Improve Network Coverage Research Articles

ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing

The Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.

A Dynamic Topology Optimization Method for Tactical Edge Networks Based on Virtual Backbone Networks.

To address the issues of low survivability and communication efficiency in wireless sensor networks caused by frequent node movement or damage in highly dynamic and high-mobility battlefield environments, we propose a dynamic topology optimization method based on a virtual backbone network. This method involves two phases: topology reconstruction and topology maintenance, determined by a network coverage threshold. When the coverage falls below the threshold, a virtual backbone network is established using a connected dominating set (CDS) and non-backbone node optimization strategies to reconstruct the network topology, quickly restore network connectivity, effectively improve network coverage, and optimize the network structure. When the coverage is above the threshold, a multi-CDS scheduling algorithm and slight position adjustments of non-backbone nodes are employed to maintain the network topology, further enhancing network coverage with minimal node movement. Simulations demonstrate that this method can improve coverage and optimize network structure under different scales of network failures. Under three large-scale failure operational scenarios where the network coverage threshold was set to 80%, the coverage was enhanced by 26.12%, 15.88%, and 13.36%, and in small-scale failures, the coverage was enhanced by 7.55%, 4.90% and 7.84%.

Coverage Optimization Strategy of UWSNs Based on Improved Sparrow Search Algorithm

Randomly distributed nodes lead to low coverage rate and low connectivity in the underwater wireless sensor networks (UWSNs). An UWSNs node coverage optimization strategy based on the Improved Sparrow Search Algorithm (ISSA) is designed to improve coverage rate and connectivity. The ISSA algorithm effectively balances the global and local optimization capabilities of nodes by introducing nonlinear inertia weights and Levy flight strategy, thereby avoiding the algorithm from getting trapped in local optima. Simulation experiments demonstrate that ISSA achieves optimal deployment results in a short time. It not only improves network coverage but also maintains good network connectivity and reduces node redundancy, thereby e ffectively reducing network energy consumption. Compared to other common algorithms , ISSA can increase network coverage up to 8.4% and improve network utilization up to11.8%, and meets the deployment requirements.

The Current Status and Strategies for Rural E-commerce Development in Yunnan Province under the Digital Economy Context

In the context of the digital economy, rural e-commerce, as an emerging business model, plays a significant role in promoting rural economic development. However, there are challenges in its development. This paper first conducts an in-depth analysis of the development status of rural e-commerce in Yunnan Province, identifying a series of challenges it faces, including inadequate infrastructure and logistical difficulties. Subsequently, through theoretical analysis, the paper proposes a set of solutions and strategies for the development of rural e-commerce. These solutions mainly include strengthening rural e-commerce infrastructure construction and improving network coverage, enhancing rural e-commerce talent development, raising awareness and skills among farmers, and strengthening government support and policy guidance to provide a better development environment and policy support for rural e-commerce. The research results of this paper contribute to further promoting the development of rural e-commerce and fostering rural economic prosperity.

Mobile services sector in Saudi Arabia: A systematic literature review of the effective strategies for enhancing customer satisfaction

The mobile services sector in Saudi Arabia has experienced significant growth in recent years, largely driven by the increasing demand for mobile communication and internet services. This systematic review aims to identify and evaluate the effective strategies for enhancing customer satisfaction in Saudi Arabia's mobile services sector. A systematic review was conducted across five databases from 1st January 2010 and 31st March 2023. The entire process was followed as recommended by the PRISMA guidelines. The findings suggest that the most effective strategies for enhancing customer satisfaction in the Saudi Arabian mobile services sector are improving network coverage, enhancing customer service, offering competitive pricing, introducing new technology and features, and providing value-added services. By adopting these strategies, mobile service providers in Saudi Arabia can enhance their customers' satisfaction, build stronger relationships with their customers, and ultimately increase customer loyalty. Moreover, the study revealed that a combination of these strategies would lead to higher levels of customer satisfaction. The study's findings indicate that mobile service providers in Saudi Arabia can enhance customer satisfaction by focusing on various strategies, such as improving network coverage, customer service, pricing, technology, and features, and providing value added services. Customer satisfaction is one of the main aspects of service delivery. Immediate measures in this regard will assist the mobile sector in Saudi Arabia to plan effective approaches to improve customer satisfaction; this, in turn, can give them a competitive edge in the market and sustain growth in the mobile services sector.

