This paper introduces a cutting-edge method for urban traffic management that harnesses the power of the RAO (named after Professor Ravipudi Venkata Rao) optimization algorithm, enhanced by the integration of the Internet of Things (IoT) and fog computing technologies. By collecting real-time traffic data from IoT devices, such as smart cameras and sensors, our approach provides a detailed and up-to-the-minute view of urban traffic conditions. This data is efficiently processed through fog computing infrastructure, which ensures minimal latency and supports immediate responses to fluctuating traffic patterns. The deployment of the RAO algorithm on this rich dataset stands out as a novel contribution, dynamically optimizing traffic flows and routes based on current conditions without the need for specific map data sources. Our research extends beyond traditional traffic management applications, offering significant benefits in emergencies by facilitating quicker medical and security responses and enhancing the management of military operations with the need for rapid and secure route planning. Through experimental simulations conducted in urban environments, our findings demonstrate substantial improvements in traffic management and congestion alleviation. The results indicate that our integrated strategy offers a viable and adaptable solution for both everyday traffic oversight and exceptional scenario planning. This study lays the groundwork for future advancements in smart city technologies, underscoring the role of sophisticated technological integration in fostering more sustainable, efficient, and secure urban landscapes.

In the dynamic field of fog computing, there is a clear trend toward exploiting local, resource-rich nodes to bypass traditional cloud infrastructure limitations. This study introduces an innovative method for enhancing the reliability of Wi-Fi systems, crucial in fog computing, by integrating quality-focused user feedback. Our approach significantly enhances the assessment of Wi-Fi system trustworthiness by emphasizing user perspectives. While traditional metrics such as Availability, Performance, and Security Parameters are crucial for defining Quality of Service (QoS), our research also integrates user feedback as a key, albeit secondary, factor in assessing Wi-Fi node trustworthiness. Designed to improve the evaluation process, this method combines system-generated QoS metrics with user feedback to subtly increase trust assessment objectivity. This not only connects technical service quality with user experiences but also strengthens trust and reliability in fog computing. Utilizing sophisticated cloud theory techniques, our model employs both backward and forward cloud generators — the backward generator converts QoS data into qualitative insights, while the forward generator combines these insights with user feedback to thoroughly evaluate Wi-Fi node service quality. The integration of user feedback allows for a more dynamic and responsive system evaluation, addressing the limitations of previous models by providing a comprehensive assessment that aligns technical service quality with user experiences. This enhancement in trust evaluation is achieved by skillfully blending QoS metrics and user feedback to create a more objective trust value.

This paper introduces iTEVAC, an advanced model of the Trusted Evacuation System (TEVAC), designed to optimize emergency evacuation procedures within commercial centers. Building upon the principles of TEVAC, iTEVAC incorporates additional features such as isolation, firefighting, and rescue intervention strategies to enhance the system's performance and efficiency. iTEVAC leverages the power of Fog computing and Internet of Things (IoT) devices to provide real-time guidance for individuals during emergencies. The system operates by executing alerts for isolated nodes and those unable to find an exit route, providing detailed guidance for nodes that need to move within the commercial center's network. A key advantage of iTEVAC is its decision-making speed, which is less than 1 second, crucial in high-stakes, time-sensitive situations. The iTEVAC model also addresses the limitations of older buildings, traditional markets, and crowded areas that often lack efficient and integrated evacuation systems. It allows users to rely not only on smart navigation guide panel but also on their smartphones to find the safest direction using local Wi-Fi and broadband cellular networks, supported by Fog computing. The iTEVAC model represents a significant advancement in emergency response strategies, contributing to the development of more effective and intelligent evacuation strategies.

