Network Performance Degradation Research Articles

Incremental RBF-based cross-tier interference mitigation for resource-constrained dense IoT networks in 5G communication system

The deployment of resource-constrained and densely distributed Internet of Things (IoT) devices poses significant challenges for 5G communication systems due to the increased likelihood of inter-tier interference. This interference can degrade network performance and hinder the transmission of data in a reliable and efficient manner. Using an incremental Radial Basis Function (RBF) technique, this paper proposes a novel approach for cross-tier interference mitigation in 5G communication among resource-constrained dense IoT networks. Utilizing the incremental RBF method to model and optimize interference patterns in resource-constrained dense IoT networks is the primary innovation of our approach. In contrast to conventional interference mitigation techniques, which view interference as a static phenomenon, our method adapts to the dynamic nature of IoT networks by incrementally updating the RBF model. This enables precise modeling of the various interference scenarios and real-time modification of interference mitigation parameters. Utilizing the spatial distribution of IoT devices, this approach improves interference mitigation. The proposed method intelligently allocates resources and optimizes interference mitigation parameters based on the location and density of IoT devices. This adaptive resource allocation improves network capacity, reliability, and overall system performance by maximizing the utilization of available resources while minimizing interference. We demonstrate the effectiveness of the incremental RBF-based approach in mitigating cross-tier interference in resource-constrained dense IoT networks within the 5G ecosystem through extensive experiments and simulations. Our findings indicate substantial improvements in communication performance, including increased throughput, decreased packet loss, and decreased latency.

IoT-based MANET performance improvement against jamming attackers in different mobile applications

Internet of Things (IoT) is a cutting-edge technology and effective solution that enables real-time communication between physical objects. In order to manage the mobility of nodes in the IoT, mobile ad hoc networks (MANETs) are crucial. It enables IoT-based systems to construct and connect networks autonomously. IoT based MANET is more vulnerable to Jamming attacks (Jammers) due to the decentralized nature of the ad hoc network. Therefore, secure communication is a crucial requirement for nodes. In this paper, jammers interfere with the IoT MANET communication resulting in order to degrade network performance in terms of delay, throughput and PDR (Packet Delivery Rate) which are the important QoS (Quality of Service) parameters to measure the network performance. The IoT ad hoc system's networking is based on MANET routing protocols so that (GRP, AODV and OLSR) routing protocols had been examined in this paper to improve the QoS (Packet Delivery rate, throughput and delay) of the IoT-MANET system using Riverbed Modeler (17.5) simulation program for different mobile applications. The outcomes showed that the selection of a suitable routing protocol would improve the degradation in delay, throughput and PDR of the ad hoc network.

Rapid Finite-Fault Models for the 2023 Mw 7.8 Kahramanmaraş, Türkiye, Earthquake Sequence

Abstract In the immediate aftermath of devastating earthquakes such as in the 6 February 2023 Kahramanmaraş sequence in southcentral Türkiye, key stakeholders and the public demand timely and accurate earthquake information. Especially for large events, finite-fault models provide important insights into the rupture process and enable interpretation of the observed ground shaking, which can improve situational awareness and facilitate rapid assessment of future hazards. Using strong-motion waveforms recorded during the Kahramanmaraş sequence, we simulate a real-time playback and calculate how a finite-source model computed with the Finite-fault rupture Detector (FinDer) algorithm would evolve for the Mw 7.8 Pazarcık, Mw 7.6 Elbistan, and Mw 6.4 Yayladağı earthquakes. Using template matching FinDer compares observed and predicted ground-motion acceleration amplitudes to determine the orientation and spatial extent of fault rupture. We test both generic crustal and fault-specific templates from ground-motion models and rupture geometries of the east Anatolian and Çardak–Sürgü faults. In the second step, we estimate the seismic slip along the source models from the backprojection of the seismic displacement amplitudes. The algorithms achieve excellent performance for all three earthquakes, and the final source models and slip profiles available within tens of seconds of the rupture nucleation match well with models computed days to weeks after the events occurred. The temporal evolution of the source models for the Pazarcık and Elbistan earthquakes suggests that FinDer can provide insight into the rupture kinematics of large earthquakes. Cascading instrument failures as well as power and data telemetry interruptions during the Pazarcık earthquake led to an early termination of signals at a significant number of near-source stations. We show that FinDer is robust enough to cope with this type of degradation in network performance that can occur in large earthquakes, in general.

