scholarly journals A Three-Tier Architecture of Large-Scale Wireless Sensor Networks for Big Data Collection

2020 ◽  
Vol 10 (15) ◽  
pp. 5382 ◽  
Author(s):  
Ado Adamou Abba Ari ◽  
Asside Christian Djedouboum ◽  
Abdelhak Mourad Gueroui ◽  
Ousmane Thiare ◽  
Alidou Mohamadou ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Smart Cities ◽  
Low Cost ◽  
Memory Storage ◽  
Wireless Sensor ◽  
Communication Model ◽  
High Loss ◽  
Technological Advances

In recent years, technological advances and the ever-increasing power of embedded systems have seen the emergence of so-called smart cities. In these cities, application needs are increasingly calling for Large-Scale Wireless Sensor Networks (LS-WSN). However, the design and implementation of such networks pose several important and interesting challenges. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities. Moreover, sensors are often required to cooperate in order to route the collected data to a single central node (or sink). The many-to-one communication model that governs dense and widely deployed Wireless Sensor Networks (WSNs) most often leads to problems of network overload and congestion. Indeed, it is easy to show that the closer a node is geographical to the sink, the more data sources it has to relay. This leads to several problems including overloading of nodes close to the sink, high loss rate in the area close to the sink, and poor distribution of power consumption that directly affects the lives of these networks. In this context, we propose a contribution to the problem of LS-WSN energy consumption. We designed a hierarchical 3-tier architecture of LS-WSNs coupled with a modeling of the activities of the different sensors in the network. This architecture that is based on clustering also includes a redeployment function to maintain the topology in case of coverage gaps. The results of the performed simulations show that our architecture maximizes the lifetime than compared solutions.

scholarly journals Wireless Sensor Networks for Smart Cities: Network Design, Implementation and Performance Evaluation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 218
Author(s):  
Ala’ Khalifeh ◽  
Khalid A. Darabkh ◽  
Ahmad M. Khasawneh ◽  
Issa Alqaisieh ◽  
Mohammad Salameh ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Experimental Evaluation ◽  
Large Scale ◽  
Performance Metrics ◽  
Smart Cities ◽  
Field Tests ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Real Field

The advent of various wireless technologies has paved the way for the realization of new infrastructures and applications for smart cities. Wireless Sensor Networks (WSNs) are one of the most important among these technologies. WSNs are widely used in various applications in our daily lives. Due to their cost effectiveness and rapid deployment, WSNs can be used for securing smart cities by providing remote monitoring and sensing for many critical scenarios including hostile environments, battlefields, or areas subject to natural disasters such as earthquakes, volcano eruptions, and floods or to large-scale accidents such as nuclear plants explosions or chemical plumes. The purpose of this paper is to propose a new framework where WSNs are adopted for remote sensing and monitoring in smart city applications. We propose using Unmanned Aerial Vehicles to act as a data mule to offload the sensor nodes and transfer the monitoring data securely to the remote control center for further analysis and decision making. Furthermore, the paper provides insight about implementation challenges in the realization of the proposed framework. In addition, the paper provides an experimental evaluation of the proposed design in outdoor environments, in the presence of different types of obstacles, common to typical outdoor fields. The experimental evaluation revealed several inconsistencies between the performance metrics advertised in the hardware-specific data-sheets. In particular, we found mismatches between the advertised coverage distance and signal strength with our experimental measurements. Therefore, it is crucial that network designers and developers conduct field tests and device performance assessment before designing and implementing the WSN for application in a real field setting.

Basic Concepts of Wireless Sensor Networks

2011 ◽  
pp. 57-64
Author(s):  
Lina M. Pestana Leão de Brito ◽  
Laura M. Rodríguez Peralta
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Cost ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Technological Advances ◽  
The Cost ◽  
Communication Equipment

As with many technologies, defense applications have been a driver for research in sensor networks, which started around 1980 due to two important programs of the Defense Advanced Research Projects Agency (DARPA): the distributed sensor networks (DSN) and the sensor information technology (SensIT) (Chong & Kumar, 2003). However, the development of sensor networks requires advances in several areas: sensing, communication, and computing. The explosive growth of the personal communications market has driven the cost of radio devices down and has increased the quality. At the same time, technological advances in wireless communications and electronic devices (such as low-cost, low-power, small, simple yet efficient wireless communication equipment) have enabled the manufacturing of sensor nodes and, consequently, the development of wireless sensor networks (WSNs).

scholarly journals Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 322 ◽  
Author(s):  
Damien Wohwe Sambo ◽  
Blaise Yenke ◽  
Anna Förster ◽  
Paul Dayang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Smart Cities ◽  
Clustering Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Environmental Behaviors ◽  
High Data ◽  
High Scalability

During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

scholarly journals Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10924
Author(s):  
Fatma H. Elfouly ◽  
Rabie A. Ramadan ◽  
Ahmed Y. Khedr ◽  
Ahmad Taher Azar ◽  
Kusum Yadav ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Large Scale ◽  
Smart Cities ◽  
Minimum Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
High Coverage ◽  
Heterogeneous Wireless Sensor Networks

 Wireless Sensor Networks (WSNs) became essential in developing many applications, including smart cities and Internet of Things (IoT) applications. WSN has been used in many critical applications such as healthcare, military, and transportation. Such applications depend mainly on the performance of the deployed sensor nodes. Therefore, the deployment process has to be perfectly arranged. However, the deployment process for a WSN is challenging due to many of the constraints to be taken into consideration. For instance, mobile nodes are already utilized in many applications, and their localization needs to be considered during the deployment process. Besides, heterogeneous nodes are employed in many recent applications due to their efficiency and cost-effectiveness. Moreover, the development areas might have different properties due to their importance. Those parameters increase the deployment complexity and make it hard to reach the best deployment scheme. This work, therefore, seeks to discover the best deployment plan for a WSN, considering these limitations throughout the deployment process. First, the deployment problem is defined as an optimization problem and mathematically formulated using Integer Linear Programming (ILP) to understand the problem better. The main objective function is to maximize the coverage of a given field with a network lifetime constraint. Nodes’ mobility and heterogeneity are added to the deployment constraints. The importance of the monitored field subareas is also introduced in this paper, where some subareas could have more importance than others. The paper utilizes Swarm Intelligence as a heuristic algorithm for the large-scale deployment problem. Simulation experiments show that the proposed algorithm produces efficient deployment schemes with a high coverage rate and minimum energy consumption compared to some recent algorithms. The proposed algorithm shows more than a 30% improvement in coverage and network lifetime. 

scholarly journals Hard Decision-Based Cooperative Localization for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4665 ◽  
Author(s):  
Zhaoyang Wang ◽  
Xuebo Jin ◽  
Xiaoyi Wang ◽  
Jiping Xu ◽  
Yuting Bai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Cost ◽  
Distance Estimation ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Accuracy ◽  
Range Free ◽  
Hard Decision

Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.

Low-Cost Collaborative Mobile Charging for Large-Scale Wireless Sensor Networks

2017 ◽  
Vol 16 (8) ◽  
pp. 2213-2227 ◽  
Author(s):  
Tang Liu ◽  
Baijun Wu ◽  
Hongyi Wu ◽  
Jian Peng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Cost ◽  
Wireless Sensor

scholarly journals A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1113 ◽  
Author(s):  
Asside Christian Djedouboum ◽  
Ado Adamou Abba Ari ◽  
Abdelhak Mourad Gueroui ◽  
Alidou Mohamadou ◽  
Ousmane Thiare ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Smart Cities ◽  
Network Connectivity ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Area Of Interest

Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.

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