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Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size. [ABSTRACT FROM AUTHOR]

Fog Manufacturing applies both Fog and Cloud Computing collaboratively in Smart Manufacturing to create an interconnected network through sensing, actuation, and computation nodes. Fog Manufacturing has become a promising research component to be integrated into the existing Smart Manufacturing paradigm and provides reliable and responsive computation services. However, Fog nodes' relatively limited communication bandwidth and computation capabilities call for reduced data communication load and computation time latency for modeling. There has long been a lack of an integrated framework to automatically reduce manufacturing data and perform computationally efficient modeling/machine learning. This research direction is increasingly important as both the computational demands and Fog/networked Manufacturing become prevalent. This paper proposes an integrated and distributed framework for data reduction and modeling of multiple systems in a Smart Manufacturing network considering the system similarities. A simulation study and a Fog Manufacturing testbed for ingot growth manufacturing validated that the proposed framework significantly reduces the sample size used for improved computational runtime metrics while outperforming various other data reduction methods in modeling performance. [ABSTRACT FROM AUTHOR]

Social emotion prediction aims to predict readers' emotion, for example, emotion distributions evoked by documents (e.g., news articles). It makes a significant contribution to social media applications, such as opinion summary, election prediction, and emotions investigation of society. While recent studies have focused on encoding consecutive word sequences in documents using neural network models and leveraging topical information, it is essential to acknowledge the influence of documents sharing similar topics or being related to similar events on evoking readers' emotions. The interactions among documents can significantly impact social emotion prediction. In this paper, we propose a novel approach to model the interactions among documents by constructing a heterogeneous graph. This graph captures the interaction among documents based on global word co-occurrence patterns in a corpus and the emotional scores of words obtained from emotion lexicons. Additionally, we develop heterogeneous graph convolution attention network (HGCA) to embed the heterogeneous graph. This network effectively captures the importance of different neighboring nodes and different node types, enabling comprehensive emotion prediction. Furthermore, we develop Taylor series expansion-based Transformer (Tayformer) to derive initialized node representations that can be co-trained with our graph network while having low memory complexity. Experimental results on four benchmark datasets show the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity. [ABSTRACT FROM AUTHOR]

Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively. [ABSTRACT FROM AUTHOR]

This paper focuses on radio resource management (RRM) in multi-user dual-function radar communication (DFRC) systems using orthogonal frequency division multiplexing (OFDM) waveforms. We propose two RRM schemes, one from the perspective of sum rate maximization and the other from the perspective of user fairness maximization. These optimization problems are non-convex due to the presence of mixed integer terms, making them difficult to solve. To address these challenges, we have employed a decomposition approach to transform these two complex problems into separate, more readily solvable ones. In addressing the sum rate maximization problem, we initially introduce a heuristic greedy algorithm to obtain a resource allocation scheme that satisfies radar performance requirements. Subsequently, we utilize a cyclic iterative method along with KKT conditions to solve the sum rate maximization problem for communication users. Concerning the fairness maximization problem for communication users, we similarly employ a heuristic greedy algorithm to obtain a resource allocation scheme that meets radar performance constraints. Then utilize the Lagrangian dual method to solve the multi-user fairness maximization problem for communication users. Our experimental results confirm the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we improve networks' spectral efficiency (SE), extend networks' lifetime, and maximize networks' energy efficiency (EE) of two-way full-duplex (FD) relay networks. Firstly, to improve networks' SE and to extend networks' lifetime simultaneously, we design a two-way FD relay transmission strategy with simultaneous wireless information and power transfer and direct links (DLs). The designed transmission strategy can complete a bidirectional communication in only one time slot with the exists of DLs and the energy-constrained relay node. With the designed transmission strategy, we further give the characteristics of relay amplification factor, the analysis of the designed transmission strategy, and the EE analysis of traditional half-duplex two-way amplify-and-forward relaying. Secondly, to maximize networks' EE, we present both the EE maximization problems and analyses of the designed transmission strategy with equal power allocation and optimal power allocation. To solve the EE maximization problems, we further propose the alternating optimal algorithm and give complexity analysis of the algorithm. Simulations show that our designed transmission strategy can improve the SE and EE of the networks. [ABSTRACT FROM AUTHOR]

