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Security and privacy of data-in-transit are critical issues in Industry 4.0 which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this work, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. Additionally, we conduct a security analysis that proves that the scheme resists several attacks in the industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works.

Today, there is a crucial need for precise monitoring and prediction of energy consumption at the building level using the latest technologies including Internet of Things (IoT) and data analytics to determine and enhance energy usage. Data-driven models could be used for energy consumption prediction. However, due to high non-linearity between the inputs and outputs of energy consumption prediction models, these models need improvement in terms of accuracy and robustness. Therefore, this work aims to predict energy usage for the optimum outline of building-extensive energy distribution strategies based on a lightweight IoT monitoring framework. To calculate accurate energy consumption, an enhanced hybrid model was developed based on Auto-Regressive Integrated Moving Average (ARIMA) and Imperialist Competitive Algorithm (ICA). The parameters of the ARIMA model were optimized by adapting the ICA technique that improved fitting accuracy while preventing over-fitting on the acquired data. Then, Exponentially Weighted Moving Average (EWMA) was applied to monitor the predicted values. The proposed AIK-EWMA hybrid model was assessed based on the actual power consumption data and validated using mathematical tests. As compared to previous works, the findings revealed that the hybrid model could accurately predict power consumption for green building automation applications.

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities.

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers' water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (R2), correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and BIAS are provided.

Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions.

The exchange of correct and reliable data among legitimate nodes is one of the most important challenges in vehicular ad hoc networks (VANETs). Malicious nodes and obstacles, by generating inaccurate information, have a negative impact on the security of 5G-VANET. The big data generated in the vehicular network is also an issue in the security of VANET. To this end, a security model based on authentication and plausibility is proposed to improve the safety of network named ‘AFPM’. In the first layer, an authentication mechanism using edge nodes along with 5G is proposed to deal with the illegitimate nodes who enter the network and broadcast wrong information. In the authentication mechanism, because of the growth of the connected vehicles to the edge nodes that lead to generating big data and hence the inappropriateness of the traditional data structures, cuckoo filter, as a space-efficient probabilistic data structure, is used. In the second layer, a plausibility model by performing fuzzy logic is presented to cope with inaccurate information. The plausibility model is based on detection of inconsistent data involved in the event message. The plausibility model not only tackles with inaccurate, incomplete, and inaccuracy data but also deals with misbehaviour nodes under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. All obtained results are validated through well-known evaluation measures such as F-measure and communication overhead. The results presented in this paper demonstrate that the proposed security model possesses a better performance in comparison with the existing studies.

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication &lrm, between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

Seamless mobility is a challenging issue in the area of research of vehicular networks that are supportive of various applications dealing with the intelligent transportation system (ITS). The conventional mobility management plans for the Internet and the mobile ad hoc network (MANET) is unable to address the needs of the vehicular network and there is severe performance degradation because of the vehicular networks’ unique characters such as high mobility. Thus, vehicular networks require seamless mobility designs that especially developed for them. This research provides an intelligent algorithm in providing seamless mobility using the media independent handover, MIH (IEEE 802.21), over heterogeneous networks with different access technologies such as Worldwide Interoperability for Microwave Access (WiMAX), Wireless Fidelity (Wi-Fi), as well as the Universal Mobile Telecommunications System (UMTS) for improving the quality of service (QoS) of the mobile services in the vehicular networks. The proposed algorithm is a hybrid model which merges the biogeography-based optimization or BBO with the Markov chain. The findings of this research show that our method within the given scenario can meet the requirements of the application as well as the preferences of the users.

In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can deal with uncertainties and risk taking from unreliable information in vehicular environments. However, inaccurate, incomplete, and imprecise information collected by vehicles as well as movable/immovable obstacles have interrupting effects on VANET. In this paper, a fuzzy trust model based on experience and plausibility is proposed to secure the vehicular network. The proposed trust model executes a series of security checks to ensure the correctness of the information received from authorized vehicles. Moreover, fog nodes are adopted as a facility to evaluate the level of accuracy of event’s location. The analyses show that the proposed solution not only detects malicious attackers and faulty nodes, but also overcomes the uncertainty and imprecision of data in vehicular networks in both line of sight and non-line of sight environments.

