Wireless Network Optimization Research Articles

Multi‐strategy enhanced coot algorithm for coverage optimization in wireless sensor networks

AbstractAn improved coot optimization algorithm (COOT) is proposed for the coverage optimization in wireless sensor networks (WSNs). To monitor areas of interest and obtain valid data, a WSNs coverage model is developed. The population is initialized with a Cubic map and an opposition‐based learning strategy. Carrying out dimension‐by‐dimensional opposition‐based learning on the leader population to strengthen the global search ability of the algorithm, the simplex method is introduced to optimize the local exploration of the population. The experimental results demonstrate that the enhanced COOT can effectively reduce energy consumption and improve network coverage.A COOT based on simplex method and dimension‐by‐dimension opposition‐based learning is proposed to optimize the coverage of WSNs. The experimental results as Figure 2a and 2c show that the improved algorithm has faster convergence speed and higher precision, which reduces the node redundancy in the coverage optimization process of wireless sensor network and improves the coverage.

Optimizing Wireless Network based on Building Design in Telkom University

Computer network designed according to its functions and building design which must be analyzed to consider usage intensity and maintenance. This paper’s study case is in one of the buildings in University at Bandung which uses fiber optic and wireless network for both 2,4GHz and 5GHz frequencies. Existing conditions were tested in site surveys which are high frequency interference of wireless signals, misconfigured channel bands caused frequent disconnections close to two different access points, and there are blank spots causing user’s devices cannot properly connect to network. Wireless network optimization needs to be done to solve its problems. Method used is the Network Development Life Cycle (NDLC) which consists of Analysis, Design, and Simulation Prototyping phases, and performance measurements using Quality of Service (QoS) parameters. Site survey results showed many high interference areas caused by channel misconfiguration in each access point that the same channel can be used for adjacent access points, misplacing wall-mounted access points on ceiling which can interfering both upper floor and lower floor networks. QoS performance measurements to match TIPHON Standardization for throughput is higher than 1kbps, packet loss lower than 3%, and delay lower than 150ms. Recommendation for optimizing network is properly configuring channel band using non-overlapping channel configuration (1-6-11) for all adjacent access points, and there is simulation for replacing wall-mounted access points that placed on the ceiling to a ceiling-mounted type access point. It showed a decreasing interference in the entire network, whether its on the same floor or between upper and lower floor.

COMPARISON OF OPTIMIZATION STRATEGIES AND ESTIMATION TECHNIQUES FOR RADIO NETWORK PLANNING AND OPTIMIZATION PROBLEMS

Background. Radio network planning is one of the main phases of the cellular network lifecycle, as it determines capital and operating costs and allows system performance evaluation at any given time. An accurate and comprehensive analysis of existing network statistics is necessary for proper cell planning during network expansion. These statistics are collected throughout the life cycle of the cellular network and usually have certain imperfections (heterogeneity of statistics, which have different densities in different parts of the search space, up to the presence of significant voids, etc.) The system describing the functioning of the radio network can be represented as a black box because its internal processes are too complex to be defined by mathematical functions. This determines the need to use appropriate tools.
 Objective. The purpose of the paper is to create a toolkit that allows finding the proper relationships between network parameters to define target values that will help to build an effective network plan in terms of performance and costs for its creation and operation. The tools should be able to work efficiently using the minimum set of available statistical data, as well as taking into account their imperfections.
 Methods. Mathematical estimation and optimization methods are used, namely Ordinary Least Squares, Ridge Regression, Lasso, Elastic-net, LARS lasso, Bayesian Ridge Regression, Automatic Relevance Determination, Stochastic gradient descent, Theil-Sen estimator, Huber Regression, Quantile regression, Polynomial regression. We consider 12 estimation methods in combination with two optimization strategies. Additionally, the method of partial analysis of the search space with different number of configurations is considered.
 Results. A software package using the Python programming language has been created, which contains a practical implementation of all the considered estimation and optimization methods, as well as tools for evaluating arbitrary configurations of the software package (benchmark) and visualizing the results. The best estimation method is Ordinary Least Squares for finding the optimal configuration of the statistical parameters of the 4G radio network to maximize the download speed. To obtain satisfactory results, it is enough to consider 25 initial and 250 estimated points - a larger number of points will not significantly increase prediction accuracy.
 Conclusions. The results indicate the possibility of using the created software package for radio network planning tasks. Further research is aimed at expanding the created software package's functionality and considering additional estimation methods and optimization strategies.
 Keywords: radio network planning; radio network optimization; cellular networks; estimation techniques; optimization strategies; big data; 2G; 3G; 4G; CQI; capacity.

