Lifeng Sang

On exploiting asymmetric wireless links via one-way estimation

A substantial percentage of links in wireless networks, especially low-power ones, is asymmetric. For the low-quality direction of asymmetric links, we observe based on testbed experiments that the reliability of synchronous acknowledgments is considerably higher than that of asynchronous messages. Thus the norm of estimating link quality in both directions via asynchronous beacons such as in ETX-based routing potentially underestimates the link reliability of asymmetric links. This leads us to investigate how to exploit asymmetric links in order to improve network functions such as convergecast routing in sensor networks via one-way link estimation. We propose a new one-way link metric ETF (for the expected number of transmissions over forward links) and present a local procedure for its estimation. We use ETF to identify high reliability forward links and use dynamic retransmission thresholding for error control and observe an improvement inconvergecast routing over ETX. This is quantified with experimental testbed results with respect to reliability, number of transmissions per packet, latency, duplicates and average hops. We also study the comparative performance improvement of ETF over ETX when no special mechanism is employed to discover asymmetric links.

On link asymmetry and one-way estimation in wireless sensor networks

Link asymmetry is one of the unique challenges that wireless sensor networks pose in the design of network protocols. We observe, based on testbed experiments, that a substantial percentage of links are asymmetric, many are even unidirectional. We also find that the reliability of synchronous acknowledgments is considerably higher than that of asynchronous messages. Thus the norm of estimating link quality bidirectionally via asynchronous beacons underestimates the link reliability of asymmetric links. This leads us to investigate how to exploit asymmetric links in order to improve network functions such as convergecast routing in sensor networks via one-way link estimation. We propose a new one-way link metric ETF (for the expected number of transmissions over forward links) and present a local procedure for its estimation. We use ETF to identify reliable forward links, and we use dynamic retransmission thresholding for error control. Via experiments on testbeds of CC1000 radios and CC2420 radios (an IEEE 802.15.4-compliant radio), we quantify the performance improvement in ETF as compared with ETX. We also study the performance improvement of ETF over ETX when no special mechanism is employed to discover asymmetric links or to control retransmissions.

Spatial Signatures for Lightweight Security in Wireless Sensor Networks

This paper experimentally investigates the feasibility of crypto-free communications in resource-constrained wireless sensor networks. We exploit the spatial signature induced by the radio communications of a node on its neighboring nodes. We design a primitive that robustly and efficiently realizes this concept, even at the level of individual packets and when the network is relatively sparse. Using this primitive, we design a protocol that robustly and efficiently validates the authenticity of the source of messages: authentic messages incur no communication overhead whereas masqueraded communications are detected cooperatively by the neighboring nodes. The protocol enables lightweight collusion-resistant methods for broadcast authentication, unicast authentication, non-repudiation and integrity of communication. We have implemented our primitive and protocol, and quantified the high-level of accuracy of the protocol via testbed experiments with CC1000 radio-enabled motes.

Comparison of Data-Driven Link Estimation Methods in Low-Power Wireless Networks

Link estimation is a basic element of routing in low-power wireless networks, and data-driven link estimation using unicast MAC feedback has been shown to outperform broadcast-beacon-based link estimation. Nonetheless, little is known about how different data-driven link estimation methods affect routing behaviors. To address this issue, we classify existing data-driven link estimation methods into two broad categories: L-NT that uses aggregate information about unicast and L-ETX that uses information about the individual unicast-physical-transmissions. Through mathematical analysis and experimental measurement in a testbed of 98 XSM motes (an enhanced version of MICA2 motes), we examine the accuracy and stability of L-NT and L-ETX in estimating the ETX routing metric. We also experimentally study the routing performance of L-NT and L-ETX. We discover that these two representative, seemingly similar methods of data-driven link estimation differ significantly in routing behaviors: L-ETX is much more accurate and stable than L-NT in estimating the ETX metric, and accordingly, L-ETX achieves a higher data delivery reliability and energy efficiency than L-NT (for instance, by 25.18 percent and a factor of 3.75, respectively, in our testbed). These findings provide new insight into the subtle design issues in data-driven link estimation that significantly impact the reliability, stability, and efficiency of wireless routing, thus shedding light on how to design link estimation methods for mission-critical wireless networks which pose stringent requirements on reliability and predictability.

Capabilities of Low-Power Wireless Jammers

In this paper, motivated by the goal of modeling the fine-grain capabilities of jammers for the context of security in low-power wireless networks, we experimentally characterize jamming in networks of CC2420 radio motes and CC1000 radio motes. Our findings include that it is easy to locate J (relative to S and R) and choose its power level so that J can corrupt Ss messages with high probability as well as corrupt individual Ss bits with nontrivial probability. Internal jammers are however limited in at least two ways: One, it is hard for them to prevent R from detecting that it has received an uncorrupted message from S. And two, the outcome of their corruptions are not only not deterministic, even the probabilities of corrupted outcomes are time-varying. We therefore conclude that it is hard to predict the value resulting from colliding Ss messages (bits) with Js messages (bits) and, conversely, to deduce the value sent by Ss or Js from the corrupted value received by R.