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the jamming precoding and transmitting precoding matrices at both the source and the relay while adhering to transmit power constraints. We present a formulation of this problem and demonstrate that it can be efficiently solved using an effective block coordinate descent (BCD) algorithm. Simulation results are conducted to validate the convergence and performance of the proposed algorithm. These findings confirm the effectiveness of our approach. Furthermore, the numerical analysis reveals that our proposed algorithm surpasses traditional schemes that lack jamming to achieve higher secrecy rates. As a result, the proposed algorithm offers the benefit of guaranteeing secure communications in a realistic channel model, even in scenarios involving colluding eavesdroppers. [ABSTRACT FROM AUTHOR]

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation. [ABSTRACT FROM AUTHOR]

The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in this direction that involve first selecting the most important nodes and then linking them, trying to extract the most useful subgraph out of the original graph. However, the current state of the art systems use costly centrality measures for identifying the most important nodes, whereas even costlier procedures have been devised for linking the selected nodes. In this paper, we address both those deficiencies by first exploiting embeddings for node selection, and then by meticulously selecting approximate algorithms for node linking. Experiments performed over two real-world big KGs demonstrate that the summaries constructed using our method enjoy better quality. Specifically, the coverage scores obtained were 0.8, 0.81, and 0.81 for DBpedia v3.9 and 0.94 for Wikidata dump 2018, across 20%, 25%, and 30% summary sizes, respectively. Additionally, our method can compute orders of magnitude faster than the state of the art. [ABSTRACT FROM AUTHOR]

How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been emerging as an essential aspect of smart applications that are presently there in wireless communication. Gateways play an important role in IoT as they are capable of filtering the data that is being communicated to the cloud platforms. The data is sent via intermediary gateways utilizing a variety of wireless technologies to application servers located in the IoT cloud. Using standard routing strategies to link millions of IoT devices to the internet results in gateway congestion and overload resulting in decrease in IoT network throughput. But for cost-effectiveness, optimal gateway placement is required and this can be possible by integrating Solution Specific Gateways (SSGs) thus it creates a multi-technology gateways management scenario. In this paper, an Enhanced Multi-technology Gateway Management (EMTGM) scheme has been proposed based on the efficient selection of SSGs along with context aware handover decision making based on multiple objectives. The proposed scheme is further strengthened as both AHP and TOPSIS concepts are integrated for effective decision making which makes it a more efficient one. The proposed framework is simulated on Mobisim and its effectiveness has been analyzed with respect to existing approaches under the network parameters like End to end Delay, Packet Delivery Ratio, Residual Energy, Energy Consumption and Throughput by varying the arrival tasks, node count and simulation time. From the results, it is inferred that the proposed scheme outperforms existing schemes and thus is a quite effective one. [ABSTRACT FROM AUTHOR]

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance. [ABSTRACT FROM AUTHOR]

As multiprocessor systems continue to grow in processor scale, the incidence of faults also increases. As a result, fault diagnosis is becoming a key mechanism for maintaining the normal operation of multiprocessor systems. To explore more effective diagnostic methods, Somani et al. introduced a generalized pessimistic diagnostic strategy, named t / k -diagnosis, in which all faulty nodes are isolated in a set of nodes and at most k fault-free nodes are misdiagnosed, provided that the quantity of faults is limited by t. By imposing certain conditions or restrictions, the t / k -diagnosability of some regular networks under the Preparata, Metze, and Chien (PMC) model has been determined. However, the t / k -diagnosability of many networks under the comparison model remains unidentified. In this paper, we provide new insights into the study of t / k -diagnosability under the comparison model. After introducing some new notions, such as the 0-test unit, 0-test set and 0-test subgraph, under the comparison model, we study the relationship in a system G between the 0-test subgraphs and the components of G − F , where F is the set of faulty nodes, and we obtain some important correlation properties. Based on these results, we study t / k -diagnosability under the comparison model. As a result, the t / k -diagnosability of some regular interconnection networks can be efficiently determined. [ABSTRACT FROM AUTHOR]

