Economic Feasibility Assessment of Motorized PV Louver System Considering the Near Shading Parameter, Array Incidence Losses and Other Environmental Factors

Seoul has good weather settings for incorporating renewable energies, hence, given its small land area living mode was mostly set in an apartment condition it is an ideal place for building applied photovoltaic (BAPV) for solar energy harvesting. On the other hand, the BAPV energy self-consumption hasn’t been thoroughly examined considering the overall energy consumption requirement. Therefore, presented in this communication are the viability of PVL to produce electricity from solar energy and insights on modulating and improving energy harvesting efficiency. To accomplish this objective, three major factors were considered: 1) the photovoltaic (PV) positioning;2) the solar tracking scenario;and 3) the mechanistic system energy consumption. The overall louver energy generation was thoroughly scrutinized from the net energy conception of the BAPV up to the mechanistic module energy expenditure. This work intends to provide insights into the economic feasibility of BAPV assessing its technological profitability in the specified location and building size.

MEC-based architecture for interoperable and trustworthy internet of moving things

The expansion of the Internet of Moving Things(IoMT)leads to limitless and continuous working playgrounds exploited by highly dynamic end devices.This requires the adoption of multi-Radio Access Technologies(RATs)-based strategies to provide IoMT units with ubiquitous connectivity.To this end,the development of secure bootstrapping and authentication mechanisms is necessary to permit the secure operation of end devices.Given the transmission and power limitations of these elements,current cryptographic solutions do not address these stringent requirements.For that reason,in the study we present a Multi-Access Edge Computing(MEC)-based endto-end architecture that enables an efficient and secure authentication and key agreement between end devices and network servers over heterogeneous resource-limited networks such as the Low Power Wide Area Networks(LPWANs).Our proposal is based on the Authentication,Authorization,and Accounting(AAA)architecture and the recent Internet Engineering Task Force initiatives Static Context Header Compression and Low-Overhead CoAP-EAP.The results obtained from experimental tests reveal the validity of the proposal as it enables constrained IoMT devices to gain IPv6 connectivity as well as performs end-to-end secure authentication with notable reliability and controlled latency.

Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks

The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence(AI)-based analytics,empowering seamless integration across the entire Continuum(Edge,Fog,Core,Cloud).This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning(FL)-based anomaly detection in B5Gnetworks.By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm,which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior,the systemorchestrates network intelligence to detect and prevent cyber-attacks.Integrated into a B5G Zero-touch Service Management(ZSM)aligned Security Framework,the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors,enhancing reaction capabilities against cyber-attacks.The proposed FL architecture can be dynamically deployed across the B5G Continuum,utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling.Implementation includes FL enforcement operations for interoperability and extensibility,enabling dynamic deployment,configuration,and reconfiguration on demand.Performance validation of the proposed solution was conducted through dynamic orchestration,FL,and real-time anomaly detection processes using a practical test environment.Analysis of key performance metrics,leveraging the 5G-NIDD dataset,demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks,including real-time network monitoring and countermeasure implementation for mitigation.

Joint Dynamic Clustering and User Scheduling for Downlink Cloud Radio Access Network with Limited Feedback

In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,propose an efficient three-phase framework and corresponding algorithms for dealing with this problem.Firstly,a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase.And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase.In the end,based on the limited feedback two zero-forcing(ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase.The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.

A Novel Soft Sensor Modeling Approach Based on Least Squares Support Vector Machines

Artificial Neural Networks (ANNs) such as radial basis function neural networks (RBFNNs) have been successfully used in soft sensor modeling. However, the generalization ability of conventional ANNs is not very well. For this reason, we present a novel soft sensor modeling approach based on Support Vector Machines (SVMs). Since standard SVMs have the limitation of speed and size in training large data set, we hereby propose Least Squares Support Vector Machines (LS_SVMs) and apply it to soft sensor modeling. Systematic analysis is performed and the result indicates that the proposed method provides satisfactory performance with excellent approximation and generalization property. Monte Carlo simulations show that our soft sensor modeling approach achieves performance superior to the conventional method based on RBFNNs.

