Wireless Sensor Networks

2019 ◽  
pp. 247-276
Author(s):  
Sachin R. Jain ◽  
Nileshsingh V. Thakur
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Architecture ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Industry Automation ◽  
Almost All ◽  
And Control ◽  
Evaluation Parameters ◽  
Human Healthcare

Wireless sensor networks (WSNs) can be classified among the blazing domains of research in the recent era. WSNs have enormous day-to-day life real-time applications due their low priced, self-computing, powerful, autonomous small sensor nodes which have a small storage capacity, restricted non-removable non-rechargeable battery, and a restricted computational capacity. The applicability of WSNs are in almost all domains, like observing environmental conditions, human healthcare tracking systems, position tracking and monitoring, industry automation, process tracking and controlling, tracking and monitoring objects, mammal, human being, and control, and many more. This chapter briefly explores the basic concepts, components, network architecture, design issues, challenges, routing protocols, application domains, implemented applications, etc. in the field of WSNs. It also focuses on the performance evaluation parameters to check, analyze, diagnose, examine, and determine the performance of WSNs. At the end, the chapter concludes with the scope of research in the field of wireless sensor networks.

scholarly journals Identification of an Optimized Network Topology for WSN Based on Reliability and Power Efficiency

2019 ◽  
Vol 9 (1S) ◽  
pp. 232-239
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Architecture ◽  
Power Efficiency ◽  
Mesh Networks ◽  
Vital Role ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Industry Automation ◽  
Selection Of

Wireless Sensor Networks have number of sensor nodes with limited energy resources that can be deployed regularly or randomly in the applications area. Compared to conventional technologies WSN plays vital role in major applications including structural health monitoring, hospitals, environment monitoring, defense, mining, manufacturing and industry automation. In WSN reliability of network architecture is the crucial parameter. In this paper the study of reliability of three different topologies of WSN (Wireless Sensor Networks) namely star, ring and mesh networks are explained. This paper explains the energy utilized by an each sensor node for during data transmission in each topology. The simulation results will provide useful idea for selection of topologies for sensor network designers for continuous transmission and reception of data for their applications.

Novel Energy Aware Algorithm to Design Multilayer Architecture for Dense Wireless Sensor Networks

2016 ◽  
pp. 79-114
Author(s):  
Naveen Chilamkurti ◽  
Sohail Jabbar ◽  
Abid Ali Minhas
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Architecture ◽  
Network Performance ◽  
Data Dissemination ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Unbalanced Load

Network layer functionalists are of core importance in the communication process and so the routing with energy aware trait is indispensable for improved network performance and increased network lifetime. Designing of protocol at this under discussion layer must consider the aforementioned factors especially for energy aware routing process. In wireless sensor networks there may be hundreds or thousands of sensor nodes communicating with each other and with the base station, which consumes more energy in exchanging data and information with the additive issues of unbalanced load and intolerable faults. Two main types of network architectures for sensed data dissemination from source to destination exist in the literature; Flat network architecture, clustered network architecture. In flat architecture based networks, uniformity can be seen since all the network nodes work in a same mode and generally do not have any distinguished role.

Novel Energy Aware Algorithm to Design Multilayer Architecture for Dense Wireless Sensor Networks

2020 ◽  
pp. 372-399
Author(s):  
Naveen Chilamkurti ◽  
Sohail Jabbar ◽  
Abid Ali Minhas
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Architecture ◽  
Network Performance ◽  
Data Dissemination ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Unbalanced Load

Network layer functionalists are of core importance in the communication process and so the routing with energy aware trait is indispensable for improved network performance and increased network lifetime. Designing of protocol at this under discussion layer must consider the aforementioned factors especially for energy aware routing process. In wireless sensor networks there may be hundreds or thousands of sensor nodes communicating with each other and with the base station, which consumes more energy in exchanging data and information with the additive issues of unbalanced load and intolerable faults. Two main types of network architectures for sensed data dissemination from source to destination exist in the literature; Flat network architecture, clustered network architecture. In flat architecture based networks, uniformity can be seen since all the network nodes work in a same mode and generally do not have any distinguished role.

