A three-dimensional sub-region query processing mechanism in underwater WSNs

Abstract

International audienceWith the advance and wide applicability of the Internet of Underwater Things, smart things are deployed under the water and form the underwater wireless sensor networks, for the exploration of vast ocean volume. Generally, a sub-region, rather than the whole network region, may be interested by a certain application in a certain time duration. To address this challenge, a sub-region query processing mechanism is proposed. Specifically, a routing tree is constructed where a sensor node corresponds to a tree node. A sensor node acknowledges the fact that whether it lies within the sub-region of interest or not through the localisation techniques. Sensory data are routed to the sink node leveraging the parent-child relation of sensor nodes specified in the routing tree. Experimental evaluation shows that our technique is more energy efficient when the network topology is relatively steady

A dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services

“…ZhangBing proposed an energy-efficient index tree which carried multi-region aggregation queries [22]. He also proposed a subregion query processing mechanism for a subregion interest in underwater wireless sensor network [23].…”

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

“…Leveraging our sub-region query processing mechanism developed in our previous work [ 22 ] and traditional underwater localization techniques [ 23 ], we propose in this article a strategy that aims to detect the event coverage. Our major contributions include the following two aspects: (i) our technique can determine the event coverage in an energy-efficient fashion; and (ii) it can identify potential event sources, which are of importance for prompt and proper response enactment.…”

“…Without loss of generality, the geographical space of an underwater WSN is defined as a three-dimensional rectangular region. The procedure for this technique includes the following steps: Our sub-region query processing mechanism developed in [ 22 ] has been improved, where a set of neighboring sensor nodes, whose sensory data deviate from a normal sensing range in a collective fashion, are identified. These sensory data are routed to sink node(s) through our routing tree [ 22 ] in an energy-efficient fashion.…”

“…The procedure for this technique includes the following steps: Our sub-region query processing mechanism developed in [ 22 ] has been improved, where a set of neighboring sensor nodes, whose sensory data deviate from a normal sensing range in a collective fashion, are identified. These sensory data are routed to sink node(s) through our routing tree [ 22 ] in an energy-efficient fashion. Note that an event is different from an error, which typically corresponds to an outlier in the network [ 19 , 24 ].…”

“…Our sub-region query processing mechanism developed in [ 22 ] has been improved, where a set of neighboring sensor nodes, whose sensory data deviate from a normal sensing range in a collective fashion, are identified. These sensory data are routed to sink node(s) through our routing tree [ 22 ] in an energy-efficient fashion.…”