A reliable trust-aware reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This doctoral research therefore aims to advance the state of the art in routing by proposing a lightweight, reliable routing protocol for WMSN. Ensuring a reliable path between the source and the destination requires making trustaware routing decisions to avoid untrustworthy paths. A lightweight and effective Trust Management System (TMS) has been developed to evaluate the trust relationship between the sensor nodes with a view to differentiating between trustworthy nodes and untrustworthy ones. Moreover, a resource-conservative Reinforcement Learning (RL) model has been proposed to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, with a view to addressing the inborn overestimation problem in Q-learning-based routing protocols, we adopted double Q-learning to overcome the positive bias of using a single estimator. An energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Finally, a realistic trust management testbed has been developed to overcome the limitations of using numerical analysis to evaluate proposed trust management schemes, particularly in the context of WMSN. The proposed testbed has been developed as an additional module to the NS-3 simulator to fulfill usability, generalisability, flexibility, scalability and high-performance requirements

ETAREE: an effective trend-aware reputation evaluation engine for wireless medical sensor networks.

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

RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks.

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

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches

TrustMod: a trust management module for NS-3 simulator.

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 survey on wireless body area networks: architecture, security challenges and research opportunities.

In the era of communication technologies, wireless healthcare networks enable innovative applications to enhance the quality of patients’ lives, provide useful monitoring tools for caregivers, and allows timely intervention. However, due to the sensitive information within the Wireless Body Area Networks (WBANs), insecure data violates the patients’ privacy and may consequently lead to improper medical diagnosis and/or treatment. Achieving a high level of security and privacy in WBAN involves various challenges due to its resource limitations and critical applications. In this paper, a comprehensive survey of the WBAN technology is provided, with a particular focus on the security and privacy concerns along with their countermeasures, followed by proposed research directions and open issues

3R: a reliable multi-agent reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This paper proposes 3R, a reliable multi-agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN

DQR: a double Q learning multi agent routing protocol for wireless medical sensor network.

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

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

Exploration is a critical component in reinforcement learning algorithms. Exploration exploitation trade-off is still a fundamental dilemma in reinforcement learning. The learning agent needs to learn how to deal with a stochastic environment in order to maximize the accumulated long-term reward. This paper proposes a robust exploration strategy (RES) based on the temporal difference error. In RES, the exploration problem is modeled using Beta probability distribution to control the exploration rate. Moreover, the most promising action is selected during the exploration with a view to maximizing the accumulated reward and avoiding un-rewardable wrong actions. RES has been evaluated on the k-armed bandit problem. The simulation results show superior performance without the need to tune parameters

LTMS: a lightweight trust management system for wireless medical sensor networks.

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