In ad-hoc mobile radio networks, nodes are organized into non-overlapping clusters. These clusters are independently controlled and dynamically reconfigured when the topology changes. This work presents a Distributed Label clustering scheme (DL) that partitions nodes into clusters using a weight-based criterion. The DL scheme allows the border nodes to determine their roles first to avoid selecting unsuitable clusterheads. In order to resolve the clusterhead change problem, the DL scheme restricts the number of clusterhead changes. The DL scheme also restricts the size of the virtual backbone by reducing the number of clusters. This scheme is distributed and can be executed at each node with only the knowledge of one-hop neighbors. The simulation results demonstrate that our scheme outperforms other clustering schemes in terms of the number of clusters, stability of the clusters and control overhead when the topology changes.

In a WSAN (Wireless Sensor and Actuator Network), most resources, including sensors and actuators, are designed for certain applications in a dedicated environment. Many researchers have proposed to use of gateways to infer and annotate heterogeneous data; however, such centralized methods produce a bottlenecking network and computation overhead on the gateways that causes longer response time in activity processing, worsening performance. This work proposes two distribution inference mechanisms: regionalized and sequential inference mechanisms to reduce the response time in activity processing. Finally, experimental results for the proposed inference mechanisms are presented, and it shows that our mechanisms outperform the traditional centralized inference mechanism.