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Summary: The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues. [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]

TCP-Transmission Control Protocol provides connection oriented and reliable communication. TCP's default consideration of any loss as a cause of network congestion degrades the end-to-end performance in wireless networks specially in MANETs-Mobile Adhoc Networks. TCP should identify various non congestion losses such as channel loss and route failure loss to act accordingly. Over the years, researchers have proposed Machine Learning based network protocols for accurate and efficient decision making. Reinforcement learning suits better for the dynamic networks with unpredictable traffic and topology. TCP-RLLD, TCP with Reinforcement Learning based Loss Differentiation is an end-to-end transport layer solution to predict cause of a packet loss. TCP's default consideration of any loss as a congestion loss is overruled by TCP-RLLD to avoid unnecessary reduction of the transmission rate. TCP-RLLD is evaluated with multiple TCP variants for the Mobile Adhoc Networks. The extensive evaluation is performed with NS-3 simulator. This paper discusses TCP-RLLD architecture along with the detail of performance improvement. [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]

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]

With the dense deployment of basic service set (BSS) in the next generation Wireless Local Access Network (WLAN). The overlapping coverage of multiple BSSs leads to intensified contention conflicts, low efficiency of buffer state reports (BSR) collection and a sharp decline in system throughput. To solve this problem, this paper proposes an access point (AP) cooperation based multi-BSS multi-user full duplex multiple access protocol (CMMFD). Firstly, access control (AC) divides the STAs in the cooperative network area into overlapping STA and non-overlapping STA. A bi-level channel resource allocation algorithm is designed. The first level is channel resource allocation in the collection stage of BSR and inter-node interference information (BI), and the second level is channel resource allocation in full duplex data transmission. Secondly, a protocol framework for parallel BI information collection of cooperative APs is designed. AC schedules cooperative APs to send trigger frame at the same time, and triggers STAs in each BSS to report BI information on the assigned channel resource with p -probability access mode. Finally, AP forwards the collected BI information to AC, and AC assigns the STAs in the multi-BSS to perform multi-user full-duplex transmission on the resource unit (RU). Theoretical analysis gives the closed form expressions of the mean number of STAs successfully collected by AC in the cooperation area and saturated network throughput. Simulation results show that compared with MU-FuPlex and EnFD-OMAX protocol, CMMFD protocol improves the network throughput by 29.6% and 31.2% respectively. [ABSTRACT FROM AUTHOR]

This research is a case study on bug bounty hunting as a successful approach to finding and uncovering vulnerabilities in software. The purpose of this study is to understand the effectiveness of bug bounty hunting and its benefits in improving company security. Qualitative research methods were used by analyzing data from bug bounty programs that were successful in finding significant vulnerabilities. The results showed that bug bounty hunting proved to be effective in capturing various types of vulnerabilities, including web application vulnerabilities, network protocol vulnerabilities, hardware security vulnerabilities, and mobile app vulnerabilities. Security researchers play a critical role in the success of the bug bounty hunt, with their expertise in bug hunting and deep understanding of the technology. The benefits of bug bounty hunting include increased software security, cost efficiency, and increased company reputation for security and credibility. However, challenges such as finding hidden vulnerabilities and coordinating with enterprises need to be overcome. Recommendations include strengthening collaboration between enterprises and security researchers, establishing effective communication channels, and proactively responding to vulnerability reports. [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]

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]

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]

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]

Protecting users' privacy over the Internet is of great importance; however, it becomes harder and harder to maintain due to the increasing complexity of network protocols and components. Therefore, investigating and understanding how data are leaked from the information transmission platforms and protocols can lead us to a more secure environment. In this paper, we propose a framework to systematically find the most vulnerable information fields in a network protocol. To this end, focusing on the transport layer security (TLS) protocol, we perform different machine-learning-based fingerprinting attacks on the collected data from more than 70 domains (websites) to understand how and where this information leakage occurs in the TLS protocol. Then, by employing the interpretation techniques developed in the machine learning community and applying our framework, we find the most vulnerable information fields in the TLS protocol. Our findings demonstrate that the TLS handshake (which is mainly unencrypted), the TLS record length appearing in the TLS application data header, and the IV field are among the most critical leaker parts in this protocol, respectively. [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]

