Worst-case Network Research Articles

BLPCA-ledger: A lightweight plenum consensus protocols for consortium blockchain based on the hyperledger indy

An examination of different distributed real-time applications operating on the blockchain platform is conducted. These applications can be broadly classified into three types: permissionless public, permissioned private, and consortium chains. In order for a decentralized network to function independently, consensus mechanisms are needed to facilitate the delivery of transactions and keep track of them in a ledger. But the fundamental idea behind Blockchain technology is the use of several consensus protocols, like Proof of Stake, Proof of Elapsed Time, etc., which requires greater processing power. In order to arrange transactions, it increases the demand for buying more computing units. Furthermore, present consortia blockchain consensus mechanisms lack a policy to collect socio-economic financial levies, including monies for charitable donations, education, and social activities. To collect socio-economic taxes, this study suggests a lightweight Plenum consensus algorithm called "BLPCA" for consortium blockchains built on Hyperledger Indy. The Byzantine Fault Tolerance concept combined with optimization is used in the suggested BLPCA systems to manage large-scale decentralized traffic of socioeconomic hierarchy. Because there are no transaction costs, BLPCA encourages government analysts to review funds using fewer resources. By including a multithreading heterogeneous technique, the BLPCA can effectively handle multi-transaction needs and execute this protocol in an industrial setting that operates in real-time. It is observed from the simulations that even in the worst-case network scenario—such as a fork—the suggested consortium blockchain does not crash a single transaction. In order to guarantee node scalability, a high propagation speed is determined. Furthermore, the BLPCA shows an excellent average time while constructing socioeconomic transaction blocks.

Enhancing Performance of Wide Area CIoT SDN by US-ML Based Optimum Controller Placement

It is a critical area of study for enhancing the effectiveness of wide-area Cellular Internet of Things (CIoT) networks. One solution is to merge Software Defined Networking (SDN) with Internet of Things (IoT) network to boost efficiency. The main challenge is determining the best location for the SDN controller and evaluating SDN clustering. This paper proposed an Un-Supervised Machine-Learning (US-ML) approach based on silhouette distance along with gap statistic for finding the optimum number of controllers for network under consideration. In addition, the Partition Around Medoids (PAM) approach is opted for allocation of controller locations. Apart from SDN, another approach is to create efficient Low-Power Wide Area Networks (LPWAN). As a result, this research contributed to the study of various LPWAN design approaches and offered a method of optimal controller location for IoT-SDN cellular networks in industries. Several outstanding research challenges are noted, and prospective research objectives for LPWAN are offered. For the case study of wide area networks (WAN), a graphical representation of the SDN controller positioning method is presented. It is determined that effective placement can improve SDN performance in worst-case network scenarios.

Multigateway Designation for Real-Time TSCH Networks Using Spectral Clustering and Centrality

This letter proposes a multi-gateway designation framework to design real-time wireless sensor networks (WSNs) improving traffic schedulability, i.e., meeting the traffic time constraints. To this end, we resort to Spectral Clustering unsupervised learning that allows defining arbitrary k disjoint clusters without knowledge of the nodes physical position. In each cluster we use a centrality metric from social sciences to designate one gateway. This novel combination is applied to a time-synchronized channel-hopping (TSCH) WSN under earliest-deadline-first (EDF) scheduling and shortest-path routing. Simulation results under varying configurations show that our framework is able to produce WSN designs that greatly reduce the worst-case network demand. In a situation with 5 gateways, 99% schedulability can be achieved with 3.5 times more real-time flows than in a random benchmark.

Worst-case traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles by factoring in the uncertain link capacity

