Forwarding Mechanism Research Articles

Big Data Processing over Cloud Computing Environment

The topics of this special paper are mainly devoted to the most recent research, development and applications in the field of cloud computing, big data processing in the various fields, e.g. Web blog, search engine, image processing, and industry. Among all of the submitted manuscripts, 15 papers were selected and included in this special paper. Creative thoughts and interesting inspirations are presented, discussed, and disseminated in this paper. First, there are three papers on the cloud architecture and optimization for big data processing. J. Peng, et al. discussed the problems of load balance for massive data processing on cloud considering the data processing efficiency and nodes’ capability. A load balance approach for massive data, based on the consistent hashing method, was presented to improve the data processing equalization and high processing efficiency. Large-scale sensor networks and internet of things are one of the most important application fields in big data. Y. Sun, et al. proposed a cloud computing system to support PaaS in energy power applications—PROXZONE. PROXZONE provides the cloud services uniform monitoring and authorization, and cloud message service, which can support good performance when dealing with big data. Meanwhile, S. Xu, et al. discussed an improved cooperative dynamic cluster model in multitasks mobile network, and proposed a cooperative message forwarding mechanism to improve the network performance.

SDN-Based Reconfigurable Edge Network Architecture for Industrial Internet of Things

Internet of Things (IoT) with edge computing capability enhances efficiency, availability and improves latency of an industrial automation system. However, to provide dynamic services at the resource-constrained edge device, reconfiguration of services is necessary. This paper proposes a programmable edge network to (re)configure different services of industrial IoT, that employs programmable layers at the edge for reconfiguring the sensor/actuator network and application services. The lowermost layer allows reconfiguring the communication-related parameters and the middle layer consists of an SDN controller that can dynamically program different modules and handles actuation decisions from the edge. An interfacing protocol between the layers is proposed to provide reliability by considering the required configuration parameters among layers. At the top-layer, a priority forwarding mechanism is designed for SDN core (control-loop) communication when sensor and actuator are on different edges. The proposed architecture significantly improves the actuation-latency and is highly energy efficient compared to the existing state-of-the-art.

PAtCH: Proactive Approach to Circumvent Holes in Wireless Sensor Networks.

The occurrence of hole regions in Wireless Sensor Networks is a significant challenge when applying a greedy technique in a geographic routing approach. The local minimum phenomenon is commonly attributed to physical obstacles, energy depletion of the nodes, failures in communication between neighbors, or even the incorrect deployment of the nodes in the sensing field. To address the problem of hole regions, most approaches choose to abandon the traditional greedy forwarding mechanism to temporarily adopt the well-known perimeter routing scheme applied to nearby nodes or along the edge of a region of a hole. However, this mechanism does not satisfy the network load balance requirement, because it imposes too much traffic to the nodes in the hole's edge, making them overloaded when compared to other network nodes more distant from holes. In this work, we propose a novel location-free geographic routing technique called PAtCH (Proactive Approach to Circumvent Holes in Wireless Sensor Network) to avoid routing holes in WSNs. Our solution can circumvent hole regions and create routing paths toward the destination. We consider that our sink has a higher communication range, and the Received Signal Strength Indicator (RSSI) is used to assist the construction of the routing paths. Our results show the efficiency achieved by our proposed solution in scenarios with hole regions, also maintaining all the benefits of a classic greedy forwarding technique.

Analyzing Coverage Probability of Reconfigurable Intelligence Surface-aided NOMA

Along with the explosive growth of wireless communication network users who require large frequency bands and low latency, it is a challenge to create a new wireless communication network beyond 5G. This is because installing a massive 5G network requires a large investment by network providers. For this reason, the authors propose an alternative beyond 5G that has better quality than 5G and a relatively lower investment value than 5G networks. This study aims to analyze the downlink of the cooperative non-orthogonal multiple access (NOMA) network, which is usually used in 5G, combined with the use of a reconfigurable intelligence surface (RIS) antenna with decode and forward relay mechanisms. RIS is processed with a limited number of objects utilizing Rayleigh fading channels. The scenario is created by a user who relays without a direct link for users near the base station and with a direct link for users far from the base station. Under the Nakagami-m fading channel, the authors carefully evaluated the probability of loss for various users as a function of perfect channel statistical information (p-CSI) utilizing simply a single input-output (SISO) system with a finite number of RIS elements. As a key success metric, the efficiency of the proposed RIS-assisted NOMA transmission mechanism is evaluated through numerical data on the outage probability for each user. The modeling outcomes demonstrate that the RIS-aided NOMA network outperforms the traditional NOMA network