[EN] Industrial Internet of Things (IIoT) plays an important role in increasing productivity and efficiency in heterogeneous wireless networks. However, different domains such as industrial wireless scenarios, small cell domains and vehicular ad hoc networks (VANET) require an efficient machine learning/intelligent algorithm to process the vertical handover decision that can maintain mobile terminals (MTs) in the preferable networks for a sufficient duration of time. The preferred quality of service parameters can be differentiated from all the other MTs. Hence, in this paper, the problem with the vertical handoff (VHO) decision is articulated as the process of the Markov decision aimed to maximize the anticipated total rewards as well as to minimize the handoffs' average count. A rewards function is designed to evaluate the QoS at the point of when the connections take place, as that is where the policy decision for a stationary deterministic handoff can be established. The proposed hybrid model merges the biogeography-based optimization (BBO) with the Markov decision process (MDP). The MDP is utilized to establish the radio access technology (RAT) selection's probability that behaves as an input to the BBO process. Therefore, the BBO determines the best RAT using the described multi-point algorithm in the heterogeneous network. The numerical findings display the superiority of this paper's proposed schemes in comparison with other available algorithms. The findings shown that the MDP-BBO algorithm is able to outperform other algorithms in terms of number of handoffs, bandwidth availability, and decision delays. Our algorithm displayed better expected total rewards as well as a reduced average account of handoffs compared to current approaches. Simulation results obtained from Monte-Carlo experiments prove validity of the proposed model. (C) 2018 Elsevier B.V. All rights reserved., This work has been supported by the "Ministerio de Economia y Competitividad'', Spain, through the "Convocatoria 2014. Proyectos I+D -Programa Estatal de Investigacion Cientifica y Tecnica de Excelencia'' in the "Subprograma Estatal de Generacion de Conocimiento'', project TIN2014-57991-C3-1-P.

The hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user's preferences. Markov processes are widely used in performance modelling of wireless and mobile communication systems. We address the problem of optimal wireless network selection during vertical handover, based on the received information, by embedding the decision problem in a Markov decision process (MDP) with genetic algorithm (GA), we use GA to find a set of optimal decisions that ensures the best trade-off between QoS based on their priority level. Then, we emerge improved genetic algorithm (IGA) with simulated annealing (SA) as leading methods for search and optimization problems in heterogeneous wireless networks. We formulate the vertical handoff decision problem as a MDP, with the objectives of maximizing the expected total reward and minimizing average number of handoffs. A reward function is constructed to assess the QoS during each connection, and the AHP method are applied in an iterative way, by which we can work out a stationary deterministic handoff decision policy. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithm that avoid the slow and massive computations. This paper compares the formulation and results of five recent optimization algorithms: artificial bee colony, GA, differential evolution, particle swarm optimization and hybrid of (GA---SA). Simulation results indicated that choosing the SA rules would minimize the cost function, and also that, the IGA---SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the `Ping-Pong' effect.

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.

Water level prediction of rivers, especially in flood prone countries, can be helpful to reduce losses from flooding. A precise prediction method can issue a forewarning of the impending flood, to implement early evacuation measures, for residents near the river, when is required. To this end, we design a new method to predict water level of river. This approach relies on a novel method for prediction of water level named as RBF-FFA that is designed by utilizing firefly algorithm (FFA) to train the radial basis function (RBF) and (FFA) is used to interpolation RBF to predict the best solution. The predictions accuracy of the proposed RBF–FFA model is validated compared to those of support vector machine (SVM) and multilayer perceptron (MLP) models. In order to assess the models’ performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results show that the developed RBF–FFA model provides more precise predictions compared to different ANNs, namely support vector machine (SVM) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real time water stage measurements. The results specify that the developed RBF–FFA model can be used as an efficient technique for accurate prediction of water stage of river.

Beacon rate adaption is a way to cope with congestion of the wireless link and it consequently decreases the beacon drop rate and the inaccuracy of information of each vehicle in the network. In a vehicular environment, the beacon rate adjustment is strongly dependent on the traffic condition. Due to this, we firstly propose a new model to detect traffic density based on the vehicle’s own status and the surrounding vehicle’s status. We also develop a model based on fuzzy logic namely the BRAIN-F, to adjust the frequency of beaconing. This model depends on three parameters including traffic density, vehicle status and location status. Channel congestion and information accuracy are considered the main criteria to evaluate the performance of BRAIN-F under both LOS and NLOS. Simulation results demonstrate that the BRAIN-F not only reduces the congestion of the wireless link but it also increases the information accuracy.

