Geospatial ontology-based mission assignment in Wireless Sensor Networks

Abstract

Ahstract-Wireless Sensor Networks (WSNs) have been a focus for research in recent years. They enable data gathering across a wide range of domains and areas of interest from health service to environment monitoring, security, etc. Sensors are becoming cheaper, and as a result are being deployed in high density, at the same time; users' requirements of the network are changing frequently, they can even exceed the capabilities of the sensors in the network sometimes. The network is expected to operate for a long period of time with many nodes operating on batteries. These challenges have raised the attention to the problem of mission assignment in WSNs. Mission assignment aims at allocating tasks to specific sensors in the network -according to their capabilities-to answer users requirements while at the same time preserving the energy of the network. This paper approaches the problem of mission assignment from a geospatial perspective: because sensors' readings and data are associated with spatial properties of the sensors like their location and sensing range; spatial reasoning is an important aspect of mission assignment.However, spatial reasoning is still very limited in WSNs and has not received much attention because of the lack of standardized modelling of space. In the center of our approach an integration of two ontologies; the W3C semantic sensor network ontology (SSN) which describes sensor nodes in the network and GeoSPARQL (the OGC standard for modelling and reasoning about spatial data). We present Geospatial Sensor Mission Assignment algorithm (GeoSMA); a branch and bound algorithm based on the spatial functions and relations between tasks' and sensors' spatial properties. The aim is to find the most appropriate set of nodes to answer various missions required by the users.

Semantic Middleware Architectures for IoT Healthcare Applications

“…BFO [61] is a spatio-temporal extension that make a distinction between describing identities that happen at a finite time and events like storm and routing. It is a hierarchical system approach where sensors collect data from the real world and send it to clusterhead-node.…”

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

“…Because of its flexibility and adaptivity, the ontology has become more general and has been used in many research projects and applied to several different domains in recent years. Some of the most recently published works that utilize the SSN ontology are the OpenIoT Project [13], the FIESTA-IoT () [14], VITAL-IoT () and GeoSMA [15].…”

Semantic Data Gathering of Physical Entities in Semantic Sensor Networks Using Software Agents

“…Calbimonte et al proposed a new method of characterizing and extracting semantic metadata via analyzing the observations of sensor raw data [7]. Ibrahim et al presented a temporal-spatial model for sensor networks [14]. The model makes it possible to reason based on sensors in any time and place.…”

“…In the second experiment, the network lifetime of different methods is compared together. The network lifetime in this research was considered as the dead time of the first node in the network [14]. The evaluation results are shown in Figure 9, which suggests that the efficiency of the proposed method is desirable.…”

“…GPSR method[14]: A basic data gathering method in sensor networks where data are gathered without clustering. In this way, data gathering does not take place semantically.…”