Connected and autonomous vehicles (CAVs) can form platoons to reduce the time headway and improve the link capacity. However, in a mixed traffic flow environment where both human-driven vehicles (HDVs) and CAVs exist, the platoon intensity is significantly impacted by the stochastic order of the HDVs and CAVs (i.e., the fleet sequence). Therefore, the link capacity involves a large uncertainty even under the same HDV and CAV flow. This uncertain link capacity can cause a large variation in network flow. In the literature, traffic assignment models for mixed traffic flows of HDVs and CAVs are developed based on expected link capacity models, in which the computed link capacity is deterministic for given HDV and CAV flows. These models ignore the impacts of uncertain link capacity on the network performance and network flow distribution, which can dramatically reduce the effectiveness of the corresponding planning strategies. To address this problem, this study proposes a worst-case mixed traffic assignment model. It aims to compute the worst network performance and corresponding equilibrium flow that may occur due to uncertain link capacity. The worst-case mixed traffic assignment is formulated as a bilevel programming problem, where the low-level problem is a variational inequality problem presented to compute the equilibrium results based on a fixed link capacity while the upper-level problem is to find the optimal input for all link capacities within their ranges to minimize the network performance. The partition-based norm relaxed method of the feasible direction solution algorithm is proposed to solve the bilevel worst-case mixed traffic assignment problem. A numerical application shows that the uncertain link capacity has drastic effects on the network flows and network performance, and the proposed algorithm can effectively and efficiently solve the bilevel worst-case mixed traffic assignment problem to compute the worst-case network equilibrium flows and network performance. These results can help traffic managers design robust planning strategies to ensure a minimum level of network performance under the impacts of uncertain link capacity.

Robust Power Allocation in Optical Satellite MIMO Links With Pointing Jitter

In this letter, an optical satellite system is considered comprised of a geostationary satellite with multiple transmitters on-board and an optical ground station with multiple receiving telescopes. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {\mathrm {A}}$ </tex-math></inline-formula> robust power allocation strategy for the downlink is investigated incorporating the effects of the atmospheric impairments as well as the optical beam pointing jitter. Firstly, a theoretical analysis for the optical satellite system under pointing jitter is given and a maximin optimization problem is formulated maximizing the worst-case network capacity. The power allocation is carried out under peak and total power constraints using standard convex techniques. The lower bound of the ergodic network capacity for independent log-normal optical channels over variable jitter variance is derived. The proposed methodologies are evaluated via simulations using experimental channel measurements from the ARTEMIS-optical satellite campaign and their performances are compared to baseline power allocation strategies. The presented results show the effects of jitter on the network capacity and confirm the superiority of the proposed allocation scheme.

DORR: A DOR-Based Non-Blocking Optical Router for 3D Photonic Network-on-Chips

Recently, researchers paid more attention on designing optical routers, since they are essential building blocks of all photonic interconnection architectures. Thus, improving them could lead to a spontaneous improvement in the overall performance of the network. Optical routers suffer from the dilemma of increased insertion loss and crosstalk, which upraises the power consumed as the network scales. In this paper, we propose a new 7×7 non-blocking optical router based on the Dimension Order Routing (DOR) algorithm. Moreover, we develop a method that can ensure the least number of MicroRing Resonators (MRRs) in an optical router. Therefore, by reducing these optical devices, the optical router proposed can decrease the crosstalk and insertion loss of the network. This optical router is evaluated and compared to Ye's router and the optimized crossbar for 3D Mesh network that uses XYZ routing algorithm. Unlike many other proposed routers, this paper evaluates optical routers not only from router level prospective yet also consider the overall network level condition. The appraisals show that our optical router can reduce the worst-case network insertion loss by almost 8.7%, 46.39%, 39.3%, and 41.4% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. Moreover, it decreases the Optical Signal-to-Noise Ratio (OSNR) worst-case by almost 27.92%, 88%, 77%, and 69.6% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. It also reduces the power consumption by 3.22%, 23.99%, 19.12%, and 20.18% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively.

Radio Resource Management for V2V Multihop Communication Considering Adjacent Channel Interference

This paper investigates schemes for multihop scheduling and power control for vehicle-to-vehicle (V2V) multicast communication, taking into account the effects of both co-channel interference and adjacent channel interference, such that requirements on latency or age of information (AoI) are satisfied. Optimal performance can be achieved by formulating and solving mixed Boolean linear programming (MBLP) optimization problems for various performance metrics, including network throughput and connectivity. Fairness among network nodes (vehicles) is addressed by considering formulations that maximizes the worst-case network node performance. Solving the optimization problem comes at the cost of significant computational complexity for large networks and requires that (slow) channel state information is gathered at a central point. To address these issues, a clustering method is proposed to partition the optimization problem into a set of smaller problems, which reduces the overall computational complexity, and a decentralized algorithm that does not need channel state information is provided.

