Avinash Sridharan

Routing without routes: the backpressure collection protocol

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, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average end-to-end packet delays for delivered packets drastically (75% under high load, 98% under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60% improvement in max-min rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks.

Max-min fair collision-free scheduling for wireless sensor networks

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 scheduled access. We present a linear programming formulation and corresponding distributed TDMA-based scheduling algorithms to provide max-min fair collision-free bandwidth allocation to all sources. We evaluate the performance of the proposed scheduled flow technique using the Tossim/Nido network simulator for the Berkeley Mote/TinyOS platform. Our results show that under high data rate conditions, the proposed scheme significantly outperforms randomized access based schemes in terms of key metrics such as fairness, energy efficiency, throughput, and delay.

Connectionless port scan detection on the backbone

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 investigates the effectiveness of existing portscan detection algorithms in the context of a large transit backbone network and proposes a new algorithm that meets the demands of aggregated high speed backbone traffic. Specifically, we evaluate two existing approaches - the portscan detection algorithm in SNORT, and a modified version of the TRW algorithm that is a part of the intrusion detection tool BRO. We then propose a new approach, TAPS, which uses sequential hypothesis testing to detect hosts that exhibit abnormal access patterns in terms of destination hosts and destination ports. We perform a comparative analysis of these three approaches using real backbone packet traces, and find that TAPS exhibits the best performance in terms of catching the maximum number of true scanners and yielding the least number of false positives. We have a working implementation of TAPS on our monitoring platform. Further implementation optimizations using bloom filters are identified and discussed

Maximizing network utilization with max---min fairness in wireless sensor networks

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. For the quantification of the problem we present a receiver capacity model to capture the interference existing in a wireless network. We also provide empirical evidence to motivate the applicability of this model to a real CSMA based wireless network. Using this model, 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 linear programs. We first show how the max–min rate can be computed efficiently for a given network. We then adopt a dual-based approach to maximize the network utilization. The analysis of the dual shows the sub-optimality of previously proposed additive increase algorithms with respect to bandwidth efficiency. Although in theory a dual-based sub-gradient search algorithm can take a long time to converge, we find empirically that setting all shadow prices to an equal and small constant value, results in near-optimal solutions within one iteration (within 2% of the optimum in 99.65% of the cases). This results in a fast heuristic distributed algorithm that has a nice intuitive explanation—rates are allocated sequentially after rank ordering flows based on the number of downstream receivers whose bandwidth they consume. We also investigate the near optimal performance of this heuristic by comparing the rank ordering of the source rates obtained from the heuristic to the solutions obtained by solving the linear program.

Explicit and precise rate control for wireless sensor networks

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 links. We address this challenge by applying a receiver capacity model that associates capacities with nodes instead of links, and use it to develop and implement the first explicit and precise distributed rate-based congestion control protocol for wireless sensor networks --- the wireless rate control protocol (WRCP). Apart from congestion control, WRCP has been designed to achieve lexicographic max-min fairness. Through extensive experimental evaluation on the USC Tutornet wireless sensor network testbed, we show that WRCP offers substantial improvements over the state of the art in flow completion times as well as in end-to-end packet delays.

Handling inelastic traffic in wireless sensor networks

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 elastic (having no bandwidth constraint requirements) and inelastic traffic. Current state of the art rate control protocols for wireless sensor networks, are however designed with focus on elastic traffic. In this work, by adapting a recently developed theory of utilityproportional rate control for wired networks to a wireless setting, and combining it with a stochastic optimization framework that results in an elegant queue backpressure-based algorithm, we have designed the first-ever rate control protocol that can efficiently handle a mix of elastic and inelastic traffic in a wireless sensor network. We implement this novel protocol in a real world sensor network stack, the TinyOS-2.x communication stack for IEEE 802.15.4 radios and evaluate the real-world performance of this protocol through comprehensive experiments on 20 and 40-node subnetworks of USCs 94-node Tutornet wireless sensor network testbed.

Implementing backpressure-based rate control in wireless networks

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 mechanisms for multihop wireless networks, in particular, the first such study in the context of wireless sensor networks. Our evaluation yields two key insights into the design of such protocols. First, for wireless sensor networks, we show that a simple backpressure scheduling policy which allows nodes to transmit so long as they have a positive queue differential (irrespective of its size) gives performance comparable to more sophisticated heuristics. The advantage of this approach is that no changes are required to the underlying MAC. Second, we show that the performance of backpressure based protocols is highly sensitive to a parameter setting that depends upon current traffic conditions. Therefore, practical backpressure protocols must provide for automatic parameter adaptation.

Making distributed rate control using Lyapunov drifts a reality in wireless sensor networks

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 optimization can be directly applied to a CSMA based wireless sensor network using our novel receiver capacity model. We back this claim by implementing our algorithm on the Tmote sky class devices. Our experimental evaluation on a 5 node testbed shows that the empirically observed rate allocation on a real sensor network testbed that uses our back pressure algorithm is close to the analytically predicted values, justifying our claims.

Empirical evaluation of querying mechanisms for unstructured wireless sensor networks

In the last few years, several studies have analyzed the performance of flooding and random walks as querying mechanisms for unstructured wireless sensor networks. However, most of the work is theoretical in nature and while providing insights into the asymptotic behavior of these querying mechanisms, does not account for the non-idealities faced by the network in real deployments. In this paper, we propose a 3-way handshake protocol as a reliable implementation of a random walk and compare its performance with flooding in real environments. The metrics considered are delay, reliability and transmission cost. Our initial results suggest that flooding is better suited for low-interference environments, while random walks might be a better option in networks with high interference. We also present possible research directions to improve the performance oflooding and random walks.

Maximizing Network Utilization with Max-Min Fairness in Wireless Sensor Networks

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 linear programs. We first show how the max-min rate can be computed efficiently for a given network. We then adopt a dual-based approach to maximize the network utilization. The analysis of the dual shows the sub-optimality of previously proposed additive increase algorithms with respect to bandwidth efficiency. Although in theory a dual-based sub-gradient search algorithm can take a long time to converge, we find empirically that setting shadow prices to 1 results in near-optimal solutions within one iteration (within 2% of the optimum in 99.65% of the cases). This results in a fast heuristic distributed algorithm that has a nice intuitive explanation - rates are allocated sequentially after rank ordering flows based on the number of downstream receivers whose bandwidth they consume.