Discovery Protocol Research Articles

Morphometric trait normalization, cluster analysis and molecular phylogenetic analysis in delimiting sympatric Neolissochilus hexastichus and N. hexagonolepis in Umngi River, Meghalaya (India)

Aim: To assess the ability of an integrative approach comprising multivariate morphometric data analysis and mitochondrial cytochrome oxidase subunit I (CO I) sequence divergence analysis in delimiting sympatric Neolissochilus hexastichus (McClleland, 1839) and N. hexagonolepis (McClleland, 1839) mahseer populations in River Umngi, Meghalaya. Methodology: Authoritative descriptions of mahseers were referred during the sampling of 40 individuals from River Umngi, Meghalaya. Multivariate morphometric data were normalized with respect to standard length. The normalization procedure maintained the particular shape factor for each sample. Normalised data were subjected to hierarchical clustering on principal components Analysis (HCPC) for grouping samples according to morphometric features. Genetic affinities based on CO I sequence divergence were assessed by Bayesian Inference (BI) and Automatic Barcode Gap Discovery (ABGD) protocols. Results: The normalization protocols removed the variations in traits due to body size effects and facilitated the identification of allometric traits. HCPC analysis revealed morphometric features that had a significant impact on clustering. Nine N. hexagonolepis and 13 N. hexastichus individuals were identified by integrative morphological and molecular phylogenetic approach. The remaining 18 individuals had incongruence in their memberships in the morphometric and phylogenetic clusters. Interpretation: The integrative approach, apart from delineating N. hexagonolepis and N. hexastichus congeners, highlighted the need to include nuclear genetic markers in future studies for confirming the presence of natural hybrids in sympatric populations of Neolissochilus mahseers. Key words: Cluster, Morphometric, Neolissochilus, Normalization, Phylogenetics

Machine-directed Gravitational-wave Counterpart Discovery

Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics or detector dynamics and the behavior policy that maximizes a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent with the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3× higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.

Sex-Specific Skeletal Muscle Gene Expression Responses to Exercise Reveal Novel Direct Mediators of Insulin Sensitivity Change.

Understanding the causal pathways, systems, and mechanisms through which exercise impacts human health is complex. This study explores molecular signaling related to whole-body insulin sensitivity (Si) by examining changes in skeletal muscle gene expression. The analysis considers differences by biological sex, exercise amount, and exercise intensity to identify potential molecular targets for developing pharmacologic agents that replicate the health benefits of exercise. The study involved 53 participants from the STRRIDE I and II trials who completed eight months of aerobic training. Skeletal muscle gene expression was measured using Affymetrix and Illumina technologies, while pre- and post-training Si was assessed via an intravenous glucose tolerance test. A novel gene discovery protocol, integrating three literature-derived and data-driven modeling strategies, was employed to identify causal pathways and direct causal factors based on differentially expressed transcripts associated with exercise intensity and amount. In women, the transcription factor targets identified were primarily influenced by exercise amount and were generally inhibitory. In contrast, in men, these targets were driven by exercise intensity and were generally activating. Transcription factors such as ATF1, CEBPA, BACH2, and STAT1 were commonly activating in both sexes. Specific transcriptional targets related to exercise-induced Si improvements included TACR3 and TMC7 for intensity-driven effects, and GRIN3B and EIF3B for amount-driven effects. Two key signaling pathways mediating aerobic exercise-induced Si improvements were identified: one centered on estrogen signaling and the other on phorbol ester (PKC) signaling, both converging on the epidermal growth factor receptor (EGFR) and other relevant targets. The signaling pathways mediating Si improvements from aerobic exercise differed by sex and were further distinguished by exercise intensity and amount. Transcriptional adaptations in skeletal muscle related to Si improvements appear to be causally linked to estrogen and PKC signaling, with EGFR and other identified targets emerging as potential skeletal muscle-specific drug targets to mimic the beneficial effects of exercise on Si.