Overbook in Advance, Trade in Future: Computing Resource Provisioning in Hybrid Device-Edge-Cloud Networks

The big data processing in distributed Internet of Things (IoT) systems calls for innovative computing architectures and resource provisioning techniques to support real-time and cost-effective computing services. This article introduces a novel overbooking-promoted forward trading mechanism named Overbook in Advance, Trade in Future (OATF), where computing resources can be traded across three parties, i.e. end-users, an edge server and a remote cloud server, under a hybrid device-edge-cloud network with uncertainties (e.g., "no shows"). More importantly, OATF encourages a feasible overbooking rate that allows the edge server to overbook resources to multiple end-users (e.g., exceed the resource supply), while purchasing backup resources from the cloud server, by determining rights and obligations associated with forward contracts in advance via analyzing historical statistics (e.g., network, resource dynamics). Such a mechanism can greatly improve time efficiency and resource utilization thanks to overbooking and pre-signed forward contracts. Critical issues such as overbooking rate design and risk management are carefully investigated in this article, while an interesting case study is proposed with mathematical analysis. Comprehensive simulations demonstrate that OATF achieves mutually beneficial utilities for different parties (cloud, edge, and end-users), as well as substantial resource usage and commendable time efficiency, in comparison with conventional trading methods.

Routing in future space‐terrestrial integrated networks with SATNET‐OSPF

SummaryConnectivity in satellite networks is governed by the spacecraft nodes' orbital dynamics together with the planet's continuous rotation where ground nodes are located. The resulting time‐dynamic but predictable topology demands the design of specific distributed routing schemes. However, terrestrial Internet routing schemes' maturity, proven scalability, and efficiency shall be leveraged whenever possible to facilitate space‐terrestrial integration while reducing risk and costs. In line with this reflection, we introduce SATNET‐OSPF: a backward‐compatible satellite extension for the widely used Open Shortest Path First routing protocol. The key features of SATNET‐OSPF are (a) accurate routing interface mapping to inter‐satellite links and ground‐to‐satellite links, (b) accelerated link‐up/link‐down event detection adapted to space‐specific wireless technologies, (c) proactive routing and forwarding mechanism to take advantage of predicted link‐down events, and (d) low memory footprint topology model to efficiently propagate the forthcoming space connectivity events via constrained telecommand links. Leveraging existing IPv6 and OSPFv3 open‐source stacks, we implemented SATNET‐OSPF in an actual space router comprising a space‐grade SPARC V8 CPU and a radiation‐hardened FPGA. Furthermore, we present the details of an emulation test bench supporting various configurations with COTS terrestrial OSPF routers that enabled a realistic performance evaluation of the SATNET‐OSPF. Results show that SATNET‐OSPF reduced OSPFv3 routing protocol overhead by up to 31%; shortened the link event detection delay by four orders of magnitude; decreased the routing outage by a factor of 22; and ensured flooding control message generation and forwarding times, as well as routing computing time, satisfy the original requirements (192, 37, and 17 ms, respectively).

