Dynamic data prioritization for quality-of-service differentiation in heterogeneous Wireless Sensor Networks

Abstract

Abstract-In many applications of Wireless Sensor Networks (WSNs), heterogeneity is a common property in terms of different sensor types and different circumstances like node location, link quality, and local node density. In many applications, there are several different sensor types with entirely different Quality-ofService (QoS) requirements. The requirements may also vary over time according to the application scenario and also due to network dynamics. Different requirements appeal different approaches while forwarding sensed data through a multi-hop communication network. This paper proposes a dynamic priority assignment strategy to be used for data routing in heterogeneous WSNs aiming to fairly propagate information according to its importance and requirements. To cope with heterogeneity and dynamics, nodes in the routing path dynamically compute priorities for individual data items according to the attached QoS requirements. We apply the proposed strategy for a healthcare monitoring application scenario which consists of an ambient network and several mobile clusters of nodes in the form of Wireless Body Area Networks (WBANs). The nodes have very different requirements and WBANs show a high mobility in the network with more stringent demands. The results show a large improvement in the achieved QoS for more demanding information. I. INTRODUCTION In many applications of Wireless sensor networks (WSNs),there is a large variety between different sensor nodes in the network in terms of Quality-of-Service (QoS) requirements. Moreover, different environment situations for different nodes cause more heterogeneity in the network. The relative sensor node position, different distances to the sink nodes, nonuniform network density, different interference levels, and varying quality of wireless links are some sources of environment heterogeneity in WSNs.Both QoS requirements and the surrounding situation for a sensor node are prone to vary over time. Mobility is one important source of environment variations and it sometimes can entirely change the network topology and density depending on the mobility level. The QoS requirements of a sensor node can also change over time according to the application scenario. Context-aware data propagation is an example of such changes in which the requirements may change considering the sampled data. Multi-scenario applications can be another example in which the behavior of the network changes over time based on the selected scenario. For instance, in an ambient intelligence application, different scenarios might be used during day and night time.

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