Throughput-efficient blockchain for Internet-of-Vehicles

Internet-of-Vehicle (IoV) is empowering smart vehicles with data collection and sharing capabilities, and blockchains have been introduced to manage the IoV data due to many advantages, including decentralization, security, reliability, and scalability. Nevertheless, existing IoV blockchain models suffer from poor security against collusion attacks instigated by malicious blockchain miners typically represented by roadside units (RSUs). To address this problem, additional block verifiers, e.g., vehicles, can be recruited during block verification, which enhances security but also can lead to the reduced throughput. Therefore, in this paper, we propose a resource management scheme for IoV blockchains to enhance the system security while maximizing the throughput by optimizing contributed computing resources from RSUs and recruited vehicles. We show that the optimal strategies of RSUs and vehicles can be found through the Karush-Kuhn-Tucker (KKT) conditions and verify (using simulations) that our scheme achieves the higher throughput with enhanced security compared to the existing IoV blockchains

Lagrange coded federated learning (L-CoFL) model for Internet of Vehicles

In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations

Thermal Performance Evaluation of a Novel Ejector-Injection Cascade Refrigeration System

In this article, a novel cascade refrigeration system is proposed for moderately low temperature applications. An ejector expansion vapor compression cycle (EEVCC) and a vapor injection cycle with the flash tank (VICFT) are coupled together through a cascade heat exchanger to develop the novel ejector injection cascade refrigeration system. The goal of this study is to investigate the potential improvement scope of a conventional cascade refrigeration system by integrating EEVCC and VICFT. A detailed energy and exergy balance analysis are conducted on the novel proposed system by considering the following three design parameters: (1) evaporator temperature; (2) pressure drop at the ejector; and (3) lower circuit condensation temperature. The results reveal that the performance of the proposed system is sensitive to pressure drop at the ejector and lower circuit condensation temperature for each evaporator temperature. Therefore, it is necessary to determine the optimal values of these factors in order to conduct an accurate performance analysis. Furthermore, a comparative study between the proposed system and the conventional system is conducted based on the first and second laws of thermodynamics. The results show that at −60 ℃ of evaporator temperature, the proposed system has 8.571 % COP improvement and 7.241 % exergy efficiency improvement over conventional cascade refrigeration system. This study also explores the suitable pair of refrigerants for this proposed system to minimize environmental impact. Among seven pairs of refrigerants investigated, R170 and R601 at LTC and HTC respectively shows the best performance. The proposed system was found to be more sensitive to change of LTC refrigerant than HTC

Contribution of morpho-physiological attributes in determining the yield of mungbean

Field experiments were conducted in 2006 and 2007 under subtropical conditions to investigate the variations in growth and reproductive characters, and yield attributes for selection of important source and sinks characters using correlation and path coefficient analyses in 45 mungbean genotypes. Large genetic variability existed in source characters viz., leaf area index (LAI) (1.22 to 3.80) and sink characters viz., number of racemes plant-1 (6.30 to 22.9), flowers plant-1 (18.1 to 51.9) and pods plant-1 (9.6 to 22.1). Genotypic correlation study revealed that among the traits investigated, LAI was the most important source that determined total dry mass (TDM) yield, and reproductive characters like number of racemes, flowers and pods plant-1 were the most important sinks that determined seed yield. Contrarily, reproductive efficiency (RE, % pod set to opened flowers) did not show significant relationship with pod number and seed yield, indicating that selection of high yield based on RE may be misleading. Path coefficient analysis further revealed that number of flowers, pods and 100-seed weight constituted central important sinks which exerted direct positive influence on seed yield. The results indicated that pod yield could be increased by increased raceme and flower production, while seed yield could be increased by increasing pod production. High yielding genotypes, in general, possessed higher earlier mentioned source (LAI) and sink (flower and pod number) characters which resulted in higher seed yield in mungbean. This information could be exploited in the future plant breeding programmes

DOLPHIN: Dynamically Optimized and Load Balanced PatH for INter-domain SDN Communication

Software-Defined Networking has become an integral technology for large scale networks that require dynamic flow management. It separates the control function from data plane devices and centralizes it in a domain controller. However, only a limited number of switches can be managed by a single and centralized controller which introduces challenges such as scalability, reliability, and availability. Distributed controller architecture resolves these issues but also introduces new challenges of uneven load and traffic management across domains. As real-world networks have redundant links, hence a significant challenge is to distribute traffic flows on multiple paths, within a domain, and across multiple independent domains. The selection of ingress and egress switches becomes even more problematic if the intermediate domain is non-cooperative. In this work, we propose a Dynamically Optimized and Load-balanced Path for Inter-domain (DOLPHIN) communication system, a customized solution for different SDN controllers. It provides control beyond the virtual switch elements in intra and inter-domain communication and extends the range of programmability to wireless devices, such as the Internet of Things or vehicular networks. Extensive simulation results show that the traffic load is distributed evenly on multiple links connecting different domains. We model data center communication and 5G vehicular network communication to show that, by load balancing the flow completion times of the different types of network traffic can be significantly improved