Cross-layer Optimization Approach Research Articles

Considering Performance Issues in Ad Hoc Networking : A Comprehensive Study

Ad hoc networks have emerged as a versatile and dynamic communication paradigm with applications spanning from mobile devices and sensor networks to emergency response and military operations. However, their inherent decentralized and self-organizing nature gives rise to a myriad of performance challenges that must be comprehensively addressed to ensure reliable and efficient communication. This paper presents a comprehensive study of performance issues in ad hoc networking, aiming to provide a holistic understanding of the various factors impacting network performance. The study begins by examining the fundamental characteristics of ad hoc networks, highlighting their unique advantages and challenges. We delve into critical performance metrics such as throughput, latency, scalability, and energy efficiency, dissecting the factors that influence each of these metrics in ad hoc environments. The role of routing protocols, MAC layer design, and network topologies in shaping network performance is thoroughly analyzed. Furthermore, this study explores the impact of mobility patterns, traffic models, and interference on ad hoc network performance. It delves into the challenges posed by dynamic network topologies, node failures, and the need for adaptive mechanisms to maintain robust connectivity. Quality of service (QoS) considerations and their implications on performance are also discussed in detail. To provide a comprehensive view, we survey state-of-the-art solutions and techniques proposed in the literature to mitigate performance issues in ad hoc networks. These include adaptive routing algorithms, cross-layer optimization approaches, and novel communication paradigms like cognitive radio networks. Finally, we present a roadmap for future research directions in the field of ad hoc networking performance optimization. The goal is to inspire further investigation and innovation in addressing the evolving challenges faced by ad hoc networks, making them more reliable, efficient, and adaptable for a wide range of applications.

Interoperable, adaptable, information exchange in NATO coalition operations

Abstract This paper summarises our work on policy-enabled inter-network routing for mobile networks and adapting information services to available networking resources in tactical networks. The work shows promise; both the policy routing and adaptive service infrastructure were part of successful interoperability trials in the Coalition Warrior Interoperability eXercise (CWIX) in 2021. This paper highlights our findings, how our work can support interoperability in NATO, and represents an enabler for future coalition operations. Although promising, the work involves research and concept development, and so, we anticipate its timeframe for seeing actual operational use as likely 3–5 years from now, typically targeting future developments within Federated Mission Networking (FMN). In our work, we have shown that we can build a federated mobile network by using a reactive routing protocol that supports policy routing in a network overlay for use in a coalition. Further, we have shown that we can leverage network-level information at the application level, through a so-called cross-layer optimization (CLO) approach. The CLO approach leverages a well-defined format, and we found that this format promotes interoperability and can be used in a multi-national setting. Since our work is experimental, we have also identified some shortcomings for future work.

A holistic cross-layer optimization approach for mitigating stragglers in in-memory data processing

Abstract In-memory data processing frameworks (e.g., Spark) make big data analysis greatly simpler and efficient. However, stragglers that take much longer to finish than other tasks significantly degrade performance. There exist multiple factors that cause stragglers, either from the hardware resource layer or application layer, e.g. hardware heterogeneity, interference, data locality and data skew. While state-of-the-art straggler mitigation techniques have presented partial solutions on data skew and data locality, we experimentally demonstrate that the other factors can also result in serious problems. We present Clio, a cross-layer interference-aware optimization system that can effectively mitigate stragglers for data processing frameworks. Clio supports the scheduling of both map and reduce tasks. It heuristically dispatches intermediate data in proportion to the actual computing ability of each worker node, which is estimated considering various straggler factors, to balance the completion times of tasks in a much finer way. We implement Clio in Apache Spark, and evaluate its performance using both synthetic and real datasets. Experiment results show that, Clio can speed up the execution of applications by up to 67%, compared with the existing algorithms.

A novel approach for energy- and memory-efficient data loss prevention to support Internet of Things networks

Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios.

