Forwarding Candidates Research Articles

Cross-Layer Routing Protocol Based on Channel Quality for Underwater Acoustic Communication Networks

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR).

Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids.

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols.

Crisis classifications in mobility: Reporting the first COVID death in Taiwan

Crisis classifications are integral to COVID-19 press conferences. During the initial outbreak of the pandemic, public health officials classified the crisis status of the community. Depending on the status assigned, the community faced various consequences such as lockdowns and travel restrictions. This study traces Taiwanese health officials’ classification practices across temporal orders and participation frameworks. I call this successive link of discursive practices ‘crisis classifications in mobility’ to elucidate the processes of category production and dissemination. The mobility moved from uncertainty to certainty as health officials and journalists negotiated whether the first death of COVID-19 constituted a sign of community infection or community spread. Officials deployed metadiscourse to render some categories mobile and others immobile. Journalists used metadiscourse to push forward candidate categories and animate conflicting accounts given by local experts and a global superpower. Classifications in mobility capture these challenges of deconstructing and reconstructing categories in building public consensus.

Abstract PR02: Deep proteomics identifies novel biomarker candidates and molecular pathways of pancreatic cancer-related diabetes

Abstract Introduction: In up to 10% of individuals newly diagnosed with type 2 diabetes (T2DM), diabetes is secondary to pancreatic disease, of which 10% is pancreatic cancer-related. Individuals over the age of 50 yr with new-onset diabetes are considered to be the highest risk group for pancreatic cancer (PDAC), with diabetes presenting as a paraneoplastic symptom and occurring, on average, 13 months prior to PDAC being identified. Screening in this population could facilitate earlier cancer detection, however, there is currently no test to distinguish PDAC-related diabetes (PDAC-DM; a form of type 3c diabetes) from T2DM. The aim of our study was to develop protein biomarkers capable of identifying T3cDM from T2DM to facilitate the identification of a PDAC enriched population suitable for screening. Methods: Mass spectrometry (SWATH)- and aptamer-based proteomics workflows were employed for the analysis of 210 plasma samples, comprising seven case and control groups, stratified according to diabetes status. Groups included PDAC with/without diabetes, chronic pancreatitis with/without diabetes, new-onset diabetes (<3 yr post-diagnosis) and long-standing diabetes (>3 yr post-diagnosis). Raw SWATH data was processed using a publicly available in-silico peptide library (generated by DIA-NN), with filtering for plasma proteins. Data was split into training and test sets prior to analysis and a T3cDM group was generated by combining PDAC- and chronic pancreatitis-related diabetes sample data. Linear models were created to identify proteins that were differentially expressed in multiple comparisons. Three main comparisons of interest were proteins differentially expressed between 1) T3cDM and new-onset diabetes 2) PDAC and new-onset diabetes and 3) long-standing diabetes and new-onset diabetes. From the differentially expressed proteins a classifier was trained for the distinction of T3cDM from T2DM (allowing identification of individuals whose diabetes was secondary to underlying pancreatic disease). An ensemble of models was created using a bootstrapping approach, taking forward candidate biomarkers appearing in 90% of runs for the creation of a composite biomarker. Results: Over 7500 proteins, from >6500 genes, were identified per sample. Our linear models identified distinct T3cDM (28 and 105 differentially expressed proteins from SWATH- and aptamer-platforms, respectively) and PDAC signals (72 and 102 differentially expressed proteins from SWATH- and aptamer-based platforms, respectively). These had minimal overlap with the long-standing diabetes comparison. Conclusion: We have undertaken the deepest reported interrogation of the PDAC plasma proteome to-date. The robustness of our approach is supported by the identification of both novel and existing biomarkers of PDAC. Further work is underway to interrogate the molecular pathways contributing to the pathophysiology of diabetes in PDAC. This knowledge will support the development of new biomarkers for earlier detection and will inform future treatment strategies for early disease. Citation Format: Lucy Oldfield, Emily Johnson, Eva Caamaño Gutiérrez, Martyn Stott, William Greenhalf, Christopher Halloran, Eithne Costello. Deep proteomics identifies novel biomarker candidates and molecular pathways of pancreatic cancer-related diabetes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr PR02.

