Link Metrics Research Articles

QoE-Oriented Routing Mixing Application KPIs and Link Metrics Through Machine Learning

QoE-Oriented Routing Mixing Application KPIs and Link Metrics Through Machine Learning

Novel Optimized Strategy Based on Multi-Next-Hops Election to Reduce Video Transmission Delay for GPSR Protocol over VANETs

Reducing transmission traffic delay is one of the most important issues that need to be considered for routing protocols, especially in the case of multimedia applications over vehicular ad hoc networks (VANET). To this end, we propose an extension of the FzGR (fuzzy geographical routing protocol), named MNH-FGR (multi-next-hops fuzzy geographical routing protocol). MNH-FGR is a multipath protocol that gains great extensibility by employing different link metrics and weight functions. To schedule multimedia traffic among multiple heterogeneous links, MNH-FGR integrates the weighted round-robin (WRR) scheduling algorithm, where the link weights, needed for scheduling, are computed using the multi-constrained QoS metric provided by the FzGR. The main goal is to ensure the stability of the network and the continuity of data flow during transmission. Simulation experiments with NS-2 are presented in order to validate our proposal. Additionally, we present a neural network algorithm to analyze and optimize the performance of routing protocols. The results show that MNH-FGR could satisfy critical multimedia applications with high on-time constraints. Also, the DNN model used can provide insights about which features had an impact on protocol performance.

Leveraging Software-Defined Networking for a QoS-Aware Mobility Architecture for Named Data Networking

The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative internet architecture. The software-defined networking (SDN) method, which is employed to provide mobility management in NDN, is one of the feasible strategies. Decoupling the network control plane from the data plane creates an improved programmable platform and makes it possible for outside applications to specify how a network behaves. The SDN is a straightforward and scalable network due to its key characteristics, including programmability, flexibility, and decentralized control. To address the problem of consumer mobility, we proposed an efficient SDPCACM (software-defined proactive caching architecture for consumer mobility) in NDN that extends the SDN model to allow mobility control for the NDN architecture (NDNA), through which the MC (mobile consumer) receives the data proactively after handover while the MC is moving. When an MC is watching a real-time video in a state of mobility and changing their position from one attachment point to another, the controllers in the SDN preserve the network layout and topology as well as link metrics to transfer updated routes with the occurrence of the handoff or handover scenario, and through the proactive caching mechanism, the previous access router proactively sends the desired packets to the new connected routers. Furthermore, the intra-domain and inter-domain handover processing situations in the SDPCACM for NDNA are described here in detail. Moreover, we conduct a simulation of the proposed SDPCACM for NDN that offers an illustrative methodology and parameter configuration for virtual machines (VMs), OpenFlow switches, and an ODL controller. The simulation result demonstrates that the proposed scheme has significant improvements in terms of CPU usage, reduced delay time, jitter, throughput, and packet loss ratio.

A Study on Development of Matrix Model of WMN for Mesh Clustering and its Validation

Tracking and tracing network nodes and monitoring network topology pose a huge challenge in Wireless Mesh Networks (WMNs). This becomes even more challenging in the scenario of multiple clusters, which keep performing hand-off handovers among each other for load management or link management purposes. In the dynamic and fragile WMN scenario, loss of connectivity is very common. In the case of multi-hop multi-gateway WMNs, such a scenario is very common. The architecture of the IEEE 802.11s WMN proposes a network wherein each node selects a gateway through which it routes its packets. A node selects the gateway based on its proximity in terms of the number of hop-distance and the Air Time Link Metric. Since the selection of gateway is done by a node on basis of its local information, more than often the scheduling of gateway becomes unfair. This leads to gateway congestion and a drop in the throughput of the network. In this paper, a model of a centralized gateway scheduling scheme is proposed. This paper proposes a matrix model of a clustered WMN. A well-defined gateway service set (cluster) is formed and then this is mapped onto the matrix model of the clustered WMN. In such a centralized scheme, an additional responsibility of tracking the nodes for connectivity is also addressed through this matrix model. This ensures no lost nodes or repeated nodes while the formation of clusters. Thereafter, the paper presents the process for validation of the integrity of WMN and its clusters through a mathematical model of WMN.

