No abstract

Wireless Medical Sensor Networks (WMSNs) offer ubiquitous health applications that enhance patients' quality of life and support national health systems. Detecting internal attacks on WMSNs is still challenging since cryptographic measures can not protect from compromised or selfish sensor nodes. Establishing a trust relationship between sensor nodes is recognized as a promising measure to reinforce the overall security of Wireless Sensor Networks (WSNs). However, the existing trust schemes for WSNs are not necessarily fit for WMSNs due to their different operation, topology, resources limitations, and critical applications. In this paper, the aforementioned factors are regarded, and accordingly, two different methods to evaluate the trust value have been proposed to fit in-body, on-body, and off-body sensor nodes. Our Lightweight Trust Management System (LTMS) provides a further line of defense to detect packet drop attacks launched by compromised or selfish sensor nodes. Moreover, simulation results show that LTMS is more robust against complicated on-off attacks and can significantly reduce the processing overhead.Index Terms-Wireless Medical Sensor Networks (WMSNs), TMS, internal attacks, on-off attacks.

Wireless Medical Sensor Networks (WMSN) will play a significant role in the advancements of modern healthcare applications. Security concerns are still the main obstacle to the widespread adoption of this technology. Conventional security approaches, such as authentication and encryption, are able to defend against external attacks effectively. However, internally launched threats, either by compromised or selfish nodes, require further security measures to be detected. In this paper, an Effective Trend-Aware Reputation Engine (ETAREE) is proposed for WMSN. ETAREE uses a novel updating mechanism to evaluate the reputation value, which makes it effective in detecting malicious nodes. Moreover, the proposed updating mechanism of ETAREE can efficiently detect on-off attacks. ETAREE security evaluations have been presented and compared with different reputation evaluation models, demonstrating faster detection of malicious behaviours.

Wireless Medical Sensor Network (WMSN) offers innovative solutions in the healthcare domain. It alleviates the patients' everyday life difficulties and supports the already overloaded medical staff with continuous monitoring tools. However, widespread adoption of these advancements is still restrained by security concerns and limitations of existing routing protocols. Routing is challenging in WMSN owing to the fact that some critical requirements, such as reliable delivery, have been neglected. To address these challenges, this paper proposes DQR, a double Q-learning routing protocol to meet WMSN requirements and overcome the positive bias estimation problem of the Q-learning based routing protocols. DQR uses a novel Reinforcement Learning (RL) model to reduce computational and communication overheads. It is combined with an effective trust management system to ensure a reliable data transfer and defeat packet dropping attacks. The experimental results demonstrate robust performance under various attacks with minimal resource footprint and efficient energy consumption.

No abstract

Trust management offers a further level of defense against internal attacks in ad hoc networks. Deploying an effective trust management scheme can reinforce the overall network security. Regardless of limitations, however, security researchers often use numerical simulations to prove the merits of novel methods. This is due to the lack of an adequate testbed to evaluate the proposed trust schemes. Therefore, there is a demanding need to develop a generic testbed that can be used to evaluate the trust relationship for different networks and protocols. This paper proposes TrustMod, an NS-3 module consisting of three main components to evaluate the different trust relationships: direct trust, uncertainty, and indirect trust. It is designed to meet usability, generalisability, flexibility, scalability and high-performance requirements. A series of experiments involving 1680 simulations were performed to prove the design and implementation accuracy of TrustMod. The performance results show that TrustMod's resource footprint is minimal, even for very large networks.

A robust exploration strategy in reinforcement learning based on temporal difference error.

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widespread adoption of WMSN advancements is still hampered by security concerns and limitations of routing protocols. Routing in WMSNs is a challenging task due to the fact that some WMSN requirements are overlooked by existing routing proposals. To overcome these challenges, this paper proposes a reliable multi-agent reinforcement learning based routing protocol (RRP). RRP is a lightweight attacks-resistant routing protocol designed to meet the unique requirements of WMSN. It uses a novel Q-learning model to reduce resource consumption combined with an effective trust management system to defend against various packet-dropping attacks. Experimental results prove the lightweightness of RRP and its robustness against blackhole, selective forwarding, sinkhole and complicated on-off attacks.