Preventive Start-Time Optimization to Determine Link Weights Against Probabilistic Link Failures

This article proposes a network design model to minimize the worst-case network congestion against multiple link failures, where open shortest path first link weights are determined at the beginning of network operation. In the proposed model, which is called the preventive start-time optimization model against multiple link failures (PSO-M), the number of multiple link failure patterns to support is restricted by introducing a probabilistic constraint called probabilistic guarantee . If the total probability of non-connected failure patterns does not exceed a specified probability, PSO-M provides a feasible solution of link weights. Otherwise, no feasible solution can be obtained. We introduce an extended model of PSO-M, called PSO-M with link reinforcement (PSO-MLR), where links are reinforced under a budget constraint. Link reinforcement in PSO-MLR has two purposes: maintaining network connectivity and reducing the worst-case congestion ratio. Numerical results show that PSO-M offers lower worst-case congestion ratios than the start-time optimization model, where link weights are obtained against the non-failure pattern assuming that multiple link failures are possible. The superiority of PSO-M strengthens as the average node degree of the network increases. Given a fixed budget, PSO-MLR allows the worst-case congestion ratio to be varied within a specific range. PSO-MLR can support a part of non-connected failure patterns to determine link weights, and so is a valuable enhancement of PSO-M.

Crosstalk Analysis and Performance Evaluation for Torus-Based Optical Networks-on-Chip Using WDM.

Insertion loss and crosstalk noise will influence network performance severely, especially in optical networks-on-chip (ONoCs) when wavelength division multiplexing (WDM) technology is employed. In this paper, an insertion loss and crosstalk analysis model for WDM-based torus ONoCs is proposed to evaluate the network performance. To demonstrate the feasibility of the proposed methods, numerical simulations of the WDM-based torus ONoCs with optimized crossbar and crux optical routers are presented, and the worst-case link and network scalability are also revealed. The numerical simulation results demonstrate that the scale of the WDM-based torus ONoCs with the crux optical router can reach 6 × 5 or 5 × 6 before the noise power exceeds the signal power, and the network scale is 5 × 4 in the worst case when the optimized crossbar router is employed. Additionally, the simulated results of OptiSystem reveal that WDM-based torus ONoCs have better signal transmission quality when using the crux optical router, which is consistent with previous numerical simulations. Furthermore, compared with the single-wavelength network, WDM-based ONoCs have a great performance improvement in end-to-end (ETE) delay and throughput according to the simulated results of OPNET. The proposed network analysis method provides a reliable theoretical basis and technical support for the design and performance optimization of ONoCs.

When Are Cyber Blackouts in Modern Service Networks Likely?

Service liability interconnections among globally networked IT- and IoT-driven service organizations create potential channels for cascading service disruptions worth billions of dollars, due to modern cyber-crimes such as DDoS, APT, and ransomware attacks. A natural question that arises in this context is: What is the likelihood of a cyber-blackout? , where the latter term is defined as the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this article, we investigate this question in general as a function of service chain networks and different cyber-loss distribution types. We show somewhat surprisingly (and discuss the potential practical implications) that, following a cyber-attack, the effect of (a) a network interconnection topology and (b) a wide range of loss distributions on the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations are mostly very small. The primary rationale behind these results are attributed to degrees of heterogeneity in the revenue base among organizations and the Increasing Failure Rate property of popular (i.i.d/non-i.i.d) loss distributions, i.e., log-concave cyber-loss distributions. The result will enable risk-averse cyber-risk managers to safely infer the impact of cyber-attacks in a worst-case network and distribution oblivious setting.

Low Power Wide Area Networks (LPWAN) at Sea: Performance Analysis of Offshore Data Transmission by Means of LoRaWAN Connectivity for Marine Monitoring Applications.

In this paper the authors discuss the realization of a Long Range Wide Area Network (LoRaWAN) network infrastructure to be employed for monitoring activities within the marine environment. In particular, transmission ranges as well as the assessment of parameters like Signal to Noise Ratio (SNR) and Received Signal Strength Indicator (RSSI) are analyzed in the specific context of an aquaculture industrial plant, setting up a transmission channel from an offshore monitoring structure provided with a LoRaWAN transmitter, to an ashore receiving device composed of two LoRaWAN Gateways. A theoretical analysis about the feasibility of the transmission is provided. The performances of the system are then measured with different network parameters (in particular the Spreading Factor—SF) as well as with two different heights for the transmitting antenna. Test results prove that efficient data transmission can be achieved at a distance of 8.33 km even using worst case network settings: this suggests the effectiveness of the system even in harsher environmental conditions, thus entailing a lower quality of the transmission channel, or for larger transmission ranges.

