On k-connectivity for a geometric random graph
Random Structures & Algorithms
On the minimum node degree and connectivity of a wireless multihop network
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
The Critical Transmitting Range for Connectivity in Sparse Wireless Ad Hoc Networks
IEEE Transactions on Mobile Computing
Effects of Correlated Shadowing: Connectivity, Localization, and RF Tomography
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
Radio characterization of 802.15.4 and its impact on the design of mobile sensor networks
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
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Radio channel fluctuations affecting links of Wireless Sensor Networks (WSNs) show an evident spatial correlation, besides the random behavior caused by obstacles and fading effects. This makes node footprints (i.e. the area covered by the radio transmitter of a node) irregular. Nonetheless, the vast majority of models used in the literature to assess the performance of WSNs in terms of network connectivity, neglect this evidence. They usually consider either the deterministic disk model (with circular footprints) or some random connection model assuming i.i.d. channel fluctuations when different links are realized at the same node with different neighbors. We show in this paper that in realistic settings the spatial correlations of the random fluctuations play a relevant role; we support this statement with an analysis starting from real measurements performed on-field. However, we also show that the i.i.d model provides results which can be close enough to reality in some cases. More precisely, assuming a constant average number of neighbors, we study the percolating properties of realistic and theoretical footprints on random graphs by computing the relative size of the largest component of the graph. Our results show that the presence of correlation may be beneficial or detrimental, depending of whether one considers undirected or directed graphs, i.e., ultimately, on the application.