A survey on wireless sensor network databases

Belfkih, Abderrahmen; Duvallet, Claude; Sadeg, Bruno

doi:10.1007/s11276-019-02070-y

Cited by 26 publications

(11 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wireless sensor network consists of several nonrechargeable sensor nodes applied for particular purposes [1]. One of the most important issues and challenges related to wireless sensor networks is the use of methods to reduce the energy consumption of sensor nodes.…”

Section: -Introductionmentioning

confidence: 99%

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

Sedighimanesh¹,

Zandhessami²,

Alborzi³

et al. 2022

jist

View full text Add to dashboard Cite

Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sensors. The selection of the appropriate cluster heads for clustering and hierarchical routing is effective in enhancing the performance and reducing the energy consumption of sensors. Aim: Clustering sensors in different groups is one way to reduce the energy consumption of sensor nodes. In the clustering process, selecting the appropriate sensor nodes for clustering plays an important role in clustering. The use of multistep routes to transmit the data collected by the cluster heads also has a key role in the cluster head energy consumption. Multistep routing uses less energy to send information. Methods: In this paper, after distributing the sensor nodes in the environment, we use a Teaching-Learning-Based Optimization (TLBO) algorithm to select the appropriate cluster heads from the existing sensor nodes. The teachinglearning philosophy has been inspired by a classroom and imitates the effect of a teacher on learner output. After collecting the data of each cluster to send the information to the sink, the cluster heads use the Tabu Search (TS) algorithm and determine the subsequent step for the transmission of information. Findings: The simulation results indicate that the protocol proposed in this research (TLSIA) has a higher last node dead than the LEACH algorithm by 75%, ASLPR algorithm by 25%, and COARP algorithm by 10%. Conclusion:Given the limited energy of the sensors and the non-rechargeability of the batteries, the use of swarm intelligence algorithms in WSNs can decrease the energy consumption of sensor nodes and, eventually, increase the WSN lifetime.

show abstract

Section: -Introductionmentioning

confidence: 99%

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

Sedighimanesh¹,

Zandhessami²,

Alborzi³

et al. 2022

jist

View full text Add to dashboard Cite

show abstract

“…To start with, several challenges arise on the data collection process: (a) raw data coming from the sensor may be inaccurate or even erroneous due to systematic and random errors; (b) some information may be partially lost due to network congestion, continuous environmental interference [ 9 ], or long distances in remote places [ 10 ]; (c) fraudulent manipulation of data by attackers affecting their veracity [ 11 ]; (d) some technical specifications from sensors manufacturers can vary the structure of their data and/or their communication interface may change [ 12 ]; or (e) huge volumes of real-time data that traditional systems cannot handle [ 13 ] may overload the systems, among others. The presence of any of these factors requires specific data operations to avoid an inadequate representation [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

Mateo

Pagès‐Bernaus

Plà-Aragonés

et al. 2021

Sensors

View full text Add to dashboard Cite

With the growing adoption of the Internet of Things (IoT) technology in the agricultural sector, smart devices are becoming more prevalent. The availability of new, timely, and precise data offers a great opportunity to develop advanced analytical models. Therefore, the platform used to deliver new developments to the final user is a key enabler for adopting IoT technology. This work presents a generic design of a software platform based on the cloud and implemented using microservices to facilitate the use of predictive or prescriptive analytics under different IoT scenarios. Several technologies are combined to comply with the essential features—scalability, portability, interoperability, and usability—that the platform must consider to assist decision-making in agricultural 4.0 contexts. The platform is prepared to integrate new sensor devices, perform data operations, integrate several data sources, transfer complex statistical model developments seamlessly, and provide a user-friendly graphical interface. The proposed software architecture is implemented with open-source technologies and validated in a smart farming scenario. The growth of a batch of pigs at the fattening stage is estimated from the data provided by a level sensor installed in the silo that stores the feed from which the animals are fed. With this application, we demonstrate how farmers can monitor the weight distribution and receive alarms when high deviations happen.

show abstract

“…These WSNs are self-organized networks that consist of thousands of sensor nodes (SNs) [6]. Generally, SNs are able to sense and collect information from the very harsh environments, and to return information according to some requests from users [7]. However, SNs have easily become attack targets of malware because of their resource constraints and weak defense capability [8][9].…”

Section: Introductionmentioning

confidence: 99%

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

2021

View full text Add to dashboard Cite

As an infrastructure of Internet of Things, WSNs (Wireless Sensor Networks) play a more and more important role. However, WSNs are vulnerable to malware and malware can destroy their data security and integrity, which motivates us to explore the role of malware propagation in WSNs. First, according to the actual propagation characteristics of malware in the WSNs and the process of the density change of all node types, we propose an epidemiology-based malware propagation model in consideration of a secondary immune mechanism. Then we set up differential equations to describe the propagation model. By solving differential equations, we can obtain two kinds of equilibrium points indicating that the density of all node types tends to be stable in the WSNs. One is to achieve an equilibrium where only susceptible nodes exist in the WSNs. The other is that malware always exists in the WSNs. Moreover, we prove the local and global stability of these two equilibrium points. Eventually, we analyze the influence and effect of the secondary immunity, forgetting mechanism and containment mechanism on malware propagation in the WSNs, and validate the proposed model through simulations.

show abstract

A survey on wireless sensor network databases

Cited by 26 publications

References 68 publications

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization

Contact Info

Product

Resources

About

A survey on wireless sensor network databases

Cited by 26 publications

References 68 publications

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

An Internet of Things Platform Based on Microservices and Cloud Paradigms for Livestock

SIR1R2: Characterizing Malware Propagation in WSNs With Second Immunization

Contact Info

Product

Resources

About

SIR₁R₂: Characterizing Malware Propagation in WSNs With Second Immunization