An Efficient Fitness Function for Clustering of Wireless Sensor Networks

2020 ◽  
Vol 10 (3) ◽  
pp. 318-324
Author(s):  
Atiieh Hoseinpour ◽  
Mojtaba Jafari Lahijani ◽  
Mohammad Hosseinpour ◽  
Javad Kazemitabar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fitness Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Evolutionary Clustering ◽  
Fitness Functions ◽  
Field Sensor ◽  
Simulation Results ◽  
And Control

Background & Objective: A sensor network is composed of a large number of sensor nodes that are deployed to perform measurement and/or command and control in a field. Sensor nodes are battery powered devices and replacement or recharging of their batteries may not be feasible. One of the major challenges with sensory wireless networks is excessive energy consumption in nodes. Clustering is one of the methods that has been offered for resolving this issue. In this paper, we pursue evolutionary clustering and propose a new fitness function that har-nesses multiple propagation indices. Methods: In this paper we develop an efficient fitness function by first selecting the best clusters, and then selecting the best attribution of cluster to clusters. The distance between the nodes and relevant cluster heads was used for the mathematical modelling necessary. In the end we develop the fitness function equation by using normalization of the raw data. Results: Simulation results show improvement compared to previous fitness functions in clustering of the wireless sensor networks.

Measurements Used in Wireless Sensor Networks Localization

2009 ◽  
pp. 33-53
Author(s):  
Fredrik Gustafsson ◽  
Fredrik Gunnarsson
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Architecture ◽  
Time Synchronization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Time Of Arrival ◽  
General Observation ◽  
Network Nodes ◽  
Sonic Wave

Wireless sensor networks (WSN) localization relies on measurements. Availability of, and the information content in, these measurements depend on the network architecture, connectivity, node time synchronization and the signaling bandwidth between the sensor nodes. This chapter addresses wireless sensor networks measurements in a general framework based on a set of nodes, where each node either emits or receives signals. The emitted signal can for example be a radio, acoustic, seismic, infrared or sonic wave that is propagated in a certain media to the receiver. This general observation model does not make any difference between localization of sensor network nodes or unknown objects, or whether the nodes or objects are stationary or mobile. The information available for localization in wireless cellular networks (WCN) is in literature classified as direction of arrival (DOA), time of arrival (TOA), time difference of arrival (TDOA) and received signal strength (RSS). This chapter generalizes these concepts to the more general wireless sensor networks.

scholarly journals The Management of Indoor Thermal Comfort with Wireless Sensor Networks

2017 ◽  
Vol 50 (9-10) ◽  
pp. 206-213 ◽  
Author(s):  
Sinan Uguz ◽  
Osman Ipek
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Environmental Factors ◽  
Thermal Comfort ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Space Heating ◽  
Monitoring And Control ◽  
Comfort Index ◽  
And Control

In this study, real-time monitoring and control platform based on thermal comfort was developed to control space heating in living spaces. To calculate the thermal comfort level in a living space, environmental factors such as indoor air temperature, mean radiant temperature, air velocity, and humidity are needed. In order to obtain the environmental factors, sensor nodes based on wireless sensor networks were developed. According to the data obtained from the sensor nodes, the thermal comfort index was calculated, and radiators used for space heating were controlled via monitoring and control software based on PC. Furthermore, several experiments were performed between living spaces where real-time monitoring and control platform was installed and living spaces heated with conventional methods. The measurements were carried out in four rooms at the Faculty of Technology of Suleyman Demirel University in Turkey during the winter season. The heat transferred from room radiators by creating proper conditions that can change the thermal comfort index was compared in the experiments. During experimental measurements, it was observed that the heat transferred to the environment through the room radiators reduced significantly, especially with closed doors and windows.

Joint Deployment Strategy of Battery-Free Sensor Networks with Coverage Guarantee

2021 ◽  
Vol 17 (4) ◽  
pp. 1-29
Author(s):  
Tuo Shi ◽  
Zhipeng Cai ◽  
Jianzhong Li ◽  
Hong Gao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Architecture ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Free Node ◽  
Deployment Strategy ◽  
Energy Limitation ◽  
Coverage Quality ◽  
Important Measurement