To assist in the broadcasting of time-critical traffic information in an Internet of Vehicles (IoV) and vehicular sensor networks (VSN), fast network connectivity is needed. Accurate traffic information prediction can improve traffic congestion and operation efficiency, which helps to reduce commute times, noise and carbon emissions. In this study, we present a novel approach for predicting the traffic flow volume by using traffic data in self-organizing vehicular networks. The proposed method is based on using a probabilistic generative neural network techniques called deep belief network (DBN) that includes multiple layers of restricted Boltzmann machine (RBM) auto-encoders. Time series data generated from the roadside units (RSUs) for five highway links are used by a three layer DBN to extract and learn key input features for constructing a model to predict traffic flow. Back-propagation is utilized as a general learning algorithm for fine-tuning the weight parameters among the visible and hidden layers of RBMs. During the training process the firefly algorithm (FFA) is applied for optimizing the DBN topology and learning rate parameter. Monte Carlo simulations are used to assess the accuracy of the prediction model. The results show that the proposed model achieves superior performance accuracy for predicting traffic flow in comparison with other approaches applied in the literature. The proposed approach can help to solve the problem of traffic congestion, and provide guidance and advice for road users and traffic regulators.

Heterogeneous wireless networks are converging towards an all-IP network as part of the so-called next-generation network. In this paradigm, different access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, an artificial bee colony (ABC) algorithm for real-time vertical handover using different objective function has been presented to find the optimal network to connect. It can select an optimal set of weights for specified values, and find the optimal network selection solution. Simulation results illustrate that the proposed ABC algorithm has better performances than the existing methods in many evaluating parameters, and the computational time is also minimized.

The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.

The use of the Internet has increased dramatically over recent years and is now regarded as the best channel for distribution of products and services of various types of businesses, such as internet banking services. This paper extends an area of information systems research into a financial services context by looking into the element of trust in Internet banking. As more financial institutions are currently seeking ways to boost Internet banking adoption rates, trust is also being examined as a significant issue in the relationship. This can be attributed to the fact that bank customers are concerned about the security involved in processing such sensitive material as financial information. Moreover, significant factors of trust are involved and these include: accessibility, privacy, security, quality, usability, users knowledge and disposition to trust. These can all have an impact on customer trust in adopting internet banking. Based on previous models with aforementioned variables that are theoretically justified as having an influence on trust, a relevant research model was developed to test eight (8) hypothesized paths among the study variables. These include, namely: accessibility, privacy, security, quality, usability, users knowledge, disposition to trust, trust, as well as the rate of internet banking adoption. Data was collected by survey questionnaires from a sample of 150 internet banking users. The Smart PLS tool was used for data analysis. The results of the data analysis generally support the model, as well as all of the proposed hypotheses. In summary, the results of this research have shown that accessibility, privacy, security, quality, usability, users knowledge and disposition to trust were found to have significant influence on customer trust. Trust, in turn, was found to be an important factor in fostering a positive attitude toward adopting the services. Several implications for both research and practice have emerged and are discussed later in this study.

To provide the seamless mobility in heterogeneous wireless networks two significant methods, simulated annealing SA and genetic algorithms GAs are hybrid. In this paradigm, vertical handovers VHs are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service QoS in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect.

The basis of vehicular ad hoc networks (VANETs) is the exchange of data between entities, and making a decision on received data/event is usually based on information provided by other entities. Many researchers utilize the concept of trust to assess the trustworthiness of the received data. Nevertheless, the lack of a review to sum up the best available research on specific questions on trust management in vehicular ad hoc networks is sensible. This paper presents a systematic literature review to provide comprehensive and unbiased information about various current trust conceptions, proposals, problems, and solutions in VANETs to increase quality of data in transportation. For the purpose of the writing of this paper, a total of 111 articles related to the trust model in VANETs published between 2005 and 2014 were extracted from the most relevant scientific sources (IEEE Computer Society, ACM Digital Library, Springer Link, Science Direct, and Wiley Online Library). Finally, ten articles were eventually analyzed due to several reasons such as relevancy and comprehensiveness of discussion presented in the articles. Using the systematic method of review, this paper succeeds to reveal the main challenges and requirements for trust in VANETs and future research within this scope.