Worst-Case Probabilistic Network Outage Identification Under Physical Disturbances

This letter presents a mathematical optimization model to identify the worst-case probabilistic network outage scenario induced by physical disturbances. That is, we seek to find the outage scenario with the maximum combined likelihood and impact on the network. This is a challenging combinatorial problem, as the search domain exponentially grows with the size of the network and the impact of outages on the network is not usually explicitly quantifiable. In this letter, we develop an iterative algorithm to tackle these challenging issues by formulating it as a mixed-integer programming problem and tightening the search domain by bounding upper and lower bounds of the solution using the upper bound estimates of outage scenario probabilities. We also apply the proposed model to identify the worst-case outage scenarios in power networks, where the impacts of outage scenarios are calculated using the security-constrained optimal power flow problem. The numerical studies, conducted on two test power networks, demonstrate the efficiency and proven convergence of the proposed model in identifying the worst-case probabilistic outage scenario.

5G New Radio Fronthaul Network Design for eCPRI-IEEE 802.1CM and Extreme Latency Percentiles

Packet-switched fronthaul networks are often designed following the rule that the worst-case network delay must be below a given target end-to-end network latency budget. However, the theoretical maximum delay can be too pessimistic in particular scenarios, where the latency budget needs to be a very small or there is a need to stretch the distance between the radio heads and the baseband units. In this paper, we propose to use a very high packet delay percentiles as an alternative to the maximum theoretical delay in order to stretch the range of the fronthaul links at the expense of a higher frame loss ratio (FLR), within the limits established by eCPRI and the IEEE 802.1 CM. Several methods to estimate the percentiles for the $\mathbf {I}_{U}~/~\mathbf {II}_{D}$ eCPRI functional splits are analyzed. Namely, G/G/1 and N*D/D/1 queueing models are tested and compared with simulation as dimensioning tools. The results support that the N*D/D/1 queue is able to model the behavior of a packet-switch fronthaul aggregator using the eCPRI standard for 5g New Radio (NR) Fronthaul streams and can be used as a tool to dimension the length of the links. The experiments show that the fronthaul links’ lengths can be increased by 60% and 10% for 50- and 100-MHz NR channels, respectively, while keeping the latency budget and frame loss ratio within the IEEE 802.1 CM limits.

Adaptive topologies against jamming attacks in wireless networks: A game-theoretic approach

Towards securing wireless networks and ensuring their dependability under jamming attacks, this paper presents and analyzes game-theoretic formulations between an adversary and a defender. The adversary jams a subset of nodes to increase the level of interference in the network, while the defender makes judicious adjustments of the transmission power level of the nodes, thereby continuously adapting the underlying network topology to reduce the impact of the attack. The defender's strategy is based on playing Nash equilibria (NE) strategies securing a worst-case network utility. First, a discrete control set is considered in which the space of strategies of the defender grows exponentially with the network size. Scalable decomposition-based approaches are developed yielding a defense strategy whose performance closely approaches that of the non-decomposed game. Second, we develop a marginal strategy to assign the power levels while satisfying a coverage constraint thereby evading the combinatorial complexity associated with enumerating all pure actions. Third, we generalize the marginal-based strategy by considering a continuous action space for both players. For this setting, we prove the existence of a unique pure Nash equilibrium. The presented numerical results show the effectiveness of the proposed defense approach against various attack policies and demonstrate the assignment strategies on a real wireless network deployed in a three-story building.

Ultra-Dense HetNets Meet Big Data: Green Frameworks, Techniques, and Approaches

Ultra-dense heterogeneous networks (Ud-HetNets) have been put forward to improve the network capacity for next-generation wireless networks. However, counter to the 5G vision, ultra-dense deployment of networks would significantly increase energy consumption and thus decrease network energy efficiency suffering from the conventional worst-case network design philosophy. This problem becomes particularly severe when Ud-HetNets meet big data because of the traditional reactive request-transmit service mode. In view of these, this article first develops a big-data-aware artificial intelligent based framework for energy-efficient operations of Ud-HetNets. Based on the framework, we then identify four promising techniques, namely big data analysis, adaptive base station operation, proactive caching, and interference-aware resource allocation, to reduce energy cost on both large and small scales. We further develop a load-aware stochastic optimization approach to show the potential of our proposed framework and techniques in energy conservation. In a nutshell, we devote to constructing green Ud-HetNets of big data with the abilities of learning and inferring by improving the flexibility of control from worst-case to adaptive design and shifting the manner of services from reactive to proactive modes.

