks.

In tactical wireless sensor networks, tactical sensors are increasingly expected to be exploited for information collection in battlefields or dangerous areas on behalf of soldiers. The main function of these networks is to use sensors to measure radiation, nuclear, and biochemical values for the safety of allies and also to monitor and carry out reconnaissance of enemies. These tactical sensors require a network traffic flow that sends various types of measured information to the gateway, which needs high reliability. To ensure reliability, it must be able to detect malicious nodes that perform packet-dropping attacks to disrupt the network traffic flow, and energy-constrained sensors require energy-efficient methods to detect them. Therefore, in this paper, we propose a stepwise and hybrid trust evaluation scheme for locating malicious nodes that perform packet-dropping attacks in a tree-based network. Sensors send a query to the gateway by observing the traffic patterns of their child nodes. Moreover, depending on the situation, the gateway detects malicious nodes by choosing between gateway-assisted trust evaluation and gateway-independent trust evaluation. We implemented and evaluated the proposed scheme with the OPNET simulator, and the results showed that a higher packet delivery ratio can be achieved with significantly lower energy consumption.

Conducting Super-Hydrophobic Thin Film for Electric Heating Applications.

Frost presents a serious problem for the human environment, resulting in such phenomena as downed power lines, damaged crops and stalled aircraft. In addition, frost and ice accumulation significantly decrease the performance of ships, wind turbines, and HVAC systems with high failure risk. Super-hydrophobic (SH) surface can be an appropriate solution for frost problems, due to its anti-icing properties that can prevent ice nucleation on the surface. In addition, in the case of conducting SH surface using carbon nanotubes (CNTs) as a filler, it can form an excellent heating unit, owing to the resistive heating effect. The purpose of this study is to produce a large-area conducting SH film that can prevent ice nucleus and remove ice formation rapidly. High aspect ratio carbon nanotubes (CNTs) as a conducting filler and adhesive polymer resin as a binder were used to form coating layer. In addition, silica particles (~7 nm) were used to stabilize nano-size roughness of the SH surface. Wet and dry etching processes were used on the substrate to improve wettability and to produce organic functional groups. To evaluate the de-icing effect, the fabricated SH surface was rapidly heated to 150 °C by applying voltage.

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks.

Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones' tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.

A Study on Mechanical Characteristics of Phosphor Film Containing Methyl Silicone Resin Based on Crosslinking Reaction Analysis.

This study reveals a methodological research for predicting mechanical properties of phosphor films through the chemical crosslinking reaction of methyl silicone resin during fabrication of the phosphor films. Crosslinking point according to the type of methyl silicone resins was verified through the magnitude of the absorption peak of the functional group and the curing reaction heat. Then, we measured mechanical properties of the fabricated phosphor films. As a result, it was figured out that the number of the crosslinking point was directly proportional to the total curing reaction heat, and also affected the mechanical properties of the phosphor films. Based on the correlation of curing reaction heat and crosslinking point of the methyl silicone resins and mechanical properties of the fabricated phosphor films, we proposed a methodology that can understand and control the phosphor films in advance of finishing the fabrication of the final phosphor products.

A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks.

In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status. The sink performs computations on the received data to estimate the object boundary. For accurate boundary estimation, nodes at the phenomenon boundary need to be carefully selected. Failure of one or multiple boundary nodes (BNs) can significantly affect the object detection and boundary estimation accuracy at the sink. We develop an efficient failure-prone object detection approach that not only detects and recovers from BN failures but also reduces the number and size of transmissions without compromising the boundary estimation accuracy. The proposed approach utilizes the spatial and temporal features of sensor nodes to detect object BNs. A Voronoi diagram-based network clustering, and failure detection and recovery scheme is used to increase boundary estimation accuracy. Simulation results show the significance of our approach in terms of energy efficiency, communication overhead, and boundary accuracy.

