Energy-Efficient Clustering for Wireless Sensor Devices in Internet of Things

Cacciagrano, Diletta; Culmone, Rosario; Micheletti, Matteo; Mostarda, Leonardo

doi:10.1007/978-3-319-93557-7_5

Cited by 24 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Behavioral simulations of CPS and IoT are increasing in their relevance with respect to analyzing reliability, because precise mathematical modeling is not straightforward [80]. These simulations are based on addressing four main topics: node localization, energy management, network multi-objective optimization, and self-capabilities approach [81,82].…”

Section: Cps-based Co-simulation Frameworkmentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

View full text Add to dashboard Cite

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

show abstract

Section: Cps-based Co-simulation Frameworkmentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

View full text Add to dashboard Cite

show abstract

“…because the mathematical modeling of those factors is so difficult [24]. Those simulations are based on addressing four main topics: node localization, energy management, network multi-objective optimization, and self-capabilities approach [25,26].…”

Section: Behavioral Simulations Of Cps and Iot Assume Importance As Amentioning

confidence: 99%

Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System

Castaño¹,

Villalonga²,

Haber³

et al. 2019

Preprint

View full text Add to dashboard Cite

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

show abstract

“…Since charging or replacement of energy sources is practically impossible. According to the environment absorption, the sensors are densely mounted . Energy efficiency is a significant problem for the application of intellectual disseminated sensor systems from node to system design level.…”

Section: Introductionmentioning

confidence: 99%

“…According to the environment absorption, the sensors are densely mounted. 4 Energy efficiency is a significant problem for the application of intellectual disseminated sensor systems from node to system design level. That kind of issue leads to the practical issues of intergalactic and life-threatening sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Pattnaik

Sahu

2020

Int J Communication

View full text Add to dashboard Cite

Summary The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.

show abstract

Energy-Efficient Clustering for Wireless Sensor Devices in Internet of Things

Cited by 24 publications

References 18 publications

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Towards Sensor Reliability Using Internet-of-Things LiDAR Data in a Cyber-Physical System

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Contact Info

Product

Resources

About