On the convergence and stability of data-driven link estimation and routing in sensor networks

The wireless network community has become increasingly aware of the benefits of data-driven link estimation and routing as compared with beacon-based approaches, but the issue of Biased Link Sampling (BLS) estimation has not been well studied even though it affects routing convergence in the presence of network and environment dynamics. Focusing on traffic-induced dynamics, we examine the open, unexplored question of how serious the BLS issue is and how to effectively address it when the routing metric ETX is used. For a wide range of traffic patterns and network topologies and using both node-oriented and network-wide analysis and experimentation, we discover that the optimal routing structure remains quite stable even though the properties of individual links and routes vary significantly as traffic pattern changes. In cases where the optimal routing structure does change, data-driven link estimation and routing is either guaranteed to converge to the optimal structure or empirically shown to converge to a close-to-optimal structure. These findings provide the foundation for addressing the BLS issue in the presence of traffic-induced dynamics and suggest approaches other than existing ones. These findings also demonstrate that it is possible to maintain an optimal, stable routing structure despite the fact that the properties of individual links and paths vary in response to network dynamics.

A shared-secret free security infrastructure for wireless networks

This article develops a shared-secret free wireless security infrastructure that provides confidentiality, identity authentication, message authentication, integrity, sender nonrepudiation, receiver nonrepudiation, and anonymity. Our infrastructure is based on two physical primitives, namely collaborative jamming and spatial signature enforcement, and a zero knowledge alternative for bootstrapping trust. Notably, it eschews the use of shared secrets, while providing a cryptosystem that is no less secure than conventional cryptosystems.

Comparison of Data-driven Link Estimation Methods in Low-power Wireless Networks

Link estimation is a basic element of routing in low-power wireless networks, and data-driven link estimation using unicast MAC feedback has been shown to outperform broadcast-beacon based link estimation. Nonetheless, little is known about the impact that different data-driven link estimation methods have on routing behaviors. To address this issue, we classify existing data-driven link estimation methods into two broad categories: L-NT that uses aggregate information about unicast and L-ETX that uses information about the individual unicast-physical transmissions. Through mathematical analysis and experimental measurement in a testbed of 98 XSM motes (an enhanced version of MICA2 motes), we examine the accuracy and stability of L-NT and L-ETX in estimating the ETX routing metric. We also experimentally study the routing performance of L-NT and L-ETX. We discover that these two representative, seemingly similar methods of data-driven link estimation differ significantly in routing behaviors: L-ETX is much more accurate and stable than L-NT in estimating the ETX metric, and, accordingly, L-ETX achieves a higher data delivery reliability and energy efficiency than L-NT (for instance, by 25.18% and a factor of 3.75 respectively in our testbed). These findings provide new insight into the subtle design issues in data-driven link estimation that significantly impact the reliability, stability, and efficiency of wireless routing, thus shedding light on how to design link estimation methods for mission-critical wireless networks which pose stringent requirements on reliability and predictability.

QoS supported efficient clustered query processing in large collaboration of heterogeneous sensor networks

Significant worldwide growth is witnessed in development and deployment of huge numbers of heterogeneous sensor networks. These all brings the issue of state-of-the-art federations or collaborations among such networks. Query Processing operation in such collaborative systems has major challenges: fast and scalable query processing, QoS support for query, flexible and robust collaborative system design etc. To our knowledge there has not been much work done on designing scalable and efficient query processing among the huge collaboration of sensor networks. The work EE-QPS designed a pipelined query optimization problem based on energy efficiency. But with varying demands (energy, delay, reliability etc.) of different queries, the Quality of Service (QoS) support becomes very important. Also, entirely sequential or entirely parallel query processing have problems with latency and scalability. Then a hybrid query processing scheme can have flexibility to deliver better performance for all kinds of collaborative systems. Considering all these aspects, we have proposed QoS-QPS, a QoS supported clustered Query Processing System. We have designed a flexible model for querying cost of sensor networks. The cost model is inexpensive to compute and general enough to apply. Then we propose clustered query processing technique, that utilizes a constrained graph partitioning algorithm. This whole QoS aware query processing technique delivers balanced and efficient clustering of sensor networks based on implication relationship. Comprehensive simulations study shows that our proposed scheme is better than existing techniques in compromising among different system requirements. The results also validate the efficiency, scalability and applicability of QoS-QPS. Further we have analyzed potential architectural issues and possible solutions.

On biased link sampling in data-driven link estimation and routing in low-power wireless networks

The wireless network community has become increasingly aware of the benefits of data-driven link estimation and routing as compared with beacon-based approaches, but the issue of biased link sampling (BLS) has not been well studied even though it affects routing convergence in the presence of network and environment dynamics. Focusing on traffic-induced dynamics, we examine the open, unexplored question of how serious the BLS issue is and how to effectively address it when the routing metric ETX is used. For a wide range of traffic patterns and network topologies and using both node-oriented and network-wide analysis and experimentation, we discover that the optimal routing structure remains quite stable even though the properties of individual links and routes vary significantly as traffic pattern changes. In cases where the optimal routing structure does change, data-driven link estimation and routing is either guaranteed to converge to the optimal structure or empirically shown to converge to a close-to-optimal structure. These findings provide the foundation for addressing the BLS issue in the presence of traffic-induced dynamics and suggest approaches other than existing ones. These findings also demonstrate that it is possible to maintain an optimal, stable routing structure despite the fact that the properties of individual links and paths vary in response to network dynamics.