Three-layer Architecture Research Articles

Q ‐learning based computation offloading for multi‐UAV‐enabled cloud‐edge computing networks

Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi-UAV-enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs-Edge-Cloud three-layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed-integer non-linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficient Q-learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three-layer network architecture achieves a 5% benefits compared with the traditional two-layer network architecture in terms of MUs' energy and time consumptions.

Mobile Medical Applications and Cloud Federation Challenges

Abstract Issue Given the importance, of cloud environments for mobile telemedicine information systems, focus is given in this paper on the challenges rising. We discuss the pros of the access to computing services and resources on demand without having own infrastructures, and the need of advanced interoperability data formats and application program interfaces (APIs) to facilitate the usage of the infrastructure. Description of the problem Cloud-Oriented Architecture (COA) describes the architecture, where applications act as services and serve other applications in the cloud environment. The aim is IT infrastructure and software applications to be optimized for their use in cloud computing environments. But what happens in the specific field of health as data interoperability for mobile telemedicine information systems? Results Two architectures were presented using a patient's compliance and engagement solution. A simple Representational State Transfer (REST) based architectural style was implemented in a three-layered architecture first and then compared to a cloud federation model. In the second the interaction goes through the federation via a middleware layer. Internal operations of cloud providers in the federation are still transparent to the cloud users. The middleware layer aims to coordinate the interaction with cloud users and providers in the federation. But, to avoid delays in those interactions interoperability of data formats and APIs. Lessons A Fast Healthcare Interoperability Resources (FHIR) based application was developed. Mobile telemedicine information systems are a strong instrument in patients' compliance. Many systems have proved that the used resources combined can solve clinical and administrative problems in a secure environment. Key messages The use of cloud is wide spread in the health sector. The challenge is to combine this infrastructure into one federated platform and maximize the added value by using advanced interoperability data formats and APIs.

Mobile Vehicles as Fog Nodes for Latency Optimization in Smart Cities

The advent of 5G technologies enables highly anticipated prospect of the internet of things in smart cities. Smart terminals become a necessity with increasing number of applications and tasks. The limited computational resources and constrained battery energy at terminals compel tasks to be offloaded for execution sometimes. For time-sensitive and safety-critical tasks, fog computing is the preferred option instead of cloud computing, since fog computing brings the computing resources to the edge of networks and thus has shorter response latency. In this context, vehicular fog computing (VFC) that supplements fog computing by contributing vehicular computing resources has attracted increasing attention in recent years. However, a few of challenging issues regarding task offloading in VFC still need to be addressed. For example, decision making for task allocation and response latency optimization are not straightforward, since on one hand the connection between vehicles and end users is usually short or intermittent; on the other hand the multi-hop inter-vehicle task forwarding is time-consuming and thus prone to packet loss. To address the latency related issue, we in this paper propose a three-layer architecture of VFC to schedule the tasks offloaded from mobile devices (MDs). The road side unit (RSU) is endowed with computing facilities and storing resources such that it can act as not only a gateway to the remote cloud center but also a centralized infrastructure responsible for task scheduling among vehicles. A greedy heuristic based scheduling strategy and algorithms are proposed in the next. Last, an extensive series of experiments are carried out to investigate the proposed scheduling strategy. The results show that the proposed scheduling strategy outperforms other well known heuristics based scheduling strategies such as Min-Min and Max-Min and thus has wide prospects.

An algorithm based on markov chain to improve edge cache hit ratio for blockchain-enabled IoT

Reasonable allocation of storage and computing resources is the basis of building big data system. With the development of IoT (Internet of Things), more data will be brought. A three-layer architecture includes smart devices layer, edge cloud layer and blockchain-based distributed cloud layer. Blockchain is used in IoT for building a distributed decentralize P2P architecture to deal with the secure issue while edge computing deals with increasing volume of data. Edge caching is one of the important application scenarios. In order to allocate edge cache resources reasonably, to improve the quality of service and to reduce the waste of bandwidth resources, this paper proposes a content selection algorithm of edge cache nodes. The algorithm adopts markov chain model, improves the utilization of cache space and reduces the content transmission delay. The hierarchical caching strategy is adopted and the secondary cache stores slides of contents to expand the coverage of cached content and to reduce user waiting time. Regional node cooperation is adopted to expand the cache space and to support the regional preference of cache content. Compared with the classical substitution algorithm, simulation results show that the algorithm in this paper has higher cache hit ratio and higher space utilization.

