Wireless sensor networks (WSNs) have been the popular targets for cyberattacks these days. One type of network topology for WSNs, the scale-free topology, can effectively withstand random attacks in which the nodes in the topology are randomly selected as targets. However, it is fragile to malicious attacks in which the nodes with high node degrees are selected as targets. Thus, how to improve the robustness of the scale-free topology against malicious attacks becomes a critical issue. To tackle this problem, this paper proposes a Robustness Optimization scheme with multi-population Co-evolution for scale-free wireless sensor networKS (ROCKS) to improve the robustness of the scale-free topology. We build initial scale-free topologies according to the characteristics of WSNs in the real-world environment. Then, we apply our ROCKS with novel crossover operator and mutation operator to optimize the robustness of the scale-free topologies constructed for WSNs. For a scale-free WSNs topology, our proposed algorithm keeps the initial degree of each node unchanged such that the optimized topology remains scale-free. Based on a well-known metric for the robustness against malicious attacks, our experiment results show that ROCKS roughly doubles the robustness of initial scale-free WSNs, and outperforms two existing algorithms by about 16% when the network size is large.

Due to the recent proliferation of cyber-attacks, improving the robustness of wireless sensor networks (WSNs), so that they can withstand node failures has become a critical issue. Scale-free WSNs are important, because they tolerate random attacks very well; however, they can be vulnerable to malicious attacks, which particularly target certain important nodes. To address this shortcoming, this paper first presents a new modeling strategy to generate scale-free network topologies, which considers the constraints in WSNs, such as the communication range and the threshold on the maximum node degree. Then, ROSE, a novel robustness enhancing algorithm for scale-free WSNs, is proposed. Given a scale-free topology, ROSE exploits the position and degree information of nodes to rearrange the edges to resemble an onion-like structure, which has been proven to be robust against malicious attacks. Meanwhile, ROSE keeps the degree of each node in the topology unchanged such that the resulting topology remains scale-free. The extensive experimental results verify that our new modeling strategy indeed generates scale-free network topologies for WSNs, and ROSE can significantly improve the robustness of the network topologies generated by our modeling strategy. Moreover, we compare ROSE with two existing robustness enhancing algorithms, showing that ROSE outperforms both.

Application-layer multicast supports group applications without the need for a network-layer multicast protocol. Here, applications arrange themselves in a logical overlay network and transfer data within the overlay. We present an application-layer multicast solution that uses a Delaunay triangulation as an overlay network topology. An advantage of using a Delaunay triangulation is that it allows each application to locally derive next-hop routing information without requiring a routing protocol in the overlay. A disadvantage of using a Delaunay triangulation is that the mapping of the overlay to the network topology at the network and data link layer may be suboptimal. We present a protocol, called Delaunay triangulation (DT protocol), which constructs Delaunay triangulation overlay networks. We present measurement experiments of the DT protocol for overlay networks with up to 10 000 members, that are running on a local PC cluster with 100 Linux PCs. The results show that the protocol stabilizes quickly, e.g., an overlay network with 10 000 nodes can be built in just over 30 s. The traffic measurements indicate that the average overhead of a node is only a few kilobits per second if the overlay network is in a steady state. Results of throughput experiments of multicast transmissions (using TCP unicast connections between neighbors in the overlay network) show an achievable throughput of approximately 15 Mb/s in an overlay with 100 nodes and 2 Mb/s in an overlay with 1000 nodes.