Identification of Indoor Radio Environment Properties from Channel Impulse Response with Machine Learning Models

The design and optimization of next-generation indoor wireless communication networks require detailed and precise descriptions of the indoor environments. Environmental awareness can serve as a fundamental basis for the dynamic adaptation of the wireless system to channel conditions and can improve the system’s performance. Methods that combine wireless technology with machine learning are promising for identifying the properties of the indoor radio environment (RE) without requiring specialized equipment or manual intervention. In the paper, we propose an approach for identifying the materials of the surfaces using channel impulse response (CIR) and RE identification models built with machine learning. To train the models and assess their performance, we acquired radio propagation data from rooms with different sizes and materials using ray tracing. We explored tree-based methods, ensemble-based methods, kernel-based methods, and neural networks for training the models. The performance of the models is evaluated in three realistic scenarios defined by the location of the radio nodes and the room sizes. The multilayer perceptron models performed best in most of the evaluation settings. The results show that the models are capable of accurately predicting the materials in rooms with sizes that were not included in the training procedure. Including CIRs from a large number of rooms with different sizes and surface materials estimated with different radio node positions in the training process results in models with wider practical applicability.

Survelliance monitoring based routing optimization for wireless sensor networks

Survelliance monitoring based routing optimization for wireless sensor networks

A score based link delay aware routing protocol to improve energy optimization in wireless sensor network

The Wireless Sensor Network (WSN) is the most appearing expertise that has potential applications broad ranges that include environment examining, smart spaces, medical systems, and robotic study. Energy efficiency is a consideration of vital design for WSN. The collision is occurred owing to the transmission of data from the sensor nodes and the traffic at the SINK node is high because of the excess data transmission by the sensor nodes. An essential division of the consumption of resources in a WSN is managed by the mechanism of Medium Access Control (MAC). An existing MAC protocols initiated for the utilization of WSNs single channel for data transmission. The deployment of wireless sensor nodes is concerned in the harsh environment in which the drastic change of conditions suffers from sudden changes in the status of a node and the quality of a link. With limited energy, the sensor nodes are supplied and a major concern is towards improving the lifetime of a network. To deal with these issues, a novel and simple routing mechanism Score based Link Delay Aware Routing (SBLDAR) is proposed, which utilizes the multi-channel MAC procedure comprises of Carrier Sense Multiple Access/ Collision Avoidance (CSMA/CA), to exchange the data, the activity of Time Division Multiple Access (TDMA) sequencing nodes and also Frequency Division Multiple Access FDMA) to allow collision-free exchange simultaneously. Also, in order to overcome this issue, an approach has been put out in this work that involves leveraging a variety of trust variables to identify malicious nodes and then using fuzzy based Modified Sun Flower Optimization (FMSFO) algorithm to elect an effective cluster head. The score based route selection increases the network lifetime and improves network performance. By comparing with the other protocols, the proposed SBLDAR combined with the technique of multi-MAC protocol has shown that it increases the network’s overall performance based on the simulation studies.

Wireless Network Design Optimization for Computer Teaching with Deep Reinforcement Learning Application

ABSTRACT Computer technology has had a significant impact on the field of education, and its use in classrooms has facilitated the spread of knowledge and has helped students become well-rounded citizens. However, as the number of users accessing wireless networks for computer-based learning continues to grow, the scarcity of spectrum resources has become more apparent, which makes it crucial to find intelligent methods to improve spectrum utilization in wireless networks. Dynamic spectrum access is a critical technology in wireless networks, and it primarily focuses on how users can efficiently access licensed spectrum in a dynamic environment. This technology is a crucial means to tackle the problem of spectrum scarcity and low spectrum utilization. This work proposes a novel approach to address the issue of dynamic channel access optimization in wireless networks and investigates the dynamic resource optimization problem with deep reinforcement learning algorithms. The proposed approach focuses on the optimization of dynamic multi-channel access under multi-user scenarios and considers the collision and interference caused by multiple users accessing a channel simultaneously. Each user selects a channel to access and transmit data, and the network aims to develop a multi-user strategy that maximizes network benefits without requiring online coordination or information exchange between users. What makes this work unique is its utilization of deep reinforcement learning algorithms and a Long Short-Term Memory (LSTM) network that keeps an internal state and combines observations over events. This approach allows the network to utilize the history of processes to estimate the true state, providing valuable insights into how deep reinforcement learning algorithms can be used to optimize dynamic channel access in wireless networks. The work’s contribution lies in demonstrating that this approach is an effective means to solve the dynamic resource optimization problem, enabling the development of a multi-user strategy that maximizes network benefits. This approach is particularly valuable as it does not require online coordination or information exchange between users, which can be challenging in real-world scenarios. The proposed approach presents an important contribution to the field of dynamic spectrum access and wireless network optimization. As the demand for computer-based learning continues to increase, the use of intelligent methods to improve spectrum utilization in wireless networks will become even more critical. The findings of this work could have significant implications for the future of computer-based learning and education, enabling more efficient use of wireless networks and the creation of well-rounded citizens.

