Network Selection Research Articles

Optimizing energy-efficient cluster head selection in wireless sensor networks using a binarized spiking neural network and honey badger algorithm

Wireless Sensor Networks (WSNs) play a vitalparton some applications, like environmental monitoring, healthcare, industrial automation. Optimizing the performance of WSN is essential to ensure efficient data delivery while conserving energy resources. This manuscript addresses the challenges of Multi-Objective Cluster Head Selection and Energy-Aware Routing in WSNs through the development of the Binarized Spiking Neural Network optimized with the Honey Badger Algorithm (EACHS-BzSpNNHBA-WSN). The appropriate cluster heads selection is vital for the overall presentation of WSNs, influencing data aggregation, routing, and energy consumption. Conventional approaches often struggle to balance the conflicting objectives of maximizing network lifetime, minimizing energy consumption, and ensuring timely data delivery. Therefore, there is a pressing need for innovative solutions capable of addressing these multi-objective optimization challenges. In this study, the EACHS-BzSpNNHBA-WSN framework is proposed, leveraging the computational power of Binarized Spiking Neural Networks and the optimization capabilities of the Honey Badger Algorithm to identify optimal cluster heads in WSNs. By formulating the cluster head selection as a multi-objective optimization problem, the aim is to achieve robust data delivery while minimizing energy consumption and extending the network lifetime. The proposed framework is implemented in MATLAB platform. The performance assessment is conducted using various metrics, including the number of active nodes, packet drop rate, energy consumption, network lifetime, delay, throughput, packet delivery ratio. Comparative analysis with existing models demonstrates the effectiveness of the EACHS-BzSpNNHBA-WSN approach.

Embedded feature selection for neural networks via learnable drop layer

Embedded feature selection for neural networks via learnable drop layer

Energy aware forwarder selection in wireless body area networks to enhance stability and lifetime

Energy aware forwarder selection in wireless body area networks to enhance stability and lifetime

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds.

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

In recent times, Wireless Sensor Networks (WSNs) have become an indispensable technology across various industries, offering diverse applications and services. Among the crucial performance metrics for WSNs, optimal cluster head (CH) selection and energy efficiency are paramount for cost-effective network operations. This paper proposes a novel approach for WSNs that tackles both challenges using Zebra Fish Optimization (ZFO) and Sea Horse Optimization (SHO) algorithms. The proposed approach focuses on dynamic cluster formation and CH selection. The ZFO algorithm, enhanced with a new multi-level threading technique, dynamically selects the most suitable CH based on a fitness function. Subsequently, the SHO algorithm, equipped with an innovative adaptive parameter tuning mechanism, optimizes energy consumption within the network. This two-phased approach ensures balanced performance. Performance evaluation is validated using key metrics like packet delivery ratio (PDR), throughput, network lifetime, and residual energy. Experimental results and statistical analysis demonstrate that the proposed hybrid scheme outperforms existing popular algorithms in all these metrics. The improvements range from 1.8 % to 6.9 % for PDR, 6.7 % to 24 % for throughput, 1.86 % to 7.40 % for network lifetime, and 9.65 % to 37.95 % for residual energy. These advancements are attributed to the innovative modifications introduced in both ZFO and SHO algorithms, ultimately contributing to the enhanced performance of the entire system.

Federated Reinforcement Learning With Knowledge Transfer for Network Selection in Hybrid WiFi-VLC Networks

Federated Reinforcement Learning With Knowledge Transfer for Network Selection in Hybrid WiFi-VLC Networks

Spectrum-Energy-Efficient Mode Selection and Resource Allocation for Heterogeneous V2X Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

Spectrum-Energy-Efficient Mode Selection and Resource Allocation for Heterogeneous V2X Networks: A Federated Multi-Agent Deep Reinforcement Learning Approach

Feedback between stochastic gene networks and population dynamics enables cellular decision-making.

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

Quick UDP Internet Connections and Transmission Control Protocol in unsafe networks: A comparative analysis

AbstractSecure data transmission and efficient network performance are both key aspects of the modern Internet. Traditionally, Transport Layer Security (TLS)/Transmission Control Protocol (TCP) has been used for reliable and secure networking communications. In the past decade, Quick User Datagram Protocol (UDP) Internet Connections QUIC has been designed and implemented on UDP, attempting to improve security and efficiency of Internet traffic. Real‐world platform investigations are carried out in this paper to evaluate TLS/TCP and QUIC/UDP in maintaining communication, security and efficiency under three different types of popular cyber‐attacks. A set of interesting findings, including delay, loss, server CPU utilisation and server memory usage are presented to provide a comprehensive understanding of the two protocol stacks in performing malicious traffic. More specifically, in terms of the efficiency in achieving short delays and low packet loss rates with limited CPU and memory resources, QUIC/UDP performs better under Denial of Service attacks but TLS/TCP overtakes QUIC/UDP when handling MitM attacks. In terms of security, the implementation of TCP tends to be more secure than QUIC, but QUIC traffic patterns are harder to learn using machine learning methods. We hope that these insights will be informative in protocol selection for future networks and applications, as well as shedding light on the further development of the two protocol stacks.