This paper concerns with the problem of fault detection for networked fuzzy systems. Considering the digital communication channel, quantization is adopted to make the data transmission feasible and the induced data distortion is converted into a bounded uncertain term. For the purpose of saving network resources, a new quantizer with an event-triggered strategy is designed to reduce the length and the number of the transmitted data packets, simultaneously. In the process of stability analysis, the T-S fuzzy model approach is employed to approximate the nonlinear networked system, and a more flexible fuzzy design method is presented to get the fault detection filter which can detect the sensor fault in the presence of external disturbance. And sufficient conditions are constructed to guarantee the stability of the filter error system in terms of linear matrix inequalities. Finally, the simulation example is given to verify the feasibility of the method. [ABSTRACT FROM AUTHOR]

Mandarin has a low rate of overtly expressed arguments in all syntactic functions without agreement marking on the verb. It has been claimed that Mandarin exhibits higher rates of zero arguments than other languages. Most previous work has compared Mandarin with English, while comparison with other languages remains a desideratum. This study compares Mandarin with seven languages (Cypriot Greek, English, Northern Kurdish, Sanzhi, Teop, Tondano, Vera'a) taken from Multi-CAST (Haig & Schnell 2019). Results suggest that while Mandarin exhibits more zero arguments than pronouns, this is not unique, with e.g. Cypriot Greek having a higher rate of zero arguments. In addition, a relatively stable rate of lexical expressions can be found across languages, relativising Mandarin's unique position with regard to referential choice even further. [ABSTRACT FROM AUTHOR]

Summary: One of the key strategies in wireless sensor network (WSN) for sending data packets to the base station (BC) is known as routing. However, malicious node outbreaks will occur, which exaggerate the functions of WSNs during the routing procedure. A secure routing protocol is necessary to protect the effectiveness of WSN and routing fortification. In real‐time scenarios, the existing routing protocol is volatile dynamically; it makes difficult to identify the performance of unprotected routing nodes. In this manuscript, a unique routing strategy is proposed for secured dynamic optimal routing (SDOR) in WSN that combines block chain technology optimized with a generative adversarial capsule network. Every routing sensor node contains a secret key, which is used to construct a crypto hash signature (CHS) token for flow access. The proposed block chain‐based WSN is used to provide SDOR information using a self‐attention‐based generative adversarial capsule network that is improved using the flamingo search algorithm. Following that, the security on the proposed block chain WSN is assessed from six angles. Block chain token transactions are evaluated using measures including average latency, average energy usage, and throughput. The proposed SDOR‐SA‐GAC‐FSA‐CHS‐BWSN delivers 53.87%, 42.57%, and 32.87% lower average packet delay, 28.97%, 37.73%, and 34.75% lesser identification time analyzed to the existing methods, such as trusted distributed routing scheme for WSNs using block chain (SDOR‐DCNN‐SSOA‐BWSN), secure authentication with DSM‐KL ascertained presentation optimization of hybrid block chain‐enabled model for multi‐WSN (SDOR‐DSM‐KL‐BWSN), and optimizing the valid transaction utilizing reinforcement learning‐based block chain ecosystem in WSN (SDOR‐RIL‐BWSN). [ABSTRACT FROM AUTHOR]

The most recent and elegant solution to address the problem of scalability in network-on-chip (NoC) architectures is WiNoCs (Wireless NoCs).WiNoC, a wired-wireless hybrid architecture in NoC designs, is becoming increasingly popular for its fast unicast message delivery. However, modern applications often require multicasting and broadcasting messages instead. In the proposed work, the widely used simulation tool BookSim is employed, which offers reconfigurability and openness and it enables the analysis of both wired and wireless NoC architectures. This paper proposes a Tree-based Wireless NoC architecture approach incorporating MDND (message duplication in non-destination), which provides multicast support for WiNoC by exploring the inherent broadcast-type communication of wireless connection. Additionally, the proposed work supports the configuration of various aspects, including its multicast injection rate, enabling or disabling its multicast capabilities, and determining the number of multicast recipients for each packet. The proposed Tree-based approach showed a significant improvement in average multicast transaction latency compared to the traditional NoC. There is a noticeable reduction in the average network latency, the flit latency, and the total power. The analysis also shows a reduction in average packet latency for unicast messages and average packet latency for broadcast messages. [ABSTRACT FROM AUTHOR]

