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The rapid surge in internet-driven smart devices and bandwidth-hungry multimedia applications demands high-capacity internet services and low latencies during connectivity. Cloud radio access networks (C-RANs) are considered the prominent solution to meet the stringent requirements of fifth-generation (5G) and beyond networks by deploying the fronthaul transport links between baseband units (BBUs) and remote radio heads (RRHs). High-capacity optical links could be conventional mainstream technology for deploying the fronthaul in C-RANs. But densification of optical links significantly increases the cost and imposes several design challenges on fronthaul architecture which makes them impractical. Contrary, Ethernet-based fronthaul links can be lucrative solutions for connecting the BBUs and RRHs but are inadequate to meet the rigorous end-to-end delays, jitter, and bandwidth requirements of fronthaul networks. This is because of the inefficient resource allocation and congestion control schemes for the capacity constraint Ethernet-based fronthaul links. In this research, a novel reinforcement learning-based optimal resource allocation scheme has been proposed which eradicates the congestion and improves the latencies to make the capacity-constraints low-cost Ethernet a suitable solution for the fronthaul networks. The experiment results verified a notable 50% improvement in reducing delay and jitter as compared to the existing schemes. Furthermore, the proposed scheme demonstrated an enhancement of up to 70% in addressing conflicting time slots and minimizing packet loss ratios. Hence, the proposed scheme outperforms the existing state-of-the-art resource allocation techniques to satisfy the stringent performance demands of fronthaul networks.

Smart home devices are vulnerable to a variety of attacks. The matter gets more complicated when a number of devices collaborate to launch a colluding attack (e.g., Distributed-Denial-of-Service (DDoS)) in a network (e.g., Smart home). To handle these attacks, most studies have hitherto proposed authentication protocols that cannot necessarily be implemented in devices, especially during Device-to-Device (D2D) interactions. Tapping into the potential of Ethereum blockchain and smart contracts, this work proposes a lightweight authentication mechanism that enables safe D2D interactions in a smart home. The Ethereum blockchain enables the implementation of a decentralized prototype as well as a peer-to-peer distributed ledger system. The work also uses a single server queuing system model and the authentication mechanism to curtail DDoS attacks by controlling the number of service requests in the system. The simulation was conducted twenty times, each with varying number of devices chosen at random (ranging from 1 to 30). Each requester device sends an arbitrary request with a unique resource requirement at a time. This is done to measure the system's consistency across a variety of device capabilities. The experimental results show that the proposed protocol not only prevents colluding attacks, but also outperforms the benchmark protocols in terms of computational cost, message processing, and response times.

Visual surveillance of on-road vehicles has emerged as a vibrant research topic within the fields of computer vision and Intelligent Transportation Systems (ITSs) to mitigate traffic problems. Road accidents result in traffic congestion, vehicle damage, injuries, and fatalities. The likelihood, severity, and fatality of automobile collisions are all increased by speeding. Autonomous Speed Limit Violation Detection (SLVD) mechanism offers the best way to estimate speed with high precision using Intelligent Transportation Systems (ITSs) technology. Vision-based Vehicle Speed Monitoring (VSM) pipeline is proposed in this work, which includes mechanisms for vehicle detection, tracking, and speed measurement. VSM pipeline is based on a three-tier architecture, exploiting a single RSU-camera. In the first tier, a real-time CNN-based Hybrid Vehicle Detection Network (HVD-Net) is designed for vehicle detection. The HVD-Net utilizes multi-level and multi-scale features to minimize the impact of vehicle scale variation and maximize detection accuracy. Secondly, a Simple Online Real-time Tracker (SORT) along with HVD-Net is adopted to track vehicles' trajectories. The tracker utilizes a Kalman filter for vehicle state estimation and a Hungarian algorithm to solve the multi-vehicle association problem. Finally, a mechanism is presented to estimate the vehicle speed in the motion plane to mitigate speed overestimation. Empirical evaluations on three datasets demonstrate that the proposed vehicle detection, tracking, and speed estimation schemes perform better when compared with the relevant and state-of-the-art schemes. The estimated pipeline vehicle speed is achieved with accuracy of 87.242%.

Abstract The Internet of Things (IoT) is characterized by the seamless integration of heterogeneous devices into information networks to enable collaborative environments, specifically those concerning the collection of data and exchange of information and services. Security and trustworthiness are among the critical requirements for the effective deployment of IoT systems. However, trust management in IoT is extremely challenging due to its open environment, where the quality of information is often unknown because entities may misbehave. A hybrid context‐aware trust and reputation management protocol is presented for fog‐based IoT that addresses adaptivity, survivability, and scalability requirements. Through simulation, the effectiveness of the proposed protocol is demonstrated.

In terms of delivery effectiveness, Vehicular Adhoc NETworks (VANETs) applications have multiple, possibly conflicting, and disparate needs (e.g., latency, reliability, and delivery priorities). Named Data Networking (NDN) has attracted the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. A vehicle in a VANET application is heavily reliant on information about the content, network, and application, which can be obtained from a variety of sources. The information gathered can be used as context to make better decisions. While it is difficult to obtain the necessary context information at the IP network layer, the emergence of NDN is changing the tide. The Pending Information Table (PIT) is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PIT overflow results in service disruptions as new Interest messages cannot be added to PIT. Adaptive, context-aware PIT entry management solutions must be introduced to NDN-based VANETs for effective content dissemination. In this context, our main contribution is a decentralised, context-aware PIT entry management (CPITEM) protocol. The simulation results show that the proposed CPITEM protocol achieves lower Interest Satisfaction Delay and effective PIT utilization based on context when compared to existing PIT entry replacement protocols.

A new stability-indicating RP-HPLC method for the determination of metformin hydrochloride and nateglinidein bulk was developed. The separation wasperformed on a Hypersil BDS-C18column (250 X 4.6 mm ID, 5 µm) by using 0.05 M potassium dihydrogen orthophosphate (pH 3.5) acetonitrile (30:70 v/v) as the mobile phase with UV detection at 226 nm. Under these chromatographic conditions, favorable retention parameters were obtained with good symmetry of peaks for the studied compounds. The validation studies performed as per ICH guidelines. The proposed method showed high degree of accuracy, and precision, with a good degree of sensitivity and robustness. The drugs were subjected to forced degradation using acidic, alkaline, oxidation, wet heat, dry heat, and photodegradation conditions. The results confirmed that the method could effectively separate the drugs in the presence of their degradation products, hence it can be regarded as stability indicating. [ABSTRACT FROM AUTHOR]