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Calcium oxalate (CaOx) crystallization is a common phenomenon that contributes to various kidney disorders and stone formation, as well as the formation of scale in industrial processes. The inhibition of CaOx is an area of intense scientific interest in the field of materials science due to its relevance to biomineralization. The present study investigated the effects of pectin (PE) and sodium alginate (SA), two natural polymers, on the growth of CaOx crystals using a batch crystallization method in aqueous solutions at 37 °C with different concentrations (0.5, 1, 5, and 10 ppm). The results of the study showed that both PE and SA were effective inhibitors of CaOx crystal growth, with the highest inhibition observed at a concentration of 10 ppm, reaching 80 %. PE did not significantly affect the size of the crystals, while SA reduced their size as the concentration increased. These findings contribute to our understanding of the potential of natural polymers as non‐toxic inhibitors of CaOx crystal growth. [ABSTRACT FROM AUTHOR]

In this study, both the optical and piezoelectric properties of polyvinylidene fluoride (PVDF) electrospun nanofibers were investigated at different needle-to-collector distances of the electrospinning process at constant applied high voltage. For piezoelectric characterization, the fabricated nanofiber mats were subjected to applied forces, including cyclic force, variable frequency-based loads, and free-falling masses (impulse loading), along with power density analysis for different load resistance values. In addition, both optical absorbance and transmittance measurements were conducted to evaluate the optical properties of the fabricated nanofibers. The piezoelectric analysis demonstrated the best piezoresponse of the fabricated nanomats at a needle-to-collector distance of 15 cm and high voltage of 22 kV. However, a trade-off between piezoelectric response and optical transmissivity was observed based on the electrospinning distance parameter. The relatively higher optically transparent sample exhibited only moderate piezoelectric response, while the less transparent sample displayed the highest piezoelectric activity. Based on the optimized sample and piezoelectric analysis, the synthesized nanofiber mat was subjected to applied mechanical stress in the form of variable velocity and momentum loads. A maximum potential of approximately 16 V was harvested through velocity and momentum impact, especially with the addition of a double-layer PVDF membrane. Furthermore, the sensing effect of airflow pressure on single/double-layer PVDF was studied. The single-layer PVDF membrane generated 79 mV under an airflow speed of 21 km/h, while the double-layer membrane produced 114 mV potential under the same airflow. This study highlights the diverse applications of PVDF nanofiber mats as multifunctional sensors and energy harvesting applications from mechanical shocks and airflow impact. [ABSTRACT FROM AUTHOR]

Machine learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. Intrusion detection in VANET is nonconvex and combinatorial problem. Thus, three intelligence optimization algorithms are used for optimizing the accuracy value of SVM classifier. These optimization algorithms include Genetic algorithm (GA), Particle swarm optimization (PSO), and ant colony optimization (ACO). Our results demonstrate that GA outperformed other optimization algorithms. [ABSTRACT FROM AUTHOR]

In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different purposes in the academic domain. Finally, we compared the results of this link with previous work, as evidence of the accuracy of leveraging this technology to improve decisions within universities. [ABSTRACT FROM AUTHOR]

Temporal information is critical for routing computation in the vehicular network. It plays a vital role in the vehicular network. Till now, most existing routing schemes in vehicular networks consider the networks as a sequence of static graphs. We need to find an appropriate method to process temporal information into routing computation. Thus, in this paper, we propose a routing algorithm based on the Hidden Markov Model (HMM) and temporal graph, namely, Prediction-Based Temporal Graph Routing Algorithm (PT-GROUT). This new algorithm considers the vehicular network as a temporal graph, in which each data transmission as an edge has its specific temporal information. To better capture the temporal information, we select Software-Defined Vehicular Network (SDVN) as our network architecture, which is a preferred architecture for processing the temporal graph regarding the vehicular network since all vehicle statuses can be easily managed. To compute the future routing path accurately and efficiently, the future temporal graph is predicted by applying HMM, in which we model the current vehicular network with dynamic programming and greedy strategies. With the temporal information and reasonable setting of HMM, PT-GROUT can better evaluate the vehicular network and discover the evolution of the internal structure of the network. The optimal routing path can be achieved more efficiently. The simulation results demonstrate that PT-GROUT can substantially improve the computation efficiency and reduce packet loss and delivery delay compared with its counterparts. [ABSTRACT FROM AUTHOR]

