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This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid "explosion of complexity" and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents' outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this study, we focus on real-time anomaly detection using the gated graph neural network (GGNN) and long short-term memory (LSTM) algorithms for the most commonly used network protocols of HTTP and TELNET. In a network, the flow of the various protocols is determined by their respective roles. Each protocol consists of options with individual tasks, and protocols are processed based on these tasks. Therefore, when analyzing flow, a unique repetitive pattern emerges according to the flow and mission. Accordingly, when an anomaly signal is involved, the pattern shows different characteristics from the existing pattern. This study identifies the flow of input and output values based on the correlation between the options defined at each port, whereby the correlation is analyzed, and the detection accuracy of the anomaly signal is determined using the GGNN and LSTM algorithms. The experimental results demonstrate an accuracy of 99.36% for GGNN and 89.46% for LSTM in detecting network anomalies. [ABSTRACT FROM AUTHOR]

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics. [ABSTRACT FROM AUTHOR]

Background: Previous studies have found that psychological interventions have a positive effect on improving physical and psychological problems in colorectal cancer survivors. However, there is still a lack of high-quality evidence reviews that summarize and compare the impact of different psychological interventions. The aim of this study was to synthesize existing psychological interventions and use network meta-analysis to explore whether psychological interventions improve anxiety, depression, fatigue and quality of life in colorectal cancer (CRC) survivors. Methods: We will extract relevant randomized controlled trials of psychological interventions for CRC survivors from eight electronic databases, including PubMed, Embase, The Cochrane Library, Web of Science, CINAHL, PsycInFO, CNKI, and Wanfang database. Two reviewers will independently screen the literature and extract data. The risk of bias of the included studies will be assessed using the RoB2: Revised Cochrane Risk of Bias Tool. We will then conduct paired meta-analyses and network meta-analyses of the extracted data, using a frequency-based framework and random effects models. Discussion: To the best of our knowledge, this study is the first proposed qualitative and quantitative integration of existing evidence using systematic evaluation and network meta-analysis. This study will inform health policy makers, healthcare providers' clinical intervention choices and guideline revisions, and will help to reduce depression and anxiety in CRC survivors, reduce fatigue, improve quality of life. [ABSTRACT FROM AUTHOR]

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another. [ABSTRACT FROM AUTHOR]

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary. [ABSTRACT FROM AUTHOR]

The industrial control and automation sector has invested in the development and standardization of new wireless (WirelessHART, ISA 100.11a, and WIA-PA) and wired (Profibus/Profinet, Modbus, and LonWORK) solutions aimed at automating processes to support standard monitoring and control functions from the perspective of addressing critical applications, as well as those integrated within the Building Internet of Things (BIoT) concept. Distributed data acquisition and control systems allow modern installations to monitor and control devices remotely. Various network protocols have been proposed to specify communication formats between a client/gateway and server devices, with Modbus being an example that has been widely implemented in the latest industrial electrical installations. The main contribution made in this paper concerns the completion of the Modbus Extension (ModbusE) specifications for the server station in the classical Modbus communication architecture, as well as their implementation and testing in an STM32F4 kit. A general-purpose control architecture is proposed for BIoT sector, comprising both intelligent touch switches and communication protocols of which the Modbus protocol is used extensively for the monitoring and control part, especially between clients, smart switches, and devices. The specific contributions concern the presentation of a scientific and practical implementation of improved specifications and their integration as software modules on ModbusE protocol server stations. A client station with a VirtualComm USB PC connection is also implemented in the lab to test the operation of the proposed server with specific Modbus applications. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and the improvement of people's living standards, smart locks have become prevalent in ordinary households. However, for enterprises and institutions requiring unified management and control capabilities for networked smart locks, the technology has been relatively lagging, and there is a lack of established brands. Existing products are mostly based on wireless low-power network protocols, leading to common issues of low stability, limited network scalability, and high maintenance efforts. This paper addresses the problems existing in current networked lock systems and leverages the centralized office characteristics of enterprises and institutions. By integrating IoT technology and Power over Ethernet- (PoE-) centralized power supply, we have developed a networked smart lock system with unified management and control capabilities. This system is primarily designed for applications in offices, collective dormitories, and hotels of enterprises, institutions, and schools. The test results demonstrate that even with a scale of over tens of thousands of networked locks under unified control, the system maintains fast response times and low packet loss rates. It also incorporates energy-saving and environmentally friendly features, as well as easy automatic updates, facilitating convenient postdeployment maintenance. Thus, it effectively meets the requirements for centralized management and control in different application scenarios with centralized use of rooms. In conclusion, this research has led to the development of a unified management and control networked smart lock system, which is well suited for enterprises, institutions, and schools. Its scalability, speed, energy efficiency, and ease of maintenance make it an excellent solution for various application scenarios requiring centralized management and control of access. [ABSTRACT FROM AUTHOR]

Background: VCI is a severe public health problem facing the world today. In addition to pharmacological treatment, non-invasive neuromodulation techniques have also been effective. At this stage, non-invasive neuromodulation techniques combined with pharmacological treatment are the mainstay of clinical treatment, and clinical trials are continuing to be conducted, which is becoming the direction of treatment for VCI. Therefore, we outline this systematic review and network meta-analysis protocol to evaluate and rank clinical data in future studies which can develop optimal protocols for the clinical treatment of VCI with non-invasive neuromodulation techniques in combination with drugs. Methods: The network meta-analysis will search eight databases, including PubMed, Embase, Cochrane Library, Web of Science, China Knowledge Infrastructure Library (CNKI), China Biology Medicine disc (CBM)), Wanfang Data Knowledge Service Platform and Vipshop Journal Service Platform (VIP), for a period of from the establishment of the library to January 30 2022. The quality of the studies will be evaluated using the Cochrane Review's Handbook 5.1 and the PEDro scale to assess the evidence and quality of the included randomised controlled trials. Risk of bias assessment and heterogeneity tests will be performed using the Review Manager 5.4 program, and Bayesian network meta-analysis will be performed using the Stata 16.0 and WinBUGS 1.4.3 program. Results: The results of the network meta-analysis will be published in a peer-reviewed journal. Conclusions: Our study is expected to provide high quality evidence-based medical evidence for the treatment of VCI by clinicians. Trial registration: PROSPERO:CRD42022308580. [ABSTRACT FROM AUTHOR]