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]

The wireless sensor network is of great interest due to its special features and applications. Due to the computational limitation and power supply of these networks, hierarchical cluster routing is mentioned as a solution to improve the problem of energy consumption and network lifetime. In our study, a fuzzy-based clustering routing protocol was proposed that uses network zoning and node residual energy parameters, node distance to the center of each zone, and node-to-base station angle as fuzzy input for cluster head selection. In the proposed method with the aim of reducing network energy consumption, the responsibility of choosing CHs in each area, determining the area of each node, calculating the distance of each node to the center and calculating the angle of each node was assigned to the BS. The proposed method was carried out with Farahzadi, LEACH, MSCR protocols in terms of network lifetime evaluation criteria, stability period, selection and number of CH and average residual energy in MATLAB environment. The comparison of the results showed that the proposed method has performed better in terms of criteria, so that the network lifetime as one of the important indicators, the proposed method has improved by 50%, 40% and 20%, respectively, compared to LEACH, Farahzadi and MSCR methods. [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]

This paper is concerned with the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems subject to unknown disturbances. Firstly, a modified fixed-time disturbance observer is devised to estimate the unknown mismatched disturbance. Secondly, a distributed fixed-time neural network control protocol is designed, in which neural network is employed to approximate the uncertain nonlinear function. Simultaneously, the technique of command filter is applied to fixed-time control, which circumvents the "explosion of complexity" problem. Under the proposed control strategy, all agents are enable to track the desired trajectory in fixed-time, and the consensus tracking error and disturbance estimation error converge to an arbitrarily small neighborhood of the origin, meanwhile, all signals in the closed-loop system remain bounded. Finally, a simulation example is provided to validate the effectiveness of the presented design method. [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]

The Internet of Things (IoT) is a system of machines, computing devices, electronic equipment, and different sensors. It forms a network, where the transmission of device-related data can be accomplished. The devices in the IoT are connected to each other through wireless links and form ad-hoc networks. In IoT based applications, the lifetime of the communicating nodes is a greater concern. The network lifetime can be maximized by introducing energy efficient data transmission in the network. Therefore, a traffic and delay-aware energy-efficient routing (TADEER) protocol for IoT-based networks are proposed in this work. The proposed technique assigns delay for transmitting data based on the criticality level of data and traffic rate at the forwarding nodes. Fixing delays for data transmission helps to avoid unnecessary transmissions. The route selection process is implemented using an optimization algorithm. A Fuzzy logic (FL) based biogeography-based optimization (BBO) algorithm is presented in this work. Thus, the number of data transmission and energy consumption can be reduced. The performance of the proposed method is evaluated by analyzing transmission delay, network lifetime, and energy consumption. By comparing the simulation results to the existing methods TEAR and ETASA, the simulation results are validated. [ABSTRACT FROM AUTHOR]

Today's Internet is built on decades-old networking protocols that lack scalability, reliability and security. In response, the networking community has developed path-aware Internet architectures that solve these problems while simultaneously empowering end hosts to exert some control on their packets' route through the network. In these architectures, autonomous systems authorize forwarding paths in accordance with their routing policies, and protect these paths using cryptographic authenticators. For each packet, the sending end host selects an authorized path and embeds it and its authenticators in the packet header. This allows routers to efficiently determine how to forward the packet. The central security property of the data plane, i.e., of forwarding, is that packets can only travel along authorized paths. This property, which we call path authorization, protects the routing policies of autonomous systems from malicious senders. The fundamental role of packet forwarding in the Internet's ecosystem and the complexity of the authentication mechanisms employed call for a formal analysis. We develop IsaNet, a parameterized verification framework for data plane protocols in Isabelle/HOL. We first formulate an abstract model without an attacker for which we prove path authorization. We then refine this model by introducing a Dolev–Yao attacker and by protecting authorized paths using (generic) cryptographic validation fields. This model is parametrized by the path authorization mechanism and assumes five simple verification conditions. We propose novel attacker models and different sets of assumptions on the underlying routing protocol. We validate our framework by instantiating it with nine concrete protocol variants and prove that they each satisfy the verification conditions (and hence path authorization). The invariants needed for the security proof are proven in the parametrized model instead of the instance models. Our framework thus supports low-effort security proofs for data plane protocols. In contrast to what could be achieved with state-of-the-art automated protocol verifiers, our results hold for arbitrary network topologies and sets of authorized paths. [ABSTRACT FROM AUTHOR]