Due to the increased number of cyberattacks, numerous researchers are motivated towards the design of such schemes that can hide digital information in a signal. Watermarking is one of the promising technologies that can protect digital information. However, traditional watermarking schemes are either slow or less secure. In this paper, a dynamic S-Box based efficient watermarking scheme is presented. The original image was extracted at the receiver's end without any loss of sensitive information. Firstly, the Secure Hash Algorithm is applied to the original image for the generation of the initial condition. Piece Wise Linear Chaotic Map is then used to generate 16 × 16 dynamic Substitution Box (S-Box). As an additional security feature, the watermark is substituted through dynamic S-Box. Hence, it is hard for the eavesdroppers to attack the proposed scheme due to the dynamic nature of S-Box. Lastly, lifting wavelet transform is applied to the host image and the High Low and High High blocks of host image are replaced with least significant bits and most significant bits of the substituted watermark, respectively. Robustness, efficiency and security of the proposed scheme is verified using Structure Similarity Index, Structure Dissimilarity Index, Structure Content, Mutual Information, energy, entropy, correlation tests and classical attacks analysis. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a multi-criteria decision making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the analytic hierarchy process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink. [ABSTRACT FROM AUTHOR]

Software-defined vehicular networks (SDVN) is a promising technology to overcome the limitations of current vehicular networking. However, existing vehicular routing schemes are not equipped to handle communication in SDVNs. In addition, routing schemes in SDVNs, in general, has been lightly addressed in the literature to well compute the flow tables. To fill this gap, this article explores the potential of SDVNs from the aspect of routing and studies the design principles of routing schemes in SDVNs and classifies the current routing solutions based on different criteria. SDVN routing schemes are then compared through comprehensive analysis, and key open issues and opportunities for future research directions are discussed. [ABSTRACT FROM AUTHOR]

Due to the limited local computation resource, industrial vehicular computation requires offloading the computation tasks with time-delay sensitive and complex demands to other intelligent devices (IDs) once the data is sensed and collected collaboratively. This article considers offloading partial computation tasks of the industrial vehicles (IVs) to multiple available IDs of the industrial mobile edge computing (MEC), including unmanned aerial vehicles (UAVs), and the fixed-position MEC servers, to optimize the system cost including execution time, energy consumption, and the ID rental price. Moreover, to increase the access probability of IV by the UAVs, the geographical area is divided into small partitions and schedule the UAVs regarding the regional IV density dynamically. A minimum incremental task allocation algorithm is proposed to divide the whole task and assign the divided units for the minimum cost increment each time. Experimental results show the proposed solution can significantly reduce the system cost. [ABSTRACT FROM AUTHOR]

Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue, that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall. [ABSTRACT FROM AUTHOR]

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency. [ABSTRACT FROM AUTHOR]

An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the same security concerns of legacy ad hoc networks. Unlike exiting works, we consider, in this paper, detection a common attack where nodes modify safety message or drop them. Unfortunately, detecting such a type of intrusion is a challenging problem since some packets may be lost or dropped in normal VANET due to congestion without malicious action. To mitigate these concerns, this paper presents a novel scheme for minimizing the invalidity ratio of VANET packets transmissions. In order to detect unusual traffic, the proposed scheme combines evidences from current as well as past behaviour to evaluate the trustworthiness of both data and nodes. A new intrusion detection scheme is accomplished through a four phases, namely, rule-based security filter, Dempster–Shafer adder, node's history database, and Bayesian learner. The suspicion level of each incoming data is determined based on the extent of its deviation from data reported from trustworthy nodes. Dempster–Shafer's theory is used to combine multiple evidences and Bayesian learner is adopted to classify each event in VANET into well-behaved or misbehaving event. The proposed solution is validated through extensive simulations. The results confirm that the fusion of different evidences has a significant positive impact on the performance of the security scheme compared to other counterparts. [ABSTRACT FROM AUTHOR]

