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Current data collection protocols for wireless sensor networks are mostly based on quasi-static minimum-cost routing trees. We consider an alternative, highly-agile approach called backpressure routing, in which routing and forwarding decisions are made on a per-packet basis. Although there is a considerable theoretical literature on backpressure routing,(More)
The state of the art congestion control algorithms for wireless sensor networks respond to coarse-grained feedback regarding available capacity in the network with an additive increase multiplicative decrease mechanism to set source rates. Providing precise feedback is challenging in wireless networks because link capacities vary with traffic on interfering(More)
The receiver capacity model is a simple model to capture flow dynamics in a multi-hop wireless network, by presenting linear constraints to define the feasible rate region of the network, taking into account interference. The model associates with each receiver in the network a notion of constant receiver capacity. Receiver capacity is defined as the(More)
The state of the art for optimal data-gathering in wireless sensor networks is to use additive increase algorithms to achieve fair rate allocation while implicity trying to maximize network utilization. We explicitly formulate the problem of maximizing the network utilization subject to a max-min fair rate allocation constraint in the form of two coupled(More)
— When the data rates in sensor networks are comparable to the available channel bandwidth, traditional randomized access schemes face the problem of energy inefficiency and reduced throughput due to increased MAC collisions as well as the problem of unfair data delivery. We argue that under such conditions it is preferable to focus on techniques for(More)
Considerable research has been done on detecting and blocking portscan activities that are typically conducted by infected hosts to discover other vulnerable hosts. However, the focus has been on enterprise gateway-level intrusion detection systems where the traffic volume is low and network configuration information is readily available. This paper(More)
1 This paper describes an ongoing project investigating embedded networked sensing for structural health monitoring applications. The vision is of many low-power sensor " motes " embedded throughout the structure with a smaller number of nodes that can provide local excitation. The challenge is to develop both the networking algorithms to reliably(More)
—The capabilities of sensor networking devices are increasing at a rapid pace. It is therefore not impractical to assume that future sensing operations will involve real time (inelastic) traffic, such as audio and video surveillance, which have strict bandwidth constraints. This in turn implies that future sensor networks will have to cater for a mix of(More)
From a theoretical standpoint, backpressure-based techniques present elegant cross-layer rate control solutions that use only local queue information. It is only recently that attempts are being made to design real world wireless protocols using these techniques. To aid this effort, we undertake a comprehensive experimental evaluation of backpressure(More)
We take a top-down approach of formulating the rate control problem, over a collection tree, in a wireless sensor network as a generic convex optimization problem and propose a distributed back pressure algorithm using Lyapunov drift based optimization techniques. Primarily, we show that existing theoretical results in the field of stochastic network(More)