Vehicular Internet of Things (VIoT) refers to integrating Internet of Things (IoT) technology into the transportation sector, specifically vehicles. It aims to enhance transportation safety, efficiency, and sustainability by using connected devices and real-time data exchange between vehicles, road infrastructure, and transportation management systems. RFID can provide valuable insights into the functioning and performance of vehicles, enabling the development of intelligent transportation systems and contributing to the growth of the VIoT. To secure essential driving data tag location, data privacy, and road safety are the primary concerns in the VIoT system. An attacker can perform a variety of attacks on the tag and compromise its privacy through the wireless channel between it and the reader. To ensure secure communication for the various application of the VIoT system, An RFID-based protocol is proposed, which is referred to as UPSRVNet (Ultralightweight, Privacy preserved, and Secure RFID-based authentication protocol for VIoT Networks). The proposed protocol guarantees secure authentication in VIoT networks using ultralightweight, privacy-preserving RFID tags by substituting bit formation and right-shift rotation for high-computing operations like hash functions and encryption/decryption algorithms. It also reduces RFID tags' storage and communication overhead and aims to secure the driver's location, personal information, and resistance from known attacks. As per the experiment result, the proposed UPSRVNet protocol showed a significant reduction in communication overhead between tag and reader, that is 50%, and the storage cost on the tag is also reduced by 33.33% compared to existing protocols. Due to this, the certification and authentication processes may be performed quickly. The protocol ensured the security requirements by informal analysis using the Scyther tool. A comprehensive analysis of the proposed protocol reveals its substantial advantages over the existing protocol concerning computation complexity, communication efficacy, and storage expenditure. [ABSTRACT FROM AUTHOR]

The complexity and isomerization of communication networks have put forth new requirements for cryptographic schemes to ensure the operation of network security protocols. Robust cryptographic schemes have been gradually favored. The robust initialization vector (RIV) instead of the synthetic initialization vector (SIV) was first introduced to support strong security and robust authenticated encryption. This paper first introduces RIV to GCM-SIV1, proposes a robust variant, GCM-RIV1, and proves that it ensures birthday-bound subtle AE (SAE) security and nonce-misuse resistance. Then, to support beyond-birthday-bound (BBB) security with graceful degradation, we introduce another, stronger security variant, GCM-RIV2, and prove that it allows gracefully degrading BBB SAE security in the faulty nonce setting. Finally, the performance of GCM-RIV1 and GCM-RIV2 is discussed and compared. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs' side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage. [ABSTRACT FROM AUTHOR]

Address Resolution Protocol (ARP) is used to resolve a host’s MAC address, given its IP address. ARP is stateless, as there is no authentication when exchanging a MAC address between the hosts. Hacking tactics using ARP spoofing are constantly being abused differently; many previous studies have prevented such attacks. However, prevention requires modification of the underlying network protocol or additional expensive equipment, so applying these methods to the existing network can be challenging. In this paper, we examine the limitations of previous research in preventing ARP spoofing. In addition, we propose a defence mechanism that does not require network protocol changes or expensive equipment. Before sending or receiving a packet to or from any device on the network, our method checks the MAC and IP addresses to ensure they are correct. It protects users from ARP spoofing. The findings demonstrate that the proposed method is secure, efficient, and very efficient against various threat scenarios. It also makes authentication safe and easy and ensures data and users’ privacy, integrity, and anonymity through strong encryption techniques. [ABSTRACT FROM AUTHOR]

Background: Vitreous hemorrhage from proliferative diabetic retinopathy can cause severe vision loss. DRCR Retina Network Protocol AB was a randomized clinical trial comparing intravitreal aflibercept versus vitrectomy with panretinal photocoagulation and found no difference in the average rate of visual recovery over 104 weeks. Herein, we describe patient-reported outcome measures from Protocol AB. Methods: Secondary analysis of a multicenter (39 sites) randomized clinical trial. The Work Productivity and Activity Impairment Questionnaire was administered at 4, 12, 24, 36, 52, 68, 84, and 104 weeks. Main outcomes were mean change in activity impairment and work productivity loss over 24 and 104 weeks (area under the curve). Results: Mean (SD) activity impairment at baseline was 58% (27%) in the aflibercept group (N = 99) and 56% (30%) in the vitrectomy group (N = 105). The mean reduction in activity impairment from baseline over 24 weeks was 21% (25%) in the aflibercept group and 27% (31%) in the vitrectomy group (adjusted difference = -6.8% [95% CI, -12.7% to -0.9%], P =.02); over 104 weeks, the adjusted mean difference was -3.1% (95% CI, -9.2% to 3.0%, P =.31). Mean work productivity loss at baseline was 51% (28%) in the aflibercept group (N = 44) and 58% (30%) in the vitrectomy group (N = 43). The mean reduction in work productivity loss from baseline over 24 weeks (area under the curve) was 19% (23%) in the aflibercept group and 31% (24%) in the vitrectomy group (adjusted difference = -8.3% [95% CI, -16.8% to 0.2%], P =.06); over 104 weeks, the adjusted mean difference was -9.1% (95% CI, -18.4% to 0.2%, P =.05). Conclusions: Participants with vitreous hemorrhage from proliferative diabetic retinopathy had less activity impairment over 24 weeks when treated initially with vitrectomy and panretinal photocoagulation versus intravitreal aflibercept. The trend was similar for work productivity but not statistically significant. By 104 weeks, the improvements were similar in the two treatment groups. Trial registration: ClinicalTrials.gov NCT02858076. [ABSTRACT FROM AUTHOR]

