Extending the lifetime of wireless sensor networks (WSNs) while delivering the expected level of service remains a hot research topic. Clustering has been identified in the literature as one of the primary means to save communication energy. In this paper, we argue that hierarchical agglomerative clustering (HAC) provides a suitable foundation for designing highly energy efficient communication protocols for WSNs. To this end, we study a new mechanism for selecting cluster heads (CHs) based both on the physical location of the sensors and their residual energy. Furthermore, we study different patterns of communications between the CHs and the base station depending on the possible transmission ranges and the ability of the sensors to act as traffic relays. Simulation results show that our proposed clustering and communication schemes outperform well-knows existing approaches by comfortable margins. In particular, networks lifetime is increased by more than 60% compared to LEACH and HEED, and by more than 30% compared to K-means clustering.
Cognitive Radio Networks (CRN) were introduced as a means to more efficiently reuse the licensed radio frequency spectrum. One of their salient features is the ability of unlicensed nodes to dynamically adapt their radio parameters according to their needs. This paper investigates the resource allocation problem in CRN by jointly considering power control and bandwidth for a set of secondary users (SU) transmitting simultaneously with a set of licensed users (PU), which transmissions must remain unaltered. The proposed allocation scheme is based on a Genetic Algorithm (GA) where the chromosome's genes represent the reconfigurable interface radio parameters, by defining genetic operations the GA is empowered to find a set of radio parameters that maximize the overall network capacity and under the physical interference model enforced to the transmissions of both PU’s and SU’s. The numerical results illustrate the prominent effect of adjusting jointly multiple radio parameters on the network capacity.