An Opportunistic Routing Based on Directional Transmission in the Internet of Underwater Things

The Internet of Underwater Things (IoUT) has attracted a lot of attention because of its promising applications in underwater environmental monitoring; however, the characteristics of acoustic communication, e.g., long propagation delay and high attenuation, pose great challenges for efficient and reliable underwater data transmission. Currently, opportunistic routing is regarded as a key technology to improve the packet delivery ratio, because it can dynamically choose several forwarding nodes and leverage their cooperative forwarding to increase network throughput. However, choosing an excessive number of forwarding nodes may result in energy waste and lengthy communication delays. Therefore, an Opportunistic Routing based on Directional Transmission (ORDT) is studied to improve packet delivery timeliness and reliability and lower energy consumption. ORDT mainly contains three phases: forwarding area division, candidate forwarder selection, and candidate forwarder coordination. The forwarding region is first established based on directional transmission. Only neighbors located in the forwarding region can forward packets. Following that, candidate forwarders in the forwarding region are chosen depending on their forwarding capability. Additionally, the chosen candidate’s time of holding packets is specified so that they might collaborate to reduce redundant data transmission and transmit packets to gateway nodes. Then, an Autonomous Underwater Vehicle (AUV) is employed to gather packets from gateway nodes. Simulation findings demonstrate that ORDT performs better than existing opportunistic routing in terms of packet delivery success rate, transmission latency, and energy usage.

Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks.

The Mine Internet of Things (MIoT), as a key technology for reconstructing post-disaster communication networks, enables a user to monitor and control the safety of an affected roadway. However, due to the challenging underground mine environment, the MIoT suffers from severe signal attenuation, vulnerable nodes, and limited energy, which result in a low level of network reliability for the post-disaster MIoT. To improve transmission reliability and reduce energy consumption, a directional-area-forwarding-based energy-efficient opportunistic routing (DEOR) approach for the post-disaster MIoT is proposed. DEOR defines a forwarding zone (FZ) for each node to route packets toward the sink. The candidate forwarding set (CFS) is constructed by the nodes within the FZ that satisfy the energy constraint and the neighboring node degree constraint. The nodes in the CFS are prioritized based on a routing quality evaluation, which takes the local attributes of the nodes, such as the directional angle, transmission distance, and residual energy, into consideration. DEOR adopts a recovery mechanism to address the issue of void nodes. The simulation results verify that the proposed DEOR approach outperforms the ORR, OBRN and ECSOR methods in terms of energy consumption, average hop count, packet delivery rate, and network lifetime.

Delay-Aware and Link-Quality-Aware Geographical Routing Protocol for UANET via Dueling Deep Q-Network.

In an unmanned aerial vehicles ad hoc network (UANET), UAVs communicate with each other to accomplish intricate tasks collaboratively and cooperatively. However, the high mobility of UAVs, the variable link quality, and heavy traffic loads can lead to difficulties in finding an optimal communication path. We proposed a delay-aware and link-quality-aware geographical routing protocol for a UANET via the dueling deep Q-network (DLGR-2DQ) to address these problems. Firstly, the link quality was not only related to the physical layer metric, the signal-to-noise ratio, which was influenced by path loss and Doppler shifts, but also the expected transmission count of the data link layer. In addition, we also considered the total waiting time of packets in the candidate forwarding node in order to decrease the end-to-end delay. Then, we modeled the packet-forwarding process as a Markov decision process. We crafted an appropriate reward function that utilized the penalty value for each additional hop, total waiting time, and link quality to accelerate the learning of the dueling DQN algorithm. Finally, the simulation results illustrated that our proposed routing protocol outperformed others in terms of the packet delivery ratio and the average end-to-end delay.

A new on the fly energy-efficient opportunistic routing in wireless multi-hop networks

ABSTRACT Wireless channels have a broadcast nature based on which opportunistic routing protocols work. In opportunistic routing protocols, packets are forwarded by the intermediate nodes that hear their transmissions, which are called candidate forwarders. In most of these algorithms, the forwarder list is pre-determined. However, the energy-efficient selection of the forwarder list is a research topic that is not considered well. Energy Efficient OPportunistic Routing algorithm (EEOPR) is presented in this paper in which the forwarders are determined on the packets’ fly. EEOPR is a flexible method that performs the routing process locally, and the candidate forwarders are selected during the routing and for each packet. The process of the candidate nodes’ selection and their packet forwarding are managed by the Genetic algorithm according to the nodes’ remaining energy and their regions. Simulation results show that network performance is improved in EEOPR compared to ROMER and CORP-M in terms of throughput, the number of duplicate packets, and the network nodes’ residual energy.

