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Network Utility Maximization (NUM) techniques, which cast resource sharing problems as one of distributed utility maximization, have been investigated for a variety of optimization problems in wireless and wired networks. Our recent work has extended the NUM framework to consider the case of resource sharing by multiple competing missions in a military-centric wireless sensor network (WSN) environment. Our enhanced NUM-based protocols provide rapid and dynamic mission-based adaptation of tactical wireless networks to support the transport of sensor data streams with very small control overhead. In particular, we focus specifically on mechanisms that capture the joint nature of mission utilities and the presence of prioritized mission demands. We then introduce a new problem, of joint utility and network lifetime maximization, as a representative of a new class of multi-metric optimization problems, and provide early evidence that techniques from optimal control theory can be used to derive distributed adaptation protocols conforming to the basic NUM paradigm. We also enumerate and motivate a list of open cross-layer dynamic adaptation problems of direct relevance to military C4I operations.

This paper extends the distributed network utility maximization (NUM) framework to consider the case of resource sharing by multiple competing missions in a military-centric wireless sensor network (WSN) environment. Prior work on NUM-based optimization has considered unicast flows with sender-based utilities in either wireline or wireless networks. We extend the NUM framework to consider three key new features observed in mission-centric WSN environments: i) the definition of an individual mission's utility as a joint function of data from multiple sensor sources ii) the consumption of each senders (sensor) data by multiple receivers (missions) and iii) the multicast-tree based dissemination of each sensors data flow, using link-layer broadcasts to exploit the "wireless broadcast advantage" in data forwarding. We show how a receiver-centric, pricing-based, decentralized algorithm can ensure optimal and proportionally-fair rate allocation across the multiple missions, without requiring any coordination among independent missions (or sensors). We also discuss techniques to improve the speed of convergence of the protocol, which is essential in an environment as dynamic as the WSN.

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 non-negligible computing cost. In this work, we have developed a unified, utility-based closed-loop control framework that permits distributed convergence to both a) the optimal level of compression performed by a forwarding node on streams, and b) 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.

In-network processing, involving operations such as filtering, compression and fusion, is widely used in sensor networks to reduce the communication overhead. In many tactical and stream-oriented wireless network applications, both link bandwidth and node energy are critically constrained resources and in-network processing itself imposes non-negligible computing cost. In this work, we have developed a unified and distributed closed-loop control framework that computes both a) the optimal level of sensor stream compression performed by a forwarding node, and b) the best set of nodes where the stream processing operators should be deployed. Our framework extends the Network Utility Maximization (NUM) paradigm, where resource sharing among competing applications is modeled as a form of distributed utility maximization. We also show how our model can be adapted to more realistic cases, where in-network compression may be varied only discretely, and where a fusion operation cannot be fractionally distributed across multiple nodes.This research was sponsored by US Army Research laboratory and the UK Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory, the U.S. Government, the UK Ministry of Defense, or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.