Background: The purpose of this network meta-analysis (NMA) is to investigate the efficacy of pharmacological and non-pharmacological therapy on pain intensity and disability of older people with chronic nonspecific low back pain, providing comprehensive evidence for an informed decision-making. Methods: We will perform a systematic search to identify randomized controlled trials of pharmacological and non-pharmacological interventions for older people with chronic nonspecific low back pain. MEDLINE, Cochrane Library, Embase, AMED, PsycINFO, and PEDro will be searched without language or date restrictions. Our primary outcomes are pain intensity and disability. Risk of bias will be assessed for all studies using the revised Cochrane risk-of-bias (RoB) tool 2.0. For each pairwise comparison between the different interventions, estimated mean differences and their 95% confidence intervals will be presented. Standard pairwise meta-analyses will be performed using random effects models in STATA version 16. The competing interventions will be ranked using the surface under the cumulative ranking curve (SUCRA) for the outcomes of interest at short and long terms. The confidence in the results from NMA will be assessed using the Confidence in Network Meta‐Analysis (CINeMA) framework. Discussion: This NMA compares efficacy of interventions for nonspecific chronic low back pain in older people. It will provide reliable evidence for patients, clinicians, stakeholders, and researchers in this field where competing therapies, many of extraordinarily little value, are commonly used in clinical practice. Systematic review registration: PROSPERO CRD42022312565 [ABSTRACT FROM AUTHOR]

This survey presents various insights on the IoT system and its architecture used to implement a smart health-care system, along with the implementation of the latest technologies such as Cloud Computing for storing data, Machine Learning for predicting various diseases. These technologies are also used to improve the existing management and administration system in hospitals. Various solutions for tackling the health-care crisis have been proposed most of them sound promising but a practical low-cost implementation and feasibility of IoT and ML integrated system is yet to be designed. The present chapter also deals with the standard protocols designed for body area networks for health monitoring applications. The IEEE 802.15.6 standard is used for the body area network which provides high data rate and low-range communication. The integration of the standard body area network protocols with 5G technology can be used for high throughput real-time reliable operations. [ABSTRACT FROM AUTHOR]

The world's first five-agent network, also called Wuxing network in ancient China, had been fully established in the second century BC. Surprisingly, the key to cracking the operation of Wuxing network is the golden ratio, the world's most astonishing number originating from ancient Greece. Wuxing network is composed of five agents located on the vertices of a pentagon such that adjacent agents cooperate with each other, while spaced-apart agents oppose each other. Although it was proposed more than 2000 years ago, it is still an unparalleled network operation protocol. This article reveals the role of the golden ratio in the balance and stability of Wuxing network, and demonstrates how to detect the golden ratio experimentally in Wuxing electronic circuits and in Wuxing formation flight of drones. [ABSTRACT FROM AUTHOR]

The user datagram protocol is the most widely used protocol for streaming multimedia data over the Internet. Like other protocols at the network and transport layers, it uses the Internet Checksum (IC) to detect channel errors. The consequence of this is that each corrupted packet is dropped, which can degrade the quality of service perceived by a user. In this paper, the authors present a solution to this problem based on integer codes capable of correcting single and double adjacent errors. The presented codes are a generalization of the IC, which makes them easy to implement in software. [ABSTRACT FROM AUTHOR]

Industry 4.0 has significantly improved the industrial manufacturing scenario in recent years. The Industrial Internet of Things (IIoT) enables the creation of globally interconnected smart factories, where constituent elements seamlessly exchange information. Industry 5.0 has further complemented these achievements, as it focuses on a human-centric approach where humans become part of this network of things, leading to a robust human–machine interaction. In this distributed, dynamic, and highly interconnected environment, functional safety is essential for adequately protecting people and machinery. The increasing availability of wireless networks makes it possible to implement distributed and flexible functional safety systems. However, such networks are known for introducing unwanted delays that can lead to safety performance degradation due to their inherent uncertainty. In this context, the Time-Sensitive Networking (TSN) standards present an attractive prospect for enhancing and ensuring acceptable behaviors. The research presented in this paper deals with the introduction of TSN to implement functional safety protocols for wireless networks. Among the available solutions, we selected Wi-Fi since it is a widespread network, often considered and deployed for industrial applications. The introduction of a reference functional safety protocol is detailed, along with an analysis of how TSN can enhance its behavior by evaluating relevant performance indexes. The evaluation pertains to a standard case study of an industrial warehouse, tested through practical simulations. The results demonstrate that TSN provides notable advantages, but it requires meticulous coordination with the Wi-Fi MAC layer protocol to guarantee improved performance. [ABSTRACT FROM AUTHOR]

In this paper, network energy assesses the capacity of a node to convey messages to others. In most cases, network energy is created when two nodes interact with one another. If a node is part of the same network as the node it is connecting with, then it will be able to make an inter-network energy connection with the node it is meeting. In the case that this does not take place, there will be an accumulation of energy within the part of the network that is facing the node. A node with higher inter-network energy is considered suitable for forwarding. The energy optimisation is achieved using efficient identification of source and destination pairs. This work has considered two scenarios, i.e. lossless transmission and lossy transmission, for our experiments and evaluated the detection probability. The performance of the proposed PPM is evaluated in terms of delivery ratio, overhead and hops count performance measures. When the buffer size is set to 100 MB, PPM delivers 59% of messages with message overhead of 750 and it has a hop count of 2, which is comparable to the state-of-the-art methods. With lengthy lifetimes of IOT networks, PPM is capable of giving higher performance while maintaining the privacy of network. The detection probability for the lossy observations model is applied to a 10-node, 20-node, 30-node and 40-node IoT network. [ABSTRACT FROM AUTHOR]

