Up-Link Capacity Derivation for Ultra-Narrow-Band IoT Wireless Networks

International audienceThanks to its low energy consumption and very long range (upto 50 km in free-space), ultra-narrow-band transmission (UNB) represents apromising alternative to classical technologies used in cellular networks to servelow-throughput wireless sensor networks (WSNs) and the Internet of things(IoT). In UNB, nodes access to the medium by selecting their frequency ina random and continuous way. This randomness leads to new behavior inthe interference which has not been theoretically analyzed, when consideringthe pathloss of nodes randomly deployed around the receiver. In this paper, inorder to quantify the system performance, we derive and exploit two theoreticalexpressions of the outage probability in a UNB based IoT network, accountingfor both interference due to the spectral randomness and path loss due to thepropagation (with and without Rayleigh fading). This enables us to estimatethe network capacity as a function of the path-loss exponent, by determiningthe maximum number of simultaneous supported nodes. We highlight that thebandwidth should be chosen based on the propagation channel properties

Optimal fusion rule for distributed detection in clustered wireless sensor networks

We consider distributed detection in a clustered wireless sensor network (WSN) deployed randomly in a large field for the purpose of intrusion detection. The WSN is modeled by a homogeneous Poisson point process. The sensor nodes (SNs) compute local decisions about the intruder’s presence and send them to the cluster heads (CHs). A stochastic geometry framework is employed to derive the optimal cluster-based fusion rule (OCR), which is a weighted average of the local decision sum of each cluster. Interestingly, this structure reduces the effect of false alarm on the detection performance. Moreover, a generalized likelihood ratio test (GLRT) for cluster-based fusion (GCR) is developed to handle the case of unknown intruder’s parameters. Simulation results show that the OCR performance is close to the Chair-Varshney rule. In fact, the latter benchmark can be reached by forming more clusters in the network without increasing the SN deployment intensity. Simulation results also show that the GCR performs very closely to the OCR when the number of clusters is large enough. The performance is further improved when the SN deployment intensity is increased