Adaptively prioritizing candidate forwarding set in opportunistic routing in VANETs

Opportunistic routing (OR) paradigms in vehicular networks via opportunistic candidate set selection have a better performance over the pre-defined next-hop node in terms of packet delivery rate. Nevertheless, uneven vehicle distribution, highly dynamic topology, varying link interference, and bandwidth restrictions impose drastic challenges for satisfying applications requirements in static prioritizing candidate set selection policies. Besides, fulfilling quality of service (QoS) requirements of heterogeneous applications, due to the limited local topology perspective and many involving conflicting routing criteria, will be further compromised. Considering the candidate set selection policy as a Multi-Criteria Decision Making (MCDM) task, an adaptive two-hop cooperative forwarding set selection strategy called APFS is proposed in this paper. By developing a local topology perspective to the two-hop neighbor information, the APFS scheme jointly considers indicators of link stability, delay assessment, and link good-put as routing criteria. Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods are employed in two different scenarios in prioritizing candidate forwarding sets. First, classical AHP-TOPSIS technique with a mutable pair-wise comparison matrix is applied, and then Neutrosophic Set (NS) is combined with the classical AHP-TOPSIS method to handle ambiguous vehicular environments. The simulation results show that the APFS strategy using both prioritization policies have significantly improved the performance of packet delivery ratio, average end-to-end delay, throughput, and hop counts as compared to other schemes.

The Recruitment Process for Grassroots Cadres in a Chinese County: The CCP Organization Department and Its Alarm Function

Through field research conducted within the Chinese Communist Party’s (CCP’s) County S Organization Department, this article finds that, under the CCP’s 2012 Eighteenth Party Congress policies of strengthening local governance and “whoever recommends a cadre is responsible for their performance,” the powers of the county organization departments have been enhanced. The main task of an organization department is the selection of cadres for postings. The County S Organization Department can put forward candidates for posts and also exercises an alarm function for the county party committee, although the party committee still has the final say in cadre appointments. The County S Organization Department has also enhanced its management of cadres’ personnel files to provide accurate reference material for use in cadre selection. The strengthening of the powers of the organization departments may provide an effective solution to the problem of information asymmetry faced by the party committees, enabling their leaders to avoid being held to account for mistakes in cadre selection. It appears that, since 2012, more norms have been developed for selecting cadres, thus facilitating the creation of a “political meritocracy” at the grassroots level.

Methylmercury-induced DNA methylation-From epidemiological observations to experimental evidence.

Methylmercury (MeHg) is a developmental neurotoxicant, and one potential mechanism of MeHg toxicity is epigenetic dysregulation. In a recent meta-analysis of epigenome-wide association studies (EWAS), associations between prenatal MeHg exposure and DNA methylation at several genomic sites were identified in blood from newborns and children. While EWASs reveal human-relevant associations, experimental studies are required to validate the relationship between exposure and DNA methylation changes, and to assess if such changes have implications for gene expression. Herein, we studied DNA methylation and gene expression of five of the top genes identified in the EWAS meta-analysis, MED31, MRPL19, GGH, GRK1, and LYSMD3, upon MeHg exposure in human SH-SY5Y cells exposed to 8 or 40 nM of MeHg during differentiation, using bisulfite-pyrosequencing and qPCR, respectively. The concentrations were selected to cover the range of MeHg concentrations in cord blood (2–8.5 μg/L) observed in the cohorts included in the EWAS. Exposure to MeHg increased DNA methylation at MED31, a transcriptional regulator essential for fetal development. The results were in concordance with the epidemiological findings where more MED31 methylation was associated with higher concentrations of MeHg. Additionally, we found a non-significant decrease in DNA methylation at GGH, which corresponds to the direction of change observed in the EWAS, and a significant correlation of GGH methylation with its expression. In conclusion, this study corroborates some of the EWAS findings and puts forward candidate genes involved in MeHg’s effects on the developing brain, thus highlighting the value of experimental validation of epidemiological association studies.

Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks

Due to the dynamic mobility feature, the proactive flow rule cache method has become one promising solution in software-defined networking (SDN)-based access networks to reduce the number of flow rule installation procedures between the forwarding nodes and SDN controller. However, since there is a flow rule cache limit for the forwarding node, an efficient flow rule cache strategy is required. To address this challenge, this paper proposes the mobility-aware hybrid flow rule cache scheme. Based on the comparison between the delay requirement of the incoming flow and the response delay of the controller, the proposed scheme decides to install the flow rule either proactively or reactively for the target candidate forwarding nodes. To find the optimal number of proactive flow rules considering the flow rule cache limits, an integer linear programming (ILP) problem is formulated and solved using the heuristic method. Extensive simulation results demonstrate that the proposed scheme outperforms the existing schemes in terms of the flow table utilization ratio, flow rule installation delay, and flow rules hit ratio under various settings.

Adaptively Prioritizing Candidate Forwarding Set in Opportunistic Routing in Vanets

Adaptively Prioritizing Candidate Forwarding Set in Opportunistic Routing in Vanets

Adaptively Prioritizing Candidate Forwarding Set in Opportunistic Routing in Vanets

Adaptively Prioritizing Candidate Forwarding Set in Opportunistic Routing in Vanets

Design of a location-based opportunistic geographic routing protocol

Highly dynamic networks face frequent topological changes. In such a network, traditional routing protocols are inefficient and even impractical, as they need to detect network topology and update locations frequently. To mitigate this problem, this paper puts forward a location-based opportunistic geographic routing (LOGR) protocol, an opportunistic packet transmission mechanism based on geographic locations. In most traditional routing protocols, the forwarding nodes are selected in advance. In the proposed protocol, the sending node does not know which is the next-hop node. Instead, the sending node broadcasts the forwarding rules and the data packet together, utilizing the broadcast nature of wireless transmission. Then, each node receiving the packet will judge whether it has the right to forward the packet, according to the preset rules. Another difference between our protocol and common protocols lies in the priority assignment of opportunistic forwarding. In common protocols, all candidate forwarding nodes are ranked in turn. In our protocol, the absolute priority method is adopted, such that any candidate receiving the packet can derive its own priority from its location information and forwarding rules, and determine the forwarding time, without knowing the information of other candidates. In this way, our protocol eliminates the need for topology detection or location update, and avoids the huge network overhead incurred in pairwise comparison of nodes for priority assignment. Therefore, unlike traditional protocols constrained by network scale and network topology, our protocol can better adapt to the frequent topology change in the highly dynamic network Experimental results show that the LOGR protocol worked effectively on the network containing fast moving nodes, and, in the same environment, the network performed much better under the LOGR protocol than under traditional routing protocols.

The Network-Based Candidate Forwarding Set Optimization for Opportunistic Routing

In wireless multi-hop networks, the opportunistic routing has advantages on packet delivery probability over deterministic routing. For reducing the transmission delay and duplicate transmission in opportunistic routing, the candidate forwarding set (CFS) optimization approach is proposed. Since the previous node-based approaches have disadvantages on improving the performance of opportunistic routing, we propose the network-based approach to address these disadvantages, named the Delay and Duplicate transmission Avoid CFS optimization algorithm, shorted as DDA. In DDA, the nodes in CFS are divided into different fully connected relay networks. For the forwarding nodes in CFS, more than one relay networks can be constructed and only one can be chosen as the final relay network. So, first, we propose the fully connected relay network recognition algorithm to judge whether the chosen network is fully connected or not. Next, the properties of these networks are investigated. Then, based on the multi-attribution decision making (MADM) theory, and taking the packet delivery probability (between sender and relay networks), the transmission delay, and the forwarding priorities of nodes into account, the DDA chooses the most appropriate relay network. The nodes in this relay network are selected as the final forwarding nodes. Based on these innovations, the transmission delay and duplicate transmission are reduced while the efficiency of the original opportunistic routing algorithms is kept. The simulation results illustrate the effectiveness of the network-based DDA algorithm.