This article models the multihop data-routing in vehicular ad-hoc networks as multiple criteria decision making (MCDM) in four steps. First, the criteria that have impact on the performance of the network layer are captured and transformed into fuzzy sets. Second, the fuzzy sets are characterized by fuzzy membership functions (FMFs), which are interpolated (curve fitting) based on the data collected from massive experimental simulations. Third, the analytical hierarchy process (AHP) is exploited to identify the relationships among the criteria. Fourth, multiple fuzzy rules are determined and the Takagi–Sugeno–Kang (TSK) inference system is employed to infer and aggregate the final forwarding decision. Through integrating techniques of MCDM, FMF, AHP, and TSK, we design a distributed and opportunistic data routing protocol, namely, vehicular environment fuzzy router which targets vehicle-to-vehicle (V2V) communication and runs in two main processes—road segment selection (RSS) and relay vehicle selection (RVS). RSS is intended to select multiple successive junctions through which the packets should travel from the source to the destination, while RVS process is intended to select relay vehicles within the selected road segment. The experimental results show that our protocol performs and scales well with both network size and density, considering the combined problem of end-to-end packet delivery ratio and end-to-end latency. [ABSTRACT FROM AUTHOR]

OpenFlow is considered as the most known protocol for Software Defined Networking (SDN). The main drawback of OpenFlow is the lack of support of new header definitions, which is required by network operators to apply new packet encapsulations. While SDN's logically centralized control plane could enhance network security by providing global visibility of the network state, it still has many side effects. The intelligent controllers that orchestrate the dumb switches are overloaded and become prone to failure. Delegating some level of control logic to the edge or, to be precise, the switches can offload the controllers from local state based decisions that do not require global network wide knowledge. Thus, this paper, to the best of our knowledge, is the first to propose the delegation of typical security functions from specialized middleboxes to the data plane. We leverage the opportunities offered by programming protocol-independent packet processors (P4) language to present two authentication techniques to assure that only legitimate nodes are able to access the network. The first technique is the port knocking and the second technique is the One-Time Password. Our experimental results indicate that our proposed techniques improve the network overall availability by offloading the controller as well as reducing the traffic in the network without noticeable negative impact on switches' performance. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, routing protocols with immutable network policies lacking the flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environment situations and application requirements. The proposed “Flexible Routing Computing Approach (FRCA)” is a novel distributed and probabilistic computing approach capable of modifying or upgrading routing policies on the fly with low cost, which effectively enhances the routing flexibility. FRCA models the routing metric as a forwarding probability distribution for routing decisions. This model depends on three elements, the physical quantities collected at sensor nodes, the built-in base math functions, and the routing parameters. These elements are all user-oriented and can be specified to implement multifarious complicated network policies meeting different performance requirements. More significantly, through distributing routing parameters from the sink to end nodes, operators are allowed to adjust network policies on the fly without interrupting the network services. Through extensive performance evaluation studies and simulations, the results demonstrate that routing protocols designed based on FRCA could achieve better performance compared to its state-of-the-art counterparts regarding network lifetime, energy consumption, and duplicate packets as well as ensure high flexibility during network policies modification or upgrade. [ABSTRACT FROM AUTHOR]

Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput. [ABSTRACT FROM AUTHOR]

Different studies have recently emphasized the importance of deploying clustering schemes in Vehicular ad hoc Network (VANET) to overcome challenging problems related to scalability, frequent topology changes, scarcity of spectrum resources, maintaining clusters stability, and rational spectrum management. However, most of these studies addressed the clustering problem using conventional performance metrics while spectrum shortage, and the combination of spectrum trading and VANET architecture have not been tackled so far. Thus, this paper presents a new fuzzy logic based clustering control scheme to support scalability, enhance the stability of the network topology, motivate spectrum owners to share spectrum and provide efficient and cost-effective use of spectrum. Unlike existing studies, our context-aware scheme is based on multi-criteria decision making where fuzzy logic is adopted to rank the multi-attribute candidate nodes for optimizing the selection of cluster heads (CH)s. Criteria related to each candidate node include: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. Our model performs efficiently, exhibits faster recovery in response to topology changes and enhances the network efficiency life time. [ABSTRACT FROM AUTHOR]

For the last twenty years, artificial intelligence (AI) has become a significant technology to influence our daily life with its ability of promoting the quality of living. Hence, they propose a repost network embedding-based diffusion popularity prediction algorithm for achieving the precise prediction. In addition, the paper entitled "LSTM based link quality confidence interval boundary prediction for wireless communication in smart grid" provided by sun et al., studies the link quality prediction of the wireless communication in smart grid. [Extracted from the article]

