Immune-Inspired Self Healing in Wireless Sensor Networks

Tiong-Hoo, Lim,; Lau, Hui Keng; Timmis, Jon; Bate, Iain

doi:10.1007/978-3-642-33757-4_4

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…Artificial Immune Systems are one of the interesting fields to investigate when working with unpredictable environments. The Receptor Density Algorithm (RDA), one of the versatile artificial immune system inspired algorithms, has been applied in a vast array of real-world applications such chemical detection [12], monitoring industrial processes [8],safety and security applications [16], error detection in swarm robotics [17], wireless sensor networks [18] , online fraud detection [13] etc. The secret behind the popularity of RDA is its ability to operate unsupervised in real-time conditions.…”

Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control

Dassanayake

Anjum

Manning

et al. 2019

Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%. CCS CONCEPTS • Computing methodologies → Online learning settings; Motion path planning; • Computer systems organization → Realtime system architecture; • Software and its engineering → Real-time systems software.

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Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control

Dassanayake

Anjum

Manning

et al. 2019

Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

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“…In SmartMATES, an accelerometer sensor is attached to patient's wrist to monitor the ADL and identify the action of consuming a medication when it is time for a patient medication. Accelerometer is commonly used for gait analysis to detect walking pattern [8] and fall detection [9] using Wireless Sensor Networks [10]. Using the same approach, SmartMATES uses accelerometer to detect non-medication adherence by applying gait analysis on the accelerometer x, y, z to determine the specific pattern feature of consuming the medication as shown in Figure 2.…”

Section: Figure 1 Smartmates System Architecturementioning

confidence: 99%

Medication Adherence using Non-intrusive Wearable Sensors

Lim¹,

Abdullah²

2017

ICST Transactions on Ambient Systems

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Activity recognition approaches have been applied in home ambient systems to monitor the status and wellbeing of occupant especially for home care systems. With the advancement of embedded wireless sensing devices, various applications have been proposed to monitor userâĂŹs activities and maintain a healthy lifestyle. In this paper, we propose and evaluate a Smart Medication Alert and Treatment Electronic Systems (SmartMATES) using a non-intrusive wearable activity recognition sensing system to monitor and alert an user for missing medication prescription. Two sensors are used to collect data from the accelerometer and radio transceiver. Based on the data collected, SmartMATES processes the data and generate a model for the various actions including taking medication. We have evaluated the SmartMATES on 9 participants. The results show that the SmartMATES can identify and prevent missing dosage in a less intrusive way than existing mobile application and traditional approaches.

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“…Lim et al [13] proposed an immune-inspired self-healing mechanism to tackle with repeated WSN problems like unreachable nodes and link failures due to interference. In this system, an individual node was able to identify network performance degradation and was able to carry out diagnostic tests.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

Rizwan

Khan

Abbas

et al. 2015

International Journal of Distributed Sensor Networks

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During the past few years, we have seen a tremendous increase in various kinds of anomalies in Wireless Sensor Network (WSN) communication. Recently, researchers have shown a lot of interest in applying biologically inspired systems for solving network intrusion detection problems. Several solutions have been proposed using Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and so forth. In this paper, we propose a bioinspired solution using Negative Selection Algorithm (NSA) of the AIS for anomalies detection in WSNs. For this purpose, we implement the enhanced NSA and make a detector set that holds anomalous packets only. Then the random packets are tested and matched with the detector set and anomalies are identified. Anomalous data packets are used for further processing to identify specific anomalies. In this way, the number of wormholes, packets delayed, and packets dropped are calculated and identified. Simulations are performed on a large dataset and the results show high accuracy of the proposed algorithm in detecting anomalies. The proposed NSA is also compared with Clonal Selection Algorithm (CSA) for the same dataset. The results show significant improvement of the proposed NSA over CSA in most of the cases.

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Immune-Inspired Self Healing in Wireless Sensor Networks

Cited by 11 publications

References 17 publications

A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control

A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control

Medication Adherence using Non-intrusive Wearable Sensors

Anomaly Detection in Wireless Sensor Networks Using Immune-Based Bioinspired Mechanism

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