Shuo Guo

Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links

Flooding service has been investigated extensively in wireless networks to efficiently disseminate network-wide commands, configurations, and code binaries. However, little work has been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of the time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. Starting with an energy-optimal tree structure, probabilistic forwarding decisions are made at each sender based on the delay distribution of next-hop receivers. Only opportunistically early packets are forwarded via links outside the tree to reduce the flooding delay and redundancy in transmission. We further propose a forwarder selection method to alleviate the hidden terminal problem and a link-quality-based backoff method to resolve simultaneous forwarding operations. We show by extensive simulations and test-bed implementations that Opportunistic Flooding is close to the optimal performance achievable by oracle flooding designs. Compared with Improved Traditional Flooding, our design achieves significantly shorter flooding delay while consuming only 20-60% of the transmission energy.

FIND: faulty node detection for wireless sensor networks

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole systems performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functionnal faults, this paper proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole systems performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

TSF: Trajectory-Based Statistical Forwarding for Infrastructure-to-Vehicle Data Delivery in Vehicular Networks

This paper proposes a data forwarding scheme called Trajectory-based Statistical Forwarding (TSF), tailored for the data delivery from infrastructure nodes (e.g., Internet access points) to moving vehicles in vehicular networks. To our knowledge, this paper presents the first attempt to investigate how to effectively utilize the packet destination vehicle’s trajectory for such an infrastructure-to-vehicle data delivery. This data delivery is performed through the computation of a target point based on the destination vehicle’s trajectory that is an optimal rendezvous point of the packet and the destination vehicle. TSF forwards packets over multi-hop to a selected target point where the vehicle is expected to pass by. Such a target point is selected optimally to minimize the packet delivery delay while satisfying the required packet delivery probability. The optimality is achieved analytically by utilizing the packet’s delivery delay distribution and the destination vehicle’s travel delay distribution. Through theoretical analysis and extensive simulation, it is shown that our design provides an efficient data forwarding under a variety of vehicular traffic conditions.

Correlated flooding in low-duty-cycle wireless sensor networks

Flooding in low-duty-cycle wireless sensor networks is very costly due to asynchronous schedules of sensor nodes. To adapt existing flooding-tree-based designs for low-duty-cycle networks, we shall schedule nodes of common parents wake up simultaneously. Traditionally, energy optimality in a designated flooding-tree is achieved by selecting parents with the highest link quality. In this work, we demonstrate that surprisingly more energy can be saved by considering link correlation. Specifically, this work first experimentally verifies the existence of link correlation and mathematically proves that the energy consumption of broadcasting can be reduced by letting nodes with higher correlation receive packets simultaneously. A novel flooding scheme, named Correlated Flooding, is then designed so that nodes with high correlation are assigned to a common sender and their receptions of a broadcasting packet are only acknowledged by a single ACK. This unique feature effectively ameliorates the ACK implosion problem, saving energy on both data packets and ACKs. We evaluate Correlated Flooding with extensive simulations and a testbed implementation with 20 MICAz nodes. We show that Correlated Flooding saves more than 66% energy on ACKs and 15%–50% energy on data packets for most network settings, while having similar performance on flooding delay and reliability.

Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links

Flooding service has been investigated extensively in wireless networks to efficiently disseminate network-wide commands, configurations, and code binaries. However, little work has been done on low-duty-cycle wireless sensor networks in which nodes stay asleep most of the time and wake up asynchronously. In this type of network, a broadcasting packet is rarely received by multiple nodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. Starting with an energy-optimal tree structure, probabilistic forwarding decisions are made at each sender based on the delay distribution of next-hop receivers. Only opportunistically early packets are forwarded via links outside the tree to reduce the flooding delay and redundancy in transmission. We further propose a forwarder selection method to alleviate the hidden terminal problem and a link-quality-based backoff method to resolve simultaneous forwarding operations. We show by extensive simulations and test-bed implementations that Opportunistic Flooding is close to the optimal performance achievable by oracle flooding designs. Compared with Improved Traditional Flooding, our design achieves significantly shorter flooding delay while consuming only 20-60% of the transmission energy.

