Trust-aware virtual network embedding algorithms for wireless sensor networks

Abstract

Network virtualization (NV) in wireless sensor networks (WSNs) enables the utilization of their shared sensing capabilities. Efficient assignment of WSN resources to maximize the infrastructure provider’s revenue can be achieved by virtual network embedding (VNE) while considering the quality of information (QoI), quality of service (QoS), and wireless interference handling constraints. Improving the acceptance rate of VNE is essential because the more the virtual network requests (VNRs) can be mapped onto the substrate network, the more revenue they will generate for the infrastructure provider. However, the shared and complex nature of VNE exposes WSNs to security risks. In this thesis, we apply security constraints and address the trust-aware VNE problem with different algorithms to maximize the VNR acceptance rate while minimizing the cost for WSNs. This research includes four main objectives. In the first objective, we develop a novel centralized trust-aware virtual wireless sensor network (TA-VWSN) algorithm to improve QoI, QoS, and security and enhance the average network throughput, measurement error efficiency, and processing time when the trust attributes are assigned, making the VNE algorithm more practical. Since centralized algorithms suffer from scalability issues, in the second objective, we design a novel distributed trust-aware virtual wireless sensor networks (DTA-VWSN) algorithm by using multiagent systems (MAS) to scale these algorithms to network size. Our heuristic TA-VWSN and DTA-VWSN algorithms achieve a high-quality sub-optimal solution in a real-time manner, enabling us to investigate the tradeoff between solution quality and search time. However, the heuristic algorithms use manual embedding rules, which are incompatible with actual VNE situations. Therefore in the third objective, we develop a novel reinforcement learning-based (RL-based) trust-aware virtual wireless sensor network (RLT-VWSN) algorithm by employing a policy network and extracting attributes of the substrate nodes to get the mapping probability of each one. This algorithm is superior to heuristic algorithms in terms of VNR acceptance rate and cost. In the fourth objective, our first approach addresses node failures during mapping with a pioneering heuristic— the survivable trust-aware virtual wireless sensor networks (STA-VWSN) algorithm. It employs a failure recovery procedure, prioritizing nodes based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. In the second approach, we enhance WSN substrate resilience against individual node and link failures through the development of the survivable and trust-aware reinforcement learning-based virtual network embedding for WSNs (SRLT-VWSN) algorithm. Utilizing the deep Q-Learning (DQL) method, this algorithm ensures end-to-end failure recovery and improves physical resource utilization intelligently.