DA-DMPF: Delay-aware differential multi-path forwarding of industrial time-triggered flows in deterministic network

The time-sensitive network (TSN) utilizes gating technology to achieve mixed traffic transmission across the common network, replacing dedicated networks with time division multiplexing, simplifying the interconnection architecture. TSN fundamentally changes the uncertainty of traditional Ethernet through clock synchronization, traffic scheduling, and network configuration. TSN can meet the requirements of industrial communication for low delay, low jitter, and high-reliable data transmission. It is also fully compatible with the existing Ethernet system and is an important communication platform supporting the development of the industrial Internet. Time-triggered flow is the key flow responsible for transmitting real-time data in time-sensitive networks. Its transmission should strictly follow the gating schedule. Selecting different transmission paths will affect the success rate of scheduling. To solve the delay optimization problem of industrial time-triggered (TT) flow path selection, this paper proposes a novel forwarding mechanism for industrial TT flow, namely delay-aware differential multi-path forwarding (DA-DMPF). This mechanism first designs a multipath forwarding index based on sending delay and processing delay by analyzing the end-to-end delay, then converts the multipath forwarding problem into a 0-1 integer linear planning problem, and finally solves the forwarding path set of the stream through differential multipath forwarding algorithm to achieve low delay parallel transmission of the stream. The simulation results show that compared with load-balanced multi-path forwarding and Hop-Load forwarding mechanisms, this scheme reduces the end-to-end average delay of industrial TT flows by 22.55% and 17.32% respectively, meeting the low delay transmission requirements of TT flows.

Secured Scheme for Privacy Preserving and Node Authentication Mechanism for a Special Mobile Ad hoc Network

Opportunistic networks are a special type of Mobile Ad hoc network which are wirelessly interlinked nodes with the absence of end to end connectivity. All nodes in an opportunistic network are free to move in an environment. Due to the high degree of mobility of nodes, opportunistic networks differ significantly from the existing traditional networks and it works on store, carry &forward mechanism in which, each node has a communication range. Within its proximity, if any node comes, it can send and receive messages. In an opportunistic network, there is no proper infrastructure available for communication and node have limited storage and computational capabilities. The major problem being faced in an opportunistic network is the identification of normal and malicious nodes because due to the open nature of the opportunistic network, malicious nodes also can join the network and perform some malicious activities like Sybil attack. We propose a remedy to address the authentication and privacy issue that can arise in an opportunistic network. According to the findings of the simulation, the proposed research work satisfies the authentication and privacy criteria of an opportunistic network.

Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks

Security is an important factor that cannot be neglected in the design of time synchronization algorithms since industrial wireless sensor networks are prone to attacks against physical nodes and communication links. The Sybil attack is an intelligent attack with a high destructive capacity in pretending multiple identities and broadcasting illegitimate messages to destroy the network operation. Existing secure time synchronization algorithms mostly focus on distributed protocols; however, they pay less attention to Sybil attacks and centralized network time synchronization. In this paper, we propose a novel reference broadcast-based secure time synchronization (RSTS) for industrial wireless sensor networks with a time source against Sybil attacks. Different from previous protocols, in converging the network structure and the clock status, RSTS employs a public neighbor forwarding mechanism based on reference broadcast to filter the illegal time information automatically. Instead of establishing a table with timestamps of packet transmission and receipt, the least square linear regression is utilized to estimate the compensation relative to the source node with the recorded time and calculated time difference in receiving packets. The simulation results demonstrate that RSTS is resilient to Sybil attacks as well as message manipulation attacks in comparison with existing algorithms.

PyTorch-FEA: Autograd-enabled finite element analysis methods with applications for biomechanical analysis of human aorta

Background and objectivesFinite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed. MethodsIn this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance. ResultsWe applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs. ConclusionsWe have presented PyTorch-FEA, a new library of FEA code and methods, representing a new approach to develop FEA methods to forward and inverse problems in solid mechanics. PyTorch-FEA eases the development of new inverse methods and enables a natural integration of FEA and DNNs, which will have numerous potential applications.

Multi-scale modeling temporal hierarchical attention for sequential recommendation