In a blockchain network, a rule set called consensus mechanism is used to create and finalize a block. In a proof-of-stake (PoS), consensus-based blockchain network, nodes become validators, minters, or stakeholders' nodes to complete the consensus mechanism. In these networks, when a node becomes a validator node, its details need to be saved because the details of the validators are used in the network for many important decisions, such as selecting block proposers for the consensus process. In this paper, we present Sea Shield, which uses a validator chain to save a node's information when it becomes a validator or leaves its responsibility as a validator in the PoS-based blockchain network. The validator chain is a blockchain that can run with the main chain of a PoS-based blockchain. The internal features of the validator chain are similar to those of the blockchain. We designed and simulated a consensus mechanism to create and finalize the block for the validator chain with no forks. We present a process by which a node may join or unjoin as a validator in a PoS-based blockchain network to improve the overall security of the main chain-consensus process. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

Among the distribution network planning problems, feeder routing is one of the most important and challenging issues. Feeder routing is defined as the optimal determination of the feeders' routes by minimizing the investment cost and satisfying the network constraints. To the best knowledge of the authors, solving feeder routing problem in multi-objective framework is rarely investigated in the previous literature. This paper develops a bi-objective modeling and optimization framework to efficiently solve the feeder routing problem of distribution network considering cost and power losses. Since feeder routing is a complex, high-dimensional and combinatorial optimization problem, a multi-objective differential crow search algorithm (MO-CSAdif) is proposed to obtain a well-distributed and widely spread Pareto front. On a real large distribution network, simulation results confirm the usefulness of the proposed optimization framework. It can be drawn that MO-CSAdif can be a promising method to optimally solve the feeder routing problem. [ABSTRACT FROM AUTHOR]

Summary: A fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods. [ABSTRACT FROM AUTHOR]

Due to the alternating loads on pumping units and the integration of new energy sources, multisource DC microgrid pumping unit well groups experience increased fluctuations in voltage and power as well as superimposed peak and valley values. This work presents a distributed control strategy for pumping unit well groups on a multisource DC microgrid based on the weighted moving average algorithm. A centralized control program is implanted in the RTU of the single-well controller of each pumping unit, and communication with each well is realized via SCADA and multicast communication, resulting in a distributed well group system. The real-time power values of the pumping well group are calculated by grouping the power values, and each group is weighted using the total power fluctuation threshold of the well group as the control target. Then, a weighted moving average algorithm is used to predict the next power value and form a table of predicted real-time power spectra. According to the power values in the community power spectrum table, the inverter frequency is proportionally adjusted downwards to reach the power peak before deceleration; after the power peak is crossed, the frequency is increased in the same way to reach the power valley before acceleration. Finally, the peak and valley power values of the bus system level off and further learn to reach the set impulse; ultimately, a stable impulse is formed. In laboratory testing and field application in the Shengli Oilfield XIN-11 block, the group control software module effectively suppressed the active power peak and valley values and voltage fluctuations of the bus system, the active power fluctuation rate range decreased by more than 70%, and the DC bus voltage fluctuation range decreased by more than 80%; moreover, the active power decreased by approximately 6% without additional hardware costs. [ABSTRACT FROM AUTHOR]

A model of the multichannel queuing system is considered. Each channel contains some service lines. There are several input flows. Each customer requires several lines to be serviced. If the channel does not have a sufficient number of service lines, it is possible to reorient this customer to another channel. The service line has a distribution function of a general form depending both on the flow and on the number of lines required by the customer. A fast simulation method aimed to evaluate the blocking probability of customers of a certain flow with a given number of service lines is proposed. The method is compared with the Monte Carlo method using numerical example, and the gain in simulation time is illustrated in particular. [ABSTRACT FROM AUTHOR]

As an important component of the smart grid, vehicle-to-grid (V2G) networks can deliver diverse auxiliary services and enhance the overall resilience of electrical power systems. However, V2G networks face two main challenges due to a large number of devices that connect to it. First, V2G networks suffer from serious security threats, such as doubtful authenticity and privacy leakage. Second, the efficiency will decrease significantly due to the massive requirements of authentication. To tackle these problems, this paper proposes a cross-domain authentication scheme for V2G networks based on consortium blockchain and certificateless signature technology. Featuring decentralized, open, and transparent transactions that cannot be tampered with, this scheme achieves good performance on both security and efficiency, which proves to be suitable for V2G scenarios in the smart grid. [ABSTRACT FROM AUTHOR]

