An analytical framework and implementation of wireless network inference and optimization

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.

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.

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.

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.

In the design of modern mobile wireless networks the effort for performance improvement has to be balanced with the effort in more effective energy management strategies. In fact, the capacity of batteries is not increasing as new devices and multimedia applications requires. In this paper a new approach to routing optimization in ad-hoc wireless networks supporting multimedia communication is investigated. It exploits an energy model for the radio interface and cross-layer information to dynamically determine routing paths connecting wireless nodes, in order to reduce energy consumption and prolong network lifetime, while preserving quality of service. A network architecture based on a modified 802.11a wireless physical interface with multi-channel capability is considered. We investigate a multi-hop environment where network devices collect cross- layers information and periodically update a central repository located on a network node elected as network coordinator. The coordinator exploits the recorded information to provide best quality routing paths to devices that need to send sensible data over the network. We propose here an algorithm for routing path optimization and compare the optimized strategy with a reference implementation of the OLSR routing protocol. The numerical results show benefits in terms of increased network life-time.

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.

Base station location selection and network optimization are critical to improving the performance of wireless communication networks in terms of latency reduction. To this end, the article proposes leveraging a convolutional neural network (CNN) to improve the accuracy of base station location selection and network latency reduction. The CNN method, based on a three-dimensional representation including signal strength data set, network topology data set, and transmission path data set, is used to select base station location and optimize the multihop relay network for latency reduction. The article presents a following method: location selection and network optimization for the wireless communication network. First, it collects the experimental data set of base station location selection and network optimization, and then uses the training data to train the CNN model to extract features. Once the training is done, the article further optimizes the network parameters and configurations, and ultimately obtains the optimal base station location and network configuration while minimizing network latency. As a result, simulation results indicate that the CNN model has remarkable performance in base station location selection, as well as in network optimization. In summary, the feature extraction and processing ability of CNN are powerful, enabling it to effectively capture factors leading to delay, hence improving the performance of base station location selection and network optimization. The article also demonstrates that the CNN model can be adjusted according to different environments and scenario settings through dynamic tuning.

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end–end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end–end delay, and 94% packet delivery ratio.

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.

BackgroundReverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective.ResultsThis paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies.ConclusionsThe approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.

Path loss models are often used by radio network engineers to predict signal coverage, optimize limited network resources, and perform interference feasibility studies. However, the propagation mechanisms of electromagnetic waves depend on the physical characteristics of the wireless channel. Therefore, efficient radio network planning and optimization requires detailed information about the specific propagation environment. In this data article, the path loss data and the corresponding information that are needed for modeling radio wave propagation in smart campus environment are presented and analyzed. Extensive drive test measurements are performed along three different routes (X, Y, and Z) within Covenant University, Ota, Ogun State, Nigeria (Latitude 6°40′30.3″N, Longitude 3°09′46.3″E) to record path loss data as the mobile receiver moves away from each of the three 1800 MHz base station transmitters involved. Also, the longitude, latitude, elevation, altitude, clutter height, and the distance information, which describes the smart campus environment, are obtained from Digital Terrain Map (DTM) in ATOLL radio network planning tool. Results of the first-order descriptive statistics and the frequency distributions of all the seven parameters are presented in tables and graphs respectively. In addition, correlation analyses are performed to understand the relationships between the network parameters and the terrain information. For ease of reuse, the comprehensive data are prepared in Microsoft Excel spreadsheet and attached to this data article. In essence, the availability of these data will facilitate the development of path loss models for efficient radio network planning and optimization in smart campus environment.

The radio network planning in TETRA digital trunking communication system including cover planning, capacity planning, frequency planning and the related optimizing technology and method such as capacity optimization and cell reselection is specifically stated in this paper. The technology and method on radio network planning and optimization is also analyzed and expressed by the practical application of Beijing TETRA digital trunking communication network.

The fifth-generation (5G) is coming. Quo vadis 5G? What architectures, network topologies and technologies will define 5G? Are methodologies to the analysis, design and optimization of current cellular networks still applicable to 5G? The proposed tutorial is intended to offer a comprehensive and in depth crash course to communication professionals and academics. It is aimed to critically illustrate and discuss essential and enabling transmission technologies, communication protocols and architectures that are expected to make 5G wireless communication networks a reality. More specifically, the present tutorial is focused on illustrating the critical and essential importance of channel and spatial models for an accurate system-level analysis and optimization of 5G networks, which are expected to use different frequency bands compared to state-of-the-art networks and to rely on a much denser deployment of access points and antenna-elements, to a scale that has never been observed in the past.

This paper deals with the method of how to carry out radio network optimization for 2G and 3G systems with an overview of general GSM radio network optimization areas with regards to analysis and troubleshooting. The general steps and methods for wireless radio network optimization are first addressed. Then optimization methods are introduced. At last the issues of radio network optimization is discussed with special focus on the 2G and 3G networks as well as the required Quality of Service (QoS) to the subscribers. This study would help operators to plan effective optimization of radio resources, enhance coverage, improve quality and increase capacity in future.