The energy limitation of wireless sensors limits the lifetime of the traditional wireless sensor networks. The <b>Battery-Free Sensor Network (BF-WSN)</b> is a new network architecture proposed in recent years to address the limitation of wireless sensor networks. In a BF-WSN, the battery-free node can harvest energy from the ambient environment, and thus the lifetime of a BF-WSN is unlimited in terms of energy. The coverage quality is an important measurement of BF-WSNs. Considering the specific features of BF-WSNs, we propose a new deployment concept for BF-WSNs, named <i>Joint Deployment</i>. It aims to determine the locations and working schedules of sensor nodes to maximize network coverage quality. Based on the joint deployment concept, we propose a new deployment problem of battery-free sensor nodes. We prove that this problem is at least NP-Hard. We also analyze the upper bound of this problem. Furthermore, we propose an approximated algorithm to solve this problem and analyze the time complexity and the ratio bound of the algorithm. Extensive simulations are carried out to examine the performance of the proposed algorithm. The simulation results show that the algorithm is efficient and effective.

scholarly journals Mobility Assisted Sensor Node Self-Deployment for Maximizing the Coverage of Wireless Sensor Networks using A Genetic Algorithm

2013 ◽  
Vol 10 (2) ◽  
pp. 33
Author(s):  
VV Juli ◽  
J Raja
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Matlab Simulation ◽  
Mobile Sensor ◽  
Adequate Coverage ◽  
And Control

Wireless sensor networks extend the capability to monitor and control far-flung environments. However, sensor nodes must be deployed appropriately to reach an adequate coverage level for the successful acquisition of data. Modern sensing devices are able to move from one place to another for different purposes and constitute the mobile sensor network. This mobile sensor capability could be used to enhance the coverage of the sensor network. Since mobile sensor nodes have limited capabilities and power constraints, the algorithms which drive the sensors to optimal locations should extend the coverage. It should also reduce the power needed to move the sensors efficiently. In this paper, a genetic algorithm- (GA) based sensor deployment scheme is proposed to maximize network coverage, and the performance was studied with the random deployment using a Matlab simulation. 

scholarly journals Dynamic Reconfiguration of Cluster-Tree Wireless Sensor Networks to Handle Communication Overloads in Disaster-Related Situations

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4707 ◽  
Author(s):  
Miguel Lino ◽  
Erico Leão ◽  
André Soares ◽  
Carlos Montez ◽  
Francisco Vasques ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Dynamic Reconfiguration ◽  
Service Level ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Tree ◽  
And Control

The development of flexible and efficient communication mechanisms is of paramount importance within the context of the Internet of Things (IoT) paradigm. IoT has been used for industrial, commercial, and residential applications, and the IEEE 802.15.4/ZigBee standard is one of the most suitable protocols for this purpose. This protocol is now frequently used to implement large-scale Wireless Sensor Networks (WSNs). In industrial settings, it is becoming increasingly common to deploy cluster-tree WSNs, a complex IEEE 802.15.4/ZigBee-based peer-to-peer network topology, to monitor and control critical processes such as those related to oil or gas, mining, or certain specific chemicals. The remote monitoring of critical events for hazards or disaster detection in large areas is a challenging issue, since the occurrence of events in the monitored environment may severely stress the regular operation of the network. This paper proposes the Dynamic REconfiguration mechanism of cluster-Tree WSNs (DyRET), which is able to dynamically reconfigure large-scale IEEE 802.15.4 cluster-tree WSNs, and to assign communication resources to the overloaded branches of the tree based on the accumulated network load generated by each of the sensor nodes. A complete simulation assessment demonstrates the proposed mechanism’s efficiency, and the results show that it can guarantee the required quality of service level for the dynamic reconfiguration of cluster-tree networks.

Energy Harvesting Models and Techniques

2016 ◽  
pp. 251-267
Author(s):  
Saira Muzafar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Supply ◽  
Resource Constraints ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Civil Infrastructure ◽  
Ambient Environment ◽  
Long Run ◽  
Almost All

Wireless sensor networks (WSNs) has gain popularity due to their wide range of applications in almost all walks of life including industry controls, environmental monitoring, health, transportation, military, civil infrastructure, science, security and more. Wireless sensor nodes are cheap and tiny in size therefore its deployment is easier. They perform well in harsh environments where human intervention is almost difficult or not possible. However, wireless sensor networks are resource constraints and its power supply has been a big challenge to keep the sensor nodes functional for a longer period. Advancement in low power electronics helped a lot but the use and maintenance of conventional batteries with a limited life span cannot address the power supply problem effectively in a long run. Therefore, harvesting energy from ambient environment is an effective alternative both in terms of power and cost, which can help sensor networks to live longer. This chapter mainly focuses on different possible energy sources available in ambient environment and current technological mechanism to harvest energy for WSNs.

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