Energy-Efficient Biocooperative Video-Aware QoS-Based Multiobjective Cross-Layer Optimization for Wireless Networks

Video streaming is becoming increasingly popular due to the emergence of different video applications and services and the exponential growth in demand for multimedia applications. Streaming video over an error-prone network is technically challenging due to unpredictable channel conditions and demands for different types of quality of service (QoS) from various types of applications. The traditional Open System Interconnection (OSI) layered approach is designed for data transfer, and it performs specific operations independent of other layers. This approach may not work efficiently for wireless video streaming applications in which video quality is greatly affected by the network condition, link quality, and ability of the system to adapt to the channel conditions. This paper is primarily aimed at developing an algorithm to enhance video streaming over wireless local area networks using a biocooperative video-aware QoS-based multiobjective cross-layer optimization (MO-CLD) approach. First, the wireless video streaming optimization problem is formulated and modeled. To solve the optimization problem, we proposed a bioinspired optimization algorithm to jointly optimize the source rate and packet rate loss using dual decomposition. The simulation results show that the proposed biocooperative multiobjective optimization scheme using particle swarm optimization (PSO) relatively maintains and enhances the video streaming quality in error-prone transmission environments to ultimately support wireless multimedia applications and services. More importantly, the end-to-end delay and complexity are reduced significantly, which eventually lead to improvements in the scheme efficiency and video quality compared to basic cross-layer optimization (B-CLD).

Cross-layer design and optimization techniques in wireless multimedia sensor networks for smart cities

The future smart cities vision can be developed through leveraging the potentials of Internet of Things (IoT) and wireless sensor network (WSN) technologies. WSN is a resource constrained network where network nodes are tiny devices that are run on battery power. Diverse types of applications such as environmental and habitual monitoring, detection, and tracking, use WSNs. The invention of new network protocols, the establishment of new models for communications, and testing the available solutions in real world environment are some of the current research issues in WSNs. Main challenges in such networks include energy conservation in an efficient way, dealing with variable channel capacity, and the resource constrained nature of such networks. The design of architecture for such networks has a vital role in solving the issues to some extent, i.e., the cross layer design approach is an architectural technique that offers the interaction of different layers together to enhance the performance, minimize the energy consumption, enhance the network life time, and provide Quality of Service (QoS) in real time communications. These are some of the current areas where cross-layer design approaches are being used. This paper presents different types of cross-layer design techniques in wireless multimedia sensor networks. Using such architectural techniques, different state of the art cross-layer optimization approaches are discussed while giving the reader an insight on prominent challenges and issues along with future directions.

Cross-layer optimized authentication and error control for wireless 3D medical video streaming over LTE

3D video for tele-medicine applications is gradually gaining momentum since the 3D technology can provide precise location information. However, the weak link for 3D video streaming is the necessary wireless link of the communication system. Neglecting the wireless impairments can severely degrade the performance of 3D video streaming that communicates complex critical medical data. In this paper, we propose systematic methodology for ensuring high performance of the 3D medical video streaming system. First, we present a recursive end-to-end distortion estimation approach for MVC (multiview video coding)-based 3D video streaming over error-prone networks by considering the 3D inter-view prediction. Then, based on the previous model, we develop a cross-layer optimization scheme that considers the LTE wireless physical layer (PHY). In this optimization, the authentication requirements of 3D medical video are also taken into account. The proposed cross-layer optimization approach jointly controls and manages the authentication, video coding quantization of 3D video, and the modulation and channel coding scheme (MCS) of the LTE wireless PHY to minimize the end-to-end video distortion. Experimental results show that the proposed approach can provide superior 3D medical video streaming performance in terms of peak signal-to-noise ratio (PSNR) when compared to state-of-the-art approaches that include joint source-channel optimized streaming with multi-path hash-chaining based-authentication, and also conventional video streaming with single path hash-chaining-based authentication.