Many previous studies showed that some nodes should be in a sleeping state while the traffic is high. These nodes can wake up periodically to transmit data while they have data to transmit. The system throughput will be increased while each node can change between wake-up and sleeping states periodically. If a node can estimate the active rate and wake up at the optimal time, the collision probability decreases. This study proposes a Q-learning-based distributed queuing medium access control (QL-based DQMAC) protocol for Internet-of-Things (IoT) networks. In the proposed QL-based DQMAC, we derive the optimal number of contention IoT nodes. Each node calculates the active rate by itself through the Q-learning algorithm. Then, each node determines whether it will be active or in sleeping mode in the next contention period according to the active rate. Finding the optimal IoT nodes in each contention period decreases the probability of collision. The energy consumption due to the contention and delay for MAC contention is reduced owing to the lower number of contentions. Protocol comparison with other DQMAC protocols shows that the proposed QL-based DQMAC protocol achieves higher performance in IoT networks. [ABSTRACT FROM AUTHOR]

The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike. [ABSTRACT FROM AUTHOR]

In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicle (UAV) networks have been widely used in military and civilian scenarios; however, they suffer various attacks. Time-delay attacks maliciously delay the transmission of packets without tampering with the contents or significantly affecting the transmission pattern, making detection difficult. In this paper, a holistic cross-layer time-delay attack detection framework (HOTD) is proposed for UAV networks. A holistic selection of the delay-related features available at all layers is performed, before adopting supervised learning to build a consistency model between these features and the corresponding forwarding delay to calculate the degree of consistency of each node. Finally, the clustering method is used to distinguish malicious from benign nodes according to their degree of consistency. Experimental results show that the performance of HOTD is superior to that of state-of-the-art detection methods, and it achieves a detection accuracy higher than 85% with less than 2.5% additional overhead. • Time-delay attacks are modelled in UAV networks for the first time. • HOTD collects delay-related features at all layers in the UAV network protocol. • The relationship between the features and delay is used to identify malicious nodes. • Simulations show the method achieves over 85% detection accuracy. • HOTD is better than other available methods in detecting time-delay attack nodes. [ABSTRACT FROM AUTHOR]

Introduction-- Smart irrigation systems require reliable and energy-efficient communication between the sensors in a Wireless Sensor Network (WSN) and the control system. Objective-- To address this challenge, this paper presents several cluster-based selection protocols, based on the Stable Election Protocol (SEP) and Distributed Energy-Efficient Clustering (DEEC). Methodology-- The presented protocols divide the agriculture field into sub-fields to reduce energy consumption between far sensors and the Base Station (BS). Results-- Comparison with traditional protocols using evaluation metrics such as network throughput, stability, instability period, and lifetime, shows that the presented protocols outperform in terms of all metrics. Conclusions-- The results indicate the effectiveness of the proposed protocols in prolonging the network's lifetime and improving energy efficiency in heterogeneous WSNs, thus supporting the performance of smart irrigation systems. Numerically, using the proposed protocols, the network lifetime increased by 23% compared to conventional SEP. [ABSTRACT FROM AUTHOR]

Vehicle Ad-hoc network (VANET) can provide technical support and solutions for the construction of intelligent and efficient transportation systems, and the routing protocol directly affects the efficiency of VANET. The rapid movement of nodes and uneven density distribution affect the routing stability and data transmission efficiency in VANET. To improve the local optimality and routing loops of the path-aware greedy perimeter stateless routing protocol (PA-GPSR) in urban sparse networks, a weight-based path-aware greedy perimeter stateless routing protocol (W-PAGPSR) is proposed. The protocol is divided into two stages. Firstly, in the routing establishment stage, the node distance, reliable node density, cumulative communication duration, and node movement direction are integrated to indicate the communication reliability of the node, and the next hop node is selected using the weight greedy forwarding strategy to achieve reliable transmission of data packets. Secondly, in the routing maintenance stage, based on the data packet delivery angle and reliable node density, the next hop node is selected for forwarding using the weight perimeter forwarding strategy to achieve routing repair. The simulation results show that compared to the greedy peripheral stateless routing protocol (GPSR), for the maximum distance–minimum angle greedy peripheral stateless routing (MM-GPSR) and PA-GPSR protocols, the packet loss rate of the protocol is reduced by an average of 24.47%, 25.02%, and 14.12%, respectively; the average end-to-end delay is reduced by an average of 48.34%, 79.96%, and 21.45%, respectively; and the network throughput is increased by an average of 47.68%, 58.39%, and 20.33%, respectively. This protocol improves network throughput while reducing the average end-to-end delay and packet loss rate. [ABSTRACT FROM AUTHOR]

The current trends in 5G and 6G systems anticipate vast communication capabilities and the deployment of massive heterogeneous connectivity with more than a million internet of things (IoT) and other devices per square kilometer and up to ten million gadgets in 6G scenarios. In addition, the new generation of smart industries and the energy of things (EoT) context demand novel, reliable, energy-efficient network protocols involving massive sensor cooperation. Such scenarios impose new demands and opportunities to cope with the ever-growing cooperative dense ad hoc environments. Position location information (PLI) plays a crucial role as an enabler of several location-aware network protocols and applications. In this paper, we have proposed a novel context-aware statistical dead reckoning localization technique suitable for high dense cooperative sensor networks, where direct angle and distance estimations between peers are not required along the route, as in other dead reckoning-based localization approaches, but they are obtainable from the node's context information. Validation of the proposed technique was assessed in several scenarios through simulations, achieving localization errors as low as 0.072 m for the worst case analyzed. [ABSTRACT FROM AUTHOR]

