Efficient self-organizing virtual clusterheads that supervise data collection based on their wireless connectivity, risk, and overhead costs, are an important element of Wireless Sensor Networks (WSNs). This function is especially critical during deployment when system resources are allocated to a subsequent application. In the presented research, a model used to deploy intrusion detection capability on a Local Area Network (LAN), in the literature, was extended to develop a role-based hierarchical agent deployment algorithm for a WSN. The resulting model took into consideration the monitoring capability, risk, deployment distribution cost, and monitoring cost associated with each node.;Changing the original LAN methodology approach to model a cluster-based sensor network depended on the ability to duplicate a specific parameter that represented the monitoring capability. Furthermore, other parameters derived from a LAN can elevate costs and risk of deployment, as well as jeopardize the success of an application on a WSN. A key component of the approach presented in this research was to reduce the costs when established clusterheads in the network were found to be capable of hosting additional detection agents. In addition, another cost savings component of the study addressed the reduction of vulnerabilities associated with deployment of agents to high volume nodes.;The effectiveness of the presented method was validated by comparing it against a type of a power-based scheme that used each node's remaining energy as the deployment value. While available energy is directly related to the model used in the presented method, the study deliberately sought out nodes that were identified with having superior monitoring capability, cost less to create and sustain, and are at low-risk of an attack. This work investigated improving the efficiency of an intrusion detection system (IDS) by using the proposed model to deploy monitoring agents after a temperature sensing application had established the network traffic flow to the sink. The same scenario was repeated using a power-based IDS to compare it against the proposed model. To identify a clusterhead's ability to host monitoring agents after the temperature sensing application terminated, the deployed IDS utilized the communication history and other network factors in order to rank the nodes. Similarly, using the node's communication history, the deployed power-based IDS ranked nodes based on their remaining power. For each individual scenario, and after the IDS application was deployed, the temperature sensing application was run for a second time. This time, to monitor the temperature sensing agents as the data flowed towards the sink, the network traffic was rerouted through the new intrusion detection clusterheads. Consequently, if the clusterheads were shared, the re-routing step was not preformed.;Experimental results in this research demonstrated the effectiveness of applying a robust deployment metric to improve upon the energy efficiency of a deployed application in a multi-application WSN. It was found that in the scenarios with the intrusion detection application that utilized the proposed model resulted in more remaining energy than in the scenarios that implemented the power-based IDS. The algorithm especially had a positive impact on the small, dense, and more homogeneous networks. This finding was reinforced by the smaller percentage of new clusterheads that was selected. Essentially, the energy cost of the route to the sink was reduced because the network traffic was rerouted through fewer new clusterheads. Additionally, it was found that the intrusion detection topology that used the proposed approach formed smaller and more connected sets of clusterheads than the power-based IDS. As a consequence, this proposed approach essentially achieved the research objective for enhancing energy use in a multi-application WSN.
展开▼