Topology Inference With Multivariate Cumulants: The Möbius Inference Algorithm

Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology. However, the standard approach to topology mapping—namely, active probing with traceroutes—relies on cooperation from increasingly non-cooperative routers, leading to missing information. Network tomography, which uses end-to-end measurements of additive link metrics (like delays or log packet loss rates) across monitor paths, is a possible remedy. Network tomography does not require that routers cooperate with traceroute probes, and it has already been used to infer the structure of multicast trees. This paper goes a step further. We provide a tomographic method to infer the underlying routing topology of an arbitrary set of monitor paths using the joint distribution of end-to-end measurements, without making any assumptions on routing behavior. Our approach, called the Möbius Inference Algorithm (MIA), uses cumulants of this distribution to quantify high-order interactions among monitor paths, and it applies Möbius inversion to “disentangle” these interactions. In addition to MIA, we provide a more practical variant called Sparse Möbius Inference, which uses various sparsity heuristics to reduce the number and order of cumulants required to be estimated. We show the viability of our approach using synthetic case studies based on real-world ISP topologies.

Minimally Equicorrelated MIMO Modelling for NLOS MM Wave Performance Improvement

The 5G physical layer has evolved rapidly. With transmissions at 28, 38, and 72 GHz, millimeter (MM) wave communications are at the core of it. Such links provide improved data rates to an increased number of users availing around 20 GHz of free spectrum. Directional arrays in multiple-input multiple-output (MIMO) setups facilitate beamsteered transmissions. Maximal ratio combining (MRC) receivers aid MM wave signaling and link metrics are analyzed in terms of antenna correlations. The system model is evaluated assuming a short range, non-line-of-sight (NLOS) MM wave user under independently correlated, Rayleigh conditions. This paper showcases analytical closed form expressions for received signal, output signal- to-noise ratio (SNR), line-of-sight (LOS), probability density function (PDF), and NLOS average capacity of the proposed MM wave link. The derivations exhibit capacity alterations with transceiver correlations. MM wave correlation matrices have unique dependency on steering vectors or corresponding transmission angles. This does not apply to millimeter wave.

A novel multi‐hop routing structure for throughput maximization in unmanned aerial vehicle assisted flying ad‐hoc networks

AbstractUnmanned aerial vehicle (UAV) aided wireless communication has many potential uses to improve connectivity in the harsh environments due to its high mobility and flexible deployment. To extend the coverage, multi‐hop communication with multiple UAVs as relays has become the prominent scheme in today's wireless network. In this article, we investigate the problem of generating a multi‐hop routing structure for data transmission to maximize the overall network throughput of a flying ad‐hoc network using UAVs. Specifically, we propose an optimization problem to jointly select a communication link and its transmission power so that the overall network throughput for a given total power budget is maximized. The problem is nonconvex over selected metrics, thus making it an NP‐hard problem. Without loss of generality, we consider the Dijkstra‐based shortest‐path tree as a routing structure, thereby converting the original problem into a solvable convex problem over the transmission link metric. We observe Dijkstra‐based routing is not optimal in our scenario, thus we propose a low complexity link swap approach to further improve the network throughput by exchanging appropriate child‐parent links of UAVs. Results are numerically validated, and optimal design insights are provided.

Performance evaluation and energy optimization of CM3A cooperative WBAN

This work presents performance evaluation and energy optimization of a cooperative Wireless body area network (WBAN). Link metrics like Bit Error Rate (BER), Signal-to-Noise ratio (SNR), spectral efficiency (SE), and outage probability have been investigated to evaluate the performance of non-cooperative and cooperative WBAN at different frequency bands supporting body-centric communication (Ultra-wideband (UWB), Worldwide Industrial, Scientific and Medical (W-ISM) band, US-ISM (U-ISM) band and Wireless Mobile Telemetry Services (WMTS)). Evaluation reveals that cooperative communication shows better performance by exhibiting improved SNR and SE and lower BER and outage probability compared to non-cooperative communication. A critical comparison of Amplify and forward (A&F) cooperation has been performed with decode and forward (D&F) cooperation. Results reveal that the D&F scheme performs better with increased SNR and SE and lower levels of BER and outage probability in comparison to the A&F scheme by 43.74%, 1.64%, 0.15%, and 99%, respectively. W-MTS with the highest SNR and SE and lowest BER and outage probability outperforms the other frequency bands under both non-cooperative and cooperative scenarios. Further, the threshold distance has been evaluated using the Eigenvalues to optimize the transmission energy for the D&F scenario to enhance the performance of on-body WBAN resulting in an average decrease of 3.8% in the transmitted energy, with UWB showing a maximum reduction of 5.25% thus making the network more energy efficient.

Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry

Recently joint radar communication (JRC) systems have gained considerable interest for several applications such as vehicular communications, indoor localization and activity recognition, covert military communications, and satellite based remote sensing. In these frameworks, bistatic/passive radar deployments with directional beams explore the angular search space and identify mobile users/radar targets. Subsequently, directional communication links are established with these mobile users. Consequently, JRC parameters such as the time trade-off between the radar exploration and communication service tasks have direct implications on the network throughput. Using tools from stochastic geometry (SG), we derive several system design and planning insights for deploying such networks and demonstrate how efficient radar detection can augment the communication throughput in a JRC system. Specifically, we provide a generalized analytical framework to maximize the network throughput by optimizing JRC parameters such as the exploration/exploitation duty cycle, the radar bandwidth, the transmit power and the pulse repetition interval. The analysis is further extended to monostatic radar conditions, which is a special case in our framework. The theoretical results are experimentally validated through Monte Carlo simulations. Our analysis highlights that for a larger bistatic range, a lower operating bandwidth and a higher duty cycle must be employed to maximize the network throughput. Furthermore, we demonstrate how a reduced success in radar detection due to higher clutter density deteriorates the overall network throughput. Finally, we show a peak reliability of 70% of the JRC link metrics for a single bistatic transceiver configuration.

A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol

Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions.

Learning automata and lexical composition method for optimal and load balanced RPL routing in IoT

Low power and lossy network, internet of things (IoT) motivates energy-efficient and load-balanced routing in the network layer to extend network lifetime. IoT application scenarios exploit the routing protocol for low-power and lossy networks (RPL) due to the significant potentials. The core components of RPL are the trickle algorithm and objective functions (OF) for creating destination oriented directed acyclic graph (DODAG) and data forwarding. The RPL needs more attention to avoid hotspot problems and unnecessary energy depletion. Most of the existing routing protocols take a single either hop count or ETX, or multiple routing decision metrics. However, the RPL cannot select appropriate link metrics efficiently against the dynamic and lossy environment without considering the relationship between those metrics. Thus, the proposed methodology takes important routing metrics, such as hop count, expected transmission count, and traffic-related metric, and composites the metrics using learning automata and lexical composition method. The special attention on network energy balancing through expected transmission energy (ETT) avoids a hotspot issue and inefficient routing energy. The proposed work supports multiple metrics-based OF with considerable routing overhead by tuning the trickle parameter. Moreover, the proposed work is evaluated to show its advantages over the dynamic and lossy network, IoT.

The Impact of Vertical Salinity Gradient on Non-Line-of-Sight Underwater Optical Wireless Communication

The non-line-of-sight (NLOS) underwater communication can offer a viable route in signal propagation and coverage, thus mitigating the pointing, acquisition, and tracking difficulties in line-of-sight optical communication. However, implementing the NLOS link is non-trivial. While the NLOS technique relies on light scattering, i.e., channel turbulence can facilitate NLOS communication, the associated path-loss (PL) can be significant. Signal fading can degrade link robustness, which arises due to ocean water temperature and salinity fluctuation and gradients. To evaluate the robustness of NLOS in natural waters, we systematically measure the link metrics, such as the bit error ratio, PL, and signal-to-noise ratio (SNR), of water bodies of uniform and nonuniform vertical salinity ranging from 30–40‰ (part-per-thousand). We found that salinity-induced turbulence can establish NLOS communication with PL reduction of 2.35 dB/m and SNR increase by 32.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for dynamic water. Furthermore, a strong correlation was obtained between the strength of signal fluctuations and the received SNR. Finally, we obtained a Gaussian distribution of the statistical scintillation behavior. These results demonstrated the benefit of using the NLOS regime for underwater wireless sensor networks for aiding designers and engineers.