As an emerging network paradigm, the space–air–ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and storage resources in mobile devices greatly impacts the quality of experiences for intelligent applications. Hence, we plan to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MECs). To facilitate efficient processing, we need to solve the optimal task offloading decisions. In contrast to existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capabilities for edge computing nodes, the uncertainty of transmission latency caused by heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we first describe the task offloading decision problem in environments characterized by these new challenges. However, we cannot use standard robust optimization and stochastic optimization methods to obtain optimal results under uncertain network environments. In this paper, we propose the 'condition value at risk-aware distributionally robust optimization' algorithm for task offloading, denoted as RADROO, to solve the task offloading decision problem. RADROO combines the distributionally robust optimization and the condition value at risk model to achieve optimal results. We evaluated our approach in simulated SAGIN environments, considering confidence intervals, the number of mobile task offloading instances, and various parameters. We compare our proposed RADROO algorithm with state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the Brute algorithm. The experimental results show that RADROO can achieve a sub-optimal mobile task offloading decision. Overall, RADROO is more robust than others to the new challenges mentioned above in SAGIN. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency. In order to improve the efficiency of the IDS, a binary classification of normal and abnormal IoT traffic is constructed to enhance the IDS performance. Our method employs various supervised ML algorithms and ensemble classifiers. The proposed model was trained on TON-IoT network traffic datasets. Four of the trained ML-supervised models have achieved the highest accurate outcomes; Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor. These four classifiers are fed to two ensemble approaches: voting and stacking. The ensemble approaches were evaluated using the evaluation metrics and compared for their efficacy on this classification problem. The accuracy of the ensemble classifiers was higher than that of the individual models. This improvement can be attributed to ensemble learning strategies that leverage diverse learning mechanisms with varying capabilities. By combining these strategies, we were able to enhance the reliability of our predictions while reducing the occurrence of classification errors. The experimental results show that the framework can improve the efficiency of the Intrusion Detection System, achieving an accuracy rate of 0.9863. [ABSTRACT FROM AUTHOR]

In order to improve the data monitoring and information management capabilities of membrane gas measure device indirect metering terminals, a sensor network based data monitoring method for membrane gas measure device indirect metering terminals was proposed. The distributed and centralized sensor network node deployment method is used to achieve data collection for membrane gas measure device indirect metering terminals, and the collected membrane gas measure device indirect metering terminal data is segmented, fused, and reorganized. The distributed energy storage and constant volume two-level optimal control method is used to analyze the operation status of gas measure device indirect metering terminal data. The ZigBee networking protocol is used to build a network structure model for gas measure device indirect metering terminals, an integrated high-precision temperature and pressure sensor arranged parallel to the inner wall of the valve body is used to detect the temperature and pressure at the outlet of the membrane gas measure device. The temperature information is stored in DSP, and the user's expenses, system operation and maintenance, and peak shaving and valley filling benefits are taken as the parameters of the energy storage and constant volume outer layer model. The output stability of the indirect metering terminal data of the membrane gas measure device is analyzed in the high switching frequency mode. Combined with adaptive parameter adjustment, the indirect metering terminal control and data dynamic collection of the membrane gas measure device are realized. Realize dynamic monitoring of metering terminal data in wireless sensor network networking control mode. The test results show that using this method for indirect metering terminal data monitoring of gas measure devices has good output stability, strong dynamic fusion of sensing information, reduced metering errors, and improved metering accuracy of gas measure devices. [ABSTRACT FROM AUTHOR]

Background: Multi-professional cooperation between healthcare providers is a key quality criterion of hospice and palliative care. While hospice and palliative care networks can support cooperation on a local level, opportunities for wider cooperation through the establishment and development of regional hospice and palliative care networks in Germany have not yet been explored systematically. Aims: The HOPAN study aims at: (1) identifying regional hospice and palliative care networks in Germany, (2) analysing these networks using an adapted quality assessment tool, and (3) proposing setting-sensitive recommendations for network development and exploring the benefits of these recommendations. Methods: HOPAN is a prospective, observational, mixed-methods study comprising three work packages (WPs). In WP1, the stock of regional hospice and palliative care networks in Germany will be identified via database, literature, and internet research. In WP2a, focus groups will be conducted to adapt an existing maturity model for healthcare networks to regional hospice and palliative care networks. In WP2b, a questionnaire will be sent to each identified regional hospice and palliative care network to gain insight into their structure and status of development. In WP2c, group discussions will be conducted to develop setting-sensitive recommendations for these networks. Finally, in WP3, these recommendations will be sent to all participating hospice and palliative care networks, and the benefits of the recommendations will be evaluated via a questionnaire. Discussion: Empirically developed setting-sensitive recommendations should enable the systematic establishment and management of regional hospice and palliative care networks in Germany, considering the specific needs and potential of each network. The study findings are expected to improve the overall development of hospice and palliative care services. Trial registration: The study was prospectively registered in the German Clinical Trials Register (Deutsches Register Klinischer Studien) (Registration N° DRKS00030629; date of registration: 02 November 2022). The study is searchable under the International Clinical Trials Registry Platform Search Portal of the World Health Organization, under the German Clinical Trials Register number. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks consist of a number of low cost, small sensor nodes powered by energy constrained batteries. Due to limited power, memory, and processing capacities of sensor nodes, it is essential to find new methods which improves energy consumption of the nodes and prolongs the lifetime of the network. Cluster head election among sensor nodes has an important role in wireless sensor networks to boost efficiency and lifetime of the network. In this study, a cluster head election method has been proposed and evaluated for heterogeneous wireless sensor networks, which extends the lifetime of the network. Simulation results reveal that the proposed cluster head election method offers a better performance than the SEP and LEACH protocols in terms of network lifetime and is able to provide throughput for a longer period of time. It is found that EECHEP provides efficient use of sensor energy and helps increase the number of surviving cluster head nodes that report to the base station. [ABSTRACT FROM AUTHOR]