ABNet: An Aggregated Backbone Network Architecture for Fine Landcover Classification

High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model’s spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach.

IDA: Improved dragonfly algorithm for load balanced cluster heads selection in wireless sensor networks

IDA: Improved dragonfly algorithm for load balanced cluster heads selection in wireless sensor networks

GaN low noise amplifier MMIC with LPF and HPF noise matching

In this paper, we introduce two innovative two-stage low noise amplifiers (LNAs), each with distinct noise-matching networks. The first LNA features a low pass filter (LPF) for noise-matching in both stages, while the second uses a high pass filter (HPF) in a similar capacity. Our research focuses on evaluating the performance differences that arise from using varied matching networks within specific frequency ranges. Highlighting the critical role of appropriate network selection for optimizing gain and noise performance, our approach includes the development of two Monolithic Microwave Integrated Circuits (MMICs) using cutting-edge 0.25μm Gallium Nitride (GaN) technology. The C-band LNA, targeting a frequency range of 4–6 GHz, achieves an impressive average noise fig. (NF) of 1.5 dB and a gain of 17 dB. For the X-band range of 8–10 GHz, the LNA records a commendable average NF of 1.7 dB and a gain of 16 dB, demonstrating the effectiveness of our novel design strategies.

On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading

The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power.

Resource allocation of cognitive relay networks under continuous transmission mechanism

This paper considers the joint power allocation, path selection and subcarrier pairing design of cognitive radio cooperative networks, where the transmission power of secondary users is limited by its own power budget and the tolerable interference threshold of primary users. In particular, a continuous transmission mechanism rather than intermittent emission, leading to severe signal interference between primary and secondary users, as well as mutual interference between secondary users, is taken into account. This cooperative cognitive radio network is seen as an improvement in the sense that the source transmitter can continuously transmit symbols without silence. Although traditional cooperative network technology has long been applied to the cognitive radio field and it has been proposed to allow continuous transmission signals by source transmitter, the deployment of such improved cooperative behavior in cognitive radio networks has not been reported yet. This improvement assumption may render serious mutual interference between the relay station and the destination receiver, and the signal interference borne by the primary user will also exceed the threshold value, if there is no elaborate signal design and resource scheduling. In order to make full use of the available resources, this paper presents the closed-form solution of the power allocation, the decision criteria of the path selection and the subcarrier pairing technique for the problem of maximization of the sum rate of secondary users in such networks under the dual constraints of the power budget and the interference threshold. Simulation results show that the continuous transmission behavior at the source significantly outperforms the traditional switching back and forth between on and off.

Deterministic theory of evolutionary games on temporal networks.

Recent empirical studies have revealed that social interactions among agents in realistic networks merely exist intermittently and occur in a particular sequential order. However, it remains unexplored how to theoretically describe evolutionary dynamics of multiple strategies on temporal networks. Herein, we develop a deterministic theory for studying evolutionary dynamics of any [Formula: see text] pairwise games in structured populations where individuals are connected and organized by temporally activated edges. In the limit of weak selection, we derive replicator-like equations with a transformed payoff matrix characterizing how the mean frequency of each strategy varies over time, and then obtain critical conditions for any strategy to be evolutionarily stable on temporal networks. Interestingly, the re-scaled payoff matrix is a linear combination of the original payoff matrix with an additional one describing local competitions between any pair of different strategies, whose weights are solely determined by network topology and selection intensity. As a particular example, we apply the deterministic theory to analysing the impacts of temporal networks in the mini-ultimatum game, and find that temporally networked population structures result in the emergence of fairness. Our work offers theoretical insights into the subtle effects of network temporality on evolutionary game dynamics.

Cluster-based wireless sensor network framework for denial-of-service attack detection based on variable selection ensemble machine learning algorithms