QoS‐aware scheduling algorithms to enhance user satisfaction in OFDMA systems

AbstractThis work aims the development of scheduling algorithms to maximize user satisfaction index in the downlink of an Orthogonal Frequency Division Multiple Access network, considering different traffic models of Non‐Real Time and Real Time services; and more realistic channel conditions, eg, imperfect Channel State Information. To solve the problem of maximizing the satisfaction with affordable complexity, a cross layer optimization approach uses the utility theory to formulate the problem as a weighted sum rate maximization. Aiming at the maximization of the satisfaction of users of Non‐Real Time and Real Time services, 2 scheduling algorithms are proposed: Modified Throughput Satisfaction Maximization and Modified Delay‐based Satisfaction Maximization, respectively. The modification of parameters of the shifted log‐logistic utility function enables different strategies of distribution of resources. System level simulations indicate the accomplishment of the objective of development of efficient and low complexity scheduling algorithms able to maximize the satisfaction indexes. These strategies can be useful to the network operator who is able to design and operate the network according to a planned user satisfaction profile.

A joint power control, time-sharing and routing scheme to minimize spectrum utilization in wireless mesh networks

Despite the abundance of the optimization models proposed so far in the literature to improve the performance or to reduce the deployment costs of wireless mesh networks, only a few works have addressed explicitly the minimization of spectrum utilization in these networks. Considering the scarcity of this critical resource mainly caused by the tremendous recent developments in wireless technologies, we address in this paper the problem of minimizing the number of used radio channels while satisfying a set of end-to-end traffic demands. We formulate to this end a mixed-integer linear program as a cross-layer optimization approach acting on various aspects, such as channel assignment, power control, medium access and flow routing. Due to the expensive computational costs required to find exact solutions, we propose various approximation schemes that provide near-optimal results in a reasonable amount of time. We illustrate the impact of various parameters such as radio patterns, requested traffic loads, network density on the total required spectrum. Results show how a knowledgeable exploitation of spatial reuse can save significantly spectrum utilization.

Spatial Reuse in Dense Wireless Areas: A Cross-Layer Optimization Approach via ADMM

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their \newt{distance from the destination} and creates a structured multi-hop configuration in which each group can relay its neighbor's data. \newt{The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups}. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. \newt{The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain} a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.

Raptor Code-Aware Link Adaptation for Spectrally Efficient Unicast Video Streaming over Mobile Broadband Networks

This paper proposes novel Raptor-aware link adaptation (LA) when application layer Forward Error Correction (AL-FEC) with Raptor codes is used for live, high quality, video unicast over mobile broadband networks. The use of Raptor code AL-FEC is taken into account for the adaptation of the modulation and coding scheme (MCS) used in the physical layer. A cross-layer optimization approach is used to select the Raptor code parameters and the MCS mode jointly, in order to maximize transmission efficiency. The proposed methodology takes into consideration the channel resources required to accommodate the Raptor overheads. Simulation results show that packet loss is eliminated and the amount of radio resource required is reduced significantly. Automatic repeat request (ARQ) based unicast systems require up to 115.6 percent more channel resources, by comparison to the proposed Raptor-aware LA system without retransmissions. Furthermore, the Raptor-aware LA system can enhance the link budget by up to 4 dB, increasing coverage in LoS locations, and can improve total goodput by 46.7 percent compared to an ARQ-based system.

A Cross-Layer Optimization and Design approach under QoS constraints for green IP over WDM networks

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demands. It is important and valuable to find a way to save power for such networking systems. In this paper, we propose a Cross-Layer Optimization and Design (CLOD) approach to improve the energy efficiency of Internet Protocol (IP) over Wavelength Division Multiplexing (WDM) backbone networks under QoS constraints. CLOD makes use of the skew spatial distribution (i.e. 80–20 law) of traffic demands to construct the virtual topology of IP layer with a consideration of the cross-layer resource constraints. Also, CLOD takes advantage of the constraint-based routing to satisfy the QoS constraints in terms of allowed maximum hop count and allowed maximum link utilization. Taking into account the connection between base network design and network operation, CLOD dimensions the base network by using pattern of network operation and optimizes network power consumption by reconfiguring network to adapt traffic variation and the available resources. Simulation results indicate that CLOD can save power significantly and achieve near energy-proportional networks. In addition, an analysis on trade-off performance between the hop count and power consumption is given.