Evaluate the differences between clinical visual acuity (VA) as recorded in medical records and electronic Early Treatment Diabetic Retinopathy Study (eETDRS) protocol VA measurements and factors affecting the size of the differences. Retrospective chart review. Study and fellow eyes of participants enrolled in DRCR Retina Network Protocols AC and AE (diabetic macular edema), and W (nonproliferative diabetic retinopathy) with clinical VA recorded within 3 months before the protocol visit. Differences and their association with patient and ocular factors were evaluated using linear mixed models with random effects for correlations within sites and participants. Difference between VA letter scores measured by eETDRS during a study visit versus measured by Snellen during a regular clinical visit (Snellen fraction converted to eETDRS). Data from 1016 eyes (511 participants) across 74 sites were analyzed. The mean VA measurements were 68.6 letters (Snellen equivalent 20/50) at the clinical visit and 76.3 letters (Snellen equivalent 20/32) at the protocol visit, with a mean (standard deviation [SD]) of 26 (21) days between visits. Mean (SD) protocol VA was better than clinical VA by 7.6 (9.6) letters overall, 10.7 (12.6) letters in eyes with clinical VA ≤ 20/50 (n = 376), and 5.8 (6.6) letters in eyes with clinical VA ≥ 20/40 (n = 640). On average, the difference between clinical and protocol VA was 1.3 letters smaller for every 1-line (5 letters) increase in clinical VA (P < 0.001). Mean (SD) differences by clinical correction of refractive error were 3.9 (9.0) letters with refraction, 6.9 (9.2) letters with glasses/contact lenses, 7.9 (11.5) letters with pinhole, and 9.8 (9.3) letters without correction (P = 0.06). On average, clinical Snellen VA is 1 to 2 lines worse than eETDRS protocol refraction and VA testing, which may partly explain why clinical practice does not always replicate clinical trial results. Eyes with lower clinical measurements and eyes tested without clinical refraction tended to have larger differences. Considering the potential discrepancies between clinical and protocol VA measurements, refracting eyes in the clinic may benefit patients when determining treatment plans and study referrals based on vision. Proprietary or commercial disclosure may be found after the references. [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]

Currently, the proportion of unknown traffic in networks continues to increase. This poses great challenges to the management and security of cyberspace. The unknown traffic refers to network traffic generated by previously unknown protocols in a preconstructed traffic identification system. Measures to address this challenge can be developed by grouping the mixed unknown traffic into multiple clusters, where, ideally, each cluster contains just one traffic class. In this paper, we propose a novel scheme for clustering unknown traffic, named dual-path autoencoder-based clustering, to discover protocol-based traffic classes. The dual-path autoencoder model refers to the combination of convolutional autoencoder and deep autoencoder, which realizes the extraction and aggregation of payload features and statistical features. Then, the fusion feature is clustered by the correlation-adjusted clustering module, and the unknown traffic flows are divided into multiple high-purity clusters. To evaluate our scheme, experiments are conducted on two public network traffic datasets and one campus network dataset. Using seven common application layer protocols to simulate unknown traffic, the evaluation results show that our scheme can achieve above 98% on each dataset when the preset number of clusters is 60. This establishes the effectiveness of the proposed scheme for clustering unknown network protocols. [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]

Summary: Recently, much of the wireless personal area network (WPAN) research concerns network protocols, scheduling, and security challenges but the major issue of resource utilization has been very rarely investigated. The design of resource sharing in a network gets more attention when the number of users increases. While optimizing performance, resource utilization plays a critical role. In this paper, the numerical performance of a wireless resource utilization algorithm for a bi‐partite scatternet is presented. This algorithm is focused to enhance the bandwidth allocation and power utilization of wireless scatternets. Every node can communicate with a single neighbor at a time with minimum resources. Finally, the performances of the RUBI algorithm are shown. This algorithm is compared with the existing algorithms such as the load adaptive scheduling algorithm and pseudorandom coordinated scheduling scheme in terms of various parametric metrics like reliability, throughput, collision probability, transmission probability, and signal‐to‐noise ratio (SINR). The proposed L‐RUBI achieves 93.4% of reliability, 93.6% of transmission probability, 91.4% of throughput, 76.8% of collision performance, and 72.2% SINR. [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]