In urban environments, traffic networks are characterized by fixed distances between nodes, representing intersections or landmarks. Efficiently identifying the shortest path between any two nodes is crucial for various applications, such as route optimization for emergency services, ride-sharing algorithms, and general traffic management. Traditional methods like Dijkstra's algorithm are computationally intensive, especially for large-scale networks. To address this challenge, we propose a novel approach that precomputes and stores the shortest paths in a dedicated database hosted on a server system. Our methodology leverages the RAO algorithm, an advanced optimization technique, to solve the shortest path problem. Unlike conventional methods, the RAO algorithm adapts to varying conditions and constraints, making it highly suitable for dynamic urban traffic networks. We construct a comprehensive database that contains pre-calculated shortest paths between any two nodes, thereby significantly reducing real-time computational load. To validate the effectiveness of our approach, we conducted experiments on networks of varying complexities: 6-node, 8-node, and 20-node configurations. These experiments serve to emulate different scales of urban traffic networks. We compared the performance of our RAO-based solution with the Particle Swarm Optimization (PSO) algorithm, using Dijkstra's algorithm as a baseline for evaluation. Our results indicate a marked improvement in computational efficiency and accuracy when using the RAO algorithm. Specifically, the RAO-based solution outperformed the PSO algorithm across all test cases, thereby confirming its suitability for real-world applications. Our research introduces a scalable and efficient solution for precomputing shortest paths in urban traffic networks using the RAO algorithm.

This paper presents an innovative approach to urban traffic management by integrating OpenStreetMap (OSM) with Dijkstra’s algorithm, augmented with the Internet of Things (IoT) and fog computing technologies. Our method leverages real-time traffic data gathered from IoT devices, such as smart cameras, to gain a comprehensive view of current traffic conditions. The data is promptly processed via fog computing, ensuring reduced latency, and enabling real-time adaptation to traffic changes. The application of Dijkstra’s algorithm to this dataset is a key innovation, optimizing traffic routes dynamically with updates from OSM. A critical aspect of our research is its application beyond conventional traffic management. The system is particularly beneficial in emergency scenarios, facilitating rapid medical response and supporting military operations that require swift and secure movement. Additionally, it proves advantageous in managing large-scale events by enabling efficient isolation of specific areas or locations as needed. This adaptability makes it a vital tool for city administrations, not only improving daily traffic flow but also enhancing public safety and operational efficiency in various critical situations. Our experimental simulations in urban settings show significant improvements in traffic management and congestion reduction. The findings suggest that this integrated approach is not only a practical solution for routine traffic control but also a versatile tool for managing emergencies, military movements, and special events. This research paves the way for future smart city innovations, highlighting the potential of advanced technology in creating sustainable, efficient, and safer urban environments.

Modern office buildings, apartments, and commercial skyscrapers are designed with advanced evacuation systems to ensure people can evacuate quickly and safely during emergencies, such as natural disasters, terrorism, or explosions. However, older buildings, traditional markets, and crowded areas, such as fairs or music tours, often lack efficient and integrated evacuation systems, relying primarily on exit signal panels that do not provide adequate warnings or alternative escape routes. These areas are particularly vulnerable during emergencies, and thus, this article proposes a new model called TEVAC to address these shortcomings. TEVAC is a highly integrated system that utilizes Fog computing to take advantage of its availability, high performance, and the close proximity of IoT devices to provide a trusted and optimal way to help people evacuate dangerous situations. In addition to smart signs that indicate the best escape routes, TEVAC allows users to rely on their smartphones to find the safest direction using local Wi-Fi and broadband cellular networks, supported by Fog computing. By leveraging these technologies, TEVAC can significantly enhance the evacuation process, particularly in areas that lack modern evacuation systems.

The trend of the next generation of the internet has already been scrutinized by top analytics enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the global population will have their personal data covered under privacy regulations. This alarming statistic necessitates the orchestration of several security components to address the enormous challenges posed by federated and distributed learning environments. Federated learning (FL) is a promising technique that allows multiple parties to collaboratively train a model without sharing their data. However, even though FL is seen as a privacy-preserving distributed machine learning method, recent works have demonstrated that FL is vulnerable to some privacy attacks. Homomorphic encryption (HE) and differential privacy (DP) are two promising techniques that can be used to address these privacy concerns. HE allows secure computations on encrypted data, while DP provides strong privacy guarantees by adding noise to the data. This paper first presents consistent attacks on privacy in federated learning and then provides an overview of HE and DP techniques for secure federated learning in next-generation internet applications. It discusses the strengths and weaknesses of these techniques in different settings as described in the literature, with a particular focus on the trade-off between privacy and convergence, as well as the computation overheads involved. The objective of this paper is to analyze the challenges associated with each technique and identify potential opportunities and solutions for designing a more robust, privacy-preserving federated learning framework.