Multi-scale modeling of items interacting in a sequence of users' historical behaviors in a sequential recommendation task is crucial. In real scenarios, the user's choice of items depends not only on static preferences (long-term interests) but also on recent dynamic preferences (short-term interests). In recent years, micro-video sharing platforms have been very favorable, and correspondingly, more efficient recommendation method will be needed to support users in finding their interested micro-videos (items). Compared to traditional online videos (such as YouTube), micro-videos are created by grassroots users, shot by smartphones, are short (typically tens of seconds), and have fewer tags or descriptive text, which makes recommending it a challenging task. In this work, we explore how to model users' historical behavior sequences at multiple scales to predict their click-through rates on micro-videos and then determine whether to recommend them to users. We present a novel Multi-scale Modeling Temporal Hierarchical Attention (MMTHA) method for modeling users' behavior sequences inspired by recent deep network-based approaches. Specifically, firstly, we capture users' short-term dynamic interests using temporal windows; secondly, we utilize a category-level attention mechanism to describe the coarse-grained interests of users and an item-level attention mechanism to capture the fine-grained interests of users; thirdly, we employ a forward multi-headed self-attention mechanism to identify and integrate long-term correlations between previously segmented temporal windows. We conducted extensive experiments on two publicly available datasets to verify their effectiveness. The experimental results show that our proposed MMTHA model achieves state-of-the-art performance in all tests.

On the use of Fourier Features-Physics Informed Neural Networks (FF-PINN) for forward and inverse fluid mechanics problems

Physics Informed Neural Networks (PINN), a deep learning tool, has recently become an effective method for solving inverse Partial Differential Equations (PDEs) where the boundary/initial conditions are not well defined and only noisy sparse measurements sampled in the domain exist. PINN, and other Neural Networks, tends to converge to the low frequency solution in a field that has multiple frequency scales, this is known as spectral bias. For PINN this happens when solving PDEs that exhibit periodic behavior spatially and temporally with multi frequency scales. Previous studies suggested that Fourier Features-Neural Networks (FF-NN) can be used to overcome the spectral bias problem. They proposed the Multi Scale-Spatio Temporal-Fourier Features-Physics Informed Neural Networks (MS-ST-FF-PINN) to overcome the spectral bias problem in PDEs solved by PINN. This has been evaluated on basic PDEs such as Poisson, wave and Gray-Scott equations. In this paper we take MS-ST-FF-PINN a step further by applying it to the incompressible Navier-Stokes equations. Furthermore, a comparative analysis between the PINN and the MS-ST-FF-PINN architectures solution accuracy, the learnt frequency components and the rate of convergence to the correct solution is included. To show this three test cases are shown (a)-Forward time independent double-lid-driven cavity, (b)-Inverse time independent free surface estimation of Kelvin wave pattern, and (c)-Inverse 2D time-dependent turbulent Von Karman vortex shedding interaction downstream of multiple cylinders. The results show that MS-ST-FF-PINN is better at learning low and high frequency components synchronously at early training iterations compared to the PINN architecture that does not learn the high frequency components even after multiple iteration numbers such as the Kelvin wave pattern and the Karman vortex shedding cases. However, for the third test case, the MS-ST-FF-PINN architecture showed a discontinuity for the temporal prediction of the pressure field due to over-fitting.

Abstract 2821: Mitigation of radiation-induced colon injuries in mice by oral sepiapterin (PTC923)