A dynamic multicast routing protocol for visible light communication networks

Due to the current challenges of wireless communications, optical wireless communications, like visible light communication (VLC), have become very popular in the last decade for local area networks. However, current VLC systems focus mainly on one-hop direct transmissions, ignoring that many networking applications require multicast communications. In this sense, we propose MCAST-VLC, a fully functional cross-layer dynamic multicast scheme over a dynamic routing protocol for VLC. MCAST-VLC considers the particular characteristics of the VLC as the need for group management, traffic isolation, a challenging multi-hop route discovery protocol, and a complex cross-layer VLC mechanism to maintain the proper system state. We evaluate our protocol following close real indoor VLC scenarios, previously discussed in the literature, where a light source in a room streams data to a few other VLC devices, and we extend our results numerically. Our results show that MCAST-VLC can handle considerably more network flows than a unicast VLC system under a negligible overhead.

Big data in sarcoidosis.

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine. Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols. Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Survey of Multiple Destination Route Discovery Protocols

Route discovery protocols for multiple destination is one of the most interesting research topics since it is applied for real-world applications and needed in smart cities services such as: delivery services. The inclusion of artificial intelligence can improve the performance of multiple destination route discovery protocols. In this paper, we studied and analyzed multiple destination route discovery protocols based on different search strategies especially artificial intelligent methods. The survey compares between multiple destination route discovery protocols relate to its applications and implementation tools.

DIS-Guard: Enhancing SDN resilience to topology and RCO attacks

SDN controllers employ discovery protocols with LLDP packets to discover the network topology; however, this approach is susceptible to topology attacks due to the static reuse of LLDP packets without proper origin verification or integrity checks. This paper proposes a novel form of topology attack called the Round-trip time Confusion Onslaught (RCO) attack. Our findings showcase the capability of the RCO attack to mislead the discovery service of SDN controllers and bypass the Real-time Link Verification (RLV) defence mechanism, which depends on timing features in Machine Learning (ML) models. The RCO attack significantly reduces the effectiveness of the RLV mechanism in terms of True Positive Rate (TPR), Precision, F1-score, and Cohen’s Kappa and increases the False Positive Rate (FPR). These results underscore the difficulties in achieving dependable ML accuracy in topology attack detection. Accordingly, we propose the DIS-Guard framework, designed to protect and improve the efficiency of discovery protocols within SDN controllers. This framework utilizes the mutual Transport Layer Security (mTLS) protocol to discover hosts proactively. Moreover, it employs a novel combination of a chaos-tent map and the proposed statistical method for link discovery. Through extensive evaluation, we illustrate DIS-Guard’s superior performance and security over the established Ryu (switches.py) controller and the RLV defence mechanism. Notably, DIS-Guard reduces host-to-host access delays, decreases LLDP packet transmission and reception significantly, and improves control channel efficiency. Collectively, these quantifiable benefits manifest the efficacy of DIS-Guard as a significantly optimized solution for SDN security and performance.

Research on SDP-BF Method with Low False Positive Face to Passive Detection System

With the rapid development of 5G, UAV, and military communications, the data volume obtained by the non-cooperative perception system has increased exponentially, and the distributed system has become the development trend of the non-cooperative perception system. The data distribution service (DDS) produces a significant effect on the performance of distributed non-cooperative perception systems. However, the traditional DDS discovery protocol has problems such as false positive misjudgment and high flow overhead, so it can hardly adapt to a large multi-node distributed system. Therefore, the design of a DDS discovery protocol for large distributed system is technically challenging. In this paper, we proposed SDP-DCBF-SFF, a discovery protocol based on the Dynamic Counter Bloom Filter (DCBF) and Second Feedback Filter (SFF). The proposed discovery protocol coarsely filters the interested endpoints through DCBF and then accurately screens the uninterested endpoints through SFF to eliminate the connection requests of false positive endpoints and avoid extra flow overhead. The experimental results indicate that the proposed discovery protocol could effectively reduce the network overhead, and eliminate the false positive probability of endpoints in small, medium, large, and super large systems. In addition, it adopts the self-adaptive extension mechanism of BF to reduce the reconfiguration delay of BF and achieve the smallest system transmission delay. Therefore, the proposed discovery protocol has optimal comprehensive performance and system adaptability.

Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system

Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

VXLAN Analysis and Design for Interconnecting Different Locations at Khairun University

Khairun University is currently divided into 4 campuses with different geographical locations. The distance between these locations is very far, this results in each location having a network configuration that is not centralised. This research aims to connect networks in geographically different locations at Khairun University between Gambesi Campus and Jati Campus to be connected using one centralised IP segmentation. This research method consists of a literature study stage, a network topology design stage, a network simulation stage, a network simulation analysis stage, and a data analysis stage. Focusing on VXLAN to answer the challenge of how to connect two different locations between Gambesi campus and Jati campus to be connected in one location. By analyzing and designing through GNS3 with the help of VMWare virtualisation, VXLAN implementation can be done on proxy devices. This test uses ping, traceroute, packet capture, and throughput. The results of this study show that VXLAN itself implements several protocols that run at Layer 2 of the network, one of which is the Cisco Discovery Protocol (CDP). And throughput shows with a buffer size of 256 KB has a data transfer of 1.5 MB and a bandwidth of 1.26 Mbs, a buffer size of 512 KB has a data transfer of 2 MB and a bandwidth of 1.68 Mbs, and with a buffer size of 1024 KB has a data transfer of 2.5 MB and a bandwidth of 2.1 Mbps. In other words, VXLAN successfully connects computer networks in different locations with the same IP subnet. Although not directly connected to the internet, data transmission between clients in two geographically different locations was successful.

Trusted Computing and Privacy Protection of Computer Internet of Things Nodes Based on Deep Fuzzy Control of Dynamic Learning Rate

Under the background of mobile Internet of Things, this paper focuses on the solutions of trusted computing and privacy protection of Internet of Things nodes. Based on the research of existing mainstream service discovery protocols and the deep fuzzy control theory of dynamic learning rate, this paper proposes a trusted computing algorithm for Internet of Things nodes in mobile Internet of Things,which takes into account service quality and user preference.This paper combines the depth fuzzy control model of IoT nodes proposed before, uses a normal data set to train it, and then makes it generate and play the prediction residuals. It will be further used to build a privacy protection model and realize the anomaly detection of privacy protection data in the Internet of Things. This paper designs and implements a trusted computing system using the Internet of Things platform,which has been tested. Experimental results show that, compared with previous elastic matching algorithms, the precision and recall of the new algorithm proposed in this paper are improved.

A multi-token sector antenna neighbor discovery protocol for directional ad hoc networks

A multi-token sector antenna neighbor discovery protocol for directional ad hoc networks

Asymmetric wake-up scheduling based on block designs for Internet of Things

In general, wireless sensors operate with a limited energy source, and energy efficiency is a critical design issue. In order to extend the operation time of wireless sensors, there have been many energy efficiency neighbor discovery protocols designed for wireless sensor networks (WSNs) such as Quorum-based, prime-number-based, and block-design-based protocols. Among them, the block-design-based approach was known to be more effective solutions for neighbor discovery in terms of the worst-case discovery latency for a given duty cycle. However, the original block- design-based approach is only applicable to a sensor network where all sensors have the same duty cycle. In order to expand a block-design-based neighbor discovery solution to asymmetric WSNs, we introduce a new neighbor discovery protocol (NDP) that combines two block-design-based schedules to produce a new set of discovery schedules for asymmetric WSNs. Furthermore, by using the Kronecker product method, we prove that any pair of neighboring sensors in the proposed protocol has at least one common active slot within a length of their discovery cycle. Furthermore, the results of the simulation study show that the proposed method is better than representative NDPs (such as Quorum, U-Connect, Disco, SearchLight, Hedis, and Todis) in terms of discovery latency and energy efficiency.

