EECLP: AWireless Sensor Networks Energy Efficient Cross-Layer Protocol

Recent advancements in wireless communications have allowed the birth of novel wireless sensor networks(WSN).A sensor network comprises several micro-sensors deployed randomly in an area of interest.A micro-sensor is provided with an energy resource to supply electricity to all of its components.However,the disposed energy resource is limited and battery replacement is generally infeasible.With this restriction,the sensors must conserve energy to prolong their lifetime.Various energy conservation strategies for WSNs have been presented in the literature,from the application to the physical layer.Most of these solutions focus only on optimizing a single layer in terms of energy consumption.In this research,a novel cross-layer technique for WSNs’effective energy usage is presented.Because most energy consumption factors exist in the Medium Access Control(MAC)layer and network layer,our EECLP protocol(Energy Efficient Cross-Layer Protocol for Wireless Sensor Networks)integrates these two layers to satisfy energy efficiency criteria.To gain access to the transmission channel,we implement a communication regime at the MAC layer based on CSMA/CA(Carrier Sense Multiple Access/Collision Avoidance)techniques.Next,depending on the activity and a standby period,we employ the RTS/CTS(Request to Send/Clear to Send)method to prevent collisions and resolve hidden node concerns by utilizing the network allocation vector(NAV)to calculate the transmission duration.Employing a greedy strategy,we establish chains amongst cluster members to mitigate the issue of high energy consumption in routing data.An objective function was utilized to determine the optimal cross-chain path based on the distances to the base station(BS)and residual energy(RE).The simulation,testing,and comparison of the proposed protocol to peer protocols have shown superior outcomes and a prolonged network lifespan.Using the suggested protocol,the network lifetime increases by 10%compared to FAMACO(Fuzzy and Ant Colony Optimization based MAC/Routing Cross-layer)protocol,and it increases by 90%and 95%compared to IFUC(Improved Fuzzy Unequal Clustering)and UHEED(Unequal Hybrid Energy Efficient and Distributed)protocols successively.

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

Multi-Agent Deep Reinforcement Learning for Cross-Layer Scheduling in Mobile Ad-Hoc Networks

Due to the fading characteristics of wireless channels and the burstiness of data traffic,how to deal with congestion in Ad-hoc networks with effective algorithms is still open and challenging.In this paper,we focus on enabling congestion control to minimize network transmission delays through flexible power control.To effectively solve the congestion problem,we propose a distributed cross-layer scheduling algorithm,which is empowered by graph-based multi-agent deep reinforcement learning.The transmit power is adaptively adjusted in real-time by our algorithm based only on local information(i.e.,channel state information and queue length)and local communication(i.e.,information exchanged with neighbors).Moreover,the training complexity of the algorithm is low due to the regional cooperation based on the graph attention network.In the evaluation,we show that our algorithm can reduce the transmission delay of data flow under severe signal interference and drastically changing channel states,and demonstrate the adaptability and stability in different topologies.The method is general and can be extended to various types of topologies.

TCP-LTE/5G Cross-layer performance analysis tool for high mobility data networking and a case study on high-speed railway

Nowadays,high mobility scenarios have become increasingly common.The widespread adoption of High-speed Rail(HSR)in China exemplifies this trend,while more promising use cases,such as vehicle-to-everything,continue to emerge.However,the Internet access provided in high mobility environments stllstruggles to achieve seamless connectivity.The next generation of wireless cellular technology 5 G further poses more requirements on the endto-end evolution to fully utilize its ultra-high band-width,while existing network diagnostic tools focus on above-IP layers or below-IP layers only.We then propose HiMoDiag,which enables flexible online analysis of the network performance in a cross-layer manner,i.e.,from the top(application layer)to the bottom(physical layer).We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR,lead to more timely optimization and ultimately help to improve the network performance.