Taking advantage of the high performance and powerful data processing capabilities of cloud computing technology, externalizing data to the cloud platform is considered as an inevitable trend in the digital field today. However, ensuring the security and privacy of data remains a major challenge. To overcome this drawback, a multi-cloud platform is proposed to improve privacy and high availability of data. A multi-cloud platform that integrates public, private, and managed clouds with a single user interface. Cloud-hosted data is distributed among different data centers in a multi-cloud environment based on cloud reliability and data sensitivity. In terms of security, current encryption algorithms are considered to be very efficient, but it requires a lot of resources to handle this, which is expensive and time consuming. In addition, they also make the data impossible to process without first decoding. To be specific, traditional public key encryption requires data to be decrypted before it can be analyzed or manipulated. In contrast, homomorphic encryption is an encryption method that allows data to be encrypted while it is being processed and manipulated. It allows user or a third party, which can be cloud provider, to apply functions on encrypted data without revealing the data's values. In this paper, we explore existing multi-cloud-based security solutions using homomorphic encryption to identify open issues and opportunities for further enhancement.

This paper proposes a model to classify six human gaits, based on accelerometer data collected from IMU sensors that humans carry while walking, combined with Multi-Layer Perceptron Neural Network (MLPNN) model. In the MLPNN model, there are 108 inputs include 105 patterns in one gait cycle and three features of one gait cycle: skewness, entropy, the distance between high-peak and low-peak, and there are six outputs which are human gaits. To train MLPNN-model, we used the back-propagation algorithm. Six human gaits classified include: walk on the toe, walk-on heel, up the stair, downstairs, sit up, and normal-walk. The results of the proposed model are also compared with two different data mining techniques (Support Vector Machine - SVM, k-Nearest Neighbor k-NN) when classifying these six gaits. An experimental data set of the accelerometer was obtained from the IMU sensor that seven different people carried as they performed six different gaits each. To evaluation the model, we test on the Matlab-2018b software package. The results show that the accuracy of the six gaits classification is as follows: ANN-BP is 93,17

A model for controlling the service quality of a Wi-Fi system based on cloud theory is discussed in this paper. The quality of service of each Wi-Fi is determined based on the current and previous data of each Wi-Fi, these data include Availability (Av), Performance (Per) and Secure Parameters (SP). The backward cloud generator model is used to map a quantity of precise data of Av – Per – SP back into the qualitative concept expressed by the expected value (Ex), the entropy (En) and the hyper entropy (He). Using the forward cloud generator model to determine the quality of service of each Wi-Fi. The operating principle of the proposed model is as follows: when the user selects a certain Wi-Fi in the Wi-Fi system to connect through the proposed control model, the control model will perform determine the quality of service of each Wi-Fi in the system through the established cloud model. From there, the control model will make a comparison, to give feedback to the user whether to change the connection to another Wi-Fi or not. The simulation results show that the proposed model has controlled the quality of service of the Wi-Fi system.

This paper proposes a new bipedal gait planning algorithm with natural Zero Moment Point (ZMP) references and preset foot-lifting magnitude. The building a model of walking pattern generation for biped robot based on model Nonlinear Autoregressive eXogenous combined with Multi-Layer Perceptron Neural Networks (NARX-MLPNN) is realized. In the NARX-MLPNN model, there are 4 inputs including 2 coordinate data of the reference ZMP point and 2 coordinate data of the actual ZMP point delayed in one step, there are 4 outputs which are gait generation parameters of biped robot. Furthermore, the Improved Differential Evolution (IDE) algorithm used to the identification the weights value of the MLPNN model, to biped walking with natural ZMP references and preset foot-lifting magnitude. It is convincingly shown that we can easily generate a biped walking gait with the efficiency and high performance. The validity of the generated patterns is confirmed by simulations adequate.