To share or not to share? The impact of mobile network sharing for consumers and operators

This paper assesses the impact of mobile network sharing in Europe during the 2000-2019 period, looking at 140 mobile operators in 29 countries. We find that - consistent with economic theory - network sharing generated significant benefits for operators and consumers, including lower prices and improved network coverage and quality. This was driven by cost reductions, higher returns on investment and increased competition. These effects materialized heterogeneously, with the impact of network sharing depending on the type of sharing, the technology cycle in which it is entered into as well as the market position and size of the operators entering the agreement. This has important implications going forward as it shows that network sharing can play a vital role in the deployment of new 5G networks and that the technological and market specificity of each type of sharing agreement can significantly affect its outcomes.

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

A fuzzy/possibility approach for area coverage in wireless sensor networks

The literature approaches, devoted to sensor improve network coverage, are deterministic in terms of deployment environment and node configuration parameters. Nevertheless, this type of approaches has not proven to be very successful in uncertain deployment environments. This paper aims to deal with this issue using theories of uncertainty. We consider deployment environment’s imperfections and the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in wireless sensor networks (WSNs) is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to Schedule the Active/Passive State of Sensor nodes Strategy (FP-3SNS). This model helps to plan the scheduling of node states (Active/Passive) based on possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated our model (FP-3SNS) with (a) a running example (that shows the best choice of the active node with a probability of 0.81215); (b) a statistical evaluation (calculation of the confidence interface), where the average coverage reliability at 95% of FP-3SNS use is between (92.94, 96.27); and (c) a comparison with maximum sensing coverage region problem (MSCR), coverage maximization with sleep scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP), Probing Environment and Adaptive Sleeping with Location Information Protocol (PEAS-LI), and Variable Length Particle Swarm Optimization Algorithm with a Weighted Sum Fitness Function (WS-VLPSO). The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem and the proposed model maintained a coverage between 99.99 and 90.00% for a significant period of time.

A Novel Energy Efficient Cluster Based Routing in Wireless Sensor Network

Objective: Network lifetime of WSN can be extended through assigning nodes with higher energy levels as cluster heads. Cluster heads selection improves the packet transmission rate and conserves energy by reducing re-transmission. The proposed algorithm assigns nodes with high energy as clusters heads and finds optimal path which improves network coverage by avoiding dead nodes. Methods: To improve network lifetime and conserve node energy, a novel approach which involves optimum cluster head selection using Firefly algorithm (FF) and Glow Swarm Optimization (GSO) algorithm for routing in WSN (FFGSO) was developed. The cluster head selection is designed to be multi-objective function which meant for not only minimizing the energy consumption but also to minimize the distance and to improve coverage. Findings: The proposed model reduces energy consumption to about 16.21% which avoids dead nodes effectively and finds the shortest path to reach sink which eventually improves network coverage and improves the throughput by 9.52%. Novelty: To overcome the limitations of dead nodes and to improve the network lifetime, the proposed method introduces cluster head selection as a multi-objective function. To improve energy consumption, network lifetime and network coverage and to overcome the limitations of PSO-GSO, Leach and FF-PSO cluster head selection is designed as multi-objective function. The proposed method improves network stability and network lifetime through reducing energy consumption and finding the shortest path. Keywords: Wireless Sensor Network; Cluster head selection; Firefly algorithm (FF); Glow Swarm Optimization (GSO); Energy efficient clustering

A Novel Bio-Inspired Energy Optimization for Two-Tier Wireless Communication Networks: A Grasshopper Optimization Algorithm (GOA)-Based Approach

Energy consumption has become one of the most challenging problems in future wireless communication networks. One of the promising methods in fifth generation (5G) cellular networks to meet the ever-increasing demand for high data traffic is wireless heterogeneous networks (HetNets). Adding more base stations may improve network coverage, but leads to the consumption of a significant amount of power. The scheme of two-tier networks contains small cell base stations (SCBs) that cooperate with macro cell base stations (MCBs) to provide wider coverage. Some small cell base station SCBs are experiencing light traffic loads due to the movement of user equipment (UEs), but these SCBs still consume a considerable amount of energy. Therefore, to reduce SCBs’ power consumption and maximize the overall energy efficiency (EE) of a two-tier network, some SCBs need to be switched off. In this paper, we extend the operation modes for BSs and present a novel mechanism to select an appropriate operation mode for each SCB that is based on bio-inspired behavior. We employ a bias function to manage the power consumption of each operation mode. Each SCB has four power mode selections: On, Standby, Sleep, and Off. We formulate the EE maximization problem under a set of constraints and present a Grasshopper Optimization Algorithm-based Variant Power Mode Selection (GOA-VPMS) to solve it. The proposed algorithm scheme outperforms previous work and provides a higher EE, according to the simulation results.