Identifikasi Kadar Semen dan Pasir Melalui Citra Permukaan Menggunakan Teknik Blok Citra

Penelitian ini mengangkat topik identifikasi jenis campuran semen dan pasir pada material yang sudah kering menggunakan kecerdasan buatan. Hal ini dilakukan karena perbandingan campuran antara semen dan pasir sangat berpengaruh pada kualitas material yang dihasilkan. Beberapa model eksperimen berpengaruh terhadap tingkat akurasi pengenalan. Pada penelitian ini model eksperimen yang digunakan adalah teknik blok citra dan citra LBP, dengan jenis campuran semen dan pasir yang digunakan adalah 1:1 , 1:1.5 , 1:2 , 1:2.5 , 1:3 , dan 1:3.5. Metode pengenalan yang digunakan adalah Jaringan Syaraf Tiruan (JST) dengan algoritma propagasi balik. Banyaknya sampel pelatihan JST adalah 600 sampel, dan 120 sampel untuk pengujian. Penelitian ini menggunakan teknik blok citra sebelum ekstraksi fitur dilakukan. Fitur yang digunakan adalah mean, standar deviasi, entropy, skewness, dan kurtosis dari citra LBP. Hasil pelatihan JST mendapatkan arsitektur tiga lapisan tersembunyi, dengan pengujian menunjukkan tingkat akurasi pengenalan sebesar 80%.

Novel Defense Schemes for Artificial Intelligence Deployed in Edge Computing Environment

The last few years have seen the great potential of artificial intelligence (AI) technology to efficiently and effectively deal with an incredible deluge of data generated by the Internet of Things (IoT) devices. If all the massive data is transferred to the cloud for intelligent processing, it not only brings considerable challenges to the network bandwidth but also cannot meet the needs of AI applications that require fast and real-time response. Therefore, to achieve this requirement, mobile or multiaccess edge computing (MEC) is receiving a substantial amount of interest, and its importance is gradually becoming more prominent. However, with the emerging of edge intelligence, AI also suffers from several tremendous security threats in AI model training, AI model inference, and private data. This paper provides three novel defense strategies to tackle malicious attacks in three aspects. First of all, we introduce a cloud-edge collaborative antiattack scheme to realize a reliable incremental updating of AI by ensuring the data security generated in the training phase. Furthermore, we propose an edge-enhanced defense strategy based on adaptive traceability and punishment mechanism to effectively and radically solve the security problem in the inference stage of the AI model. Finally, we establish a system model based on chaotic encryption with the three-layer architecture of MEC to effectively guarantee the security and privacy of the data during the construction of AI models. The experimental results of these three countermeasures verify the correctness of the conclusion and the feasibility of the methods.

Online recognition of unsegmented actions with hierarchical SOM architecture

Automatic recognition of an online series of unsegmented actions requires a method for segmentation that determines when an action starts and when it ends. In this paper, a novel approach for recognizing unsegmented actions in online test experiments is proposed. The method uses self-organizing neural networks to build a three-layer cognitive architecture. The unique features of an action sequence are represented as a series of elicited key activations by the first-layer self-organizing map. An average length of a key activation vector is calculated for all action sequences in a training set and adjusted in learning trials to generate input patterns to the second-layer self-organizing map. The pattern vectors are clustered in the second layer, and the clusters are then labeled by an action identity in the third layer neural network. The experiment results show that although the performance drops slightly in online experiments compared to the offline tests, the ability of the proposed architecture to deal with the unsegmented action sequences as well as the online performance makes the system more plausible and practical in real-case scenarios.

A Privacy-Preserving and Scalable Authentication Protocol for the Internet of Vehicles

One of the most important and critical requirements for the Internet of Vehicles (IoV) is security under strict latency. Typically, authentication protocols for vehicular ad hoc networks need to authenticate themselves frequently. This results in reduced application traffic and increased overhead. Moreover, the mobile nature of vehicles makes them a prime target for physical, side channel, and cloning attacks. To address these issues, this article presents an efficient protocol for authentication in the IoV. The proposed protocol uses physical unclonable functions to provide the desired security characteristics. To reduce the overhead of authentication and improve the throughput of application layer packets, the proposed protocol uses a three-layered infrastructure architecture for IoVs, i.e., roadside units (RSUs), RSU gateways, and trusted authority. A vehicle needs to authenticate only once when it enters the area of an RSU gateway which may engulf multiple RSUs. A performance analysis of the protocol shows that the proposed strategy significantly reduces the number of authentication packets and MAC/PHY overhead while the security analysis demonstrates its robustness against various types of attacks.

Quantum Diffie-Hellman Extended to Dynamic Quantum Group Key Agreement for e-Healthcare Multi-Agent Systems in Smart Cities.