TSOR: Thompson Sampling-Based Opportunistic Routing

Routing is a fundamental problem and has been extensively studied in various networks. However, in highly dynamic networks (e.g., wireless ad hoc networks), nodes have limited transmission opportunities due to high mobility, noise and interference, where traditional routing is often not the best approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Opportunistic routing (OR)</i> , on the other hand, can effectively minimize the routing cost (e.g., the number of hops) and improve the success of routing by utilizing link metrics. However, the link metrics are usually unknown in advance and changing. In this paper, we design an adaptive algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Thompson sampling-based opportunistic routing (TSOR)</i> motivated by the distributed Bellman-Ford algorithms. TSOR is able to learn the link metrics and route packets simultaneously to reduce the overall cost. Theoretically, we show a lower bound and an upper bound of the cumulative regret (i.e., performance gap) between TSOR and the optimal routing algorithm that knows all link metrics in advance. The regret increases sublinearly with respect to the number of packets, and has a lower order in terms of the network size than the best-known results. Furthermore, we compare TSOR with the state-of-the-art algorithms, and the evaluation results show that TSOR has a lower regret and a faster convergence rate to the optimal policy than the state-of-the-art algorithms.

Downlink aware data scheduling with delay guarantees in resource-limited leo satellite networks

In recent years, data scheduling in satellite networks has drawn much attention. The nodes in the satellite network change dynamically, and there is no continuous connectivity between nodes. Due to the intermittent connection between satellites, data transmission is based on a storage-carry-forward mode. In such a network, it will inevitably result in transmission delay. It is challenging to schedule a large amount of observation data with delay guarantees in resource-limited satellite networks. In this paper, we propose a downlink aware data scheduling algorithm based on a decomposition strategy of rolling horizon mechanism. The data flows with deadlines are allocated to ground stations orderly through feasible routes during each sliding horizon. Considering the issues of time-varying node centrality and downlink capacity, a link metric is provided to solve the problem of downlink contact conflict. Then a heuristic routing algorithm is exploited to design inter-satellite link contact. Further simulation results demonstrate the effectiveness of the proposed algorithms in network profit improvement while ensuring the transmission delay demand.

Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery

The massive development of virtualized infrastructures, Internet of Things (IoT), and Wireless Sensor Network (WSN) in recent years has led to an increase in quality requirements for the management and reliability of underlay communication networks. Existing converged networks must therefore guarantee specific quantitative and qualitative parameters of different network communication services to meet customer requirements. However, the quality of the services operated is very negatively affected by an unpredictable failure of a communication link or a network node. In such situations, communication is typically interrupted for a period that is difficult to predict, and which can lead to significant financial losses and other negative effects. Internet Protocol Fast Reroute (IP FRR) technology was developed for these reasons. The paper presents the proposal of the new Enhanced Bit Repair (EB-REP) IP FRR mechanism, which offers significant improvements over its predecessor, the B-REP mechanism. The B-REP offers protection against a single failure and only for selected critical IP flows. The EB-REP provides advanced protection against multiple failures in a protected network domain and the protection can be provided for all network flows. The EB-REP calculates alternative paths in advance based on link metrics, but also allows the construction of alternative paths independently of them. The construction of alternative FRR paths uses a standardized tunneling approach via a unique field Bit-String. Thanks to these features, EB-REP is an advanced contribution to solving IP FRR-related problems, which enables the use of EB-REP in many network deployments, but especially in network solutions that require reliable data transmission.

Efficient Routing Using Flexible Ethernet in Multi-Layer Multi-Domain Networks

Routing in multi-layer multi-domain (MLMD) networks is challenging due to different technologies and cooperation between different layers and domains. The MLMD routing problem has been considered in prior work, however most of them paid no attention to the inter-layer (or boundary) links, and the inter-domain routing is not yet optimized due to the lack of visibility over the intra-domain network topology. In this article, we investigate the problem of orchestrating MLMD networks by a hierarchical path computation engine (PCE) to leverage the performance of FlexE-the new Flexible Ethernet technology used to link IP and optical domains. We model the routing and FlexE assignment problem for FlexE-Aware and FlexE-Unaware modes and derive an efficient algorithm that optimizes the network utilization by a hierarchical allocation of bandwidth. Facing the issue of missing network information in an MLMD network due to privacy and security reasons, our orchestrator uses a new implicit routing strategy for gathering intra-domain information where the boundary link metrics are considered. Experimental results show that the proposed solution achieves 87% of the optimal throughput, a performance significantly higher than the current practices.

