Lightweight and confidential data aggregation in healthcare wireless sensor networks

Soufiene, Ben Othman; Bahattab, Abdullah Ali; Trad, Abdelbasset; Youssef, Habib

doi:10.1002/ett.2993

Cited by 24 publications

(14 citation statements)

References 25 publications

(30 reference statements)

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“… End‐to‐end delay: the average time that packets spend to reach destinations Communication overhead: the number of bytes generated by vehicles to achieve a better packet delivery ratio …”

Section: Resultsmentioning

confidence: 99%

Toward a lightweight and efficient UAV‐aided VANET

Sedjelmaci

Messous

Senouci

et al. 2018

Trans Emerging Tel Tech

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Connectivity in a smart vehicular network is quite sensitive and highly affected by its dynamic network topology. Issues related to the intermittent nature of connectivity may arise due to the high mobility of nodes and network heterogeneity. In sparse areas, a vehicular network is basically a disruption-tolerant network suffering from frequent disconnections, long delays, and messages loss.Facing these issues, and specifically for time-sensitive applications, Unmanned Aerial Vehicle (UAV) can provide valuable assistance to Vehicular Ad Hoc Network (VANET) by assuming a relay node role between disconnected segments in the road. In such scenarios, effective communication is either between vehicles (V2V) or between vehicles and UAVs (V2U), which would form subsequently a UAV-assisted vehicular network. In the present paper, we propose a new framework for using small UAVs as mobile infrastructure nodes in order to enhance the connectivity between vehicles. The first part of this framework accurately predicts the disconnected segments such that drones can be deployed to reestablish lost links. We have implemented a theoretical game for this prediction process. Furthermore, since the battery reserves of the UAVs are limited, we have proposed a mitigation technique based on game theory to reduce the power draining effects that a broadcast storm would have on the energy consumption of the network. For the validation of the proposed UVE (UAV-aided VANET framework, with energy-conscious constraints), we conducted thorough simulation scenarios with Network Simulator 3. The obtained performances are promising in the sense that our approach achieves high packet delivery ratio, low end-to-end delay, and low overhead communication costs.Trans Emerging Tel Tech. 2019;30:e3520.wileyonlinelibrary.com/journal/ett

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Section: Resultsmentioning

confidence: 99%

Toward a lightweight and efficient UAV‐aided VANET

Sedjelmaci

Messous

Senouci

et al. 2018

Trans Emerging Tel Tech

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show abstract

“…Some works in connected healthcare are focused on reducing big data collection using aggregation, compression and prediction methods [16]- [23]. In [16], the authors propose a Priority-based Compressed Data Aggregation (PCDA) technique in order to reduce the amount of heath data transmitted. PCDA uses compressed sensing approach followed by a cryptographic hash algorithm to save information accuracy before sending data for diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

et al. 2021

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Nowadays, keeping a strong and good health is one of the main concern of the general public or governments. The Internet of Things (IoT) has been emerged as an efficient solution to build smart healthcare systems deployed either at hospitals or in-home. Such networks rely on biomedical sensors which are used in electronics-based medical equipment to remotely collect vital signs of patients (pressure, temperature, hart rate, oxygen saturation etc.). Generally, these biosensors are implemented on or inside the patient's body and take three types of record data such as numerical, images and videos. However, the big data collected by various biomedical sensors along with the need of emergency detection, the limited sensor energies, and the prediction of the progress of patient situation are the major challenges for heath-based IoT applications. In order to overcome these challenges, we propose, in this paper, an efficient sensor-based data analytics for real-time patient monitoring and assessment to help both hospital and medical staff. The proposed mechanism consists in three phases: Emergency detection, adapting sensing frequency and real time prediction of patient situation. Through simulations on real health data, we show the effectiveness of our mechanism compared to other exiting techniques.

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“…Some works in sensing-based healthcare are focused on reducing big data collection using aggregation, compression, and prediction methods [6][7][8][9][10][11][12]. The authors of [6] proposed the Priority-based Compressed Data Aggregation (PCDA) technique to reduce the amount of heath data transmitted. PCDA uses a compressed sensing approach followed by a cryptographic hash algorithm to save information accuracy before sending data for any diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

Harb

Mroue

Mansour

et al. 2020

Sensors

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Nowadays, the increasing number of patients accompanied with the emergence of new symptoms and diseases makes heath monitoring and assessment a complicated task for medical staff and hospitals. Indeed, the processing of big and heterogeneous data collected by biomedical sensors along with the need of patients' classification and disease diagnosis become major challenges for several health-based sensing applications. Thus, the combination between remote sensing devices and the big data technologies have been proven as an efficient and low cost solution for healthcare applications. In this paper, we propose a robust big data analytics platform for real time patient monitoring and decision making to help both hospital and medical staff. The proposed platform relies on big data technologies and data analysis techniques and consists of four layers: real time patient monitoring, real time decision and data storage, patient classification and disease diagnosis, and data retrieval and visualization. To evaluate the performance of our platform, we implemented our platform based on the Hadoop ecosystem and we applied the proposed algorithms over real health data. The obtained results show the effectiveness of our platform in terms of efficiently performing patient classification and disease diagnosis in healthcare applications.

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Lightweight and confidential data aggregation in healthcare wireless sensor networks

Cited by 24 publications

References 25 publications

Toward a lightweight and efficient UAV‐aided VANET

Toward a lightweight and efficient UAV‐aided VANET

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

A Hadoop-Based Platform for Patient Classification and Disease Diagnosis in Healthcare Applications

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