Abstract
Wireless sensor networks are being increasingly used for remote environmental monitoring. Despite advances in technology, there will always be a disparity between the number of competing sensor devices and the amount of network resources available. Auction-based strategies have been used in numerous applications to provide efficient/optimal solutions for determining how to fairly distribute system resources. This paper investigates the suitability of using online auctions to allow sensors to acquire preferential access to network resources. A framework is presented that allocates network priority to sensor devices based on their characteristics such as cost, precision, location, significant changes to readings, and amount of data collected. These characteristics are combined to form the value for a particular sensor's bid in an auction. The sensor with the highest bid wins preferential access to the network. Priority can be dynamically updated over time with regard to these characteristics, changing conditions for the phenomenon under observation, and also with input from a back-end environmental model. We present an example scenario for monitoring a flood's progress down a river to illustrate how the proposed auction-based system operates. A series of simulations were undertaken with a preliminary auction structure to examine how the system functions under different conditions.
Highlights
Effective remote monitoring of sensitive ecosystems is vital to ensuring their sustainability into the future
Performance is dependent on the core Media Access Control (MAC) layer method that has been designed to control Carrier Sense Multiple Access (CSMA)/CA network contention
The order and details of the simulation tests include: The baseline node-based simulation running for 10 separate test runs for 30 seconds; The baseline network-based simulation running varied network traffic loading (0.25 – 4 packets per second) test runs and each for 30 seconds; The MAC layer method is tested by running node-based simulations with the same default baseline simulation parameters and adjusting with a combination of macMinBE, macMaxBE and macMaxCSMABack-offs parameters over multiple different simulation tests
Summary
Effective remote monitoring of sensitive ecosystems is vital to ensuring their sustainability into the future. Advances in technology have led to high-speed broadband, cloud storage, pervasive computing, and semantically-enabled networked devices (i.e., “The Internet of Things”) [3] This makes sensor network systems more affordable and ubiquitous, thereby allowing the collection of more accurate and larger datasets on phenomena under study. Despite increases in network capacity (i.e., bandwidth), the number of sensor devices grows proportionally, which maintains an inequality between required bandwidth and actual bandwidth for transmitting large amounts of sensed data. This problem continues to limit the potential of environmental monitoring systems because the lack in required capacity leads to data loss at the point of collection. This predicament cannot be addressed through advancements in hardware technology alone – a smart software solution is required
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