ROP: A Resource Oriented Protocol for Heterogeneous Sensor Networks
Yong Ma*, Siddharth Dala1**, Majd Alwan**, James Aylor*
*Medical Automation Research Center
University of Virginia Health System
Charlottesville VA 22908
*Department of Electrical and Computer Engineering
University of Virginia
Charlottesville VA 22904
ABSTRACT: Sensor networks have been an active area of research during the past several years. Much previous work deals with issues related to networks having homogenous sensor nodes. In reality, sensors with different radio coverage, power capacity, and processing capabilities are deployed. In addition, not all of the sensors are mobile or have the same mobility freedom or mobility attributes (e.g. speed). The architecture and routing protocol for this type of heterogeneous sensor network must be based on the resources and characteristics of their member nodes. In this paper, we propose a network model that is adaptively formed according to the resources of its members. A protocol named Resource Oriented Protocol (ROP) was developed to build the network model. This protocol principally entails two phases. In the topology formation phase, nodes report their available resource characteristics , based on which network architecture is optimally built . We stress that due to the existence of nodes with limitless resources, a top-down appointment process can build the architecture with minimum consumption of resources. In the topology update phase, mobile sensors and isolated sensors are accepted into the network with an optimal balance of resources. To avoid overhead of periodic route updates, we use a reactive strategy to maintain route cache. This paper provides encouraging simulation results of ROP in GlomoSim.
In this paper, we describe a network model that is adaptively formed according to the resources of its members. A protocol we named Resource Oriented Protocol (ROP) was developed to create the network model. This protocol entails two phases: topology formation and topology update.
In the topology formation phase, first, sensors report their characteristics of available resources, and then local cluster heads aggregate these reports and send to sensors with largest resource capacity (LRC). After this step, based on the reports, LRCs decide the topology and appoint cluster heads from the top to bottom levels. In the topology update phase, sensors maintain their route cache reactively. We stress in ROP that energy efficiency cannot always result in longer system lifetime especially in heterogeneous
networks. Instead, balancing resources among sensors and saving energy for those more resource-constrained sensors are greatly helpful in lengthening the overall system lifetime. The simulation results in GlomoSim show that ROP can build the topology of a heterogeneous sensor network efficiently and the routes based upon the topology are quite appropriate for balancing resources among sensors in the network.
In general, our Resource Oriented Protocol has several unique characteristics. First, network topology is based on the resources of its member sensors. Sensors with larger resource capacity have more communication and computational workload. Therefore, the lifetime of the whole network is lengthened by minimizing workload on battery-powered nodes. Second, several powerful sensors play the major role in defining network architecture and transferring messages, which once again saves limited resources of more energy-constrained sensors. Third, it considers the location and characteristics of mobility. Only in clusters whose members have moved will change structure. Finally, since the model takes resources of each sensor into consideration, a security model designed for heterogeneous sensor networks can easily fit into this architecture. Such a security model suited for resource constrained sensor network is our future research direction. We aim to enhance such sensor networks to vastly improve data privacy and security. The targeted areas of applications include tele-health applications, health care facilities and other care settings, in addition to more secure automation applications.
Acknowledgement The authors would like to thank Mr. Steve Kell, General Manager at the Medical Automation Research Center, for the enriching discussions
that helped refine the ideas implemented in this protocol and for kindly reviewing the manuscript.
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