On the efficiency of sporadic servers on ethernet with FTT-SE

Ethernet is generating a growing interest as a network for real-time embedded systems such as airplanes and automobiles. In this realm, network reservations appear as important design elements that favor composability in the time domain. One Ethernet protocol that provides such reservations is FTT-SE. In this work-in-progress we make initial steps to assess the efficiency of one particular worst-case network delay analysis for sporadic reservations associated to asynchronous messages using extensive simulation. With over 1000 message sets we found that analytic values match the observations, on average, in 31% of the messages across all generated sets whereas for 85% of the cases, the analysis upper bounds are within 2.85 times the observed values.

Repeated Intersession Network Coding Games: Efficiency and Min-Max Bargaining Solution

Recent results have shown that selfish users do not have an incentive to participate in intersession network coding in a static noncooperative game setting. Because of this, the worst-case network efficiency (i.e., the price-of-anarchy) can be as low as 20%. In this paper, we show that if the same game is played repeatedly, then the price-of-anarchy can be improved to 36%. We design a grim-trigger strategy that encourages users to cooperate and participate in the intersession network coding. A key challenge is to determine a common cooperative coding rate that the users should mutually agree on. We resolve the conflict of interest among the users through a bargaining process and obtain tight upper bounds for the price-of-anarchy that are valid for any possible bargaining scheme. Moreover, we propose a simple and efficient min-max bargaining solution that can achieve these upper bounds, as confirmed through simulation studies. The coexistence of multiple selfish network coding sessions as well as the coexistence of selfish network coding and routing sessions are also investigated. Our results represent a first step toward designing practical intersession network coding schemes that achieve reasonable performance for selfish users.

Modeling towards incremental early analyzability of networked avionics systems using virtual integration

With the advance of hardware technology, more features are incrementally added to already existing networked systems. Avionics has a stronger tendency to use preexisting applications due to its complexity and scale. As resource sharing becomes intense among the network and the computing modules, it has become a difficult task for the system designer to make confident architectural decisions even for incremental changes. Providing a tailored environment to model and analyze incremental changes requires a combination of software tools and hardware support. We have built a virtual integration tool called ASIIST which can provide a worst-case end-to-end latency of data that is sent through a network and the internal bus architecture of the end-systems. Also, we have devised a new real-time switching algorithm which guarantees the worst-case network delay of preexisting network traffic under feasible conditions. With the real-time switch support, ASIIST can provide an early modularized analysis of the end-to-end latency to make architectural design choices and incremental changes easier for the user.

Optimal design of virtual links in AFDX networks

The Avionics Full Duplex Switched Ethernet (AFDX) backbone constitutes one of the major technological breakthroughs in modern avionic architectures. This network is based on routing Ethernet frames through isolated data tunnels referred to as Virtual Links (VL). VLs can be thought of as multicast trees, each serving for data transmission between one and only one end of the network to several others. Multiple VLs are deployed for exchanging data between avionic systems with a reserved amount of bandwidth. In this paper, we propose different methods to define VL characteristics and to route VLs in the network while minimizing the maximum utilization rate of the links. The proposed methods provide the basis for a more efficient design of the VLs, and have to be completed later on by the verification of the worst-case network latencies. The industrial applicability is shown on experimental results and on a representative benchmark.

The robust joint solution for channel assignment and routing for wireless mesh networks with time partitioning

Joint channel assignment and routing is an essential yet challenging issue for multi-radio multi-channel wireless mesh networks. Though several works are presented in the existing literature to approach this problem, the key question – how to ensure that the resulting network performance can closely track the optimal solution under high traffic variability without incurring too much overhead, remains unanswered.In this work, we present a new solution called “Robust joint Channel Assignment and Routing with Time partitioning (RCART)” for WMNs. RCART consists of three steps: (1) Time Partitioning and Traffic Characterization, which accomplishes the goal of partitioning time into periodic intervals with consistent properties which can be routed efficiently, (2) Robust Routing, which finds a robust routing scheme that provides an upper bound on the worst-case network performance for traffic demands that fall into a convex region, (3) Channel Assignment, which allocates radios to fixed channels during the time interval identified in step 1 and based on the knowledge of traffic distribution from step 2, using the worst-case congestion ratio as a robustness metric in its objective. Introducing time partitions as an additional control variable in the robust mesh routing RCART solution significantly improves average-case performance. Performance evaluation is conducted for RCART using real traffic demand traces. The results show that our RCART solution significantly outperforms the existing works without time partitioning or with simpler traffic profile models.