D2D coded caching, originally introduced by Ji, Caire, and Molisch, significantly improves communication efficiency by applying the multi-cast technology proposed by Maddah-Ali and Niesen to the D2D network. Most prior works on D2D coded caching are based on the assumption that all users will request content at the beginning of the delivery phase. However, in practice, this is often not the case. Motivated by this consideration, this paper formulates a new problem called request-robust D2D coded caching. The considered problem includes K users and a content server with access to N files. Only r users, known as requesters, request a file each at the beginning of the delivery phase. The objective is to minimize the average and worst-case delivery rate, i.e., the average and worst-case number of broadcast bits from all users among all possible demands. For this novel D2D coded caching problem, we propose a scheme based on uncoded cache placement and exploiting common demands and one-shot delivery. We also propose information-theoretic converse results under the assumption of uncoded cache placement. Furthermore, we adapt the scheme proposed by Yapar et al. for uncoded cache placement and one-shot delivery to the request-robust D2D coded caching problem and prove that the performance of the adapted scheme is order optimal within a factor of two under uncoded cache placement and within a factor of four in general. Finally, through numerical evaluations, we show that the proposed scheme outperforms known D2D coded caching schemes applied to the request-robust scenario for most cache size ranges. [ABSTRACT FROM AUTHOR]

Technical organizations increasingly rely on innovation contests to find novel ideas for designing complex systems. These activities involve outsiders in the early stages of the design process, leading to ground-breaking designs that often surpass expectations. Here, the contest's rules document plays a crucial role: this design artifact communicates the organization's problem and the desired system performance to the participants--significantly impacting the resulting solutions. However, the contest's nature amplifies the challenges of communicating complex design problems across boundaries. Existing strategies for formulating--i.e., requirement and objective allocation--might not suit this context. We developed an inductive model of their formulation process based on a multiyear field study of five complex innovation contests. We found that a formulation team (or "seeker") balanced the need to communicate their problem in detail with the risk of excluding valuable participants. Here, they chose among three approaches--incentivize, impose, or subsume--depending on their knowledge of potential solutions and the participants' capabilities. Notably, the seeker formulated more granularly than the literature describes, employing multiple approaches within each rules document. These findings shed light on a poorly understood aspect of innovation contests, shed new light on a longstanding debate in the engineering design literature, and guide practitioners' formulation processes. [ABSTRACT FROM AUTHOR]

Photonic devices based on silicon waveguides are essential to versatile high‐performance and low‐cost photonic integrated systems. Extremely complex silicon photonic devices with hundreds or even thousands of degrees of freedom (DOF) are successfully designed and manufactured based on recent advances in data science and nanofabrication technology. At this level, conventional forward‐reasoning may no longer be suitable for designing high‐performance silicon photonic devices with novel functionalities since the light‐matter interaction is complex and non‐intuitive. Therefore, the timely development of sub‐wavelength silicon photonic devices that can precisely mold the flow of light is a critical and urgent issue requiring joint engineering and scientific efforts. In this paper, an inverse design strategy based on heuristic and gradient descendant algorithms, enabling the realization of large‐scale integrated devices is first introduced. Subsequently, the burgeoning deep learning technology, which offers a promising direction for the automation design of silicon photonics with a data‐driven approach, is discussed. Finally, the obstacles and prospects in this emerging research direction are revealed. Detail discussions from multiple perspectives are provided. This review aims to provide general guidance and a comprehensive reference for scientists developing photonic integrated systems. [ABSTRACT FROM AUTHOR]

The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives. [ABSTRACT FROM AUTHOR]