Tantamount with the increasing application of blockchain technologies around the world, the use of blockchain-based smart contracts has rapidly risen. In a “smart contract,” computer protocols automatically facilitate, verify, and enforce arrangements made between parties on a blockchain. Such smart contracts offer a variety of commercial benefits, notably immutability and increased efficiency facilitated by removing the need for a trusted intermediary. However, as discussed in recent legal scholarship, it is difficult for smart contracts to uphold certain fundamental principles of contract law. Translating concepts of individual intention and responsibility into the decentralized space of blockchain is problematic. Aggregating such individual intention into the combined will and intention of the blockchain entity is at best challenging, and at worst unfeasible. Further, while traditional contracts accommodate change and allow for the amendment of terms in response to evolving circumstances, blockchain smart contracts do not. As the difficulties of blockchain smart contracts become apparent, attention is turning to hybrid smart contracts. [ABSTRACT FROM AUTHOR]

Today, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage. [ABSTRACT FROM AUTHOR]

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10−4 over a distance of 590 m. [ABSTRACT FROM AUTHOR]

Acoustic energy mapping provides the functionality to obtain characteristics of acoustic sources, as: presence, localization, type and trajectory of sound sources. Several beamforming-based techniques can be used for this purpose. However, they rely on the difference of arrival times of the signal at each capture node (or microphone), so it is of major importance to have synchronized multi-channel recordings. A Wireless Acoustic Sensor Network (WASN) can be very practical to install when used for mapping the acoustic energy of a given acoustic environment. However, they are known for having low synchronization between the recordings from each node. The objective of this paper is to characterize the impact of current popular synchronization methodologies as part of the WASN to capture reliable data to be used for acoustic energy mapping. The two evaluated synchronization protocols are: Network Time Protocol (NTP) y Precision Time Protocol (PTP). Additionally, three different audio capture methodologies were proposed for the WASN to capture the acoustic signal: two of them, recording the data locally and one sending the data through a local wireless network. As a real-life evaluation scenario, a WASN was built using nodes conformed by a Raspberry Pi 4B+ with a single MEMS microphone. Experimental results demonstrate that the most reliable methodology is using the PTP synchronization protocol and audio recording locally. [ABSTRACT FROM AUTHOR]

Computing directed Minimum Spanning Tree ( D M S T ) is a fundamental problem in graph theory. It is applied in a wide spectrum of fields from computer network and communication protocol design to revenue maximization in social networks and syntactic parsing in Natural Language Processing. State-of-the-art solutions are online algorithms that compute  D M S T  for a given graph and a root. For multi-query requirements, the online algorithm is inefficient. To overcome the drawbacks, in this paper, we propose an indexed approach that reuses the computation result to facilitate single and batch queries. We store all the potential edges of  D M S T  in a hierarchical tree in  O (n)  space complexity. Furthermore, we answer the  D M S T  query of any root in  O (n)  time complexity. Experimental results demonstrate that our approach can achieve a speedup of 2–3 orders of magnitude in query processing compared to the state-of-the-art while consuming O(n) index size. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are challenged by the dynamic nature of the underwater environment, large propagation delays, and global positioning system (GPS) signal unavailability, which make traditional medium access control (MAC) protocols less effective. These factors limit the channel utilization and performance of UASNs, making it difficult to achieve high data rates and handle congestion. To address these challenges, we propose a reinforcement learning (RL) MAC protocol that supports asynchronous network operation and leverages large propagation delays to improve the network throughput.he protocol is based on framed ALOHA and enables nodes to learn an optimal transmission strategy in a fully distributed manner without requiring detailed information about the external environment. The transmission strategy of sensor nodes is defined as a combination of time-slot and transmission-offset selection. By relying on the concept of learning through interaction with the environment, the proposed protocol enhances network resilience and adaptability. In both static and mobile network scenarios, it has been compared with the state-of-the-art framed ALOHA for the underwater environment (UW-ALOHA-Q), carrier-sensing ALOHA (CS-ALOHA), and delay-aware opportunistic transmission scheduling (DOTS) protocols. The simulation results show that the proposed solution leads to significant channel utilization gains, ranging from 13% to 106% in static network scenarios and from 23% to 126% in mobile network scenarios.oreover, using a more efficient learning strategy, it significantly reduces convergence time compared to UW-ALOHA-Q in larger networks, despite the increased action space. [ABSTRACT FROM AUTHOR]

In order to solve the problem that there are a large number of unknown protocols on the network, which affect the network management and network security in varying degrees, an unknown binary protocol identification method is proposed. On the premise that the center cluster of unknown protocols is obtained by the clustering algorithm, the unknown protocols for network traffic are identified by combining one-class of classification with one-dimensional CNN classification technology. At first, a class of classification algorithm is used to select the unknown protocols, and then, the labeled protocol data obtained by clustering are used to train the one-dimensional CNN model, and the classified binary protocol packets are directly used as the input of the one-dimensional convolution neural network. After the classification of the CNN model, the unknown protocols are finally identified. The experimental results show that the proposed classification and recognition method is better than the traditional CNN and SVM algorithms, and the maximum frequency pooling is better than the traditional pooling method. [ABSTRACT FROM AUTHOR]