A Cross-Layer Optimization Approach for Lower Layers of the Protocol Stack in Sensor Networks

A cross-layer optimization approach for the physical and medium access control layers of wireless sensor networks is introduced in this article. This approach includes a Markov chain model, simulations, and analytical derivations that are applied to the analysis of sensor networks using impulse radio ultra-wideband signals with noncoherent energy detection. This type of communication system has low-power transmission requirements and noise like signal characteristics with low interference to other wireless systems. The energy efficiency of different Reed-Solomon code rates and uncoded case are studied in a star topology network, where slotted Aloha, as defined in the IEEE 802.15.4a standard, is used as the medium access protocol. Analytical and simulation results clearly show the potential energy gains that can be achieved with the proposed optimization approach that can be also used in the evaluation and optimization of other combinations of physical and medium access control protocols.

Spectrum efficient cross-layer adaptation of Raptor codes for video multicasting over mobile broadband networks

This paper explores the use of rateless application layer Forward Error Correction (AL-FEC) based on Raptor codes in a spectrally efficient way for real-time video multicasting over mobile broadband networks. The link between channel errors at the PHY, MAC and application layers and the required Raptor code parameters for quasi-error free video transmission is investigated. Low Raptor code rates are shown to place high demands on the limited bandwidth of a wireless channel. The selection of Modulation and Coding Scheme (MCS) is extended to take into account the channel resources required to accommodate the additional Raptor overheads. A cross-layer optimization approach and a novel metric are proposed to select the Raptor code parameters and the MCS mode jointly to maximize wireless transmission efficiency. Simulation results show that 166% more bandwidth is required by a non-Raptor enabled multicast WiMAX system, assuming a Mobile Station (MS) speed of 10 km/h, in comparison to the optimized Raptor AL-FEC multicast WiMAX system. Results show that more bandwidth is required at lower MS speeds. Furthermore, the optimized Raptor AL-FEC system is shown to eliminate packet loss, to enhance the link budget by up to 6 dB, which quadruples multicast coverage in LoS locations, and to increase the total goodput by 67%, in comparison to a non-Raptor enabled multicast WiMAX system.

Cross-layer optimization for performance trade-off in network code-based wireless multi-hop networks

Maximizing network lifetime and optimizing aggregate system utility are important but usually conflict goals in wireless multi-hop networks. For the trade-off particularly for such networks equipped with the capability of network coding, we introduce a cross-layer optimization approach that can seamlessly accommodate routing, scheduling and stream control to simultaneously meet the diverse objectives with the aid of network utility maximization. Specifically, by taking into account both intra- and inter-session network coding and cross-layer formalization, the algorithms resulted are considered to be more general than those obtained from, e.g., poison-remedy based methods that focus on the methodology of network coding itself. In particular, along with a Markov Chain Monte Carlo method and a CSMA approximation, these algorithms can dynamically approach the optimal solution while solving the scheduling subproblem involved, which is NP-hard in general, in a distributed manner. Finally, we present numerical results for the insight that can be gained from the cross-layer optimization and show that this work is capable on achieving an optimal trade-off between the lifetime and utility.

Towards an energy-aware Internet: modeling a cross-layer optimization approach

The containment of power consumption and the use of alternative green sources of energy are the new main goals of telecommunication operators, to cope with the rising energy costs, the increasingly rigid environmental standards, and the growing power requirements of modern high-performance networking devices. To address these challenges, we envision the necessity of introducing energy-efficiency and energy-awareness in the design, configuration and management of networks, and specifically in the design and implementation of enhanced control-plane protocols to be used in next generation networks. Accordingly, we focus on research and industrial challenges that foster new developments to decrease the carbon footprint while leveraging the capacities of highly dynamic, ultra-high-speed, networking. We critically discuss current approaches, research trends and technological innovations for the coming green era and we outline future perspectives towards new energy-oriented network planning, protocols and algorithms. We also combine all the above elements into a comprehensive energy-oriented network model within the context of a general constrained routing and wavelength assignment problem framework, and analyze and quantify through ILP formulations the savings that can be attained on the next generation networks.