Concoction Node Fault Discovery (CNFD) on Wireless Sensor Network Using the Neighborhood Density Estimation in SHM

Structural Health Monitoring (SHM) has been considered to afford up-to-date information about the state of structures by assessing structural vibration responses and further physical phenomena and disorders. Determining the structure conditions is not an easy task which is associated with complex civil structures. The main objective of SHM is earlier damage detection, inspection cost reduction and lifetime estimation. The modern improvements of wireless sensor networks paid attention in the field of structural health monitoring. The deployment of sensors are expected to provide the rich information about civil structures in an effective manner. The main limitations of sensors (small memory size, small communication throughput, limited speed of the CPU) reduce the effectiveness of SHM. We propose concoction node fault discovery approach in order to provide reliable communication in the wireless environment specifically having lot of obstacles. The HRPD algorithm select the Line of sight (LOS) nodes based on extracted RSS features which is higher than non LOS. The selected intermediate LOS nodes transmit the collected sensing information to the base station with reduced energy consumption. The Statistical link parameter estimation supports to detect the optimal path through continuous analysis of link metrics such like link bandwidth, response time and queue size. The LOS fault tolerant approach determines the faulty LOS nodes, and direct the system towards survivability. The simulation results shows that our proposed approach minimize the energy depletion and enhances the sensor node’s life time. The application of the proposed work helps to monitor the structural health of buildings, bridges and towers with high quality monitoring mechanism.

A New Objective Function Based on Additive Combination of Node and Link Metrics as a Mechanism Path Selection for RPL Protocol

Since its development by IETF, the IPv6 routing protocol for low power and lossy networks (RPL) remains the subject of several researches. RPL is based on objective function as a mechanism selection of paths in the network. However, the default objective functions standardized selects the routes according to a single routing metric that leads to an unoptimized path selection and a lot of parent changes. Thus, we propose in this paper weighted combined metrics objective function (WCM-OF) and non-weighted combined metrics objective function (NWCM-OF) that are based both on additive link quality and energy metrics with equal weights or not to achieve a tradeoff between reliability and saved energy levels. The proposed objective functions were implemented in the core of Contiki operating system and evaluated with Cooja emulator. Results show that the proposed objective functions improved the network performances compared to default objective functions.

Quantizing Radio Link Data Rates to Create Ever-Changing Network Conditions in Tactical Networks

Several sources of randomness can change the radio link data rate at the edge of tactical networks. Simulations and field experiments define these sources of randomness indirectly by choosing the mobility pattern, communication technology, number of nodes, terrain, obstacles and so on. Therefore, the distribution of change in the network conditions is unknown until the experiment is executed. We start with the hypothesis that a model can quantize the network conditions, using a set of states updated within a time window, to define and control the distribution of change in the link data rate before the experiment is executed. The goal is to quantify how much variation in the link data rate a tactical system can handle and how long it takes to resume IP data-flows after link disconnections. Our model includes functions to combine patterns of change together, transforming one pattern into another, jumping between patterns, and creating loops among different patterns of change. We use exemplary patterns to show how the change in the data rate impacts other link metrics, such as latency and jitter. Our hypothesis is verified with experiments using VHF radios over different patterns of change created by our model. We compute the inter-packet latency of three types of IP data-flows (broadcast, unicast and overlay) to highlight the time to resume data-flows after long link disconnections. The experimental results also support the discussion on the advantages and limitations of our model, which was designed to test tactical systems using military radios.

Measurement Integrity Attacks against Network Tomography: Feasibility and Defense

Network tomography is an important tool to estimate link metrics from end-to-end network measurements. An implicit assumption in network tomography is that observed measurements indeed reflect the aggregate of link performance (i.e., seeing is believing ). However, it is not guaranteed today that there exists no anomaly (e.g., malicious autonomous systems and insider threats) in large-scale networks. Malicious nodes can intentionally manipulate link metrics via delaying or dropping packets to affect measurements. Will such an assumption render a vulnerability when facing attackers? The problem is of essential importance in that network tomography is developed towards effective network diagnostics and failure recovery. In this article, we demonstrate that the vulnerability is real and propose a new attack strategy, called measurement integrity attack , in which malicious nodes can substantially damage a network (e.g., delaying packets) and at the same time maliciously manipulate end-to-end measurement results such that a legitimate node is misleadingly identified as the root cause of the damage (thereby becoming a scapegoat) under network tomography. We formulate three basic attack approaches and show under what conditions attacks can be successful. We also reveal conditions to detect and locate such attacks in a network. Our theoretical and experimental results show that simply trusting measurements leads to measurement integrity vulnerabilities. Thus, existing methods should be revisited accordingly for security in various applications.