Next-generation Internet-of-Things applications pose challenges for sixth-generation (6G) mobile networks, involving large bandwidth, increased network capabilities, and remarkably low latency. The possibility of using ultra-dense connectivity to address the existing problem was previously well-acknowledged. Therefore, placing base stations (BSs) is economically challenging. Drone-based stations can efficiently address Next-generation Internet-of-Things requirements while accelerating growth and expansion. Due to their versatility, they can also manage brief network development or offer on-demand connectivity in emergency scenarios. On the other hand, identifying a drone stations are a complex procedure due to the limited energy supply and rapid signal quality degradation in air-to-ground links. The proposed method uses a two-layer optimizer based on a pre-trained VGG-19 model to overcome these issues. The non-orthogonal multiple access protocol improves network performance. Initially, it uses a powerful two-layer optimizer that employs a population of micro-swarms. Next, it automatically develops a lightweight deep model with a few VGG-19 convolutional filters. Finally, non-orthogonal multiple access is used to schedule radio and power resources to devices, which improves network performance. We specifically examine how three scenarios execute when various Cuckoo Search, Grey Wolf Optimization, and Particle Swarm Optimization techniques are used. To measure the various methodologies, we also run non-parametric statistical tests, such as the Friedman and Wilcoxon tests. The proposed method also evaluates the accuracy level for network performance of DBSs using number of Devices. The proposed method achieves better performance of 98.44% compared with other methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts. [ABSTRACT FROM AUTHOR]

The 802.11ah standard is a new energy-efficient, wireless networking protocol which allows thousands of indoor and outdoor devices to be connected to the same access point. The Centralized Authentication Control (CAC) method, described in the standard, enables up to 8000 stations to be authenticated and associated with the same access point. A baseline implementation of the CAC method has been reported; however, it suffers from a few limitations. In this paper, an efficient methodology is proposed to minimize the CAC method's association time. The proposed methodology allows the association of a large number of stations by predicting the size of the network followed by selecting the best step size that will enable fast association between the access point and the stations of the network. The methodology consists of three stages. The first stage provides a baseline implementation of the 802.11ah standard, while the second stage eliminates the effect of too large or too small step sizes. The third stage finds the optimal step size and step repeat for each network size and predicts network size based on queue size. The performance of the proposed methodology is evaluated and compared in terms of total association time, number of total trials and percentage of ineffective trials. The methodology outperforms the baseline implementation by achieving a 30% reduction in the total association time, 30% reduction in the total number of trials and 45% reduction in the total number of ineffective trials. [ABSTRACT FROM AUTHOR]

Most of the existing sender-based message logging protocols cannot commonly handle simultaneous failures because, if both the sender and the receiver(s) of each message fail together, the receiver(s) cannot obtain the recovery information of the message. This unfortunate situation may happen due to their asymmetric logging behavior. This paper presents a novel sender-based message logging protocol for broadcast network based distributed systems to overcome the critical constraint of the previous ones with the following three features. First, when more than one process crashes at the same time, the protocol enables the system to ensure the always no rollback property by symmetrically replicating the recovery information at each process or group member connected on a network. Second, it can make the first feature persist even if the general form of communication for the system is a combination of point-to-point and group ones. Third, the communication overhead resulting from the replication can be highly lessened by making full use of the capability of the standard broadcast network in both communication modes. Experimental outcomes verify that, no matter which communication patterns are applied, it can reduce about 4.23∼9.96% of the total application execution time against the latest enabling the traditional ones to cope with simultaneous failures. [ABSTRACT FROM AUTHOR]

Introduction: Patent ductus arteriosus (PDA) is one of the most common congenital heart diseases. Once the PDA is diagnosed, it needs to be dealt with in time. At present, main methods include pharmacological treatment, surgical closure, and interventional closure for treatment of PDA. However, the effect of different interventions in PDA management is still controversial. Thus, our study aims to assess the effectiveness of different interventions together and estimate the sequence of these therapies for PDA children. Meanwhile, it is necessary to conduct a Bayesian network meta-analysis to compare the safety of different interventions comprehensively. Methods and analysis: To the best of our knowledge, this is the first Bayesian network meta-analysis comparing the efficacy and safety of different interventions for the treatment of PDA. PubMed, Embase, Cochrane Library, Web of Science, gray literature, and trial registry databases were searched from inception to December 2022. We will extract and report data according to methodological guidelines for Bayesian network meta-analysis by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P). Primary PDA closure, overall PDA closure, technical success, surgical success rate, mortality during hospital stay, operation time, intensive care unit stay, intraoperative radiation dose, radiation exposure time, total postoperative complication rate, and postoperative major complication rate will be defined as the outcomes. The quality of all random studies will be assessed using ROB, and quality of evidence for all outcomes will be judged by using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Ethics and dissemination: The results will be disseminated through peer-reviewed publication. Since no private and confidential patient data will be contained in the reporting, there are no ethical considerations associated with this protocol. Systematic review registration: INPLASY2020110067. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) approach relies on the use of the Internet Protocol (IP) as a pervasive network protocol. IP acts as a "glue" for interconnecting end devices (on the field side) and end users, leveraging on very diverse lower-level and upper-level protocols. The need for scalability would suggest the adoption of IPv6, but the large overhead and payloads do not match with the constraints dictated by common wireless solutions. For this reason, compression strategies have been proposed to avoid redundant information in the IPv6 header and to provide fragmentation and reassembly of long messages. For example, the Static Context Header Compression (SCHC) protocol has been recently referenced by the LoRa Alliance as a standard IPv6 compression scheme for LoRaWAN-based applications. In this way, IoT end points can seamlessly share an end-to-end IP link. However, implementation details are out of the specifications' scope. For this reason, formal test procedures for comparing solutions from different providers are important. In this paper, a test method for assessing architectural delays of real-world deployments of SCHC-over-LoRaWAN implementations is presented. The original proposal includes a mapping phase, for identifying information flows, and a subsequent evaluation phase, in which flows are timestamped and time-related metrics are computed. The proposed strategy has been tested in different use cases involving LoRaWAN backends deployed all around the world. The feasibility of the proposed approach has been tested by measuring the end-to-end latency of IPv6 data in sample use cases, obtaining a delay of less than 1 s. However, the main result is the demonstration that the suggested methodology permits a comparison of the behavior of IPv6 with SCHC-over-LoRaWAN, allowing the optimization of choices and parameters during deployment and commissioning of both infrastructure components and software. [ABSTRACT FROM AUTHOR]