In RPL routing protocol, the destination advertisement object (DAO) control messages are announced by the child nodes to their parents to build downward routes. A malicious insider node can exploit this feature to send fake DAOs to its parents periodically, triggering those parents, in turn, to forward the fake messages upward to the root node. In this letter, we show how this behavior can have a detrimental side effect on the performance of the network, increasing power consumption, latency, and reducing reliability. To address this problem, a new scheme is introduced to mitigate significantly the effect of the DAO attack on network performance. [ABSTRACT FROM AUTHOR]

Traffic management in roads is one of the major challenges faced in developing efficient intelligent transportation systems. Recently, wireless networks have received significant attention for tackling this challenge. However, wireless technologies face the well-known spectrum scarcity problem due to the explosive demand for radio resources. To overcome this challenge, this study presents a novel intelligent traffic control system, utilizing the unused spectrum. Unlike existing works, the spectrum owners in this study hire free spectrum to drivers. The hired spectrum is deployed to build a short-range cost-effective wireless communication for monitoring traffic and enabling drivers to exchange warning messages, and thus enhancing road safety. Our objectives include minimizing crash probability, utilizing unused spectrum, and enabling spectrum owners to generate extra revenue. Numerical analysis demonstrates the capability of our approach to minimize the crash probability among vehicles under different operating road conditions. [ABSTRACT FROM AUTHOR]

Wireless Body Area Network (WBAN) has been a key element in e-health to monitor bodies. This technology enables new applications under the umbrella of different domains, including the medical field, the entertainment and ambient intelligence areas. This survey paper places substantial emphasis on the concept and key features of theWBAN technology. First, the WBAN concept is introduced and a review of key applications facilitated by this networking technology is provided. The study then explores a wide variety of communication standards and methods deployed in this technology. Due to the sensitivity and criticality of the data carried and handled by WBAN, fault tolerance is a critical issue and widely discussed in this paper. Hence, this survey investigates thoroughly the reliability and fault tolerance paradigms suggested for WBANs. Open research and challenging issues pertaining to fault tolerance, coexistence and interference management and power consumption are also discussed along with some suggested trends in these aspects. [ABSTRACT FROM AUTHOR]

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions. [ABSTRACT FROM PUBLISHER]

With the resource-constrained nature of mobile devices and the resource-abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource intensive tasks from mobile devices to the cloud. Although these schemes address similar questions within the same domain of mobile cloud application (MCA) optimisation, evaluation is tailored to the scheme and also solely mobile focused, thus making it difficult to clearly compare with other existing counterparts. In this work, we first analyse the existing/commonly adopted evaluation technique, then with the aim to fill the above gap, we propose the behaviour-driven full-tier green evaluation approach, which adopts the behaviour-driven concept for evaluating MCA performance and energy usage-ie, green metrics. To automate the evaluation process, we also present and evaluate the effectiveness of a resultant application program interface and tool driven by the behaviour-driven full-tier green evaluation approach. The application program interface is based on Android and has been validated with Elastic Compute Cloud instance. Experiments show that Beftigre is capable of providing a more distinctive, comparable, and reliable green test results for MCAs. [ABSTRACT FROM AUTHOR]

Wireless community networks (WCNs) have emerged as a cost-effective ubiquitous broadband connectivity solution, offering a wide range of services in a given geographical area. QoS-aware multicast over WCNs is among the most challenging issues and has attracted a lot of attention in recent times. The existing multicast schemes in WCNs suffer in terms of several key performance metrics, such as, latency, jitter and throughput, particularly in large-scale networks. Consequently, these schemes cannot accommodate the desired performance levels, especially when dealing with high-bandwidth applications that require efficient gateway-based management. To fill in this gap, a new strategy for supporting QoS-aware multicast in large-scale WCNs is proposed in this paper. Specifically, a new Gateway based Multi-hop Routing algorithm (GMR) is firstly proposed to enhance the routing management capability of the network. Built upon GMR, a new Multicast Gateway Multi-hop Routing algorithm (MGMR) is devised to cope with high-bandwidth applications in WCNs. The MGMR is the first of its kind that considers both the capability of gateway-based management and the requirements of high-bandwidth applications. Extensive simulation experiments and performance results demonstrate the superiority of both GMR and MGMR when compared to other methods under various operating conditions. [ABSTRACT FROM PUBLISHER]