This paper studies the problem of under what conditions the general nonlinear networks with directional competition and perturbations to achieve fully distributed BS (bipartite synchronization). For the norm bounded disturbances with unknown upper bound, under the assumption that the signed digraph is structurally balanced and contains a directed spanning tree, a fully distributed observer-based adaptive protocols is designed for BS problem of general nonlinear networks with no leader and neural networks approximation. For the disturbances generated by exosystems, based on a disturbance observer, a fully distributed disturbance observerbased adaptive protocol is proposed to make network achieve bounded BS. In order to reduce the chattering phenomenon, some adaptive gains and adaptive parameter vector of neural networks approximation are proposed. Finally, the theoretical results are verified by two numerical simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a tree-based method called Multivariate RE-EM tree, which combines the regression tree and the linear mixed effects model for modeling multivariate response longitudinal or clustered data. The Multivariate RE-EM tree method estimates a population-level single tree structure that is driven by the multiple responses simultaneously and object-level random effects for each response variable, where correlation between the response variables and between the associated random effects are each allowed. Through simulation studies, we verify the advantage of the Multivariate RE-EM tree over the use of multiple univariate RE-EM trees and the Multivariate Regression Tree. We apply the Multivariate RE-EM tree to analyze a real data set that contains multidimensional nonfinancial characteristics of poverty of different countries as responses, and various potential causes of poverty as predictors. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of guiding a flexible needle from its entry point to a target location through an obstacle-laden environment. To achieve this, we propose a path planning approach grounded in an enhanced particle swarm optimization algorithm (PSO). Our method uses the number and angle of needle rotations to govern the flexible needle's movement. By optimizing for parameters such as path length, distance from obstacles, and needle rotation, we transform the flexible needle puncture path planning problem into a multi-objective optimization task. The optimal path is determined by assigning distinct weights to these four optimization objectives and solving them using the improved PSO algorithm. To demonstrate the effectiveness and practicality of our approach, we conduct MATLAB simulations and compare the results with the unimproved PSO algorithm and the rapidly-exploring random trees algorithm. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Networks (MANETs) are dynamic and self-organizing networks where nodes constantly change positions and communicate wirelessly. The stability of routing protocols in MANETs is crucial for effective data transmission, considering factors like node mobility, bandwidth, and signal strength. This research introduces an innovative approach to enhance MANET routing stability by integrating optoelectronic devices and machine learning. Optoelectronic components are leveraged to optimize signal detection in 5G networks and beyond, particularly in Massive Multiple-Input, Multiple-Output (MIMO) systems. The proposed protocol, "Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol," utilizes machine learning algorithms to intelligently select routes based on real-time data, improving network efficiency. Moreover, the integration of optoelectronic devices enhances signal detection and quality. Comprehensive evaluations were conducted to validate the effectiveness of this approach, comparing it with conventional routing algorithms such as AODV. The Network Simulator 2 evaluated key performance indicators such as round-trip latency, packet delivery ratio, and route lifetime. Specifically for 5G networks and Massive MIMO systems, the results show that this Optoelectronic-Aided Machine Learning-Based Stable Routing Protocol has the potential to improve the stability and efficiency of MANETs considerably. This study aids in the development of cutting-edge wireless network communication protocols. [ABSTRACT FROM AUTHOR]

Due to the advancement of high definition, 5G technologies, the Internet of Things, and Artificial Intelligence, the demand for optical networks has increased widely. Optical communication networks offer various metrics, including high transmission capacity, efficient anti-interference, minimum transmission loss and robustness, and so on, that offer opportunities for communication networks. To satisfy the optical network demands, effective network resource utilization is essential. Therefore, developing a tool with improved Quality of Transmission (QoT) accuracy in optical networks is necessary. Recently, Artificial intelligence (AI) approaches have provided various opportunities to resolve these issues and the deep learning (DL) algorithms offer improved performance over the conventional methods. This paper developed a novel DL-based cognitive QoT prediction model for Quantum optical communication networks. This proposed model predicts the QoT for the QoS (Quality of Service) setup using the DL model with the transmission computation. The proposed model utilized an optimized DL model called CNN-LSTM for the prediction process using the signal and link characteristics as input features. The DL model is trained using the transmission equations. The hyperparameters of the neural network are optimized using frog leap optimization to improve the predictive performance. The experimental results highlight the enhanced and improved version of the proposed model, and the results are compared with the conventional systems in terms of performance measures. [ABSTRACT FROM AUTHOR]

Gratitude goes to the heart of discussions about ethics, care, and responsibility in a more-than-human world. Surprisingly, gratitude remains peripheral to geographical considerations of human-environment interrelations and sustainability. Moving beyond questions about whether gratitude to nature is sensible, we develop an understanding of gratitude that is relational, emotional, and practical (or enacted). We argue that focus on gratitude draws attention to a kind of attachment thus far neglected in geography, as well as enabling a new lens for what people value about their everyday lives, the labour of nonhuman-others in fostering a 'good life', and efforts to recognise and reinforce human-nonhuman interconnection. Drawing on emails written to trees in Naarm/Melbourne, we illustrate gratitude to trees for: support of life, aesthetic pleasures, moments of distraction, and solace in difficult times. Whether tantalisingly short or in-depth narration, individually and collectively, the emails demonstrate how people conceptualise and practice gratitude to nature in their everyday lives. Through attending to the power of small, embodied, emplaced gestures of thanks to trees, our analysis also suggests new ways of understanding people's commitments to nature and possibilities for securing better futures for urban forests. [ABSTRACT FROM AUTHOR]

