Analysis of Mobile and Internet Network Coverage: Propagation of Electromagnetic Waves and Concept of Digital Divide in Burundi

Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is still a long process. 2G networks have developed the messaging service, which complements the already operational voice service. 2G technology has rapidly progressed to the third generation (3G), incorporating multimedia data transmission techniques. It then progressed to fourth generation (4G) and LTE (Long Term Evolution), increasing the transmission speed to improve 3G. Currently, developed countries have already moved to 5G. In developing countries, including Burundi, a member of the East African Community (ECA) where more than 80% are connected to 2G technologies, 40% are connected to the 3G network and 25% to the 4G network and are not yet connected to the 5G network and then still a process. The objective of this article is to analyze the coverage of 2G, 3G and 4G networks in Burundi. This analysis will make it possible to identify possible deficits in order to reduce the digital divide between connected urban areas and remote rural areas. Furthermore, this analysis will draw the attention of decision-makers to the need to deploy networks and coverage to allow the population to access mobile and Internet services and thus enable the digitalization of the population. Finally, this article shows the level of coverage, the digital divide and an overview of the deployment of base stations (BTS) throughout the country to promote the transformation and digital inclusion of services.

Intelligent Deployment Model for Target Coverage in Wireless Sensor Network

Target coverage and continuous connection are the major recital factors for Wireless Sensor Network(WSN).Several previous research works studied various algorithms for target coverage difficulties;however they lacked to focus on improving the network’s life time in terms of energy.This research work mainly focuses on target coverage and area coverage problem in a heterogeneous WSN with increased network lifetime.The dynamic behavior of the target nodes is unpredictable,because the target nodes may move at any time in any direction of the network.Thus,target coverage becomes a major problem in WSN and its applications.To solve the issue,this research work is motivated to design and develop an intelligent model named Distributed Flexible Wheel Chain(DFWC)model for efficient target coverage and area coverage in WSN applications.More number of target nodes is covered by minimum number of sensor nodes that can improve energy efficiency.To be specific,DFWC motivated at obtaining lesser connected target coverage,where every target is available in the monitoring area is covered by a smaller number of sensor nodes.The simulation results show that the proposed DFWC model outperforms the existing models with improved performance.

Energy Proficient Reduced Coverage Set with Particle Swarm Optimization for Distributed Sensor Network

Retransmission avoidance is an essential need for any type of wireless communication.As retransmissions induce the unnecessary presence of redundant data in every accessible node.As storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective node.In this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space.PSO is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting nodes.Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole network.The proposed algorithm has experimented with a different number of nodes.Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.

A Sensor Network Coverage Planning Based on Adjusted Single Candidate Optimizer

Wireless sensor networks(WSNs)are widely used for various practical applications due to their simplicity and versatility.The quality of service in WSNs is greatly influenced by the coverage,which directly affects the monitoring capacity of the target region.However,low WSN coverage and uneven distribution of nodes in random deployments pose significant challenges.This study proposes an optimal node planning strategy for net-work coverage based on an adjusted single candidate optimizer(ASCO)to address these issues.The single candidate optimizer(SCO)is a metaheuristic algorithm with stable implementation procedures.However,it has limitations in avoiding local optimum traps in complex node coverage optimization scenarios.The ASCO overcomes these limitations by incorporating reverse learning and multi-direction strategies,resulting in updated equations.The performance of the ASCO algorithm is compared with other algorithms in the literature for optimal WSN node coverage.The results demonstrate that the ASCO algorithm offers efficient performance,rapid convergence,and expanded coverage capabilities.Notably,the ASCO achieves an archival coverage rate of 88%,while other approaches achieve coverage rates below or equal to 85%under the same conditions.

Hybrid Power Bank Deployment Model for Energy Supply Coverage Optimization in Industrial Wireless Sensor Network

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.

A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.

Coverage Optimization of LTE Networks Based on Antenna Tilt Adjusting Considering Network Load

In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs' antenna tilt angles(ATA). The coverage is optimized by optimizing the number of served users based on the Modified Particle Swarm Optimization(MPSO)algorithm. Simulation results show that both the number of served users by each e NB and the system throughput are significantly increased. As well,the average load and the bandwidth efficiency of the network are improved.

A Power- and Coverage-aware Clustering Scheme for Wireless Sensor Networks

A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.

Voronoi-Based Coverage Optimization for Directional Sensor Networks

Sensing coverage is a fundamental problem in sensors networks. Different from traditional isotropic sensors with sensing disk, directional sensors may have a limited angle of sensing range due to special applications. In this paper, we study the coverage problem in directional sensor networks (DSNs) with the rotatable orientation for each sensor. We propose the optimal coverage in directional sensor networks (OCDSN) problem to cover maximal area while activating as few sensors as possible. Then we prove the OCDSN to be NP-complete and propose the Voronoi-based centralized approximation (VCA) algorithm and the Voronoi-based distributed approximation (VDA) algorithm of the solution to the OCDSN problem. Finally, extensive simulation is executed to demonstrate the performance of the proposed algorithms.

Optimization method of linear barrier coverage deployment for multistatic radar

To address the problem of building linear barrier coverage with the location restriction, an optimization method for deploying multistatic radars is proposed, where the location restriction splits the deployment line into two segments. By proving the characteristics of deployment patterns, an optimal deployment sequence consisting of multiple deployment patterns is proposed and exploited to cover each segment. The types and numbers of deployment patterns are determined by an algorithm that combines the integer linear programming(ILP)and exhaustive method(EM). In addition, to reduce the computation amount, a formula is introduced to calculate the upper threshold of receivers’ number in a deployment pattern. Furthermore, since the objective function is non-convex and non-analytic, the overall model is divided into two layers concerning two suboptimization problems. Subsequently, another algorithm that integrates the segments and layers is proposed to determine the deployment parameters, such as the minimum cost, parameters of the optimal deployment sequence, and the location of the split point. Simulation results demonstrate that the proposed method can effectively determine the optimal deployment parameters under the location restriction.