A Self-Supervised Learning Approach for Accelerating Wireless Network Optimization

The prevailing issue in multi-hop wireless networking is interference management, which militates against the efficiency of traditional routing and scheduling algorithms. We develop a self-supervised learning approach to address the classic NP-hard problem of capacity optimization over a multi-hop wireless network, where the routing and scheduling decisions are deeply coupled. Our two-stage design leverages historical computation experiences to accelerate the optimization of new problem instances, where an instance represents the application-level input containing network topology, interference model, and other user-level traffic constraints. The first stage, Scheduling Structure Classification (SSC), distills the scheduling structure of the historical optimization instances into an appropriate number of classes, through a properly designed clustering algorithm without prior assumption or knowledge. The second stage uses the instances labelled with class information from the first stage to train an Application Identification (AID) neural network model capable of predicting a future problem instance's scheduling class given its application-level information. When solving the new instance, its predicted scheduling class and the associated scheduling structure are exploited to compute an efficient approximate solution, avoiding the time-consuming iterative search for such scheduling structure as practiced by the conventional approaches. We apply our method to different types of wireless multi-commodity flow problems across various network sizes and disparate flow requirements. The results demonstrate that our method significantly reduces the computation time robustly by at least 70% with only slight loss in the solution quality.

TOPSIS-based Optimal Cluster Head Selection for Wireless Sensor Network

Due to the advancement of electronics engineering technology, many types of sensors have been developed. But sensors are still battery-powered devices. Once the battery is dead, the sensors are of no use. So, energy optimization in wireless sensor networks is still a hot topic among researchers. This paper proposed a novel clustering method that uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm to select the Cluster Heads (CHs). TOPSIS is a Multi-Attribute Decision Making (MADM) based model which uses several conflicting attributes to select the best alternative. We have compared our proposed model with two other comparable models to evaluate the performance of our proposed model. The result shows that our proposed model performs better than other models.

Radio Channel Measurements and Characterization in Substation Scenarios for Power Grid Internet of Things

Wireless communication technologies play a key role to support the implementation of power grid Internet of Things (PIoT). An in-depth knowledge of the radio channel is vital to the application of wireless communication technologies in PIoT. This paper investigates the radio channel measurements and characterization for PIoT substation scenarios. A novel channel sounder is designed for achieving omnidirectional measurements and phased array antennas based directional measurements, and a directional multipath components extraction algorithm is proposed. The channel sounding system is verified and is used to perform a series of 3.35 GHz omnidirectional and directional channel measurements in four substation scenarios, involving a 10 kV switch room, a 110 kV GIS room, a semi-indoor 110 kV substation and an outdoor 220 kV substation. Based on the measured channel impulse responses data, both large-scale and small-scale fading characteristics of substation channels are extracted and analyzed. Empirical models of path loss, shadow fading, Ricean K-factor and root-mean-square (RMS) delay spread are proposed. In addition, results of RMS angle spread of departure and RMS angle spread of arrival are presented. These results will provide useful reference for the deployment and optimization of wireless communication networks in PIoT substation scenarios.