We build a multi-period pricing model between a blockchain-technology-supported platform and a traditional platform, where the blockchain-technology-supported platform provides a higher value for customers. Customers are influenced by network effect, that is, they value a platform more if the platform has more users. As either platform can adopt static pricing or dynamic pricing, four scenarios may occur. By deriving the equilibrium of each scenario, we reveal the 'Matthew effect' caused by network effect, that platform advantage (from adopting blockchain technology) or disadvantage (from not adopting blockchain technology) accumulates as time goes by. Thus, platforms are advised to adopt the blockchain technology antecedent to the competitors. Network effect, which amplifies the benefit of initial users, may intensify price competition and harm both platforms. By comparing the four scenarios, we derive the equilibrium pricing strategies: when network effect is weak, one platform adopts static pricing and the other adopts dynamic pricing; when network effect is medium, the blockchain-technology-supported platform adopts static pricing and the traditional platform adopts dynamic pricing; and when network effect is strong, both platforms adopt dynamic pricing. Dynamic pricing is more desirable for the traditional platform relative to the blockchain-technology-supported platform. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the role of brokers in the regional innovation network and its influence on innovative and collaborative outcomes. For this purpose, we make use of data from the European Patent Office and Eurostat in the period 1986–2015. We first build the regional collaboration network based on co-inventorship ties, and then we identify the brokerage roles played by each region, using the original taxonomy proposed by Gould and Fernandez (1989), to disentangle their impact on innovation and collaboration. Finally, we investigate regional collaboration intensity and how it interacts with brokerage roles, highlighting its mediating effect. Our findings indicate that brokerage roles contribute to the extension of collaboration networks, but also that they are not efficient for the creation of innovation. Collaboration intensity, on the other hand, enhances both innovation and collaborative outcomes and shows how a region can benefit from being a broker. [ABSTRACT FROM AUTHOR]

In this paper, a practical scenario of multicasting through heterogeneous cellular network has been considered. The macro base station of macro cell transmits confidential information to its intended multicast macro users and multiple eavesdroppers try to decode this information and pico base station of pico cell transmits confidential information to its intended multicast pico users. Authors are interested to develop a mathematical model consists of the closed-form analytical expression for the secrecy multicast capacity (SMC) to protect that confidential information from eavesdropping. The results show that the simultaneous effects of double scatterer and correlation significantly reduce the security in multicasting but these reduction in the security can be compensated by antenna diversity at the multicast users. The results also show the effect of double scatterer on different correlation scenario (Constant Correlation and Jakes Correlation) in terms of the correlation figures of transmit, receive and scatterer correlation matrices. [ABSTRACT FROM AUTHOR]

A cloud manufacturing (CMfg) system is presented as a novel service- and customer-oriented manufacturing paradigm that integrates the distributed manufacturing enterprises to share their manufacturing capabilities or resources and collaborate as an interconnected system in a dynamic environment. Since the high performance of this system depends on the formation of a suitable group of manufacturing service providers, this paper develops an integrated c onfiguration design and capacity planning problem for the CMfg system by considering the dynamic environment of this system. In this regard, dynamic service providers and dynamic demand are considered as two aspects of the dynamic nature of this system. A multi-period multi-objective mathematical model is proposed by maximising the utilities of all three stakeholders of the system. Moreover, three extensions of a discrete multi-objective grey wolf optimiser (DMOGWO) algorithm are devised to solve the medium- and large-scale instances. A comprehensive computational experiment is conducted to assess the performance of the developed meta-heuristic algorithms. Furthermore, by carrying out a sensitivity analysis, some managerial insight is suggested for the managers. [ABSTRACT FROM AUTHOR]