Survey on Opportunistic Routing Protocols in Multihop Wireless Networks

Routing is portrayed as one of the most important prevailing challenges in research with reference to multi-hop networks in a wireless environment. Opportunistic routing (OR) protocol is an emerging area related to research, due to the improvement in communication reliability, compared to the traditional routing models. The major perception related to OR is to determine a group of neighboring node candidates, named as a candidate set using the advantages of broadcast capability of the wireless medium thereby to collaboratively transmit data packets towards the destination using the coordination of the forwarded candidate set. The design and performance of OR protocols over multi-hop wireless networks mainly depend on the processes of forwarding selection of candidates and assignment of priorities. Therefore, the researchers have designed and developed several different algorithms for those OR processes. In this paper, following a short outline on traditional routing and OR protocols, metrics involved in the design of existing OR protocols, classification of OR based protocols, and hurdles in the design of OR protocols over multi-hop wireless networks are examined. More precisely, the OR protocols are divided into two categories, based on the forwarding candidate set selection and forwarding candidate coordination methods. Furthermore, the most significant challenges of OR protocol design, such as prioritization of forwarding candidates, utilizing the cross-layer approach for candidate coordination, and achieving the quality of service also investigated.

Link-Correlation-Aware Opportunistic Routing in Low-Duty-Cycle Wireless Networks.

In low-duty-cycle wireless networks with unreliable and correlated links, Opportunistic Routing (OR) is extremely costly because of the unaligned working schedules of nodes within a common candidate forwarder set. In this work, we propose a novel polynomial-time node scheduling scheme considering link correlation for OR in low-duty-cycle wireless networks (LDC-COR), which significantly improves the performance by assigning nodes with low correlation to a common group and scheduling the nodes within this group to wake up simultaneously for forwarding packets in a common cycle. By taking account of both link correlation and link quality, the performance of the expected transmission count (ETX) is improved by adopting the LDC-COR protocol. As a result, the energy consumption of low-duty-cycle OR is significantly reduced. LDC-COR only requires the information of one-hop neighboring nodes which introduces minimal communication overhead. The proposed LDC-COR bridges the gap between the nodes’ limited energy resource and the application lifetime requirements. We evaluate the performance of LDC-COR with extensive simulations and a physical wireless testbed consisting of 20 TelosB nodes. The evaluation results show that both transmission efficiency and energy consumption of low-duty-cycle OR are significantly improved with only a slight increase of end-to-end delay.

An Energy-Efficient and Reliable Opportunistic Routing for Wireless Sensor Networks

Introduction: Routing protocols in Wireless Sensor Networks (WSN) are of major concern because of the important factors like energy efficiency and reliability. Opportunistic Routing (OR) is the simplest and reliable routing technique for ad-hoc networks and WSN. The OR protocol guarantees data delivery in the network. As WSN need energy-efficient routing, therefore, energy- efficient OR protocols gained popularity in the last three years. Objectives: Opportunistic routing improves the performance of the network by utilizing the broadcasting abilities of wireless channels. In this paper, a new energy-efficient and reliable routing protocol has been proposed that is based on the opportunistic routing technique. Methods: The proposed protocol is based on the energy-efficient routing metric. The proposed OR protocol prioritizes among the candidate forwarders on the basis of energy-efficient routing metric and this will lead to the achievement of improved network lifetime. Results: The end-to-end delay is reduced because of the selection of candidate forwarders from the neighbor list of each node. The proposed protocol uses selective acknowledgment mechanisms. The simulation results of the proposed protocol depict the improved performance as compared to other recent OR protocols. Conclusion: The proposed protocol can directly be used with existing WSN applications. Further improvements can be made by utilizing more parameters like distance and the quality of transmission.

Adaptive Beamforming-Based Gigabit Message Dissemination for Highway VANETs

Millimeter-wave (mmWave) directional communication has been viewed as a means to achieve the goal of multiple gigabit data rate with low latency in vehicular networks, so as to fulfill the requirement of dissemination of a huge amount of sensory data. However, most of the existing researches focus on implementing the vehicular mmWave communications by point-to-point (P2P) transmissions between vehicles or base stations, which is not conductive to efficient dissemination of messages in a whole road segment. In this paper, we propose a novel gigabit message dissemination scheme called P2MP in mmWave/Dedicated Short Range Communications (DSRC) multi-hop vehicle-to-vehicle (V2V) network for highway scenario. P2MP includes: 1) a clustering-based transmitter beamforming method which helps a transmitter to search a relatively optimal transmission beam according to the collected environmental information. 2) a positioning-errors-based receiver beamforming which is used to determine the beam direction and beamwidth at every receiver. 3) a candidate forwarder selection mechanism which is devised to select reliable relays for next hop broadcast. Based on these three components, the proposed P2MP transmission scheme is able to broadcast massive messages to vehicles in the road. The simulation results show that our proposed scheme can effectively improve the efficiency of broadcasting large messages through mmWave.