Adaptive In-Network Processing for Bandwidth and Energy Constrained Mission-Oriented Multihop Wireless Networks

In-network Processing, involving operations such as filtering, compression, and fusion is a technique widely used in wireless sensor and ad hoc networks for reducing the communication overhead. In many tactical stream-oriented applications, especially in military scenarios, both link bandwidth and node energy are critically constrained resources. For such applications, in-network processing itself imposes nonnegligible computing cost. In this work, we have developed a unified, utility-based closed-loop control framework that permits distributed convergence to both 1) the optimal level of compression performed by a forwarding node on streams, and 2) the best set of nodes where the operators of the stream processing graph should be deployed. We also show how the generalized model can be adapted to more realistic cases, where the in-network operator may be varied only in discrete steps, and where a fusion operation cannot be fractionally distributed across multiple nodes. Finally, we provide a real-time implementation of the protocol on an 802.11b network with a video application and show that the performance of the network is improved significantly in terms of the packet loss, node lifetime, and quality of video received.