Currently, the most widely used protocol for the transportation layer of computer networks for reliable transportation is the Transmission Control Protocol (TCP). However, TCP has some problems such as high handshake delay, head-of-line (HOL) blocking, and so on. To solve these problems, Google proposed the Quick User Datagram Protocol Internet Connection (QUIC) protocol, which supports 0-1 round-trip time (RTT) handshake, a congestion control algorithm configuration in user mode. So far, the QUIC protocol has been integrated with traditional congestion control algorithms, which are not efficient in numerous scenarios. To solve this problem, we propose an efficient congestion control mechanism on the basis of deep reinforcement learning (DRL), i.e., proximal bandwidth-delay quick optimization (PBQ) for QUIC, which combines traditional bottleneck bandwidth and round-trip propagation time (BBR) with proximal policy optimization (PPO). In PBQ, the PPO agent outputs the congestion window (CWnd) and improves itself according to network state, and the BBR specifies the pacing rate of the client. Then, we apply the presented PBQ to QUIC and form a new version of QUIC, i.e., PBQ-enhanced QUIC. The experimental results show that the proposed PBQ-enhanced QUIC achieves much better performance in both throughput and RTT than existing popular versions of QUIC, such as QUIC with Cubic and QUIC with BBR. [ABSTRACT FROM AUTHOR]

Networking proposed protocol, programming, node placement, positioning techniques, and data security are just a few of the study disciplines where the System has been used. As a consequence of the superior network topology, nodes may be separated into different clusters. The establishing groupings of device nodes is known as convergence. The persons in charge of all of the groups are known as cluster chiefs (CHs). It is Computationally to reduce total duration using segmenting techniques. The current study provides a hybrid evolutionary algorithm with an adjustable hybridization bird optimizer for successful clustering. The CH approach considers operational costs, as well as residual energy and data rate. According to simulation data, the Adaptive Hybrids Bird Swarm Optimization technique surpasses CL-LEACH and Hybrid Bird Swarm Optimized in terms of packet delay, packet delivery, and network longevity. Finally, AHBSO's speed is crucial that compare with partial swarm optimization, Combination Swarm Optimization, and its results. The proposed technique improves transmission speed by 25.34--35.50 percent depending on node density. [ABSTRACT FROM AUTHOR]

This paper proposes a solution for ensuring the security of IoT devices in the cloud environment by protecting against distributed denial-of-service (DDoS) and false data injection attacks. The proposed solution is based on the integration of simple network management protocol (SNMP), Kullback–Leibler distance (KLD), access control rules (ACL), and moving target defense (MTD) techniques. The SNMP and KLD techniques are used to detect DDoS and false data sharing attacks, while the ACL and MTD techniques are applied to mitigate these attacks by hardening the target and reducing the attack surface. The effectiveness of the proposed framework is validated through experimental simulations on the Amazon Web Service (AWS) platform, which shows a significant reduction in attack probabilities and delays. The integration of IoT and cloud technologies is a powerful combination that can deliver customized and critical solutions to major business vendors. However, ensuring the confidentiality and security of data among IoT devices, storage, and access to the cloud is crucial to maintaining trust among internet users. This paper demonstrates the importance of implementing robust security measures to protect IoT devices in the cloud environment and highlights the potential of the proposed solution in protecting against DDoS and false data injection attacks. [ABSTRACT FROM AUTHOR]

Environmental monitoring of delicate ecosystems or pristine sites is critical to their preservation. The communication infrastructure for such monitoring should have as little impact on the natural ecosystem as possible. Because of their wide range capabilities and independence from heavy infrastructure, low-power wide area network protocols have recently been used in remote monitoring. In this regard, we propose a mobile vehicle-mounted gateway architecture for IoT data collection in communication-network-free areas. The limits of reliable communication are investigated in terms of gateway speed, throughput, and energy consumption. We investigate the performance of various gateway arrival scenarios, focusing on the trade-off between freshness of data, data collection rate, and end-node power consumption. Then we validate our findings using both real-world experiments and simulations. In addition, we present a case study exploiting the proposed architecture to provide coverage for Wadi El-Gemal national park in Egypt. The results show that reliable communication is achieved over all spreading factors (SFs) for gateway speeds up to 150 km/h with negligible performance degradation at SFs = 11 , 12 at speeds more than 100 km/h. The synchronized transmission model ensures the best performance in terms of throughput and power consumption at the expense of the freshness of data. Nonsynchronized transmission allows time-flexible data collection at the expense of increased power consumption. The same throughput as semisynchronized transmission is achieved using four gateways at only five times the energy consumption, while a single gateway requires seventeen times the amount of energy. Furthermore, increasing the number of gateways to ten increases the throughput to the level achieved by the synchronized scenario while consuming eight times the energy. [ABSTRACT FROM AUTHOR]