Computational Fluid Dynamics of Intracranial and Extracranal Arteries using 3-Dimensional Angiography: Technical Considerations with Physician's Point of View.

We investigate the potentials and limitations of computational fluid dynamics (CFD) analysis of patient specific models from 3D angiographies. There are many technical problems in acquisition of proper vascular models, in pre-processing for making 2D surface and 3D volume meshes and also in post-processing steps for display the CFD analysis. We hope that our study could serves as a technical reference to validating other tools and CFD results.

Computational modeling with fluid-structure interaction of the severe m1 stenosis before and after stenting.

PURPOSE: Image-based computational models with fluid-structure interaction (FSI) can be used to perform plaque mechanical analysis in intracranial artery stenosis. We described a process in FSI study applied to symptomatic severe intracranial (M1) stenosis before and after stenting. MATERIALS AND METHODS: Reconstructed 3D angiography in STL format was transferred to Magics for smoothing of vessel surface and trimming of branch vessels and to HyperMesh for generating tetra volume mesh from triangular surface-meshed 3D angiogram. Computational analysis of blood flow in the blood vessels was performed using the commercial finite element software ADINA Ver 8.5. The distribution of wall shear stress (WSS), peak velocity and pressure was analyzed before and after intracranial stenting. RESULTS: The wall shear stress distributions from Computational fluid dynamics (CFD) simulation with rigid wall assumption as well as FSI simulation before and after stenting could be compared. The difference of WSS between rigid wall and compliant wall model both in pre- and post-stent case is only minor except at the stenosis region. These WSS values were greatly reduced after stenting to 15~20 Pa at systole and 3~5 Pa at end-diastole in CFD simulation, which are similar in FSI simulations. CONCLUSION: Our study revealed that FSI simulation before and after intracranial stenting was feasible despite of limited vessel wall dimension and could reveal change of WSS as well as flow velocity and wall pressure.

Computational flow dynamics of the severe m1 stenosis before and after stenting.

PURPOSE: Computational flow dynamic (CFD) study has not been widely applied in intracranial artery stenosis due to requirement of high resolution in identifying the small intracranial artery. We described a process in CFD study applied to symptomatic severe intracranial (M1) stenosis before and after stenting. MATERIALS AND METHODS: Reconstructed 3D angiography in STL format was transferred to Magics (Materialise NV, Leuven, Belgium) for smoothing of vessel surface and trimming of branch vessels and to HyperMesh (Altair Engineering Inc., Auckland, New Zealand) for generating tetra volume mesh from triangular surface-meshed 3D angiogram. Computational analysis of blood flow in the blood vessels was performed using the commercial finite element software ADINA Ver 8.5 (ADINA R & D, Inc., Lebanon, MA). The distribution of wall shear stress (WSS), peak velocity and pressure in a patient was analyzed before and after intracranial stenting. RESULTS: Computer simulation of wall shear stress, flow velocity and wall pressure before and after stenting could be demonstrated three dimensionally by video mode according to flow vs. time dimension. Such flow model was well correlated with angiographic finding related to maximum degree of stenosis. Change of WSS, peak velocity and pressure at the severe stenosis was demonstrated before and after stenting. There was no WSS after stenting in case without residual stenosis. CONCLUSION: Our study revealed that CFD analysis before and after intracranial stenting was feasible despite of limited vessel wall dimension and could reveal change of WSS as well as flow velocity and wall pressure.

High shear stress at the surface of enhancing plaque in the systolic phase is related to the symptom presentation of severe M1 stenosis.

The computational fluid dynamics methods for the limited flow rate and the small dimensions of an intracranial artery stenosis may help demonstrate the stroke mechanism in intracranial atherosclerosis. We have modeled the high wall shear stress (WSS) in a severe M1 stenosis. The high WSS in the systolic phase of the cardiac cycle was well-correlated with a thick fibrous cap atheroma with enhancement, as was determined using high-resolution plaque imaging techniques in a severe stenosis of the middle cerebral artery.