Abstract The rectum is the primary dose limiting structure for radiation therapy (RT) in treating prostate cancer. Two clinical forms of rectal injury have been described: early and late. Early injury typically develops during RT and occurs to some degree in most patients. The condition is usually self-limiting, but can predispose patients to the development of late injury. Late rectal injury occurs in up to 25% of patients months to years after pelvic RT and is more concerning, since it can be irreversible and can have a major impact on a patient’s lifestyle. Development of new pharmacologic approaches to eliminate the risk of RT-dependent rectal injury remains an important goal. Although detailed mechanisms underlying normal tissue toxicities vary from tissue to tissue, the central underlying mechanism is abnormal wound healing involving endothelial dysfunction (ED) and subsequently fibrosis. Regardless of its cause, ED is characterized by an “uncoupled” endothelial NOS (eNOS) activity that generates superoxide (O2 -) and peroxynitrite (ONOO-) rather than NO. One mechanism for uncoupling in inflammatory conditions is oxidation of the reduced cofactor tetrahydrobiopterin (BH4). The product of uncoupling, ONOO-, oxidizes BH4, initiating a feed forward mechanism to sustain uncoupling. Oral treatment with sepiapterin (PTC923), a tetrahydrobiopterin precursor, decreased infiltrating inflammatory cells and cytokine levels in mice with colitis. We therefore tested whether a synthetic PTC923 might mitigate radiation-induced colon injuries. C57L/J wild-type 6-8-week-old males mice received 13.5 Gy total-body irradiation (TBI). Starting from 24 h post-irradiation, mice were treated once daily with 1 mg/kg PTC923 for six days by oral gavage. Colon injuries were accessed on 6th day after IR by colonoscopy and scored by using distal-proximal endoscopic colitis scoring system (D-PECS). The system has a core inflammatory component of four non-co-linear parameters and decimal units to notable lesions or complications. Relative expression of cytokines IL-1β, IL-6, IL-17α, and TGF-β1 in colon endothelial cells were measured by RT-PCR 8 days after IR. According with D-PECS estimation, post-irradiation colon injury was significantly reduced by PTC923 treatment: on the 6th day after TBI mice demonstrated D-PECS score 5.8±1.48 vs 1.6±0.55 D-PECS score for animals received PTC923 treatment after TBI (p-value=0.00). Increase of cytokines IL-6 and TGF-β1 in the colon endothelium after TBI was effectively blocked by PTC923 treatment (IL-6: IR+Vehicle = 4.31±1.31 fold increase vs IR+PTC923 = 1.29±0.37 fold increase (p-value=0.0016); TGF-β1: IR+Vehicle = 5.18±0.66 fold increase vs IR+PTC923 = 2.01±0.75 fold increase (p-value=0.00029)). In conclusion, these findings support the proposal that oral treatment with sepiapterin is a potential mitigator of colon injury caused by RT. Citation Format: Vasily A. Yakovlev, Christopher S. Rabender, Ross B. Mikkelsen. Mitigation of radiation-induced colon injuries in mice by oral sepiapterin (PTC923) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2821.

경사진 전방 접힘 날개의 전개 모델링 및 전개 성능 예측

This paper deals with the development of prediction method of deployment performance of a inclined forward folding wing. It uses the inclined forward folding wing mechanism, whose motion is three-dimensional. Within the analysis axis, this motion is well described and the governing equation can be well established. External moments, such as the deploying moment due to torsional spring, friction moment, aerodynamic moment, and gravity moments are well modelled and the governing equations is solved numerically. Thus the deployment performance of a inclined forward folding wing can be predicted successfully.

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory.

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

Perception of self-generated and externally-generated visual stimuli: Evidence from EEG and behavior.

Efference copy-based forward model mechanisms may help us to distinguish between self-generated and externally-generated sensory consequences. Previous studies have shown that self-initiation modulates neural and perceptual responses to identical stimulation. For example, event-related potentials (ERPs) elicited by tones that follow a button press are reduced in amplitude relative to ERPs elicited by passively attended tones. However, previous EEG studies investigating visual stimuli in this context are rare, provide inconclusive results, and lack adequate control conditions with passive movements. Furthermore, although self-initiation is known to modulate behavioral responses, it is not known whether differences in the amplitude of ERPs also reflect differences in perception of sensory outcomes. In this study, we presented to participants visual stimuli consisting of gray discs following either active button presses, or passive button presses, in which an electromagnet moved the participant's finger. Two discs presented visually 500-1250 ms apart followed each button press, and participants judged which of the two was more intense. Early components of the primary visual response (N1 and P2) over the occipital electrodes were suppressed in the active condition. Interestingly, suppression in the intensity judgment task was only correlated with suppression of the visual P2 component. These data support the notion of efference copy-based forward model predictions in the visual sensory modality, but especially later processes (P2) seem to be perceptually relevant. Taken together, the results challenge the assumption that N1 differences reflect perceptual suppression and emphasize the relevance of the P2 ERP component.

