Dynamic random replication for data centric storage
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
IEEE/ACM Transactions on Networking (TON)
Wireless Personal Communications: An International Journal
STARR-DCS: Spatio-temporal adaptation of random replication for data-centric storage
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 754.84 |
In this paper, we propose a stochastic geometric model to study the energy burdens seen in a large scale hierarchical sensor network. The network makes use of aggregation nodes, for compression, filtering, and/or data fusion of locally sensed data. Aggregation nodes (AGNs) then relay the traffic to mobile sinks. While aggregation may substantially reduce the overall traffic on the network, it may have the deleterious effect of concentrating loads on paths between AGNs and the sinks-such inhomogeneities in the energy burden may in turn lead to nodes with depleted energy reserves. To remedy this problem, we consider how one might achieve a more balanced energy burden across the network by spreading traffic, i.e., using a multiplicity of paths between AGNs and sinks. The proposed model reveals, how various aspects of the task at hand impact the characteristics of energy burdens on the network and in turn the lifetime for the system. We show that the scale of aggregation and degree of spreading can be optimized. Additionally, if the sensing activity involves large amounts of data flowing to sinks, then inhomogeneities in the energy burdens seen by nodes around the sinks will be hard to overcome, and indeed the network appears to scale poorly. By contrast, if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.