SLCRM： Subjective Logic-Based Cross-Layer Reputation Mechanism for Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are vulnerable to various security threats because of their special infrastructure and communication mode, wherein insider attacks are the most challenging issue. To address this problem and protect innocent users from malicious attacks, it is important to encourage cooperation and deter malicious behaviors. Reputation systems constitute a major category of techniques used for managing trust in distributed networks, and they are effective in characterizing and quantifying a node's behavior for WMNs. However, conventional layered reputation mechanisms ignore several key factors of reputation in other layers; therefore, they cannot provide optimal performance and accurate malicious node identification and isolation for WMNs. In this paper, we propose a novel dynamic reputation mechanism, SLCRM, which couples reputation systems with a cross-layer design and node-security-rating classification techniques to dynamically detect and restrict insider attacks. Simulation results show that in terms of network throughput, packet delivery ratio, malicious nodes identification, and success rates, SLCRM implements security protection against insider attacks in a more dynamic, effective, and efficient manner than the subjective logic and uncertainty-based reputation model and the familiarity-based reputation model.

A CROSS-LAYER STRATEGY FOR COOPERATIVE DIVERSITY IN WIRELESS SENSOR NETWORKS

We investigate the problem of how to minimize the energy consumption in multi-hop Wireless Sensor Network (WSN),under the constraint of end-to-end reliability Quality of Seervice (QoS) requirement.Based on the investigation,we jointly consider the routing,relay selection and power allocation algorithm,and present a novel distributed cross-layer strategy using opportunistic relaying scheme for cooperative communication.The results show that under the same QoS requirement,the proposed cross-layer strategy performs better than other cross-layer cooperative communication algorithms in energy efficiency.We also investigated the impact of several parameters on the energy efficiency of the cooperative communication in WSNs,thus can be used to provide guidelines to decide when and how to apply cooperation for a given setup.

Social-ecological perspective on the suicidal behaviour factors of early adolescents in China:a network analysis

Background In early adolescence,youth are highly prone to suicidal behaviours.Identifying modifiable risk factors during this critical phase is a priority to inform effective suicide prevention strategies.Aims To explore the risk and protective factors of suicidal behaviours(ie,suicidal ideation,plans and attempts)in early adolescence in China using a social-ecological perspective.Methods Using data from the cross-sectional project‘Healthy and Risky Behaviours Among Middle School Students in Anhui Province,China',stratified random cluster sampling was used to select 5724 middle school students who had completed self-report questionnaires in November 2020.Network analysis was employed to examine the correlates of suicidal ideation,plans and attempts at four levels,namely individual(sex,academic performance,serious physical llness/disability,history of self-harm,depression,impulsivity,sleep problems,resilience),family(family economic status,relationship with mother,relationship with father,family violence,childhood abuse,parental mental illness),school(relationship with teachers,relationship with classmates,school-bullying victimisation and perpetration)and social(social support,satisfaction with society).Results In total,37.9%,19.0%and 5.5%of the students reported suicidal ideation,plans and attempts in the past 6 months,respectively.The estimated network revealed that suicidal ideation,plans and attempts were collectively associated with a history of self-harm,sleep problems,childhood abuse,school bullying and victimisation.Centrality analysis indicated that the most influential nodes in the network were history of self-harm and childhood abuse.Notably,the network also showed unique correlates of suicidal ideation(sex,weight=0.60;impulsivity,weight=0.24;family violence,weight=0.17;relationship with teachers,weight=-0.03;school-bullying perpetration,weight=0.22),suicidal plans(social support,weight=-0.15)and suicidal attempts(relationship with mother,weight=-0.10;parental mental llness,weight=0.61).Conclusions This study identified the correlates of suicidal ideation,plans and attempts,and provided practical implications for suicide prevention for young adolescents in China.Firstly,this study highlighted the importance of joint interventions across multiple departments.Secondly,the common risk factors of suicidal ideation,plans and attempts were elucidated.Thirdly,this study proposed target interventions to address the unique influencing factors of suicidal ideation,plans and attempts.