Video communication gained wide popularity since it offers a more real-time mode of face-to-face interaction. This paper mainly aims to enhance the video transmission quality of multimedia applications via communication network security improvement and an adaptive resource allocation strategy. The main aim of the resource allocation strategy is to achieve efficient usage of spectrum resources since video communication utilizes the orthogonal subcarriers for data transmission and the need for conversion of non-flat channels to flat. An optimized Hybrid Deep Random Neural Network (HDRaNN) architecture is used to overcome the adaptive spectrum resource allocation problem in the single-frequency network transmission by taking into account the different time–frequency resources and regions for overlapping areas and the same time–frequency for non-overlapping areas. The Sheep Flock Optimization algorithm is used to minimize the network error rate of the HDRaNN architecture by optimizing the weights and biases. The basic purpose of communication network security in this paper is to ensure that data communicated over a network connection are safe and secure. The network security protocol offers protection from unauthorized data retrieval and attempts to obtain data. The protocols utilized for secure communication in the proposed technique are Secure Hypertext Transfer Protocol, Secure Socket Layer, Simple Network Management Protocol, and Secure File Transfer Protocol. This protocol also assures that unauthorized users, devices, or services do not have access to your network data and works on all network mediums and data types. The extensive experiments conducted using the proposed methodology show improvements in the video transmission quality in terms of high diversity gain, security, increased throughput, and minimized bit error rate. [ABSTRACT FROM AUTHOR]

Blockchain is increasingly gaining interest in both the academic and professional worlds. The implementation of this decentralised and distributed network has started taking place, firstly in the financial world and in recent years reaching to other industries. Nevertheless, not all blockchain networks are the same, and choosing the right consensus algorithm is important for companies willing to invest in this technology. Companies eager to implement blockchain should understand the underlying architecture when selecting a particular network. Through an in-depth analysis, this paper aims to explore and compare from a technical perspective the three biggest permissionless blockchain networks by market capitalisation: Bitcoin, Ethereum and Ripple. Our research shows that Bitcoin gains a competitive advantage as a widely adopted means of payment, Ethereum excels in adopting a robust and flexible smart contract functionality whilst Ripple is most suitable for cross-border payments due to its scalability and fast processing speed. Based on the comprehensive analysis of the networks and their consensus protocols, this paper is designed to set the ground for further studies on the emerging applications of blockchain in numerous sectors. [ABSTRACT FROM AUTHOR]

The advancement of the Internet of Things (IoT) has tremendously influenced many fields of human life. The Internet of Medical Things, Internet of Flying Things, Internet of Floating Things and Internet of Autonomous Things are recent evolution of IoT. The protocols used in all forms of IoT are critical during the execution demanding formal verification methods to ensure correctness. However, the most challenging part of formal verification is abstracting the system under test, making many systems unverified. Formal protocol verification is essential for detecting specification and design flaws that go undetected and corrected during the testing phase. This paper proposes novel methods for abstracting communication patterns for IoT protocols. Message broadcasts, periodic message advertisements, topology encoding, and topology change are examples of communication patterns. An attempt is made to model these patterns using pi-calculus and PROMELA. Finally, the trickle, a wireless sensor network dissemination protocol, has been modelled using pi-calculus and PROMELA. The Spin model checker verifies design flaws, such as deadlocks and non-progress cycles. The verification results ensure that there are no deadlocks or non-progress loops. The protocol is statically verified for message transmission semantics. The analysis revealed that the protocol could only guarantee message transmission for lossy connections if an alternative route covers all other nodes. The empirical results and theorem show that the abstraction mechanism can be directly utilised for automated and theoretical verification. Researchers can use the abstraction framework described in this paper to create validation models for static and automated verification of existing and new IoT protocols. [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]

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]

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]

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]