Coupled CUBIC Congestion Control for MPTCP in Broadband Networks

Recently,multipath transmission control protocol(MPTCP)was standardized so that data can be transmitted through multiple paths to utilize all available path bandwidths.However,when high-speed long-distance networks are included in MPTCP paths,the traffic transmission performance of MPTCP is severely deteriorated,especially in case the multiple paths’characteristics are heavily asymmetric.In order to alleviate this problem,we propose a“Coupled CUBIC congestion control”that adopts TCP CUBIC on a large bandwidth-delay product(BDP)path in a linked increase manner for maintaining fairness with an ordinary TCP traversing the same bottleneck path.To verify the performance excellence of the proposed algorithm,we implemented the Coupled CUBIC Congestion Control into Linux kernels by modifying the legacy MPTCP linked-increases algorithm(LIA)congestion control source code.We constructed asymmetric heterogeneous network testbeds mixed with large and small BDP paths and compared the performances of LIA and Coupled CUBIC by experiments.Experimental results show that the proposed Coupled CUBIC utilizes almost over 80%of the bandwidth resource in the high BDP path,while the LIA utilizes only less than 20%of the bandwidth for the same path.It was confirmed that the resource utilization and traffic transmission performance have been greatly improved by using the proposed Coupled CUBIC in high-speed multipath networks,as well as maintaining MPTCP fairness with competing single-path CUBIC or Reno TCP flows.

Performance Assessment of Motorized Solar Photovoltaic Louvers System Using PVSYST Software

In the realm of technological market penetration of solar photovoltaic louvers(PVL)addressing environmental difficulties and the industrial revolution,a new avenue of renewable energy is introduced.Moreover,solar energy exploitation through building façades was addressed through motorized solar photovoltaic louvers(MPVL).On the other hand,proponents exalted the benefits of MPVL overlooking the typical analyses.In this communication,we attempted to perform a thorough industrial system evaluation of the MPVL.This communication presents a methodology to validate the industrial claims about MPVL devices and their economic efficiency and the insight on how geographical location influences their utilization and augment their potential benefits.This task is carried out by evaluating the extent of solar energy that can be harvested using solar photovoltaic system(PVSYST)software and investigating whether existing product claims are associated with MPVL are feasible in different locations.The performance and operational losses(temperature,internal network,power electronics)were evaluated.To design and assess the performance of different configurations based on the geographical analogy,simulation tools were successfully carried out based on different topographical locations.Based on these findings,various factors affect the employment of MPVL such as geographical and weather conditions,solar irradiation,and installation efficiency.tt is assumed that we successfully shed light and provided insights into the complexity associated with MPVL.

Physical Layer Security with Its Applications in 5GNetworks： A Review

5G network is expected to support massive user connections and exponentially increasing wireless services,which makes network security unprecedentedly important.Unlike traditional security-guaranteeing techniques which rely heavily on cryptographic approaches at upper layers of the protocol stack,physical-layer security(PLS) solutions fully take advantages of the characteristics of wireless channels to degrade the received signal qualities at the malicious users,and realize keyless secure transmission via signal design and signal processing techniques.PLS avoids the difficulties in the distribution and management of secret keys,and provides flexible security levels through adaptive transmission protocol design.Moreover,PLS techniques match the features of 5G networks well.Therefore,the application of PLS to 5G networks is a promising solution to address the security threats.This article presents a comprehensive review of the state-of-the-art PLS techniques,and discusses their applications in 5G networks.We first summarize the principle and advantages of PLS techniques,and point out the reasons why PLS is suitable for 5G networks.Then,we review the existing PLS methods in literature,and highlight severalPLS solutions that are expected to be applied in 5G networks.Finally,we conclude this article and figure out some further research directions.

Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks

With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data(big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network's performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete(or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4 G LTE-A to detect network's anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.

A Lightweight ABE Security Protection Scheme in Cloud Environment Based on Attribute Weight

Attribute-based encryption(ABE)is a technique used to encrypt data,it has the flexibility of access control,high security,and resistance to collusion attacks,and especially it is used in cloud security protection.However,a large number of bilinear mappings are used in ABE,and the calculation of bilinear pairing is time-consuming.So there is the problem of low efficiency.On the other hand,the decryption key is not uniquely associated with personal identification information,if the decryption key is maliciously sold,ABE is unable to achieve accountability for the user.In practical applications,shared message requires hierarchical sharing in most cases,in this paper,we present a message security hierarchy ABE scheme for this scenario.Firstly,attributes were grouped and weighted according to the importance of attributes,and then an access structure based on a threshold tree was constructed according to attribute weight.This method saved the computing time for decryption while ensuring security and on-demand access to information for users.In addition,with the help of computing power in the cloud,two-step decryption was used to complete the access,which relieved the computing and storage burden on the client side.Finally,we simulated and tested the scheme based on CP-ABE,and selected different security levels to test its performance.The security proof and the experimental simulation result showthat the proposed scheme has high efficiency and good performance,and the solution implements hierarchical access to the shared message.

