Naive Bayes based Trust Management Model for Wireless Body Area Networks

Remu, Sm Rakibul Hasan; Faruque, M. Omar; Ferdous, Rezowan; Arifeen, Murshedul; Sakib, Sudman; Reza, Shamim

doi:10.1145/3377049.3377084

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The objective of trust systems is to evaluate the probability expectation that a given event occurred. In the case of sensor data, this event would be that the sensor data really reflects the actual physical environment [48][49]. Reputation systems have been developed in order to identify compromised nodes based on their behavior.…”

Section: A Trust and Reputation Systemsmentioning

confidence: 99%

“…The patient health data observed by the sensor nodes must be secure, has limited access, and should not mix with other patient data during collection as well as transmission. Moreover, an efficient security model for resource-constrained WBANs should be accurate, costeffective, real-time responsive, scalable, transparent, and less complex since BANs deals with sensitive and significant health data [45][46][47][48][49][50][51][52].…”

Section: B Motivationmentioning

confidence: 99%

“…Although, no mathematical, as well as theoretical analysis, are given in favor of its robustness. Remu et al [49] proposed a "Naive Bayes based Trust Management Model for Wireless Body Area Networks" to ensure security from selfish nodes. The authors have employed a naïve based classifier to classify a biomedical sensor node as a trusted or faulty node.…”

Section: Related Workmentioning

confidence: 99%

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ETAS: An Efficient Trust Assessment Scheme for BANs

et al. 2021

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A wireless body area network (WBAN) is a wireless network of wearable computing devices and intelligent physiological sensors. The intelligent physiological sensors collect and process sensitive data from the patient body. The security, reliability, and trustworthiness of sensitive data collected and processed by intelligent physiological sensors are critical due to its unique application domain. Moreover, consistent and reliable data gathering along with its transmission also plays a vital role in WBANs. Trust management (in BANs) has been found as a useful tool to improve cooperation among sensor nodes, security as well as reliability. The paper recommends a novel and efficient, lightweight trust assessment scheme (ETAS) suitable for health application domains and does not rely purely on any encryption technique. The primary purpose is to develop an exciting comprehensive, novel trust estimation framework for BANs to enhance reliability, dependability, security by isolating compromised (hotspot) nodes with great resource efficiency. ETAS incorporates several exclusive (unique) features like efficient trust evaluator, secure and attack resistant, along with competent trust aggregator function to achieve comprehensive trust score. The trust evaluator function is a multi-trust (communication trust, data trust, and energy trust) strategy to deal with severe internal security threats such as badmouthing attack, ballot-stuffing attack, sybil attack, traitor attack, etc. with less resource consumption. Moreover, ETAS incorporates both the success rate and misbehavior component during trust evaluation. The success rate record the number of successful/unsuccessful interactions among sensor nodes in terms of packet send/receive. The misbehavior component keeps records of current and past misbehavior of sensor nodes for effective decision making. Moreover, ETAS focus on the frequency of interaction among biomedical sensor nodes within a specified period to analyze their behavior for efficient trust decisions. Furthermore, ETAS incorporates temperature, data trust as well as the trust score of biomedical sensors to identify hotspot nodes in BSN. ETAS offers full flexibility to adjust the trust threshold, trust domain, reward, and punishment term according to system and application requirements. ETAS's efficiency is validated through several outcomes (MATLAB R2019a) along with theoretical analysis in terms of energy consumption, attack detection, mitigation, trust computation cost, and packet delivery ratio.

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Section: A Trust and Reputation Systemsmentioning

confidence: 99%

Section: B Motivationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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ETAS: An Efficient Trust Assessment Scheme for BANs

et al. 2021

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“…Machine learning algorithms occupy the vital role to make the devices as more smart and intelligent. Several supervisory algorithms such as the support vector machines (SVM) [11], naive Bayes (NB) [12], and neural network (NB) [13] algorithms were used for training the devices to obtain the smarter characteristics. But still, the performance of the existing algorithms still needs improvisation in terms of accuracy of detection and achieving the increased life time in the network.…”

Section: Introductionmentioning

confidence: 99%

“…Fig 12. Accuracy detection for the proposed learning algorithms for the obtained datasets using the test_bed_1…”

mentioning

confidence: 99%

A novel energy efficient architecture for wireless body area networks

Kalpana

Annadurai²

2021

Pers Ubiquit Comput

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With advent of the Internet of things (IoT), body area networks (BAN) has reached its different new dimension in terms of implementation and smart monitoring applications. Integration of the Internet of things along with the body area network leads to design of pervasive smart and wearable devices. Even though these devices are omnipresence, several design challenges need its solutions for an effective implementation. Energy and performance are the most important challenge which still remains on the darker side of the research. Hence, we proposed energy efficient architecture for IoT-enabled body area networks. The proposed architecture has been designed and developed with the integration of fog nodes along with the extreme learning machine for achieving the low power network and high performance. The proposed BAN architecture has been tested with the different transceivers such as WIFI and ZigBee interfaced with the versions of embedded architectures. Also, the proposed architecture has been compared with existing algorithm such as DARE and M-ATTEMPT in which the energy consumption is reduced to 40% and high performance has been obtained.

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