MODEL OF A CHAOTIC ULTRA-WIDEBAND INFORMATION TRANSMISSION SYSTEM FOR WIRELESS SENSOR NETWORKS

Distributer measuring systems, consisting of interconnected wireless sensor nodes with autonomous supply, have plenty of applications in different areas of engineering end technology. Therefore, wireless sensor network design and optimization is a vital problem in informational technologies and computer engineering. Regardless of particular application and architecture, a wireless sensor network consists of several small sensor nodes somehow distributed in an area of inspection. Such a design ensures a distributed parameter measuring, according to which, each node performs preprocessing procedures for the data, which are to be aggregated by a so-called sink node and sent via other networks, e.g., wired or wireless local area network. For healthcare solutions, it is important to ensure a stable connection with the network and server back-end to provide a high-level analysis based on predicting methods of machine learning algorithms. This paper describes an application of an ultra-wideband communication model based on chaos synchronization as a low-power and efficient alternative solution for building wireless sensor networks. Thus, using chaotic signals as information carriers in wireless communication has several advantages, including a wide smooth spectrum, high information capability, cybersecurity, and low power consumption. The synchronization problem is one of the most vital tasks to be solved to design a chaos application for ultra-wide-band communication. Being well-studied for periodic signals, modern synchronization theory contains plenty of solutions for classical telecommunication and radio engineering systems, however, it is not developed enough for chaotic systems. Hence, a one-directional dissipative synchronization between two Chen systems is studied in the first section. The second section is devoted to the computer simulation of the model, described in the previous section. All the models built and the simulations performed have been done using MATLAB/Simulink software. The negative impact of channel noise and inequality of system parameters is considered. The possible way how to improve technical characteristics is also provided. Proposed models are to be used to design and develop low-cost wireless sensor networks for multi-channel healthcare solutions.

A Multi-Strategy Improved Sparrow Search Algorithm for Coverage Optimization in a WSN.

To address the problems of low monitoring area coverage rate and the long moving distance of nodes in the process of coverage optimization in wireless sensor networks (WSNs), a multi-strategy improved sparrow search algorithm for coverage optimization in a WSN (IM-DTSSA) is proposed. Firstly, Delaunay triangulation is used to locate the uncovered areas in the network and optimize the initial population of the IM-DTSSA algorithm, which can improve the convergence speed and search accuracy of the algorithm. Secondly, the quality and quantity of the explorer population in the sparrow search algorithm are optimized by the non-dominated sorting algorithm, which can improve the global search capability of the algorithm. Finally, a two-sample learning strategy is used to improve the follower position update formula and to improve the ability of the algorithm to jump out of the local optimum. Simulation results show that the coverage rate of the IM-DTSSA algorithm is increased by 6.74%, 5.04% and 3.42% compared to the three other algorithms. The average moving distance of nodes is reduced by 7.93 m, 3.97 m, and 3.09 m, respectively. The results mean that the IM-DTSSA algorithm can effectively balance the coverage rate of the target area and the moving distance of nodes.

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression.

Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random obstruction or climate change. This study explores the capability of multi-boundary fuzzy linear regression (MBFLR) to establish uncertainty relationships between related variables for path loss predictions of WSN in agricultural farming. Measurement campaigns along various routes in an agricultural area are conducted to obtain terrain profile data and path losses of radio signals transmitted at 433 MHz. Proposed models are fitted using measured data with "initial membership level" (μAI). The boundaries are extended to cover the uncertainty of the received signal strength indicator (RSSI) and distance relationship. The uncertainty not captured in normal measurement datasets between transmitter and receiving nodes (e.g., tall grass, weed, and moving humans and/or animals) may cause low-quality signal or disconnectivity. The results show the possibility of RSSI data in MBFLR supported at an μAI of 0.4 with root mean square error (RMSE) of 0.8, 1.2, and 2.6 for short grass, tall grass, and people motion, respectively. Breakpoint optimization helps provide prediction accuracy when uncertainty occurs. The proposed model determines the suitable coverage for acceptable signal quality in all environmental situations.

O-RAN architecture, interfaces, and standardization: Study and application to user intelligent admission control

Open radio access network (O-RAN), driven by O-RAN Alliance is based on the disaggregation of the traditional RAN systems into radio unit (RU), distributed unit (DU) and central unit (CU) components. It provides a unique opportunity to reduce the cost of wireless network deployment by using open-source software, serving as a foundation for O-RAN compliant functions, and by utilizing low-cost, generic white-box hardware for radio components. Relying on the two core pillars of openness and intelligence, there has been a coordinated global effort from operators and equipment providers to enhance the RAN architecture and improve its performance through virtualized network elements and open interfaces that incorporate intelligence in RAN. With the increased complexity of 5G networks and the demand to fulfill requirements, intelligence is becoming a key factor for automated deployment, operation, and optimization of open wireless networks. The first thrust of this paper surveys the AI/ML architecture in O-RAN specifications, key discussion points and future standardization directions, respectively. In the second part, we introduce a proof-of-concept use case on AI-driven network optimization within the near real-time RAN intelligent controller (near-RT RIC) and non-real time RIC (non-RT RIC). In particular, we investigate the user admission control problem, led by a deep learning-based algorithm, implemented as an xApp for network performance enhancement. Extensive system-level simulations are performed with NS-3 LTE to assess the proposed admission control algorithm. Accordingly, the proposed dynamic algorithm shows a significant admission control performance improvement and flexibility, compared to existing admission control static techniques, while satisfying the stringent quality of service targets of admitted devices. Finally, the paper offers insightful conclusions and findings on the AI-based modeling, model inference performance, key performance challenges and future research directions, respectively.