Li-MSD: A lightweight mitigation solution for DAO insider attack in RPL-based IoT

Many IoT applications run on a wireless infrastructure supported by resource-constrained nodes which is popularly known as Low-Power and Lossy Networks (LLNs). Currently, LLNs play a vital role in digital transformation of industries. The resource limitations of LLNs restrict the usage of traditional routing protocols and therefore require an energy-efficient routing solution. IETF’s Routing Protocol for Low-power Lossy Networks (RPL, pronounced “ripple”) is one of the most popular energy-efficient protocols for LLNs, specified in RFC 6550. In RPL, Destination Advertisement Object (DAO) control message is transmitted by a child node to pass on its reachability information to its immediate parent or root node. An attacker may exploit the insecure DAO sending mechanism of RPL to perform “DAO insider attack” by transmitting DAO multiple times. This paper shows that an aggressive DAO insider attacker can drastically degrade network performance. We propose a Lightweight Mitigation Solution for DAO insider attack, which is termed as “Li-MSD”. Li-MSD uses a blacklisting strategy to mitigate the attack and restore RPL performance, significantly. By using simulations, it is shown that Li-MSD outperforms the existing solution in the literature.

Video anomaly detection guided by clustering learning

With the fuzzy boundary between normal and abnormal video data, which cannot be well distinguished by most methods, anomaly detection in video requires better characterization of the data. First we give a convolution-enhanced self-attentive video auto-encoder based on the U-Net architecture, which can extract richer image features. Secondly we design a dual-scale feature clustering structure for this encoder, which simultaneously compresses the channel and spatial structure features of the image to represent the features to obtain good coding characteristics and expand the boundary between normal and abnormal data. We also verify that our approach is equivalent to a class of auto-encoders for memory-guided learning. Finally, in the reconstruction task, since video auto-encoders are capable of triggering temporal time leakage phenomena that can lead to network performance degradation, we propose an anomaly score computation paradigm for video auto-encoders that utilizes the average frame anomaly score of a video clip to compute the first frame anomaly score in that video clip.Extensive experiments on three benchmark datasets show that our method outperforms most existing methods on large datasets with complex patterns. The code will be published at the following link: Anomaly-detection-guided-by-clustering-learning

Micro-Burst Aware ECN in Multi-Queue Data Centers: Algorithm and Implementation

With the development of multi-queue data centers, various efforts have leveraged Explicit Congestion Notification (ECN) to achieve high throughput and low latency for data communication. However, one of the deep-seated problems is that the micro-burst traffic in networks could cause the instantaneous queue length to exceed the ECN threshold, leading to significant mismarkings of ECN. Such mismarkings could further lead to severe network performance degradation. In this paper, we propose Micro-Burst aware ECN (MBECN+) to mitigate this issue. MBECN+ is essentially a novel scheme that decouples ECN threshold setting and ECN marking. First, it finds a more appropriate ECN threshold on a per-queue basis to eliminate the spurious congestion signals caused by micro burst traffic. Second, MBECN+ consists of a double-threshold ECN marking scheme that it marks the packets with ECN in a finer grain. Considering the queue backlog caused by micro-burst traffic, it marks the packets when they are dequeuing, instead of enqueuing. Furthermore, we analyze the feasibility of implementing MBECN+ on commodity layer-3 multi-queue switches. Through testbed experiments and large scale simulations, we demonstrate that MBECN+ can improve the throughput by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20% and reduce FCT (flow completion time) by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 40%. The throughput under MBECN+ improves by 1.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 2.4\times$</tex-math></inline-formula> than DCTCP and 1.26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 1.35\times$</tex-math></inline-formula> than ECN*.

On the dynamic vulnerability of an urban rail transit system and the impact of human mobility

Urban rail transit (URT) plays a pivotal role in facilitating human mobility within urban environments. It is significant to understand its vulnerability, i.e., the variation in capacity and demand when confronted with unexpected events, particularly operational disruptions. Although the network topology is generally fixed, the hourly-changing travel demand greatly impacts the actual vulnerability of a URT system. Unfortunately, few existing studies consider the combined influence of dynamic travel demand and network topology. To fill the gap, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. This method includes a defined reasonable path, an accessibility-based identification of station importance with time-varying passenger demand, and a new dynamic vulnerability delay index considering affected travel demand. An empirical analysis was carried out by taking the URT system of Beijing, China as an example, and the impact of the more realistic multiple consecutive station failures in a URT system is also examined. Results show that the distribution of high-importance stations indeed varies with the time of day, affected by both static topology and hourly-changing passenger flow. When the disturbance of operation delay occurs, the impact of high-importance stations on the network vulnerability changes nonlinearly with the increase of delayed travel demand. Some stations that serve as bridges and are visited by large passenger flows have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main cause of network performance degradation, and some high-importance stations within the lines act as catalysts to accelerate the performance degradation. The proposed method not only offers a valuable reference for quantifying network vulnerability arising from fluctuations in passenger mobility but also introduces a novel vulnerability evaluation index to the URT system.

Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks

This paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the users. The dynamic allocation of the connections may cause spectrum fragmentation and lead to network performance degradation as connection blocking increases. Predicting potential blocking situations can be helpful during EON operations. For example, this prediction could be used in real networks to trigger proper spectrum defragmentation mechanisms at suitable moments, thereby enhancing network performance. Extensive simulations over the well-known NSFNET (National Science Foundation Network) backbone network topology were run by generating realistic traffic patterns. The obtained results are later used to train the developed machine learning models, which allow the prediction of connection-blocking events. Resource use was continuously monitored and recorded during the process. Two different Convolutional Neural Network models, a 1D CNN (One-Dimensional Convolutional Neural Network) and 2D CNN (Two-Dimensional Convolutional Neural Network), are proposed as the predicting methods, and their behavior is compared to other conventional models based on an SVM (Support Vector Machine) and KNN (K Nearest Neighbors). The results obtained show that the proposed 2D CNN predicts blocking with the best accuracy (92.17%), followed by the SVM, the proposed 1D CNN, and KNN. Results suggest that 2D CNN can be helpful in blocking prediction and might contribute to increasing the efficiency of future EON networks.

Evaluation of rainfall rates and rain-induced signal attenuation for satellite communication in the South-South Region of Nigeria

Rain fade causes signal attenuation which can relatively results in signal quality performance degradation, increased pathloss and coverage area reduction in satellite communication networks, most especially in the tropics and at higher operational frequencies above 10GHz. Analysis of rain attenuation provides useful insight for satellite communication engineers for efficient network planning and design within the region of study. Hence this paper presents threeyear analysis of signal-induced attenuation in the South-South States of Nigeria for the cities of Port-Harcourt, Uyo, Calabar and Eket, whose terrain characteristics could be generalized to represent urban, sub-urban and rural terrains in Nigeria. The International Telecommunications Union Radio-wave propagation (ITU-R P618-13) model was used to estimate the long-term rain attenuation at 10GHz, 20GHz and 30GHz for satellite communication applications. Results obtained at 10GHz, 20GHz and 30GHz operational frequencies show that at high frequency, rain fade can cause signal attenuation levels in the south-south region of Nigeria. In all, Calabar recorded the highest rain rate and corresponding highest attenuation level of 96.13dB at 30GHz compared to 50.29dB at 20GHz for the period under review.

Stability, Bifurcation and Dynamics in a Network with Delays

In real-world networks, due to complex topological structures and uncertainties such as time delays, uncontrolled systems may generate instability and complexity, thereby degrading network performance. This paper provides a detailed analysis of the stability, Hopf bifurcation, and complex dynamics of a networked system under delayed feedback control. Based on the linear stability method and Hopf bifurcation theorem, the stability of the equilibrium of the error system and the existence of Hopf bifurcation are studied. The stability of periodic solutions bifurcating from the trivial equilibrium is analyzed using normal form theory and central manifold theorem. Special focus is on the effects of the network topology and time delays on the stability and Hopf bifurcation. The theoretical results are also extended to the complex networks with asymmetric adjacent matrices. In addition, the controlled model exhibits complicated dynamical behavior via three types of codimension two bifurcations and period-doubling bifurcations that eventually lead to chaos. Numerical experiments have validated the theoretical results and indicated that delayed feedback control can effectively generate or annihilate the complicated behavior of complex networks.