Building an interoperable space for smart agriculture

The digital transformation in agriculture introduces new challenges in terms of data,knowledge and technology adoption due to critical interoperability issues,and also challenges regarding the identification of the most suitable data sources to be exploited and the information models that must be used.DEMETER(Building an Interoperable,Data-Driven,Innovative and Sustainable European Agri-Food Sector)addresses these challenges by providing an overarching solution that integrates various heterogeneous hardware and software resources(e.g.,devices,networks,platforms)and enables the seamless sharing of data and knowledge throughout the agri-food chain.This paper introduces the main concepts of DEMETER and its reference architecture to address the data sharing and interoperability needs of farmers,which is validated via two rounds of 20 large-scale pilots along the DEMETER lifecycle.This paper elaborates on the two pilots carried out in region of Murcia in Spain,which target the arable crops sector and demonstrate the benefits of the deployed DEMETER reference architecture.

Research on Federated Learning Data Sharing Scheme Based on Differential Privacy

To realize data sharing,and to fully use the data value,breaking the data island between institutions to realize data collaboration has become a new sharing mode.This paper proposed a distributed data security sharing scheme based on C/S communication mode,and constructed a federated learning architecture that uses differential privacy technology to protect training parameters.Clients do not need to share local data,and they only need to upload the trained model parameters to achieve data sharing.In the process of training,a distributed parameter update mechanism is introduced.The server is mainly responsible for issuing training commands and parameters,and aggregating the local model parameters uploaded by the clients.The client mainly uses the stochastic gradient descent algorithm for gradient trimming,updates,and transmits the trained model parameters back to the server after differential processing.To test the performance of the scheme,in the application scenario where many medical institutions jointly train the disease detection system,the model is tested from multiple perspectives by taking medical data as an example.From the testing results,we can know that for this specific test dataset,when the parameters are properly configured,the lowest prediction accuracy rate is 90.261%and the highest accuracy rate is up to 94.352.It shows that the performance of the model is good.The results also show that this scheme realizes data sharing while protecting data privacy,completes accurate prediction of diseases,and has a good effect.

Efficient nonlinear companding scheme for substantial reduction in peak-to-average power ratio of OFDM

Orthogonal frequency division multiplexing(OFDM)produces a high peak-to-average power ratio(PAPR) that adversely affects high-speed OFDM data transmission. In order to reduce the high PAPR, an efficient nonlinear companding transform(NCT) function is proposed. With the proposed NCT function,the compression and expansion weights can be applied independently with suitably chosen function parameter values. As a result, the proposed function can easily maintain the average signal power approximately unchanged during the companding process.In this regard, the proposed function is superior to previously proposed schemes. Also, the simulations show the outstanding PAPR reduction performance of the proposed function. It is demonstrated that the proposed scheme performs well with nonlinear transmitter amplifiers and delivers superior error performance, compared with error function and exponential function based schemes.

Unmanned Aerial Vehicle Multi-Access Edge Computing as Security Enabler for Next-Gen 5G Security Frameworks

5G/Beyond 5G(B5G)networks provide connectivity to many heterogeneous devices,raising significant security and operational issues and making traditional infrastructure management increasingly complex.In this regard,new frameworks such as Anastacia-H2020 or INSPIRE-5GPlus automate the management of next-generation infrastructures,especially regarding policy-based security,abstraction,flexibility,and extensibility.This paper presents the design,workflow,and implementation of a security solution based on Unmanned Aerial Vehicles(UAVs),able to extend 5G/B5G security framework capabilities with UAV features like dynamic service provisioning in specific geographic areas.The proposed solution allows enforcing UAV security policies in proactive and reactive ways to automate UAV dynamic deployments and provisioning security Virtual Network Functions(VNFs)in the onboard Multi-access Edge Computing(MEC)node.A UAV has been ensembled from scratch to validate the proposal,and a raspberry-pi has been onboarded as compute node.The implementation provides a VNF for dynamic UAV management,capable of dynamically loading waypoints into the flight controller to address reactive autonomous flights,and anMLbased VNF capable of detecting image patterns.According to the security policies,the onboard VNFs can be dynamically configured to generate alerts to the framework and apply local reactions depending on the detection made.In our experiments,we measured the time it takes for the solution to be ready after receiving a security policy for detecting patterns in a specific geographical area.The time it takes for the solution to react automatically was also measured.The results show that the proactive flow configuration considering ten waypoints can be enforced in less than 3 s,and a local reactive flow can be enforced in around 1 s.We consider that the results are promising and aligned with other security enabler solutions as part of existing 5G/6G security frameworks.