Multi-Agent Systems can support e-Healthcare applications for improving quality of life of citizens. In this direction, we propose a healthcare system architecture named smart healthcare city. First, we divide a given city into various zones and then we propose a zonal level three-layered system architecture. Further, for effectiveness we introduce a Multi-Agent System (MAS) in this three-layered architecture. Protecting sensitive health information of citizens is a major security concern. Group key agreement (GKA) is the corner stone for securely sharing the healthcare data among the healthcare stakeholders of the city. For establishing GKA, many efficient cryptosystems are available in the classical field. However, they are yet dependent on the supposition that some computational problems are infeasible. In light of quantum mechanics, a new field emerges to share a secret key among two or more members. The unbreakable and highly secure features of key agreement based on fundamental laws of physics allow us to propose a Quantum GKA (QGKA) technique based on renowned Quantum Diffie–Hellman (QDH). In this, a node acts as a Group Controller (GC) and forms 2-party groups with remaining nodes, establishing a QDH-style shared key per each two-party. It then joins these keys into a single group key by means of a XOR-operation, acting as a usual group node. Furthermore, we extend the QGKA to Dynamic QGKA (DQGKA) by adding join and leave protocol. Our protocol performance was compared with existing QGKA protocols in terms of Qubit efficiency (QE), unitary operation (UO), unitary operation efficiency (UOE), key consistency check (KCC), security against participants attack (SAP) and satisfactory results were obtained. The security analysis of the proposed technique is based on unconditional security of QDH. Moreover, it is secured against internal and external attack. In this way, e-healthcare Multi-Agent System can be robust against future quantum-based attacks.

Cognitive Similarity-Based Collaborative Filtering Recommendation System

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures

The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.

Edge Intelligence for Mission Cognitive Wireless Emergency Networks

Emergency communication infrastructures are of critical importance in disaster rescue scenarios, responsible for providing reliable connection services among victims, rescuers, and public safety command centers. Moreover, many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing, and the availability of low-latency computation platforms. In this article, we propose an edge intelligence-based MCWEN to address these challenges, by leveraging edge-based technologies including edge caching, edge computing, and edge learning. In particular, MCWEN showcases a three-layer architecture, consisting of end devices, edge servers, and remote clouds. Intelligent mission cognition involves the understanding of key features of various tasks, as well as the environment and available rescue resources, and it plays an essential role in the MCWEN. We highlight edge-assisted approaches to support the key functionalities of MCWEN, including data collection, information extraction, and decision making. Typical application examples are provided to illustrate the practical significance of the proposed MCWEN framework.

A dynamic delay-based reliability evaluation model for communication networks

The traditional network reliability research generally focuses on the topological connectivity among given nodes based on graph theory. Without considering the relationship between network failures and network performance degradation, traditional network reliability analysis cannot reflect actual network ability reaching up to a certain accomplishment level over a utilization interval. As for the performability analysis of network with specific requirement of transmission time, this paper proposes a dynamic delay-based network reliability evaluation method, which considers network’s ability that transmission delay can be controlled under expected upper limit level in the course of network normal operating period, and puts forward a three-layer network reliability analysis architecture. By integrating a network component reliability model, a network topology model and a network performance model, the dynamic delay-based network reliability model is established, and the network reliability influence factors are integrated into network reliability evaluation. A simple example shows that for a given network, network reliability is different from specified performance requirement. Furthermore, network failures will result in network performance degradation in network long-term operation. It is also proven that network reliability relates not only to network topological connectivity but also to network application environment and specified performance requirement. The key contribution of this paper is to guide network engineers to design a high performance network by optimizing the network parameters according to the delay-based network reliability evaluation method proposed in this paper.

A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing

Owing to the acceleration of urbanization and the rapid development of mobile Internet, mobile crowd sensing (MCS) has been recognized as a promising method to acquire massive volume of data. However, due to the massive perception data in participatory MCS system, the data privacy of mobile users and the response speed of data processing in cloud platform are hard to guarantee. Stimulating the enthusiasm of participants could be challenging at the same time. In this paper, we first propose a three-layer MCS architecture which introduces edge servers to process raw data, protects users’ privacy and improve response time. In order to maximize social welfare, we consider two-stage game in three-layer MCS architecture. Then, we formulate a Markov decision process (MDP)-based social welfare maximization model and investigate a convex optimization pricing problem in the proposed three-layer architecture. Combined with the market economy model, the problem could be considered as a Walrasian equilibrium problem according to market exchange theory. We propose a pricing approach toward incentive mechanisms based on Lagrange multiplier method, dual decomposition and subgradient iterative method. Finally, we derive the experimental data from real-world dataset and extensive simulations demonstrate the performance of our proposed method.

