Middleware Guidelines for Future Sensor Networks

Abstract

In the near future, we envision sensor networks to transport high bandwidth, low latency streaming data from a variety of sources, such as cameras and microphones. Sensor networks will be called upon to perform sophisticated in-network processing such as image fusion and object tracking. It is not too difficult to imagine that computational capabilities of network nodes will scale up relative to the fairly limited resources of current motes. However, it is likely that energy will continue to remain a constrained resource in such futuristic sensor networks. Recently, there have been proposals for middleware that provide capabilities for higher-level in-network processing while minimizing energy drain on the network.

In this work, we analyze the interplay between resource requirements for compute- and communication-intensive in-network processing and resultant implications on figures of merit of interest to an application including latency, throughput, and lifetime. We use a surveillance application workload along with middleware capabilities for data fusion, role migration (simple relaying versus in-network processing), and prefetching. Through a simulation-based study, we shed light on the impact of device characteristics such as CPU speed and radio features on application figures of merit. We show, in the presence of prefetching, that increasing radio bandwidth may not improve latency nor throughput for compute-intensive workloads and may actually decrease productivity of the network. We show that cost function directed migration can significantly extend application lifetime in sensor networks with topologies two orders of magnitude larger than previous studies. We also show that a simple minded cost function may not be sufficient to guide migration decisions in the middleware.