Angle-Based Interference-Aware Routing Algorithm for Multicast over Wireless D2D Networks

Wireless devicetodevice （D2D） communications sharing the spectrum of cellular networks is important for improving spec trum efficiency. Furthermore, introducing multicast and multi hop communications to D2D networks can expand D2D ser vice functions. In this paper, we propose an anglebased inter ferenceaware routing algorithm for D2D multicast communica tions. This algorithm reuses the uplink cellular spectrum. Our proposed algorithm aims to reduce the outage probability and minimize the average hop count over all multicast destina tions （i.e., multicast receivers）, while limiting interference to cellular users to a tolerable level. In particular, our algorithm integrates two design principles for hopbyhop route selec tion. First, we minimize the distance ratio of the candidateto destination link to the candidatetobasestation link, such that the selected route advances closer to a subset of multi cast receivers. Second, we design the anglethreshold based merging strategy to divide multicast receivers into subsets with geographically close destinations. By applying the two principles for selection of each hop and further deriving an adaptive powerallocation strategy, the message can be more effieiently delivered to destinations with fewer branches when constructing the multicast tree. This means fewer duplicated data transmissions. Analyses and simulations are presented to show the impact of system parameters on the routing perfor mances. Simulation results also demonstrate the superiority of our algorithm over baseline schemes in terms of outage proba bility and average hop count.

Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative Clustering Algorithm

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

Scale variant vehicle object recognition by CNN module of multipooling- PCA process

The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances.The premise of advanced driver assistance system(ADAS)system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle.The recognition of the same vehicle at different scales requires feature learning with scale invariance.Unlike existing feature vector methods,the normalized PCA eigenvalues calculated from feature maps are used to extract scale-invariant features.This study proposed a convolutional neural network(CNN)structure embedded with the module of multi-pooling-PCA for scale variant object recognition.The validation of the proposed network structure is verified by scale variant vehicle image dataset.Compared with scale invariant network algorithms of Scale-invariant feature transform(SIFT)and FSAF as well as miscellaneous networks,the proposed network can achieve the best recognition accuracy tested by the vehicle scale variant dataset.To testify the practicality of this modified network,the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.

Adaptive cache policy optimization through deep reinforcement learning in dynamic cellular networks

We explore the use of caching both at the network edge and within User Equipment(UE)to alleviate traffic load of wireless networks.We develop a joint cache placement and delivery policy that maximizes the Quality of Service(QoS)while simultaneously minimizing backhaul load and UE power consumption,in the presence of an unknown time-variant file popularity.With file requests in a time slot being affected by download success in the previous slot,the caching system becomes a non-stationary Partial Observable Markov Decision Process(POMDP).We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic(A2C)algorithm,comparing Feed Forward Neural Networks(FFNN)with a Long Short-Term Memory(LSTM)approach specifically designed to exploit the correlation of file popularity distribution across time slots.Simulation results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality,demonstrating superior performance for the non-stationary POMDP problem.For caching at the UEs,we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network,with minimum energy consumption at the UEs,and minimum communication overhead.

Beacon-Less Geographic Routing in Real Wireless Sensor Networks

Geographic Routing （GR） algorithms require nodes to periodically transmit HELLO messages to allow neighbors to know their positions （beaconing mechanism）. Beacon-less routing algorithms have recently been proposed to reduce the control overheads due to these messages. However, existing beacon-less algorithms have not considered realistic physical layers. Therefore, those algorithms cannot work properly in realistic scenarios. In this paper we present a new beacon-less routing protocol called BOSS. Its design is based on the conclusions of our open-field experiments using Tmote-sky sensors. BOSS is adapted to error-prone networks and incorporates a new mechanism to reduce collisions and duplicate messages produced during the selection of the next forwarder node. We compare BOSS with Beacon-Less Routing （BLR） and Contention-Based Forwarding （CBF） algorithms through extensive simulations. The results show that our scheme is able to ache.eve almost perfect packet delivery ratio （like BLR） while having a low bandwidth consumption （even lower than CBF）. Additionally, we carried out an empirical evaluation in a real testbed that shows the correctness of our simulation results.

Non-coherent space-time code based on full diversity space-time block coding

A non-unitary non-coherent space-time code which is capable of achieving full algebraic diversity is proposed based on full diversity space-time block coding, The error performance is optimized by transforming the non-unitary space-time code into unitary space-time code, By exploiting the desired structure of the proposed code, a grouped generalized likelihood ratio test decoding algorithm is presented to overcome the high complexity of the optimal algorithm, Simulation results show that the proposed code possesses high spectrum efficiency in contrast to the unitary space-time code despite slight loss in the SNR, and besides, the proposed grouped decoding algorithm provides good tradeoff between performance and complexity,