A new wind power interval prediction approach based on reservoir computing and a quality-driven loss function

Understanding the uncertainty of wind power forecasting is crucial for its practical application. This paper proposes a new forecasting approach to estimate the wind power prediction intervals (PIs) to quantify the prediction uncertainty. This approach integrates the reservoir computing methodology into a three-layer neural network architecture, and outputs the final PIs by minimizing a quality-driven loss function. The reservoir computing methodology can help study the nonlinear relationship implicit in data as well as accelerate computational time, while the quality-driven loss function is assumption-free and can help improve the forecasting capability of the proposed model. The proposed model is applied to real wind power data to test its effectiveness. Case studies show that for the data used in this paper, the proposed model can reduce the mean prediction interval width (MPIW) by up to 16.69%, reduce root mean square error (RMSE) by up to 7.36%, and save up to 5 times computation time compared to the benchmark models, these indicate that the proposed model has strong predictive capability.

Development of a Piezoelectric Transducer-Based Integrated Structural Health Monitoring System for Impact Monitoring and Impedance Measurement

Structural health monitoring (SHM) techniques, which are also considered as online nondestructive testing methods, are significant in modern structural engineering due to their ability to guarantee structure safety while reducing maintenance cost. It is often necessary to combine different SHM methods to achieve a more reliable damage detection result. However, the hardware of the SHM systems is usually expensive, bulky, and heavy when they are designed separately. Therefore, this paper proposes a three-layer architecture for designing an integrated multi-function SHM system to achieve a small, lightweight, and low power consumption SHM system. Based on the architecture, an integrated SHM system with impact monitoring and electromechanical impedance measurement is developed. In addition, a scheduling module is developed to manage the two functions of the system. Furthermore, an integrated interface is developed to transfer the data and the command. Then, an integrated printed circuit board is designed and manufactured to achieve the aforementioned functions. The designed system is applied for impact monitoring and damage detection for a supporting structure of a sailplane.

Digital twin for geometric feature online inspection system of car body-in-white

ABSTRACT With the development of digital twin, the research on the fusion of intelligent manufacturing system and digital twin technology attracts more attention. In this paper, the realization of digital twin is studied for geometric feature online inspection system of body-in-white (BIW). A three-level virtual modeling approach of Element-Behavior-Rule is proposed for the physical environment modeling in a digital environment. An element model, behavior model and rule model are defined, respectively, and the relationship among the three-layer model is given for real-time communication. A three-layer communication architecture of the digital twin system is designed for the real-time mapping between physical and virtual space, and the process of the online inspection digital twin system is analyzed. Finally, on the basis of the online inspection physical experimental platform and JHIM (Jiangheng Intelligent Manufacturing) software platform, the digital twin system for geometric feature online inspection of BIW is developed, which shows that the virtual model can be driven in real time by real-time physical inspection data. Digital twin technology provides a feasible way for real-time monitoring of the online inspection system.

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

Performance Improvement of P2P using Location Awareness and Interest Clustering

ABSTRACT Because Peer-to-Peer (P2P) has the characteristics of providing scalable network services, related applications of P2P are one of the hot topics of network services today. However, too many P2P applications may lead to inefficient use of network resources or extremely poor application execution performance. At the same time, it may also occupy a large amount of bandwidth, causing an increase in the load of Internet Service Providers (ISPs). In order to avoid excessive network traffic burden, ISPs often use a flow control mechanism to prevent P2P from generating excessive traffic, which leads to the degradation of P2P software service quality. Since P4P (Proactive network Provider Participation for P2P) is one of the technologies used for P2P network optimization, it provides communication between ISPs and P2P service providers in a cooperative manner to reduce P2P traffic and ISP operating costs. Therefore, in order to reduce the operating costs of ISPs and the bandwidth required for P2P application services, a three-layer architecture combining location awareness with interest clustering and P4P framework is proposed in this study, which is called ICP4P (interest clusters based P4P ). Using the ICP4P proposed by this research will reduce Internet traffic and increase the robustness of the network. In addition, this study provides a candidate peer election mechanism to improve the service quality of P2P. When super peer fails, leaves or overloads, the backup super peer or cooperative peers will be enabled to maintain the stability of whole network.

Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing

This paper has proposed an integrated healthcare monitoring solution for the soldiers deployed in adverse environmental conditions, using the internet of things (IoT) with distributed computing. For these soldiers, the health parameters of every individual need to be monitored on a real-time basis and subsequent analysis of the dataset to be made for initiating appropriate medical support with the lowest possible delay. In this paper, a three-layer service-oriented IoT architecture has been proposed where the computational functionalities are distributed among all the layers. The proposed distributed computing mechanism has implemented two levels of filtration of redundant information that belongs to safe soldiers. The first level of filtering is done at the end-node using the Fuzzy classification approach and the second level of filtering is done at the intermediate node using the time-series pattern analysis approach. This layer-wise filtration process results in a reduction in data flooding and computational burden on the cloud due to which system response time improves to suit emergency applications. A prototype has been developed to validate the effectiveness of the proposed solution.