A neighbor discovery protocol with adaptive collision alleviation for wireless robotic networks

Recently, several movement control algorithms have been proposed to move robots to the desired locations and complete various collaborative tasks. These algorithms usually require information exchange among robots, which in turn relies on efficient neighbor discovery. However, neighbor discovery of robots is a challenging problem due to limited communication range and beacon collision. Here, how to set the time slot length and contention window to alleviate the beacon collisions is a major issue: too large or too small slot length and contention window values cannot achieve good performance in the robotic network. Therefore, we propose a neighbor discovery protocol with an adaptive collision alleviation mechanism, i.e. ND-ACA. ND-ACA adopts a simple mechanism to detect and avoid potential collisions before sending beacon, and adaptively adjusts both slot length and backoff window according to the number of historical beacon collections and beacon collisions. Extensive simulation results show the efficiency of our proposed approach. To the best of our knowledge, this is the first work in the literature that explore the joint implementation effects that simultaneously consider the robot mobility model and neighbor discovery.

A practical and robust method to evaluate metabolic fluxes in primary pancreatic islets

ObjectiveEvaluation of mitochondrial oxygen consumption and ATP production is important to investigate pancreatic islet pathophysiology. Most studies use cell lines due to difficulties in measuring primary islet respiration, which requires specific equipment and consumables, is expensive and poorly reproducible. Our aim was to establish a practical method to assess primary islet metabolic fluxes using standard commercial consumables. MethodsPancreatic islets were isolated from mice/rats, dispersed with trypsin, and adhered to pre-coated standard Seahorse or Resipher microplates. Oxygen consumption was evaluated using a Seahorse Extracellular Flux Analyzer or a Resipher Real-time Cell Analyzer. ResultsWe provide a detailed protocol with all steps to optimize islet isolation with high yield and functionality. Our method requires a few islets per replicate; both rat and mouse islets present robust basal respiration and proper response to mitochondrial modulators and glucose. The technique was validated by other functional assays, which show these cells present conserved calcium influx and insulin secretion in response to glucose. We also show that our dispersed islets maintain robust basal respiration levels, in addition to maintaining up to 89% viability after five days in dispersed cultures. Furthermore, OCRs can be measured in Seahorse analyzers and in other plate respirometry systems, using standard materials. ConclusionsOverall, we established a practical and robust method to assess islet metabolic fluxes and oxidative phosphorylation, a valuable tool to uncover basic β-cell metabolic mechanisms as well as for translational investigations, such as pharmacological candidate discovery and islet transplantation protocols.

Adaptive HELLO protocol for vehicular networks

In vehicular networks, the update of car Firmware Over The Air (FOTA) is becoming a challenging issue and it mainly relies on topology discovery of neighbouring nodes. Topology discovery in mobile wireless networks is usually done by using HELLO messages. Due to mobility, topology changes occur frequently and must be quickly discovered to avoid routing failures. Since the optimal HELLO frequency depends on parameters that are subject to changes (e.g., speed of nodes, density of nodes), it must be dynamically adjusted to obtain the best trade-off between the network load and the freshness of routing tables. Existing solutions assume random mobility, constant node density and average speed, which do not hold in vehicular networks because vehicles follow specific trajectory patterns (the roads) and density and speed evolve as a function of time (rush hour vs non-rush hour) and area (urban, rural, highway). In this paper, we first draw the specific features of a vehicular network at different times and spaces by analysing real datasets and then propose a dynamic neighbour discovery protocol, Vehicular Adaptive Neighbour discovery Protocol (VANP). VANP is a fully-distributed protocol that sends beacons at an optimal frequency without knowing it a priori. The objective is to reduce the frequency at which HELLO messages are sent to save bandwidth and energy while still preserving the quality of the neighbour discovery. Through extensive simulations run on real datasets, we show that the optimal HELLO frequency can be reached by maintaining a constant optimal turnover, independent from the speed of the nodes and by aiming at this turnover, nodes automatically use the optimal HELLO frequency. Results show that VANP allows the discovery of relevant neighbours by missing at most two neighbours over all scenarios and reducing the number of HELLO messages up to twice, hence saving bandwidth and energy.

