Biblio

RPL, the IPv6 Routing Protocol for low-power and lossy networks, was standardized by the Internet Engineering Task Force (IETF) in 2011. It is developed to connect resource constrained devices enabled by low-power and lossy networks (LLNs). RPL prominently becomes the routing protocol for IoT. However, the RPL protocol is facing many challenges such as trustworthiness among the nodes which need to be addressed and resolved to make the network secure and efficient. In this paper, a multicasting technique is developed that is based on trust mechanism to resolve this issue. This mechanism manages and protects the network from untrusted nodes which can hamper the security and result in delayed and distorted transmission of data. It allows any node to decide whether to trust other nodes or not during the construction of the topology. This is then proved efficient by comparing it with broadcasting nature of the transmission among the nodes in terms of energy, throughput, percentage of alive and dead nodes.

The main method for long-distance underwater communication is underwater acoustic communication(UAC). The bandwidth of UAC channel is narrow and the frequency band resources are scarce. Therefore, it is important to improve the frequency band utilization of UAC system. Cognitive underwater acoustic (CUA) technology is an important method. CUA network can share spectrum resources with the primary network. Spectrum sensing (SS) technology is the premise of realizing CUA. Therefore, improving the accuracy of spectral sensing is the main purpose of this paper. However, the realization of underwater SS technology still faces many difficulties. First, underwater energy supplies are scarce, making it difficult to apply complex algorithms. Second, and more seriously, CUA network can sometimes be attacked and exploited by hostile forces, which will not only lead to data leakage, but also greatly affect the accuracy of SS. In order to improve the utilization of underwater spectrum and avoid attack, an underwater spectrum sensing model based on the two-threshold energy detection method and K of M fusion decision method is established. Then, the trust mechanism based on beta function and XOR operation are proposed to combat individual attack and multi-user joint attack (MUJA) respectively. Finally, simulation result shows the effectiveness of these methods.

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graphbased Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.