CRAS: Curriculum Regularization and Adaptive Semi-Supervised Learning with Noisy Labels

This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning and eventually memorize data with noisy labels. This property allows for the separation of clean and noisy samples from a loss distribution. In recent years, semi-supervised learning, which divides training data into a set of labeled clean samples and a set of unlabeled noisy samples, has achieved impressive results. However, this strategy has two significant problems: (1) the accuracy of dividing the data into clean and noisy samples depends strongly on the network’s performance, and (2) if the divided data are biased towards the unlabeled samples, there are few labeled samples, causing the network to overfit to the labels and leading to a poor generalization performance. To solve these problems, we propose the curriculum regularization and adaptive semi-supervised learning (CRAS) method. Its key ideas are (1) to train the network with robust regularization techniques as a warm-up before dividing the data, and (2) to control the strength of the regularization using loss weights that adaptively respond to data bias, which varies with each split at each training epoch. We evaluated the performance of CRAS on benchmark image classification datasets, CIFAR-10 and CIFAR-100, and real-world datasets, mini-WebVision and Clothing1M. The findings demonstrate that CRAS excels in handling noisy labels, resulting in a superior generalization and robustness to a range of noise rates, compared with the existing method.

An artificial immune system‐based algorithm for selfish node detection in Mobile Ad Hoc Networks (MANETs)

AbstractThe Mobile Ad Hoc Network (MANET) depending on dynamic distributed topology presents several challenges such as decentralized infrastructure. In MANET, each node act as a host as well as a router to exchange packet. From Source to destination, the MANET nodes offer the ability of transmission to send, receive, and route traffic. Moreover, the presence of selfish nodes or malicious nodes in MANETs significantly degrades network performance. Identifying and isolating such nodes poses a formidable challenge. Consequently, this research introduces a security model founded on the principles of an Artificial Immune System (AIS) to detect and defend against Selfish Node (SN) attacks. This research work employs the principles of AIS to categorize a node's behavioral state based on the Mature Context Immune Antigen Rate (MCIAR). Subsequently, a hybrid protocol named Reliable History‐dependent Resource Conscious Clustered and Defensive AODV (RRCC‐DAODV) is introduced to detect Selfish Nodes (SNs) and counteract their attacks. Within the RRCC algorithm, the identification of selfish nodes relies on both Trust Value (TV) and Residual Energy (RE). Additionally, the proposed DAODV protocol incorporates the V‐detector algorithm to enhance defense mechanisms against potential attacks. The simulation of the proposed model is carried out in NS3. The proposed mechanism achieves a detection ratio of 94.05%. The simulation outcomes demonstrate the excellence of the implemented system. This superiority is observed when compared to other standard protocols, as indicated by the parameters of throughput, residual energy, packet delivery ratio, and delay.

An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM’s network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model’s efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM’s superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.

Virtual Private Network—Firewall Integration for Wireless Local Area Network Improvement against Jammers

Wireless networks are susceptible to many security problems since the wireless medium is open. Jammer (jamming attack) is the most important problems in wireless networks. It is denial-of-service attack in which attackers send malicious signals or messages on a channel that is intended for normal traffic by intentionally interfering with the network. One of the solutions is the development of tunneling technology as an attempt to secure communication networks. Virtual Private Network (VPN) tunneling technology when integrated with firewall would provide a suitable security. In this study, number of Jammers would interfere with the normal operation of the network resulting in performance degradation of network. These Jammers decrease throughput and increase delay and data dropped. The aim of this study is to improve the performance of Wireless Local Area Network (WLAN) by integrating Firewall with VPN in number of different Riverbed Modeler simulation scenarios which it is utilized in this study for video and data applications. Firewall is a technology employed to regulate the level of network connectivity. It can block unauthorized access from an external network to an internal network’s resources. In this study, the tunneling technology of VPN was integrated with firewall to improve the performance by increasing the throughput and decreasing delay and data dropped. The firewall would block any access to the server from the workstations and the VPN would filter the packets to allow a specific access to the specific server. The results showed that VPN when integrated with firewall increased the throughput from 1,350,000 to 1,500,000 bits/sec and decreased the delay from 0.0054 sec to 0.0047 sec and data dropped from 7,800 to 5,000 bits/sec so that this study proved that VPN incorporated with firewall provides a good throughput and delay improvement for wireless local area network.

Predicting Device Anomalous Condition in a Collaborated Industrial Environment

The industrial environment augments resource-constrained devices to bring services closer to autonomous devices. However, over time, these devices get overburdened due to computational workload, which results in degraded network performance. Therefore, the devices are programmed to share resources with nearby devices. However, due to real-time collaboration, there is the possibility that the device moves to an undefined state and starts behaving maliciously. This can impact the entire collaborative environment laid to meet the industrial products deadline. In this work, we proposed an industrial simulation framework that enables the resource-sharing environment and identifies the undefined device behavior. Further, our detection scheme is based on an intelligent model trained on device behavior through the machine-in-a-loop mechanism and deployed at network intersections, i.e., edge nodes. The proposed technique improved the efficiency of the collaborative network by 30%.

Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization.

In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired "self-repairing" mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural "evolution." We demonstrate that the proposed automatic architecture optimization leads to significant performance gains over existing manually designed RSNNs: we achieve 96.44% on TI46-Alpha, 94.66% on N-TIDIGITS, 90.28% on DVS-Gesture, and 98.72% on N-MNIST. To the best of the authors' knowledge, this is the first work to perform systematic architecture optimization on RSNNs.

Impact of late gadolinium enhancement image acquisition resolution on neural network based automatic scar segmentation

BackgroundAutomatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data. MethodsThirty-six high-resolution (0.7×0.7×2.0 mm3) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δxtrain = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δxtest = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δxtrain = 0.7 to 1.7 mm) was tested. ResultsThe prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and − 0.5 - 0.0 p.p. (2.00 – 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 – 1.69) for all investigated test resolutions. ConclusionA mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.

Tolerance Analysis of Cyber-Manufacturing Systems to Cascading Failures

In practical cyber-manufacturing systems (CMS), the node component is the forwarder of information and the provider of services. This dual role makes the whole system have the typical physical-services interaction characteristic, making CMS more vulnerable to cascading failures than general manufacturing systems. In this work, in order to reasonably characterize the cascading process of CMS, we first develop an interdependent network model for CMS from a physical-service networking perspective. On this basis, a realistic cascading failure model for CMS is designed with full consideration of the routing-oriented load distribution characteristics of the physical network and selective load distribution characteristics of the service network. Through extensive experiments, the soundness of the proposed model has been verified and some meaningful findings have been obtained: (1) attacks on the physical network are more likely to trigger cascading failures and may cause more damage; (2) interdependency failures are the main cause of performance degradation in the service network during cascading failures; and (3) isolation failures are the main cause of performance degradation in the physical network during cascading failures. The obtained results can certainly help users to design a more reliable CMS against cascading failures.

Gravity-based network traffic abstraction and laser ON/OFF control in optical satellite networks

As the cost of launching low Earth orbit satellites continuously decreases, satellite-based communications networks are emerging as a new area for both academia and industry. Lasers are already employed for building inter-satellite links, forming optical satellite networks. The orbiting nature of satellites determines that the optical satellite networks are usually uniformly distributed around the Earth, to provide seamless coverage to any place at all times. However, the end users on the Earth are non-uniformly distributed. As a result, many satellites with laser links might not be utilized efficiently. From the energy perspective, this work studies the energy-efficiency issues of the inter-satellite laser links. We first model the optical satellite networks by presenting the satellite constellation with inter-satellite laser design principles and introduce the laser ON/OFF control problem accordingly. To explore the possibility of saving energy in the massive satellite deployment, we further introduce the gravity-based network traffic model and propose a gravity-based network traffic abstraction (GNTA) model to evaluate the importance of each laser link. Accordingly, we further propose a GNTA-based ON/OFF control (GOOC) algorithm to improve the energy efficiency of inter-satellite laser links by switching OFF parts of the laser links that are less utilized. We evaluate the GOOC’s performance using simulation, and results show that switching OFF 20% of the laser terminals in the full-gridmesh topology can improve the energy efficiency by about 10%, with an acceptable cost of network performance degradation.

ELIXIR: An Expedient Connection Paradigm for Self-Powered IoT Devices

IoT devices usually work under power-constrained scenarios like outdoor environmental monitoring. Considering the cost and sustainability, in the long run, energy-harvesting technology is preferable for powering IoT devices. Since harvesting power is intrinsically weak and transient, the connection between IoT devices not only cannot be maintained constantly but is also extremely difficult to be established. In order to communicate, those devices should have a synchronized timeline so that both transmitter and receiver can start connection simultaneously. Yet due to the transient nature of ambient energy, volatile time data can be easily tampered with by the unstable power supply or corrupted completely by frequent power outages. As a result, unsynchronized IoT devices require significant efforts in time and energy to reconnect and communicate, which further escalates the performance degradation of the edge network. To adapt to ubiquitous self-powered scenarios on the IoT edge, we propose ELIXIR, an expedient connection paradigm, to synchronize swiftly and autonomously in a joint effort for self-powered IoT devices. The lightweight and highly efficient natures enable ELIXIR to be integrated into low-power IoT devices easily and efficiently. The experimental results show that the proposed ELIXIR can avoid a connection loop and help speed up the connection 2.83X and 1.84X faster than baseline methods.