Cross-Layer Optimization for Downlink Wavelet Video Transmission

Cross-layer optimization for efficient multimedia communications is an important emerging issue towards providing better quality-of-service (QoS) over capacity-limited wireless channels. This paper presents a cross-layer optimization approach that operates between the application and physical layers to achieve high fidelity downlink video transmission by optimizing with respect to a quality criterion termed “visual entropy” using Lagrangian relaxation. By utilizing the natural layered structure of wavelet coding, an optimal level of power allocation is determined, which permits the throughput of visual entropy to be maximized over a multi-cell environment. A theoretical approach to optimization using the Shannon capacity and the Karush-Kuhn-Tucker (KKT) conditions is explored when coupling the application with the physical layers. Simulations show that the throughput gain for cross-layer optimization by visual entropy is increased by nearly 80% at the cell boundary as compared with peak signal-to-noise ratio (PSNR).

Cross-layer design and analysis of WSN-based mobile target detection systems

The limited node capabilities typical of Wireless Sensor Networks (WSNs) call for cross-layer design optimization. In this paper, we address the problem of designing and operating long-lasting surveillance mobile target detection applications for unattended WSNs with a priori knowledge of the nodes’ positions. In particular, we focus on the cross-layer interaction between the sensing layer (devoted to the detection of a mobile target crossing the monitored area) and the communication layer (devoted to the transmission of the alert, upon detection, from a sensing node to the network sink). The performance of the sensing layer is characterized by the probability of target missed detection and the delay before the first sensor detection act. The communication layer is investigated considering two Medium Access Control (MAC) protocols: X-MAC [1] and the novel Cascade (Cas)-MAC protocol, inspired by the principles of the D-MAC protocol [2]. At both layers, we validate analytical models through realistic simulations and experiments. The cross-layer interaction between the two layers is achieved considering a proper model for the network lifetime, based on the average energy depletion at the node level. Finally, to highlight the benefits of the proposed framework, we present a cross-layer optimization approach for the configuration of the system parameters, considering several relevant network topologies.

A metamodeling approach for cross-layer optimization: A framework and application to Voice over WiFi

Cross-layer design has been proposed to optimize the performance of networks by exploiting the interrelations among parameters and procedures at different levels of the protocol stack. This paper introduces a quantitative framework for the study of cross-layer interactions, which enables design engineers to analyze and quantify interlayer dependencies and to identify the optimal operating point of the system, by using network economic theory principles. The framework is then used for performance optimization of a single-cell Voice over WiFi (VoWiFi) system. Insights gained from the considered scenario enable us to define a novel cross-layer Call Admission Control (CAC) scheme. The multistage CAC takes into account Quality of Service (QoS) criteria, which provide satisfaction to the end user, as well as revenue criteria that maximize the possible profit of the WiFi provider.

Optimization scheme with pre-processing for cooperative relay multicast networks in cellular system

For cooperative relay multicast networks, the general cross-layer optimization approaches converge to the global optimal value slowly because of the large quantity of relay terminals. However, the mobility of relay terminals requires quick converging optimization strategies to refresh the relay links frequently. Based on the capacity analysis of multiple relay channels, an improved cross-layer optimization scheme is proposed to resolve this problem, in which the bound of the relay selecting region is determined as a pre-processing. Utilizing the primal-dual algorithm, a cross-layer framework with pre-processing optimizes both the relay terminal selection and power allocation with quick convergence. The simulation results prove the effectiveness of the proposed algorithm.