IMF 2 O 2 : A Fully Connected Sensor Deployment Algorithm for Underwater Sensor Networks

To address the problems of node deployment schemes in existing underwater sensor networks that lack consideration of network connectivity and high deployment costs, this article constructs an optimization model that maximizes network coverage and minimizes deployment costs while ensuring full connectivity. For the NP-hard property of this optimization model, an improved moth flame optimization node deployment algorithm based on fuzzy operators (IMF 2 O 2 ) is proposed. First, comprehensively considering the two performance metrics of network coverage and network connectivity, a multi-objective selection mechanism based on fuzzy operators is proposed to improve network coverage while ensuring full connectivity. Second, a fixed number of nodes are used to monitor the target event points, transforming the node deployment of sensors into an optimal problem and proposing an improved moth flame optimization algorithm to solve this problem. Finally, the two metrics of coverage and deployment cost are measured and the fuzzy operator is used to select the optimal number of nodes to be deployed. Numerical results showed that the proposed algorithm improved network coverage rate by 10%, 22%, and 25%, and improved network connectivity rate by 12%, 20%, and 8% as compared to PSSD, RAWS, and VODA, respectively, while ensuring full connectivity.

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes' energy consumption and lengthen the network's lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network's lifetime.

Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance. The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’ access network. The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness. Coverage and energy usage are mostly determined by successful sensor placement strategies. Nature-inspired algorithms are the most effective solution for short sensor lifetime. The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks (WSNs’) maximum network coverage. Moreover, it identifies quantity of installed sensor nodes for the given area. Superiority of algorithm has been identified based on value of optimized energy. The first half of the paper’s literature on nature-inspired algorithms is discussed. Later six metaheuristics algorithms (Grey wolf, Ant lion, Dragonfly, Whale, Moth flame, Sine cosine optimizer) are compared for optimal coverage of WSNs. The simulation outcomes confirm that whale optimization algorithm (WOA) gives optimized Energy with improved network coverage with the least number of nodes. This comparison will be helpful for researchers who will use WSNs in their applications.

A novel method for optimizing energy consumption in wireless sensor network using genetic algorithm

Advancements in information technology and wireless sensors networks (WSN) create new possibilities in a variety of industries, including environmental testing, healthcare, and industrial control. The sensor has become more intelligent, has begun to go wireless, and has become smaller. WSN is used in various industries thanks to its benefits such low cost, simple installation, consistent transmission, and excellent anti-interference ability. Although wireless network sensors have numerous benefits, they also have certain drawbacks. The main obstacle limiting the lifespan of wireless sensor networks is energy, and it might be difficult or even impossible to charge or replace the power sources on wireless network nodes. The battery puts limits on the network nodes responsible for data processing and information transfer. The key to network optimization is thus to increase the network's overall life. WSN have a wide range of recognized uses, and this diversity necessitates improvements to the existing protocols and their particular properties. The lifespan of the network and energy usage for routing are two important criteria that are important in every application. In order to create the perfect set of parameters for routing and application-based WSNs, the study discussed here improved network coverage, clustering, node placement, and data aggregation. A unique fitness function was created, enhanced, and customised for usage in all stages of WSN operation based on the outcomes of evolutionary algorithms and NS simulations. The results of the comparison indicated that, while to varied degrees, these logarithms and methods might reduce that energy. It has been determined that the suggested methodology could lead to a 50% reduction in energy consumption.

On the impact of IQI on cooperative NOMA with direct links in the presence of imperfect CSI

Cooperative non-orthogonal multiple access (CNOMA) is considered as a promising technique to improve network coverage, reliability and transmission for future wireless communication networks. Meanwhile, transceivers can suffer from a number of hardware imperfections that will significantly reduce their performance, such as phase noise and in-phase/quadrature-phase imbalance (IQI). In this paper, we investigate the effect of in-phase and quadrature-phase imbalance (IQI) on CNOMA with direct links in the presence of imperfect channel state information (ICSI). The outage probability (OP) and throughput expressions are derived to evaluate the performance behaviors of the CNOMA with direct links under the IQI and ICSI imperfections. Theoretical analyzes are verified by Monte Carlo simulation. The effect of the IQI on the CNOMA with the direct links has been studied with different parameters (image rejection ratio (IRR), power allocation) and compared with conventional NOMA to clearly observe the degrading effects of imperfections on the systems. The simulation results demonstrate that the IQI and ICSI have a negative impact on the outage and throughput performance.

Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory

Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time.

A Coverage Hole Patching Algorithm for Heterogeneous Wireless Sensor Networks

The improvement of coverage is a critical issue in the coverage hole patching of sensors. Traditionally, VOPR and VORCP algorithms improve the coverage of the detection area by improving the original VOR algorithm, but coverage hole patching algorithms only target homogeneous networks. In the real world, however, the nodes in the wireless sensor network (WSN) are often heterogeneous, i.e., the sensors have different sensing radii. The VORPH algorithm uses the VOR in a hybrid heterogeneous network and improves the original algorithm. The patched nodes are better utilized, and the detection range is enlarged. However, the utilization rate of the patched nodes is not optimized, making it impossible to patch the coverage holes to the maximum degree. In the environment of hybrid heterogeneous WSN, we propose a coverage hole patching algorithm with a priority mechanism. The algorithm determines the patching priority based on the size of the coverage holes, thereby improving network coverage, reducing node redundancy, and balancing resource allocation. The proposed algorithm was compared under the same environment by simulation and analysis. The results show that our algorithm is superior to the traditional coverage hole patching algorithms in coverage rate, and can reduce node redundancy.

Quality of Service Reliability: A Study of Received Signal Quality in GSM Networks

Every radio frequency (RF) design, after its implantation, should be regularly evaluated. The kernel of this article is to evaluate the performance of four GSM networks, taking its received signal quality (RxQual) into consideration. A total of 10501, 10140, 10415 and 10690 RxQual measurements were obtained for MTN, 9mobile, Airtel and Globacom network. These generated data were subjected to statistical analysis in the form of bar charts, quality plots and calculations of measures of central tendency and dispersion. Result shows that 78.43%, 92.18%, 90.68% and 86.93% of the drive test route for MTN, 9mobile, Airtel and Globacom network had good signal quality and met with the Nigerian telecommunication regulatory benchmark of at least 4dB for RxQual. It is therefore deduced that in terms of RxQual, 9mobile was the best GSM network, followed by Airtel network, Globacom network and then MTN network. The result provided in this article will help mobile network operators to improve signal quality, ensure improved network coverage and increase network capacity in the future.

Performance Evaluation of Reconfigurable Intelligent Surface against Distributed Antenna System at the Cell Edge

In small spaces in which typical cell towers cannot be constructed, distributed antenna systems (DASs) are the preferable approach for increasing network coverage, as they are a superior solution for congested, high-volume areas. However, due to their high cost and complexity in backhaul routing, reconfigurable intelligent surfaces (RISs) are a promising solution to overcome the major drawbacks of DAS systems while improving network coverage. Thus, this work investigated a correlation of execution in an RIS-aided system cell framework and DAS-aided system cell framework where a simple and precise structure for the performance measurement of area spectral efficiency (ASE) and energy efficiency (EE) under realistic channel presumptions was introduced. The analysis started with the downlink ergodic capacity with regards to the RIS framework and DAS framework under a generalized Nakagami-m fading channel with the presence of path-loss attenuation and interference from co-channel base stations (BSs), and was simplified further by utilizing a moment-generating function (MGF)-based approach. From the computed expression, the effects of traffic activity, EE and ASE were derived and analyzed for both systems in the presence of co-channel interference. The results were then verified by comparing them with Monte Carlo simulations, and the findings show that the two outcomes generally match. Based on these, it is demonstrated that the ASE performance of the RIS-assisted system in various traffic activity conditions outperforms the DAS-aided system; however, in high signal-to-noise (SNR) regions with full traffic activity, the ASEs are highest for both systems; by only 0.005 bits/s/Hz/km2.

Multiple reconfigurable intelligent surfaces assisted downlink NOMA system

SummaryPaper has considered multiple reconfigurable intelligent surfaces (RISs) assisted downlink non‐orthogonal multiple accesses (NOMA) system, referred to as MR‐NOMA. Multiple RISs create a line‐of‐sight (LOS) channel between BS and users to improve network coverage, especially in high frequencies and severe path attenuation scenarios. In MR‐NOMA, all RISs are in series and activated simultaneously; therefore, multiplicative beamforming gain from the inter‐RIS channels is achieved. Bit error probability (BEP) under the Rician fading channel is derived and compared with simulation results. It is observed that BEP performance depends on the number of passive reflecting elements, Rician factor, and positions of RISs panels in the MR‐NOMA.