Event-driven wireless sensor networks (WSNs) are equipped with the sensor nodes which are able to capture the vital changes in the measurements and report them to the base station. Due to the nature of harsh and unattended environments, it is impossible to install sensor nodes manually. Therefore, a random deployment fashion such as by aircraft is required. However, with high probability, sensor nodes will not have uniform distribution in the environment which will cause “hole” in the network. In another issue, sensor nodes are limited in energy supply and transmission range, and thus an appropriate technique is essential to calculate energy efficient routes to relay data from the sensor nodes to the base station. In hierarchical routing category, clustering techniques attempt to partition the sensor nodes in the network to appropriate groups and select the cluster heads to contact with the base station directly. However, in large scale WSNs, most of the sensor nodes are far from the base station and cannot contact directly. Most of the clustering techniques do not consider how cluster heads can reach to the base station. In our research, A Delaunay triangulation approach is employed to detect holes in the network. Then, due to the overhead of clustering methods to define cluster areas, a virtual gridding scheme is applied to define cluster areas. To overcome uncertainties in the environment, a fuzzy logic-based approach is designed to select appropriate cluster heads and hop-nodes in a distributed manner. The experimental results prove the effectiveness and accuracy of our proposed model and applicability to large scale WSNs.

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.