Optimal Node Scheduling for Desired Percentage of Coverage in Wireless Sensor Networks

Recent developments in wireless communication and embedded computing technologies have led to the advent of wireless sensor network technology. Hundreds of thousands of these micro sensors can be deployed in many areas including health, environment and battlefield in order to monitor the domain with desired level of accuracy. When wireless sensors are deployed in an area, the lifetime of the network should last as long as possible according to the original amount of energy. Therefore, reducing energy consumption in WSNs is of primary concern. We have proposed a node scheduling solution that solves the coverage and connectivity problem in sensor networks in an integrated manner. In this way we will divide network life time to finite number of rounds and in each round we will generate a coverage bitmap of sensors of the domain and based on this bitmap it will decided which sensors remain active or go to sleep. We will check the connection of the sensor network by using Laplacian of adjancy graph of active nodes in each round. Also the network will be capable of producing desired percentage of coverage by using coverage bitmap. We will define the connected coverage problem as an optimization problem and we will seek a solution for the problem by using Genetic Algorithm optimization method.

Civil Aircraft Assisted Space-Air-Ground Integrated Networks: Architecture Design and Coverage Analysis

In this paper, we propose a novel AIenabled space-air-ground integrated networks(SAGIN). This new integrated networks architecture consists of LEO satellites and civil aircrafts carrying aerial base stations, called "civil aircraft assisted SAGIN(CAA-SAGIN)". The assistance of civil aircrafts can reduce the stress of satellite networks, improve the performance of SAGIN, decrease the construction cost and save space resources. Taking the Chinese mainland as an example, this paper has analyzed the distribution of civil aircrafts, and obtained the coverage characteristics of civil aircraft assisted networks(CAAN). Taking Starlink as the benchmark, this paper has calculated the service gap of CAAN, and designed the joint coverage constellation. The simulation results prove that the number of satellites in CAASAGIN can be greatly reduced with the assistance of civil aircrafts at the same data rate.

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.

Energy-Efficient Multi-UAV Coverage Deployment in UAV Networks:A Game-Theoretic Framework

UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

Dynamic Coverage with Wireless Sensor and Actor Networks in Underwater Environment

This paper studies the problem of dynamic coverage with wireless sensor and actor networks(WSANs) in underwater environment.Different from most existing works,the WSANs consist of two kinds of nodes,i.e.,sensor nodes(SNs) which cannot move autonomously and actor nodes(ANs) which can move autonomously according to the performance requirement.The problem of how to coordinate two kinds of nodes to facilitate dynamic coverage in underwater environment is challenging due to their heterogeneous capabilities.To reduce redundancy of communication links and improve connectivity between ANs and SNs in underwater WSANs,a min-weighted rigid graph based topology optimization scheme is first developed,such that the underwater communication energy consumption can be saved.With the optimized topology,a dynamic coverage strategy is proposed to improve the coverage among SNs and ANs for underwater WSAN where underwater fluid motions are considered.Furthermore,it is proved that the network coverage area is connected by using the min-weighted rigid graph.Finally,simulation results are presented to show the effectiveness of the main results.

Energy-Efficient Area Coverage in Heterogeneous Energy Wireless Sensor Networks

Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-efficient Area Coverage protocol for Heterogeneous Energy sensor networks (ACHE) is proposed. ACHE can achieve a good performance in terms of sensing area coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, ACHE selects the active nodes based on the average residual energy of neighboring nodes and its own residual energy parameters. Our simulation demonstrates that ACHE not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, ACHE can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.

A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

Analysis of coverage problem under border effects in wireless sensor networks

Wireless sensor networks can be used to monitor the interested region by deploying dense sensornodes.Coverage is a primary metric to evaluate the capacity of monitoring.In this paper,we focus on thecoverage problem under border effects,where the sensor nodes are distributed in a circle-shaped regionrandomly.Under this scenario,we derive the expected coverage of the sensor node and the total networkcoverage provided by n sensor nodes accurately by probability.These findings are useftd to determine therelated parameters(sensing range,number of sensor nodes and radius of monitored region)for a specificnetwork coverage ratio.Simulation results demonstrate that our analysis is correct and effective.

Localized Coverage Connectivity Based on Shape and Area Using Mobile Sensor Robots in Wireless Sensor Networks

A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.

A NOVEL INTEREST COVERAGE METHOD BASED ON JENSEN-SHANNON DIVERGENCE IN SENSOR NETWORKS

In this paper, the idea of interest coverage is provided to form clusters in sensor network, which mean that the distance among data trends gathered by neighbor sensors is so small that, in some period, those sensors can be clustered, and certain sensor can be used to replace the cluster to form the virtual sensor network topology. In detail, the Jensen-Shannon Divergence (JSD) is used to characterize the distance among different distributions which represent the data trend of sensors. Then, based on JSD, a hierarchical clustering algorithm is provided to form the virtual sensor network topology. Simulation shows that the proposed approach gains more than 50% energy saving than Sta- tistical Aggregation Methods (SAM) which transmitted data gathered by sensor only when the differ- ence among data exceed certain threshold.