Since the emergence of blockchain, how to improve its transaction throughput and reduce transaction latency has always been an important issue. Hostuff has introduced a pipeline mechanism and combined it with a chain structure to improve the performance of blockchain networks. HCA has introduced a revoting mechanism on the basis of Hostuff, further reducing transaction latency, but it has also brought some problems. In HCA, if the leader is malicious, it would be possible to continuously call on the replica nodes to revote, which can lead to network congestion. This paper employs the global perfect coin technology to guarantee that every replica can obtain a globally consistent and the freshest candidate proposal during the Revote phase, thereby improving the robustness of the HCA protocol. The performance improvement of RHCA in attack scenarios has been verified through experiments. [ABSTRACT FROM AUTHOR]

To enhance the reliability of traditional current protection systems in the evolving landscape of future power grids, this paper proposes a differential protection data synchronization approach that leverages satellite and 5G network timing. This method addresses the risk of inadvertent operation due to 5G communication jitter. It employs the BeiDou satellite system for synchronizing the timing of 5G base stations and terminals. This synchronization extends to the 5G terminal and the protection device, thereby mitigating time deviations caused by air interface delays and reducing the 5G network's end-to-end delay by approximately 4 ms on average. A simulation was conducted to assess the reliability and effectiveness of the proposed method under normal satellite signal conditions. The results indicated a significant improvement in protection accuracy, with the method reducing the protection activation time by approximately 5–7 ms compared to the 5G timing data synchronization method. This advancement offers technical support for the safe and stable operation of new power systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider to solve the problem of reducing the end-to-end quantum entanglement establishment latency in quantum networks. In some existing schemes, "doubling" entanglement swapping (ES) method is usually utilized in a nested quantum repeater structure, while we investigate parallel-measurement techniques. We propose a protocol for end-to-end entanglement establishment in a quantum network where heterogeneous Bell pairs are previously shared on physical links. The protocol is proven to be feasible theoretically in a generalized scenario with multiple quantum repeater nodes. A general decision mechanism is developed for the destination node to read out the state of end-to-end quantum entanglement locally based on knowledge of the received measurement outcomes and entangled links information. Numerical evaluation shows that our protocol outperforms the "doubling" ES scheme regarding to the end-to-end entanglement establishment latency. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. [ABSTRACT FROM AUTHOR]

Correlations between exchange rates are valuable for illuminating the dynamics of international trade and the financial dynamics of countries. This paper explores the changing interactions of the US foreign exchange market based on detrended cross-correlation analysis. First, we propose an objective way to choose a time scale parameter appropriate for comparing different samples by maximizing the summed magnitude of all DCCA coefficients. We then build weighted signed networks under this optimized time scale, which can clearly display the complex relationships between different exchange rates. Our study shows negative cross-correlations have become pyramidally rare in the past three decades. Both the number and strength of positive cross-correlations have grown, paralleling the increase in global interconnectivity. The balanced strong triads are identified subsequently after the network centrality analysis. Generally, while the strong development links revealed by foreign exchange have begun to spread to Asia since 2010, Europe is still the center of world finance, with the euro and Danish krone consistently maintaining the closest balanced development relationship. Finally, we propose a fluctuation propagation algorithm to investigate the propagation pattern of fluctuations in the inferred exchange rate networks. The results show that, over time, fluctuation propagation patterns have become simpler and more predictable. [ABSTRACT FROM AUTHOR]

To facilitate wireless communication, ocean exploration, and other military and scientific uses, underwater acoustic sensor networks have been developed. This network's location in a complex and demanding underwater environment increases failure rates and lowers the network's efficiency in terms of hardware and software. As a result, defining the quality of service in wireless sensor networks operating underwater is a developing study field. Supporting QoS is a difficult problem because of resource limits in underwater sensor networks, such as computing power, memory, bandwidth, and power source. The hardware architecture, elements, and structure of underwater sound sensor networks have been described in this study, along with a detailed explanation of the network levels and the difficulties that each layer faces. A summary of different QoS-aware routing methods for underwater sensor networks is also provided, along with a discussion of the QoS requirements for each layer. Finally, based on the crucial quality standards for the routing services in the underwater sensor network, each of the aforementioned protocols has been assessed and contrasted. In the conclusion, the service quality in underwater sensor network routing has been enhanced by the presentation of a routing solution based on multi-cast routing, hybrid clustering, and fuzzy logic. [ABSTRACT FROM AUTHOR]

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks. [ABSTRACT FROM AUTHOR]