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

Due to the limited and difficult access to sensor energy, energy conservation has always been an important issue in wireless body area network (WBAN). How to make full use of the limited energy of heterogeneous sensors in WBAN to achieve lightweight and high-reliable data transmission has also become key to the sustainable development of telemedicine services. This paper proposes a two-tier cooperation based high-reliable and lightweight forwarding (TTCF) mechanism via minimizing the amount of transmitted data and optimizing forwarding performance, so as to improve the efficiency and reliability of WBAN and reduce system energy consumption. In TTCF, an adaptive semi-tensor product compressed sensing evolution (STPCSE) model is first constructed to minimize the amount of data to be transmitted and extend the lifetime of sensors. Then, the important factors closely related to the energy consumption of human body sensors, including sampling frequency, residual energy and their importance in the network, are analyzed and redefined, and a high-reliable and lightweight forwarding model based on a multi-factor dynamic fusion is built. Finally, the performance and energy-saving effect of TTCF in a dynamic WBAN environment are compared and analyzed. Simulation results show that the system with our TTCF always performs the best in terms of data reconstruct accuracy, cumulative delivery rata, energy consumption and throughput. For example, its cumulative delivery rate is about 12% and 20.8% higher than that of UC-MPRP and CRPBA, and its residual energy and throughput are 1.22 times and 1.41 times, 1.35 times and 1.6 times of the latter two, respectively.

An Energy-Efficient Protocol based on Recursive Geographic Forwarding Mechanisms for Improving Routing Performance in WSN

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols.

Joint Routing and Scheduling With Cyclic Queuing and Forwarding for Time-Sensitive Networks

Various Industrial Internet of Things (IIoT) devices and their compelling applications have witnessed exponential growth over the past few years. Compared to the consumer internet, the industrial IoT often faces tough communication latency boundaries and low packet loss. Recently, time-sensitive networks (TSN) technique has been considered the most promising solution to tackle these issues, providing deterministic communication guarantees for time-sensitive applications. In TSN, to achieve bounded low latency, it introduces the Cyclic Queuing and Forwarding (CQF) mechanism to shape and regulate the transmission selection to provide the protected transmission window for time-triggered (TT) flows. Currently, a considerable amount of literature has explored traffic scheduling with the CQF mechanism in TSN. However, these methods ignore the impact of routing policy on scheduling optimization. In this paper, we design two algorithms to tackle the problem of joint routing and scheduling optimization with the CQF mechanism for TSN. We propose a TT flow sorting algorithm (JDPS) to measure the scheduling priority weight of each TT flow, which uses the normalization method to calculate the weight based on the flow's deadline, period, and size. Then, we propose a joint routing and scheduling optimization with load balance algorithm (JRS-LB) to improve the scheduling success radio and the network resource utilization, which jointly considers the injection time slot offset, scheduling, and routing of TT traffic. The evaluation results mainly focus on the scheduling success ratio, execution time, and resource utilization for the proposed algorithm compared to the Tabu and Naive algorithms. Extensive simulations indicate that the proposed JDPS and JRS-LB algorithms can improve the scheduling success radio compared with the other algorithms.

Integrated Policies to Reduce Australia’s Electricity Sector Greenhouse Gas Emissions to Net Zero by 2050

Recent events within the Australian National Electricity Market have demonstrated that the system of an energy-only market (a market that only compensates power that has been produced) is no longer fit for purpose. The rate of change in installed capacity and generation requires better planning to ensure reliability is maintained at the lowest total system cost during the transition to net zero. Australian National Electricity Market participants will need sufficient incentives and confidence to invest in new capacity. This paper assesses a “no constraints” scenario and recommends a range of policy and market mechanisms that could be utilized to achieve a net zero National Electricity Market in Australia by 2050. This paper adopts the perspective of total system cost, which allows multiple factors relating to decision-making to be incorporated. In the absence of a carbon price, this paper seeks to put forward technology-based policy and market mechanisms to incentivise the changes required. The “Modelling Energy and Grid Services” model used in this study has shown that this “no constraints” future grid will need to contain approximately 100 GW of variable renewable energy, almost 20 GW of firm, low-emissions generation, such as carbon capture, utilisation and storage, bioenergy with carbon capture and storage, hydroelectric power, or nuclear power. It will also require more than 10 GW of storage, including pumped hydro energy storage and other energy storage technologies, and over 30 GW of firm, dispatchable peaking plants, including thermal power generation.