Failure recovery mechanism for BDI agents based on abilities and discovery protocols

Failure recovery mechanism for BDI agents based on abilities and discovery protocols

Developing a method for the detection and identification of rootstock blockchain network nodes

The object of this study is the protocol for detecting nodes in the Rootstock blockchain network and crawling tools. Node discovery protocols are the foundation of any decentralized peer-to-peer network. In blockchain systems, full nodes store and maintain a complete copy of all transactions performed in the network. However, they do not store information about all other nodes, such as their IDs or IP addresses. Each node usually maintains an incomplete list of nodes to which it connects to exchange blockchain data. In decentralized networks, nodes join and leave the network and their IP addresses can change, making it impractical to maintain a complete, up-to-date list of all nodes. Therefore, the only way to get a list of all nodes in the network is to poll each node sequentially. The developed method involves sending specially formed messages to nodes to obtain their neighbors. The graph search algorithm is used to traverse all received neighboring nodes. This allows one to consistently detect all network nodes. Identifying the desired sequence of messages requires a preliminary analysis of the node software RSKj in the part of node discovery protocol. Effectiveness of the proposed method was verified using the developed software and an experiment in the main network. 6 verification nodes were deployed in different physical locations and at different times. All test nodes were detected in less than 10 minutes. The developed method found 222 nodes that have 209 unique IP addresses. Results of this study show how to perform analysis of node discovery protocol. They provide the means to obtain information about the available nodes of the Rootstock blockchain system, enabling the analysis of both the blockchain network in general and individual node

Exploring the Role of Wi-Fi Direct's Service Discovery Protocol in Forming Wireless Collaboration Networks: A Scoping Review

The increasing ubiquity of mobile devices and the Internet has become a hallmark of modern society, yet challenges persist in ensuring reliable communication networks during unforeseen disruptions, such as pandemics, war, or natural disasters. Despite the advancement of Wi-Fi technology, the development of a comprehensive model for temporary communication networks based on Wi-Fi interfaces during these disruptions remains inconclusive. The temporary and infrastructure-less wireless network created by mobile devices is referred to as a Wireless Collaboration Network (WCN). To assess the extent to which wireless protocols have been leveraged in forming Wireless WCN, a scoping review was conducted. This review aimed to provide a comprehensive overview of the existing literature, specifically focusing on the Service Discovery (SD) protocol of Wi-Fi Direct within the Mobile Ad Hoc Network (MANET) domain. Our investigation delved into solutions developed by researchers in this domain and the various metrics employed to evaluate the performance of these solutions. The scoping review protocol encompassed nine distinct steps, ranging from formulating research questions to synthesizing results. The review process involved examining three major databases: IEEE Xplore, ACM Digital Library, and Scopus, with the assistance of Mendeley for data organization and filtering. In total, our search identified 186 records across these databases. After rigorous screening, which included removing duplicates, irrelevant articles, and inaccessible content, we selected 35 articles for comprehensive review. These reviewed articles were then summarized and presented in a table, offering valuable insights into article titles, objectives, measurement parameters, techniques employed, and the noteworthy contributions of each work. The table serves as an invaluable resource for researchers in the field, facilitating a deeper understanding of the research landscape and enabling easier access to the collective knowledge within this domain.

Neighbor discovery protocol anomaly-based detection system using neural network algorithm

Neighbor discovery protocol anomaly-based detection system using neural network algorithm