Ricerc@Sapienza

This paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced.

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed operating level.

The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs.

Superior performance in wireless sensor networks is obtained by taking key protocol decisions based on the outcome of local learning-based computations, informing nodes on past and expected availability of resources. This paper investigates the impact on protocol performance of local computational requirements of learning techniques.

Wake-up radio technology helps to attenuate unnecessary power consumption by allowing a node to keep its main radio off until it is woken up by a signal to an auxiliary low-power radio receiver. In this paper, we evaluate the range performance of an ultra-low power wake-up radio receiver (WuR) integrated into a wireless device suitable for wireless sensor networking deployments. We run several ranging experiments, both indoors and outdoors, where a transmitter sends wake-up sequences to a receiver positioned meters away.

The advent of various wireless technologies has paved the way for the realization of new infrastructures and applications for smart cities. Wireless Sensor Networks (WSNs) are one of the most important among these technologies. WSNs are widely used in various applications in our daily lives.