Reliability Evaluation for Clustered WSNs under Malware Propagation.

Area of Science:

• Computer Science
• Network Security
• Reliability Engineering

Background:

• Wireless Sensor Networks (WSNs) face significant reliability challenges due to malware propagation.
• Ensuring continuous and dependable data transmission from sensor nodes to the sink is critical.
• Existing models struggle to reconcile deterministic malware intentions with the inherent randomness of network processes.

Purpose of the Study:

• To develop a method for evaluating the reliability of clustered WSNs under epidemic-malware propagation.
• To introduce a novel metric for calculating the Mean Time to Failure (MTTF) of individual sensor nodes.
• To validate the proposed reliability evaluation framework for clustered WSNs.

Main Methods:

• A strategic game is introduced to model and predict malware infection as a Continuous-Time Markov Chain (CTMC) state transition.
• A novel measure for computing the Mean Time to Failure (MTTF) of sensor nodes is devised.
• Classical reliability theory is applied to evaluate clustered WSNs, treating them as parallel-serial-parallel systems.

Main Results:

• The study quantifies the impact of true positive and false positive rates on sensor node MTTF.
• The proposed reliability evaluation method for clustered WSNs is validated against varying network parameters.
• The Mean Time to Failure (MTTF) is shown to be a key indicator of sensor node reliability.

Conclusions:

• The developed strategic game and MTTF measure provide an effective approach to assess WSN reliability against malware.
• The reliability evaluation method is adaptable to different clustered WSN architectures and sizes.
• This research contributes to ensuring efficient, continuous, and dependable data transmission in WSNs.