Energy-aware routing optimization in ad-hoc wireless networks under QoS constraints

Wireless communication links are time-varying in nature, causing degradation of network's performance; this however, could be alleviated by allowing cross layer interaction in the protocol stack. Hence, optimization problems formulated in this paper considered parameters from the physical, MAC, and network layers where an optimal transmission power vector that minimizes the overall transmission power of the network, satisfying the SINR, required data rate, and maximum transmission power constraints was formulated to exist. Distributed algorithms for the joint scheduling, power control and routing were also derived and an energy-aware routing algorithm which maximizes network lifetime was proposed. The energy consumed by each node in the routing path and delays associated on each transmission were considered as link cost such that a route with minimum link cost is utilized. Basically, the routing algorithm searched an energy-efficient route, satisfying the energy constraint of each node in a routing path at a tolerable delay.

The layering principle has been long identified as a way to increase the interoperability and to improve the design of telecommunication protocols, where each layer offers services to adjacent upper layers and requires functionalities from adjacent lower ones. In the past, layering has enabled fast development of interoperable systems, but at the same time limited the performance of the overall architecture, due to the lack of coordination among layers. This issue is particularly relevant for wireless networks, where the very physical nature of the transmission medium introduces several performance limitations for protocols designed for wired networks. To overcome these limitations, a modification of the layering paradigm has been proposed, namely, cross-layer design, or “cross-layering.” Several cross-layering approaches have been proposed in the literature so far. Nevertheless, little formal characterization of the cross-layer interaction among different levels of the protocol stack is available yet. A clear need exists for identifying approaches able to analyze and provide quantitative guidelines for the design of cross-layer solutions, and, more importantly, to decide, in each case, whether cross-layering represents an effective solution or not. This chapter provides a detailed survey of the state-of-the-art and future directions in the usage of formal methods for cross-layer modeling and optimization of wireless networks. The text starts by detailing the principles of layered (ISO/OSI and TCP/IP) protocol stacks as well as the cross-layer paradigm. An overview of the architectures of existing and perspective wireless networks is presented along with an analysis of the potential limitations deriving from the layering approach and detailed description of possible optimization solutions enabled by cross-layer design. Subsequent sections are devoted to the issues of modeling and optimization of wireless networks. The remaining sections cover performance optimization as well as architecture optimization (specifically in terms of signaling). The chapter ends with a summary and outlines about future directions of research on the topic.

Cross-layer optimization (physical, data link and network layers) in wireless networks has been limited by its combinatorial complexity (Toumpis and Goldsmith, 2003). Existing literature has traditionally been able to provide numerical analysis only for networks with less than 8 nodes (Madan et al., 2005), limiting our ability to understand the benefits of cross-layer optimization for more realistic-sized networks. In Barreto (2004) and Johansson and Xiao (2006) the authors showed that column generation could be used to reduce the complexity of the problem, with Johansson and Xiao (2006) providing in-depth analysis for 10-node networks and limited results for 20-node networks - all with minimum SNR and transmission rate restrictions. In this paper we show how decomposition methods can be used for the joint optimization of the physical (rate and power control), data link (MAC/scheduling), and networking (routing) layers, increasing the size of networks that can be analyzed. We present exact numerical solutions for ad-hoc networks with up to 30 nodes and mesh networks with up to 40 nodes, a significant scalability improvement. We continue by showing how bounded solutions for larger networks can be obtained with decreased computational complexity and present to heuristic implementations that increase the analysis to arbitrary-sized networks (Barreto and Chiu, 2006). We show how decomposition methods effectively decouple routing from the MAC/physical layers and allow us to solve the problems independently while reaching a global optimal solution.

With the multi network cooperative development of 4G, 3G and 2G, it is an essential and important task to comprehensively identify and classify the network optimization scenarios in the light of coverage, services, interference, malfunction and resource allocation from geography dimension on each layer, which can directly have an impact on the programs and strategies of network optimization and planning at each level. Meanwhile, precise identification of network optimization scenarios can bring an important guiding significance to the wireless network scheming, planning and optimization. In the network optimization of different scenarios, even with the same network quality, the parameters selection or adjustment during the optimization process may be different, namely, there should be the different parameter optimization models to match to different environments. Based on machine learning and big data technology, this study analyzes the wireless network multi-dimensional attributes and realize the automate identification and classification for cells under different scenarios. As such, the purpose of quantifying and identifying wireless network scenarios can be achieved.