Trajectory-Based Statistical Forwarding for Multihop Infrastructure-to-Vehicle Data Delivery

This paper proposes Trajectory-based Statistical Forwarding (TSF) scheme, tailored for the multihop data delivery from infrastructure nodes (e.g., Internet access points) to moving vehicles in vehicular ad hoc networks. To our knowledge, this paper presents the first attempt to investigate how to effectively utilize the packet destination vehicles trajectory for such an infrastructure-to-vehicle data delivery. This data delivery is performed through the computation of a target point based on the destination vehicles trajectory that is an optimal rendezvous point of the packet and the destination vehicle. TSF forwards packets over multihop to a selected target point where the vehicle is expected to pass by. Such a target point is selected optimally to minimize the packet delivery delay while satisfying the required packet delivery probability. The optimality is achieved analytically by utilizing the packets delivery delay distribution and the destination vehicles travel delay distribution. Through theoretical analysis and extensive simulation, it is shown that our design provides an efficient data forwarding under a variety of vehicular traffic conditions.

Robust multi-pipeline scheduling in low-duty-cycle wireless sensor networks

Data collection is one of the major traffic pattern in wireless sensor networks, which requires regular source nodes to send data packets to a common sink node with limited end-to-end delay. However, the sleep latency brought by duty cycling mode results in significant rise on the delivery latency. In order to reduce unnecessary forwarding interruption, the state-of-the-art has proposed pipeline scheduling technique by allocating sequential wakeup time slots along the forwarding path. We experimentally show that previously proposed pipeline is fragile and ineffective in reality when wireless communication links are unreliable. To overcome such challenges and improve the performance on the delivery latency, we propose Robust Multi-pipeline Scheduling (RMS) algorithm to coordinate multiple parallel pipelines and switch the packet timely among different pipelines if failure happens in former attempts of transmissions. RMS combines the pipeline features with the advantages brought by multi-parents forwarding. Large-scale simulations and testbed implementations verify that the end-to-end delivery latency can be reduced by 40% through exploiting multi-pipeline scheduled forwarding path with tolerable energy overhead.

Link-Correlation-Aware Opportunistic Routing in Wireless Networks

Recent empirical studies have shown clear evidence that wireless links are not independent and that the packet receptions on adjacent wireless links are correlated. This finding contradicts the widely held link-independence assumption in the calculation of the core metric, i.e., the expected number of transmissions to the candidate forwarder set, in opportunistic routing (OR). The inappropriate assumption may cause serious estimation errors in the forwarder set selection, which further leads to underutilized diversity benefits or extra scheduling costs. We thus advocate that OR should be made aware of link correlation. In this paper, we propose a novel link-correlation-aware OR scheme, which significantly improves the performance by exploiting the diverse low correlated forwarding links. We evaluate the design in a real-world setting with 24 MICAz nodes. Testbed evaluation and extensive simulation show that higher link correlation leads to fewer diversity benefits and that, with our link-correlation-aware design, the number of transmissions is reduced by 38%.

Link correlation aware opportunistic routing

By exploiting reception diversity of wireless network links, researchers have shown that opportunistic routing can improve network performance significantly over traditional routing schemes. However, recently empirical studies indicate that we are too optimistic, i.e. diversity gain can be overestimated if we continue to assume that packet receptions of wireless links are independent events. For the first time, this paper formally analyzes the opportunistic routing gain under the presence of link correlation considering the loss of DATA and ACK packets. Based on the model, we introduce a new link-correlation-aware opportunistic routing scheme, which improves the performance by exploiting the diverse uncorrelated forwarding links. Our design is evaluated using simulation where we show (i) link correlation leads to less diversity gain, (ii) and with our link-correlation-aware design; improvement can be gained. We also provide a unique model to generate strings of randomly correlated receptions.