Cross-Layer Framework for Capacity Analysis in Multiuser Ultra-Dense Networks with Cell DTx

Cell discontinuous transmission(Cell DTx)is a key technology to mitigate inter-cell interference(ICI)in ultra-dense networks(UDNs).The aim of this work is to understand the impact of Cell DTx on physical-layer sum rates of SBSs and link-layer quality-of-service(QoS)performance in multiuser UDNs.In this work,we develop a cross-layer framework for capacity analysis in multiuser UDNs with Cell DTx.In particular,we first extend the traditional one-dimensional effective capacity model to a new multidimensional effective capacity model to derive the sum rate and the effective capacity.Moreover,we propose a new iterative bisection search algorithm that is capable of approximating QoS performance.The convergence of this new algorithm to a unique QoS exponent vector is later proved.Finally,we apply this framework to the round-robin and the max-C/I scheduling policies.Simulation results show that our framework is accurate in approximating 1)queue length distribution,2)delay distribution and 3)sum rates under the above two scheduling policies,and further show that with the Cell DTx,systems have approximately 30% higher sum rate and 35% smaller average delay than those in full-buffer scenarios.

An Energy-Efficient Access Control Algorithm with Cross-Layer Optimization in Wireless Sensor Networks

This paper presents a wireless sensor network (WSN) access control algorithm designed to minimize WSN node energy consumption. Based on slotted ALOHA protocol, this algorithm incorporates the power control of physical layer, the transmitting probability of medium access control (MAC) layer, and the automatic repeat request (ARQ) of link layer. In this algorithm, a cross-layer optimization is preformed to minimizing the energy consuming per bit. Through theory deducing, the transmitting probability and transmitting power level is determined, and the relationship between energy consuming per bit and throughput per node is provided. Analytical results show that the cross-layer algorithm results in a significant energy savings relative to layered design subject to the same throughput per node, and the energy saving is extraordinary in the low throughput region.

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

Novel E2E-QoE Metric for PHY Optimization:A Cross-Layered Framework

Existing systems use key performance indicators(KPIs)as metrics for physical layer(PHY)optimization,which suffers from the problem of overoptimization,because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste.Therefore,it is necessary to utilize directly the quality of experience(QoE)of user as a metric of optimization,which can achieve the global optimum of QoE under cost and energy constraints.However,QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY.To address this problem,we in this paper propose a novel end-to-end QoE(E2E-QoE)based optimization architecture at the user-side for the first time.Specifically,a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE.Based on this,an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies.Finally,we investigate to optimize the PHY algorithm directly based on the E2E-QoE.The proposed frameworks and algorithms are all validated using the data from real fifth-generation(5G)mobile system,which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Wireless sensor networks in relic protection:deployment methodology and cross-layer design

This paper describes the deployment optimization technology and the cross-layer design of a surveil-lance WSN system applied in relic protection.Facing the typical technical challenges in the applicationcontext of relic protection,we firstly propose a deployment technology based on ant colony optimization al-gorithm(DT-ACO)to overcome the difficulties in communication connectivity and sensing coverage.Meanwhile,DT-ACO minimizes the overall cost of the system as much as possible.Secondly we proposea novel power-aware cross-layer scheme(PACS)to facilitate adjustable system lifetime and surveillanceaccuracy.The performance analysis shows that we achieve lower device cost,significant extension of thesystem lifetime and improvement on the data delivery rate compared with the traditional methods.

Delay-Based Cross-Layer QoS Scheme for Video Streaming in Wireless Ad Hoc Networks

With correlating with human perception, quality of experience(Qo E) is also an important measurement in evaluation of video quality in addition to quality of service(Qo S). A cross-layer scheme based on Lyapunov optimization framework for H.264/AVC video streaming over wireless Ad hoc networks is proposed, with increasing both Qo E and Qo S performances. Different from existing works, this scheme routes and schedules video packets according to the statuses of the frame buffers at the destination nodes to reduce buffer underflows and to increase video playout continuity. The waiting time of head-ofline packets of data queues are considered in routing and scheduling to reduce the average end-to-end delay of video sessions. Different types of packets are allocated with different priorities according to their generated rates under H.264/AVC. To reduce the computational complexity, a distributed media access control policy and a power control algorithm cooperating with the media access policy are proposed. Simulation results show that, compared with existing schemes, this scheme can improve both the Qo S and Qo E performances. The average peak signal-to-noise ratio(PSNR) of the received video streams is also increased.

