Gossip-based density estimation in dynamic heterogeneous wireless sensor networks

Review of the Effect of Heterogeneity on Wireless Sensor Networks: A Topological Perspective

“…The authors tried to ensure that low-energy nodes do not become cluster heads and sensor diversity is maximized in the network. Pros: All the techniques proposed in [30][31][32] exploit energy diversity among the nodes to achieve power-efficient operation. Cons: However, faster energy depletion of the frequently selected cluster heads is the common problem of all BCDEEC, BECC, and DEC.…”

“…Then, they estimated the expected number of cluster heads in a subsequent round using the current status of the remaining energy of the nodes. Dulman et al [32] explored energy level and sensor diversity to form clusters in WSN. They proposed Diversity-based Energy aware Clustering (DEC) protocol which designates a node with maximum residual energy as the cluster head and ensures maximum sensor diversity in every cluster.…”

Identifying Surrounds and Engulfs Relations in Mobile and Coordinate-Free Geosensor Networks

“…As the nodes are mobile, this must be refreshed periodically. This approach is similar in concept to [Malazi et al 2013], which uses mass-based diffusion to "push" out and maintain approximate Voronoi boundaries between a set of points in a mobile geosensor network. (3) Voronoi boundary propagation: Using the boundary table (B t ), nodes on a boundary between Voronoi regions will add that boundary to their maptree table (M t ).…”

“…Qualitative spatial relations provide discrete domains, smaller than quantitative alternatives, where the relations correspond to distinctions salient to humans [Galton 2000]. Thus, a body of existing research has focused on tracking the qualitative spatial relations of entities monitored by geosensor networks [Jeong and Duckham 2013;Ercan et al 2013;Malazi et al 2013;Avci et al 2014;Jeong et al 2014].…”

“…The paper also develops and tests new efficient algorithms for identifying these relationships for regions monitored by a network of dynamic (mobile) geosensor nodes. In common with most research into algorithms for geosensor networks today, our algorithms are decentralized-a type of distributed algorithm where no single system element has knowledge of the entire system state [Lynch 1996]. Decentralization ensures algorithms can operate in the network with no centralized control, making them more scalable and resilient to network and node failures.…”