This paper aims to quantify the performance improvement due to the use of fixed relays in the uplink of a wireless cellular network. Consider an orthogonal frequency division multiplex (OFDM) based cellular network in which each cell consists of a base station, multiple mobile users, and a number of relays. The questions of which frequency tones each link should use, whether the mobile station should communicate directly to the base station or though a relay, and how much power should be allocated on each frequency tone, form a simultaneous routing, frequency planning, and power allocation problem. This paper presents a dual decomposition approach to this problem and illustrates that while the use of relays does not necessarily increase the total cell throughput, it significantly improves the minimum common rate achievable across all the mobile users. Thus, the main benefit for deploying relays is in the improvement in fairness, rather than the total throughput.

In this paper, we optimize the routing and transmission power in hybrid wireless networks based on an interference-aware criterion. In particular, we consider a hybrid network consisted of a base-station (BS)-oriented network and a device-to-device (D2D) system cooperatively working on the same spectrum. Firstly, we formulate the joint optimization problem which aims to minimize the expected network cost for package delivery. Secondly, in order to analyze the relationship between the transmission power and the link cost, we develop a novel cost model considering the interference impact. Then we propose an efficient algorithm to solve the optimization problem. Finally we simulate the proposed scheme in the hybrid network based on LTE. The result shows an improved overall throughput, and demonstrates that the proposed scheme could well solve the compatibility problem of two wireless networks.

Wireless ad-hoc networks are collections of wireless nodes having no predefined infrastructure. Most of the existing routing protocols for wireless ad-hoc networks use the shortest path or minimum distance path to the destination. However, the shortest path routing does not provide a good balance of the traffic load among all nodes. When many users utilize the shortest paths, some nodes become highly congested and become what is known as hotspots. These hotspots degrade the performance of wireless ad-hoc networks by creating long queuing and contention delays. This paper investigates the effects of shortest path routing on the network by varying different network parameters. A load distributed path routing is also proposed to enhance the performance of shortest path routing. Matlab simulations show that the load distributive routing provides 35% and 44% less contentions in a 20-node and 30-node network respectively, when compared to shortest path routing.

Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad-hoc networks

Wireless sensor network (WSN) contains a huge number of spatially distributed sensor nodes that are connected by wireless to monitor and record information from the environment. The WSN nodes are battery-powered, thus reducing energy after a certain period which affects the network lifetime. To overcome this issue, this research proposed a spiral exploration mechanism with honey badger optimization (SEM-HBO) for cluster head (CH) and route path selection in WSN. The objective of this research is to reduce energy consumption and enhance network lifespan in WSN. The distance, communication cost, residual energy and cluster density are considered as fitness functions for selecting CH and route path in WSN. Through the SEM-HBO search behavior, it explores different routes and recognizes best one for reducing energy consumption and delays thereby enhancing network lifetime. The SEM-HBO performance is calculated based on packet delivery ratio (PDR), delay, energy consumption (EC), network lifetime (NL), and throughput for 100-500 nodes. The SEM-HBO performance is efficient and it achieves 99.62% and 99.59% of PDR for 100 and 200 nodes when compared to harmony search algorithm and competitive swarm optimization (HSA-CSO).

The formation of a cooperative alliance is an effective means of approaching the vehicle routing optimization in two-echelon reverse logistics networks. Cooperative mechanisms can contribute to avoiding the inefficient assignment of resources for the recycling logistics operations and reducing long distance transportation. With regard to the relatively low performance of waste collection, this paper proposes a three-phase methodology to properly address the corresponding vehicle routing problem on two echelons. First, a bi-objective programming model is established to minimize the total cost and the number of vehicles considering semitrailers and vehicles sharing. Furthermore, the Clarke–Wright (CW) savings method and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are combined to design a hybrid routing optimization heuristic, which is denoted CW_NSGA-II. Routes on the first and second echelons are obtained on the basis of sub-optimal solutions provided by CW algorithm. Compared to other intelligent algorithms, CW_NSGA-II reduces the complexity of the multi-objective solutions search and mostly converges to optimality. The profit generated by cooperation among retail stores and the recycling hub in the reverse logistics network is fairly and reasonably distributed to the participants by applying the Minimum Costs-Remaining Savings (MCRS) method. Finally, an empirical study in Chengdu City, China, reveals the superiority of CW_NSGA over the multi-objective particle swarm optimization and the multi objective genetic algorithms in terms of solutions quality and convergence. Meanwhile, the comparison of MCRS method with the Shapley value model, equal profit method and cost gap allocation proves that MCRS method is more conducive to the stability of the cooperative alliance. In general, the implementation of cooperation in the optimization of the reverse logistics network effectively leads to the sustainable development of urban and sub-urban areas. Through the reasonable reorganization of the entire network, recycling companies can provide more reliable services, contribute to the reduction of environmental pollution, and guarantee significant profits. Thus, this paper provides manufacturing companies, logistics operators and local governments with tools to protect the environment, while still making profits.

Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.

The paper addresses the problem of inference and optimization in wireless networks. An information-geometric framework is presented with scalable solutions to infer the end-to-end rate distributions of stochastic network flows from link rate measurements. Low-complexity solutions are provided by decomposing the network inference problem to smaller-size subproblems that are solved independently. By using the inferred distributions of flow rates, a recursive mechanism is presented to optimize the network performance. The distributions of all links rates are computed first from the inferred distributions of flow rates and then the weighted sum of link outage probabilities is minimized by adapting routing decisions. This approach is iterated between network inference and optimization, and provides link outage and end-to-end throughput gains compared to static approaches with fixed network parameters. The joint network inference and optimization approach is implemented with real radios and the performance is verified with hardware-in-the-loop emulation test results obtained with actual radio transmissions over emulated channels.

This study focuses on performance analysis and optimization of enterprise wireless networks. Fundamental performance parameters of Wi-Fi networks such as signal strength, signal-to-noise ratio (SNR), data rate, and channel interference were evaluated in detail in the study. The analysis process was carried out using Ekahau AI Pro software and Ekahau Sidekick device in a corporate facility, consisting of three main buildings. The obtained data revealed that signal strength dropped to -85 dBm levels in certain areas, negatively affecting the network's coverage area. Particularly in the ground floor of building B-C, secondary signal levels were found to be insufficient for roaming. Across the campus, SNR levels were observed to be 30 dB and above, and these values were found to provide ideal connectivity. During the analysis, it was discovered that in some areas, the number of access points broadcasting signals on the same channel increased up to 6. It has been assessed that this situation may negatively affect network performance in areas where interference is intense. Data rates varied between 1 - 300 Mbps in the 2.4 GHz frequency band and 1 - 585 Mbps in the 5 GHz band. The study provides significant data for performance analysis and optimization of enterprise wireless networks.

Developments in last decade in the field of electronics & communication, computer science and information technology field have resulted in the new technology paradigm, Wireless Sensor Networks. WSN is becoming increasingly popular due to their wide range of applications. For successful adaption of WSN in real field applications, there are certain issues which need to be addressed particularly the issue of energy consumption associated with sensor nodes. This limitation combined with node deployment has added many challenges to design and management of wireless sensor networks. The WSNs are involved in applications such as remote environment monitoring, event tracking in areas where providing electrical energy is very difficult. Moreover, the battery fed sensor nodes have very small energy associated with them, so preserving that energy and maximizing the usage of that energy is most critical issue in WSNs. Here, we present a survey of various energy optimization and lifetime enhancement techniques in wireless sensor networks and makes the reader aware about various energy saving techniques in WSN. They are analyzed from various points of view: Device hardware, Transmission, MAC protocols and Routing protocols.

The purpose is to realize the intelligent reform of piano online teaching and the intelligent optimization of wireless networks. Empirical research is realized with quantitative research and algorithm simulation as the starting point. First, regression fitting algorithm and Relief F weight algorithm are adopted to extract the effectiveness of each characteristic variable. Next, under the guidance of metric learning theory, K‐Nearest Neighbors (KNN) in Projected Feature Space (P‐KNN) algorithm is proposed to complete the hierarchical recognition of piano teaching influence features. Metric Learning With Support Vector Machine (ML‐SVM) classification algorithm is employed to identify the feature performance affecting piano teaching. Finally, the performance of P‐KNN algorithm and ML‐SVM algorithm is compared with KNN algorithm and Information‐Theoretic‐Metric‐Learning (ITML) algorithm. It is concluded that the recognition accuracies of P‐KNN and ML‐SVM are 82.78% and 83.97%, respectively. Based on the quantitative research on the characteristics affecting piano teaching, artificial intelligence and wireless network optimization are combined to explore the implementation path of intelligent technology in piano teaching reform, reflect the use value of modern science and technology in piano teaching, and innovate the process of music online education reform of piano teaching.