A Cluster-Based Wireless Sensor Network (CBWSN) is a system designed to remotely control and monitor specific events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. The wide range of applications of technology in almost every field of human activity exposes it to various security threats from cybercriminals. One of the pressing concerns that requires immediate attention is the risk of security breaches, such as intrusions in wireless sensor network traffic. Poor detection of denial-of-service (DoS) attacks, such as Grayhole, Blackhole, Flooding, and Scheduling attacks, can deplete the energy of sensor nodes. This can cause certain sensor nodes to fail, leading to a degradation in network coverage or lifetime. The detection of such attacks has resulted in significant computational complexity in the related works. As new threats arise, security attacks get more sophisticated, focusing on the target system's vulnerabilities. This paper proposed the development of Cluster-Based Wireless Sensor Network and Variable Selection Ensemble Machine Learning Algorithms (CBWSN_VSEMLA) as a security threats detection system framework for DoS attack detection. The CBWSN model is designed using a Fuzzy C-Means (FCM) clustering technique, whereas VSEMLA is a detection system comprised of Principal Component Analysis (PCA) for feature selection and various ensemble machine learning algorithms (Bagging, LogitBoost, and RandomForest) for the detection of grayhole attacks, blackhole attacks, flooding attacks, and scheduling attacks. The experimental results of the model performance and complexity comparison for DoS attack evaluation using the WSN-DS dataset show that the PCA_RandomForest IDS model outperforms with 99.999 % accuracy, followed by the PCA_Bagging IDS model with 99.78 % accuracy and the PCA_LogitBoost model with 98.88 % accuracy. However, the PCA_RandomForest model has a high computational complexity, taking 231.64 s to train, followed by the PCA_LogitBoost model, which takes 57.44 s to train, and the PCA_Bagging model, which takes 0.91 s to train to be the best in terms of model computational complexity. Thus, the models surpassed all baseline models in terms of model detection accuracy on flooding, scheduling, grayhole, and blackhole attacks.

Kajian Alternatif Proyeksi Jaringan Jalan Hutan untuk Perencanaan Pemanenan Kayu di PT Inhutani I Labanan, Propinsi Jawa Barat

Forests are one of the natural resources that can be utilized in the form of both wood and non-wood forest products. Harvesting carried out to extract wood results in negative impacts such as soil damage and damage to standing stands. The negative impacts of wood harvesting can be reduced by planning in advance, such as creating a forest road network. The research was carried out in the mahogany room, utilization division, forest management department, while the assessment of the forest road network used the case study of PT Inhutani I Lebanan. The aim of the research is to examine the planning and selection of the best alternative forest road network for harvesting wood using multi-criteria decision making techniques. The research will be carried out in March 2023. The research stage begins with sketching the road network; assess road networks using economic, ecological and technical criteria; as well as conducting literature studies from various sources. The research results show that there are six alternative forest road networks for harvesting wood. This is based on forest road network assessment parameters that the best alternative road network in the PT Inhutani I Labanan road scheme is the 6th road network alternative. Sustainable management of forest resources can be achieved through a well-planned road network. The highest total utility value (rank 1) is found on the 6th road network which is the best alternative forest road network to be implemented at PT Inhutani Labanan I both in terms of technical aspects, ecological aspects and economic aspects.

HPS: A Hybrid Proactive Scheduler with adaptive channel selection for industrial 6TiSCH networks

The Industrial Internet of Things (IIoT) plays a vital role in Industry 4.0, thanks to advanced wireless protocols like IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) and their incorporation in the IPv6 stack via 6TiSCH. These protocols rely on carefully constructed schedules generated by a Scheduling Function (SF). The Minimal Scheduling Function (MSF) serves as the standard SF established by the 6TiSCH WG, prescribing the management and adaptation of the communication schedule based on traffic patterns. However, MSF has a notable drawback in adapting to traffic variability and burstiness and fails to account for link quality when updating TSCH cells. To address these limitations, this paper introduces the Hybrid Proactive Scheduler with Adaptive Channel Selection (HPS). HPS calculates cell requirements by considering resource utilization, ETX (Expected Transmission Count), and buffer allocation. It incorporates a fast response mechanism for burst traffic, implemented through an autonomous scheduling mechanism. Additionally, HPS utilizes a whitelisting mechanism to select optimal communication channels. The proposed scheme is implemented in Contiki-ng and evaluated in both simulated environments and real-world testbed. Obtained results demonstrate the potential of HPS to significantly enhance reliability and reduce delays compared to state-of-the-art SFs. Specifically, HPS consistently demonstrates reliability levels surpassing 90% across diverse scenarios accompanied with a noticeable gain in latency and power consumption of approximately 70% and 25%, respectively. These findings highlight the effectiveness of HPS in improving network performance and addressing the limitations of existing SFs.

Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks

Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network.

The Latency Performance Analysis and Effective Relay Selection for Visible Light Networks.

In visible light communication (VLC), the precise latency evaluation of wireless access networks and the efficient forwarding strategy of core networks are the crux for end-to-end reliability provisioning. Leveraging martingale theory, an elegant latency-bounded reliability analysis framework is studied for the VLC network. Considering the characteristic that VLC links are easy to block, the martingale of the service process is constructed. Based on the time shift features, the martingale process related to latency is proposed for the VLC system, which models the influence of entanglement between aggregate arrivals and random service on latency. A stopping time event about latency is defined. Renting the stopping time theory, a tight upper bound of the unreliability with regard to latency is derived. In the core network, a dynamic forward backhaul framework is proposed, which uses the relay selection algorithm based on back-pressure theory to improve data transmission quality. The theoretical latency-bounded reliability matches the simulation results well, which verifies the effectiveness of the proposed analysis framework, and the proposed relay selection algorithm can also improve network performance under data-intensive transmission.