A Delaunay Edges and Simulated Annealing-Based Integrated Approach for Mesh Router Placement Optimization in Wireless Mesh Networks.

Wireless Mesh Networks (WMNs) can build a communications infrastructure using only routers (called mesh routers), making it possible to form networks over a wide area at low cost. The mesh routers cover clients (called mesh clients), allowing mesh clients to communicate with different nodes. Since the communication performance of WMNs is affected by the position of mesh routers, the communication performance can be improved by optimizing the mesh router placement. In this paper, we present a Coverage Construction Method (CCM) that optimizes mesh router placement. In addition, we propose an integrated optimization approach that combine Simulated Annealing (SA) and Delaunay Edges (DE) in CCM to improve the performance of mesh router placement optimization. The proposed approach can build and provide a communication infrastructure by WMNs in disaster environments. We consider a real scenario for the placement of mesh clients in an evacuation area of Kurashiki City, Japan. From the simulation results, we found that the proposed approach can optimize the placement of mesh routers in order to cover all mesh clients in the evacuation area. Additionally, the DECCM-based SA approach covers more mesh clients than the CCM-based SA approach on average and can improve network connectivity of WMNs.

Research on Efficiency Optimization of Logistics Vehicle Monitoring Model Based on Wireless Sensor Network

For enhance running effectiveness efficiency of logistics carriage supervisory depended on wireless sensor network (WSN), a novel wireless sensor network optimized by improved bat algorithm is established. Firstly, working theory and system framework of WSN depended on logistics monitoring system are analyzed. Secondly, B-MAC protocol is used in proposed wireless sensor network, and corresponding models are established, and recovery ratio of wireless sensor network is defined, and node deployment optimization of wireless sensor network is constructed. Thirdly, the optimization algorithm of node deployment of WSN is designed based on amended bat algorithm, and analysis procedure of this algorithm is established. Finally, a simulation analysis is carried out, analysis results show that performance of proposed WSN logistics carriage supervisory based on improved bat algorithm is better, which has quicker convergence speed and higher convergence precision, and reliability of proposed WSN logistics carriage supervisory is improved, the energy consumption of sensor node data transmission is reduced, and the life of WSN is improved. Proposed WSN based on logistics carriage supervisory based on improved BA has higher coverage ratio and higher efficiency. Therefore proposed wireless sensor network based on logistics carriage supervisory based on improved bat algorithm can obtain better monitoring efficiency, which has prospect application view.

A smart optimizer approach for clustering protocol in UAV-assisted IoT wireless networks

The Internet of Things (IoT) is a network in the socio-physical space that provides a platform for large-scale data collection. In this context, Wireless Sensor Nodes (WSN) and Unmanned Aerial Vehicles (UAVs) can play an essential role in reducing costs and ease of usage. One of the critical challenges of this network is energy consumption, the main part is related to the transmission unit. In this way, the Smart Optimizer Approach (SOA) to solving global optimization, engineering problems, and clustering in UAV-Assisted IoT Wireless Networks is presented. The SOA uses a Hybrid Vector-Based Operator (HVO), a Smart Selection Search Mode (S3M), and a Smart Re-Randomize Schema (SRS). Numerical comparison and discussions are done on CEC 2017 functions and benchmark functions and engineering problems. Comparison of performance with other algorithms shows the high generality of the SOA. Finally, this paper presented a new SOA-based Clustering protocol (SOAC) for large-scale UAV-Assisted IoT Wireless Networks. In this network, UAVs are used as Air Base Station (ABS). According to this large-scale network and the UAVs' movement, traditional protocols with high control message overhead aren't suitable. Clustering is presented with the aim of reducing overhead and increasing throughput by UAVs simultaneously. SOAC uses two new mechanisms, Assistant to the Cluster Head (ACH) and discretion license (DL), which have been introduced recently. The results of the simulations show the proposed clustering has a good improvement in stability, energy/load balancing, network lifetime, and throughput.

Wireless Network Analysis and Optimization Based on the Social Media Data

Wireless Network Analysis and Optimization Based on the Social Media Data

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α = α ∗ , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.

2.4GHz Wireless Local Area Network Optimization in University Dormitory Buildings

This article describes the wireless network problems met in University dormitory buildings, especially under 2.4GHz spectrum. Through wireless network optimization such as channel re-allocation and transmission power adjustment manually, the performance of the wireless LAN could be improved a lot even for real-time services such as video and live broadcast.