Advances in technology have lead to the creation of a connected world. Due the increase in the number of smart and autonomous cars, the safety and associated comfort level of driving has led to attempts to adopt conventional vehicular access network to the world of connected vehicles. Consolidating the cooperative safety and collected mobility management from different distributed devices are of the utmost importance. However, the prime objective of connected vehicles is not only to impose security and trust measures for individual vehicles, but the strategy of connected vehicle should also concentrate on the cooperative and collective environment on fleets of vehicles. Therefore, keeping simple authentication and access control may not be efficient to evaluate trust and assurance for all the distributed stakeholders. Trust being an important entity for this entire system, the strategy for trust evaluation becomes also, crucial. In this paper we propose a broader content matching model of trusted strings and block chain based filtering for connected Vehicles. Where a content and subject headings are first matched and then the outcome of that is consolidated by a distributed block chain consensus voting mechanism for any decision taken with respect to trust evaluation.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

The stability plays important role in power system’s operation. It refers to the ability of generators to maintain synchronization after failures. In fact, the traditional transient stability analysis methods of power system are time-consuming as well as not suitable for working online. Artificial Neural Network (ANN) is considered as the preferred method for solving problems that traditional methods cannot solve, especially in calculation speed. However, in ANN training, the critical drawback of backpropagation (BP) algorithm is the optimal solution that can fall locally extreme point. This paper proposes to use the advanced differential evolution algorithm (MDE) for optimizing the multilayer perceptron (MLP) neural network problem in order to identify the stability of power system.

The emergence of the Internet of Things (IoT) and the advantages of computer network have attracted the attention of technological experts. However, network security issues remain a challenge. Controlling Web traffic and preventing attack to Web server, especially DoS/DDoS, are tremendous in the current computing ecosystem. In this paper, we propose a solution for controlling connections of the inside as well as the outside of network systems by using an integrated hardware, which would be deployed at gateway of the networks. It is not only used to implement rules but also monitor the network traffic, especially for web traffic which is considered as the common type in the Internet. In our solution, we prefer to use the IP SLA to control the web traffic, rather proxy to set up rules as well as a data stream algorithm for fast detecting Hot-IPs to prevent attacks from outside networks to the inside servers. The proposed solution is trouble-free to use and enhance security in small & medium networks as well.

SummaryThe surge of the presence of personal mobile devices in multi‐environment makes a significant attention to the mobile cloud computing (MCC). Along with this concern, security issues also appear as a barrier to prevent the propagation of this trend. This paper focuses on an important feature in many security protocols and application, which is the device attestation in the MCC. The existing remote attestation mechanisms are currently used in trusted computing environment such as binary attestation and property‐based attestation. In this paper, by taking advantage of the combination of technologies and trends, such as trusted platform module, cloud computing, and bring your own device, we introduce property‐based token attestation to secure the mobile user in the enterprise cloud environment. In order to accomplish a secure MCC environment, security threats need to be studied and acted accordingly, and therefore, we first represent the common threats and then explain a novel attestation schema for addressing these threats by providing security proofs. In addition, Scyther is in use to verify the correctness of our protocol.

Mobile Cloud Computing (MCC) is an emerging and popular mobile technology which uses fully available Cloud Computing services and functionalities. This technology provides rich computational services to the users, network operators and Cloud service providers as well. However due to users mobility and high computational operations, consumption of energy is a major issue. Energy efficiency over MCC is needed since 57% of generated energy is used by ICT related devices and other negative impacts over environment. This paper investigates different mobile Cloud computing architectures and their performance over energy efficiency by examining different approaches: OSGi, overlay, and container based solutions.

The advantages of virtualization are tremendous in the current computing ecosystem. The virtualization technology is an abstract mechanism that enables a single hardware platform to run multiple environments. This technology is presented in many forms such as process, storage, and network virtualization. In this paper, we introduce the different techniques of virtualization such as those implemented by hypervisors or by isolated containers, in order to investigate the convergence between trusted computing and virtualized systems. By introducing Trusted Platform Module (TPM) in the virtualized environment, we describe the way VMs are attested to be used as a building block for any trusted intelligent system.