Abstract
In common distributed sensing scenarios, a number of local wireless sensor networks perform sets of acquisitions that must be sent to a central collector which may be far from the measurement fields. Hence, readings from individual nodes may reach their destination by exploiting both local and long-range transmission capabilities. The compressed sensing (CS) paradigm may help finding a convenient mix of the two options, especially if it follows the rakeness-based design flow that has been recently introduced. CS is exploited by identifying local hubs that aggregate many sensor readings in a smaller number of quantities that are then transmitted to the central collector. We here show that, depending on the relative cost of local versus long-range transmission, carefully administering the choice of the hubs, the breadth of the neighborhood from which they collect readings, as well as the coefficients with which those readings a linearly aggregated, one may significantly reduce the energy needed to sample the field. Simulations indicate that savings may be over 50% for values of the parameters modeling nowadays local and long-range transmission technologies.
Original language | English (US) |
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Pages (from-to) | 2220-2233 |
Number of pages | 14 |
Journal | IEEE Internet of Things Journal |
Volume | 5 |
Issue number | 3 |
DOIs | |
State | Published - Jun 1 2018 |
Externally published | Yes |
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Information Systems and Management
- Computer Science Applications
- Hardware and Architecture
- Computer Networks and Communications