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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.

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper’s main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

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.

Trust is a salient feature in the context of health care, which is characterized by uncertainty and an element of risk. It is a fundamental requirement for the acceptance and adoption of new services related to health care. Soft trust, based on social control mechanisms, has yielded to evidence-based trust management, where the level of trust is explicitly computed by a trust engine termed the trust and reputation system (TRS). In e-health, soft trust can be used for access control, for assessing the quality of data represented in electronic health records, to address privacy and security requirements of healthcare systems, and to compute the credibility of an entity in the presence of unknown, possibly harmful entities. Despite the importance of soft trust in healthcare, related literature that explore soft trust issues, associated challenges and requirements are largely missing. To address this deficiency, the main contributions of this paper include: (i) taxonomy related to the soft trust in healthcare systems; (ii) reference model for measuring the performance and features of TRSs; and (iii) future areas of research related to the soft trust in healthcare.

Vehicular ad-hoc network (VANET) is a technology that allows ubiquitous mobility to mobile users. Inter-vehicle communication is an integral component of intelligent transportation systems that enables a wide variety of applications where vehicles interact and cooperate with each other, from safety applications to non-safety applications. VANETs applications have different needs (e.g., latency, reliability, delivery priorities, etc.) in terms of delivery effectiveness. In the last decade, named data networking (NDN) gained the attention of the research community for effective content retrieval and dissemination in mobile environments such as VANETs. In NDN, the content’s name has a vital role in storing and retrieving the content effectively and efficiently. In NDN-based VANETs, adaptive content dissemination solutions must be introduced that can make decisions related to forwarding, cache management, etc., based on context information represented by a content name. In this context, our main contributions are two-fold: (i) we present the hierarchical context-aware content-naming (CACN) scheme for NDN-based VANETs that enables naming the safety and non-safety applications, and (ii) we present a decentralized context-aware notification (DCN) protocol that broadcasts event notification information for awareness within the application-based geographical area. Simulation results show that the proposed DCN protocol succeeds in achieving reduced transmissions, bandwidth, and energy compared to existing critical contents dissemination protocols.

With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process.

Recent advancement in digital and communication technologies has brought privacy aspects to the forefront. Although e-health has many advantages and it facilitates the patients and health service providers significantly, the possibility of privacy breaches can allow sensitive health care information to move into the wrong hands. Designing robust privacy preserving policies to strengthen the trust of patients in electronic health records is imperative for its wide spread acceptance and success. In this paper, we propose, a framework to solve the privacy problem in a heterogeneous network of many clinical institutions while preserving data utility and patients' privacy. The contributions of this paper include: (1) scalable privacy-enabled architecture supporting re-identification of patient identity, and (2) context-aware privacy-preserving scheme supporting named and anonymous linked access to medical data stored at one or more health service providers. Moreover, to demonstrate the correctness of proposed privacy-aware scheme, we performed formal modeling and verification using high-level Petri nets and Z3 Solver.

OBJECTIVES: The main aim was to assess the frequencies of accidents of motorbike in previous one year and also to estimate the factors related with the accidents. METHODOLOGY: The study design was quantitative and the data was collected from motorcyclists. In order to collect the primary data; a structured questionnaire was used. We interviewed 412 motorcyclists. Almost all the motorcyclist responded and thus we achieved 99% response. Statistical Package for Social Sciences (SPSS) version 21 was used to enter data, clean it and analysis. RESULTS: After the analysis we got hold of some factors, which were associated with accidents. Some of these factors were human, vehicle and environment related factors completing the epidemiological triad of agent, host and environment. CONCLUSION: Based on the results of the study we concluded that bikers must shun the practice of speeding over and above the limits of speed given by the government, one-wheeling, and listening to music while driving. Moreover, the bikers must be taught the basic skills of motorbike riding by authorized personnel and keep latest models of motorbikes, which are in excellent condition. The guardians or parents along with the law enforcement agencies must make ensure that biker must get a license before using the bike. KEYWORDS: District Kohat, Road Traffic Accidents, Burden, Motorbikes

In health sector, trust is considered important because it indirectly influences the quality of health care through patient satisfaction, adherence and the continuity of its relationship with health care professionals and the promotion of accurate and timely diagnoses. One of the important requirements of TRSs in the health sector is rating secrecy, which mandates that the identification information about the service consumer should be kept secret to prevent any privacy violation. Anonymity and trust are two imperative objectives, and no significant explicit efforts have been made to achieve both of them at the same time. In this paper, we present a framework for solving the problem of reconciling trust with anonymity in the health sector. Our solution comprises Anonymous Reputation Management (ARM) protocol and Context-aware Trustworthiness Assessment (CTA) protocol. ARM protocol ensures that only those service consumers who received a service from a specific service provider provide a recommendation score anonymously with in the specified time limit. The CTA protocol computes the reputation of a user as a service provider and as a recommender. To determine the correctness of the proposed ARM protocol, formal modelling and verification are performed using High Level Petri Nets (HLPN) and Z3 Solver. Our simulation results verify the accuracy of the proposed context-aware trust assessment scheme.

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]