Joint channel assignment and cross-layer routing protocol for multi-radio multi-channel Ad Hoc networks

To study multi-radio multi-channel （MR-MC） Ad Hoc networks based on 802.11, an efficient cross-layer routing protocol with the function of joint channel assignment, called joint channel assignment and cross-layer routing （JCACR）, is presented. Firstly, this paper introduces a new concept called channel utilization percentage （CUP）, which is for measuring the contention level of different channels in a node’s neighborhood, and deduces its optimal value for determining whether a channel is overloaded or not. Then, a metric parameter named channel selection metric （CSM） is designed, which actually reffects not only the channel status but also corresponding node’s capacity to seize it. JCACR evaluates channel assignment by CSM, performs a local optimization by assigning each node a channel with the smaller CSM value, and changes the working channel dynamically when the channel is overloaded. Therefore, the network load balancing can be achieved. In addition, simulation shows that, when compared with the protocol of weighted cumulative expected transfer time （WCETT）, the new protocol can improve the network throughput and reduce the end-to-end average delay with fewer overheads.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

A data-driven model of drop size prediction based on artificial neural networks using small-scale data sets

An artificial neural network(ANN)method is introduced to predict drop size in two kinds of pulsed columns with small-scale data sets.After training,the deviation between calculate and experimental results are 3.8%and 9.3%,respectively.Through ANN model,the influence of interfacial tension and pulsation intensity on the droplet diameter has been developed.Droplet size gradually increases with the increase of interfacial tension,and decreases with the increase of pulse intensity.It can be seen that the accuracy of ANN model in predicting droplet size outside the training set range is reach the same level as the accuracy of correlation obtained based on experiments within this range.For two kinds of columns,the drop size prediction deviations of ANN model are 9.6%and 18.5%and the deviations in correlations are 11%and 15%.

An Ant Colony Algorithm Based on Cross-Layer Design for Routing and Wavelength Assignment in Optical Satellite Networks

This paper introduces an ant colony routing and wavelength assignment algorithm based on cross-layer design(CL-ACRWA),which can overcome the adverse effects of Doppler wavelength shift on data transmission in optical satellite networks. Firstly, a cross-layer optimization model is built, which considers the Doppler wavelength shift, the transmission delay as well as wavelength-continuity constraint. Then an ant colony algorithm is utilized to solve the cross-layer optimization model, resulting in finding an optimal light path satisfying the above constraints for every connection request. The performance of CL-ACRWA is measured by the communication success probability, the convergence property and the transmission delay. Simulation results show that CL-ACRWA performs well in communication success probability and has good global search ability as well as fast convergence speed. Meanwhile, the transmission delay can meet the basic requirement of real-time transmission of business.

Cross-layer Enhancement to Multi-hop Routing in Sensor Networks: an Empirical Study

Lossy link is one of the unique characteristics in random-deployed sensor networks. We envision that robustness and reliability of routing cannot be ensured purely in network layer. Our idea is to enhance the performance of routing protocol by cross-layer interaction. We modified mint protocol, a routing protocol in TinyOS and proposed an enhanced version of mint called PA-mint. A transmission power control interface is added to network layer in PA-mint. When routing performance of the current network is not satisfied, PA-mint monotonically increases the transmission power via the interface we added. PA-mint is able to connect orphan nodes and robust to node mobility or key nodes failure. In the case that automatic request retransmission is employed, the number of retransmissions can be reduced by PA-mint. Results from experiments show that PA-mint increases the reliability and robustness of routing protocol by cross-layer interaction.