Wireless sensor networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pairwise based, in which discovery is passively achieved when two nodes are prescheduled to wake up at the same time. In contrast, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Since proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pairwise one. Our testbed experiments using 40 sensor nodes and extensive simulations confirm the theoretical analysis, showing one order of magnitude reduction in discovery delay compared with legacy pairwise methods in dense, uniformly distributed sensor networks with at most 8.8 percent increase in energy consumption.

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%.

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.

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 system's 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.

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.

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.

Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%~8.8% increase in energy consumption.

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 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 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 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.

The number of real-world applications that require QoS guarantees is constantly increasing and they often follow the publish/subscribe (pub/sub)messaging paradigm, which provides loosely coupled many-to-many communication. Many QoS-aware systems use overlay networks as they allow flexible routing. To provide QoS-aware pub/sub messaging in overlay networks, the messaging system should be adaptive to the changes in network conditions (such as delay and failures). However, many pub/sub systems depend on a flexed routing topology and it is costly to rebuild this topology in case of failures. This study seeks to address this challenge with Delay-Cognizant Reliable Delivery (DCRD), a novel and delay-aware dynamic routing algorithm to provide reliable message delivery for pub/sub overlay networks. For reliable message delivery, DCRD no longer uses a flxed routing topology. Instead, it dynamically switches among different links to bypass link failures and increase the chance to meet QoS requirement. Each node tries different neighboring nodes in an order that is mathematically proven to minimize the expected delay of packet delivery. With all possible neighboring nodes sorted this way, DCRD guarantees that packets are delivered as long as there exists a path between the publisher and subscriber and that the expected delay is minimized. DCRD is extensively evaluated in simulation with comparison to existing tree-based routing approaches as well as a multi path approach using different network topologies, delay constraints, and loss probabilities. Simulation results show that DCRD performs better than all the baselines, providing reliable message delivery and satisfying the delay requirement for more than 98% of messages when the link failure probability is 4% or less.

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.

Vehicular ad hoc networks (VANETs) represent promising technologies for improving driving safety and efficiency. Due to the highly dynamic driving patterns of vehicles, it has been a challenging research problem to achieve effective and time-sensitive data forwarding in vehicular networks. In this paper, a Shared-Trajectory-based Data Forwarding Scheme (STDFS) is proposed, which utilizes shared vehicle trajectory information to address this problem. With access points sparsely deployed to disseminate vehicles' trajectory information, the encounters between vehicles can be predicted by the vehicle that has data to send, and an encounter graph is then constructed to aid packet forwarding. This paper focuses on the specific issues of STDFS such as encounter prediction, encounter graph construction, forwarding sequence optimization and the data forwarding process. Simulation results demonstrate the effectiveness of the proposed scheme.

Based on spatiotemporal properties of neighborhood and mobile properties of nodes in the networks, we propose Selective Reference Mechanism to trade off between the delay and overhead of neighbor discovery in low-duty-cycle WSNs. Extensive simulation and test-bed experiment confirm our theoretical analysis, showing as much as 35.4% increase in discovery probability, 38.6% reduction in discovery delay and 27.7% reduction in total energy consumption.

Road networks are one of important surveillance areas in military scenarios. In these road networks, sensors will be sparsely deployed (hundreds of meters apart) for the cost-effective deployment. This makes the existing localization solutions based on the ranging ineffective. To address this issue, this paper introduces a novel approach based on the passive vehicular traffic measurement, called Autonomous Passive Localization (APL). Our work is inspired by the fact that vehicles move along routes with a known map. Using binary vehicle-detection time stamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of the road map, in order to identify where sensors are located on roadways. We evaluate our design outdoors on Minnesota roadways and show that our distance estimate method works well despite traffic noises. In addition, we show that our localization scheme is effective in a road network with 18 intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.07 sec and a vehicle speed deviation of 10 km/h.

This paper proposes a Trajectory-Based Data (TBD) Forwarding scheme, tailored for the data forwarding for roadside reports in light-traffic vehicular ad hoc networks. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds). These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to effectively utilize vehicles' trajectory information in a privacy-preserving manner. In our design, such trajectory information is combined with the vehicular traffic statistics for a better performance. In a distributed way, each individual vehicle computes its end-to-end expected delivery delay to the Internet access points based on its position on its vehicle trajectory and exchanges this delay with neighboring vehicles to determine the best next-hop vehicle. For the accurate end-to-end delay computation, this paper also proposes a link delay model to estimate the packet forwarding delay on a road segment. Through theoretical analysis and extensive simulation, it is shown that our link delay model provides the accurate link delay estimation and our forwarding design outperforms the existing scheme in terms of both the data delivery delay and packet delivery ratio.

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.

This paper proposes a trajectory-based data forwarding (TBD) scheme, tailored for the data forwarding in light- traffic vehicular ad-hoc networks. State-of-the-art schemes have demonstrated the effectiveness of their data forwarding strategies by exploiting known vehicular traffic statistics (e.g., densities and speeds) in these vehicular networks. These results are encouraging, however, further improvements can be made by taking advantage of the growing popularity of GPS-based navigation systems. This paper presents the first attempt to investigate how to effectively utilize vehicles" trajectory information in a privacy-preserving manner. In our design, the trajectory information is combined with the traffic statistics to improve the performance of data forwarding in road networks. Through theoretical analysis and extensive simulation, it is shown that our design outperforms the existing scheme.

Intended for network-wide dissemination of commands, configurations and code binaries, flooding has been investigated extensivelyinwirelessnetworks. However,littleworkhasyetbeendone on low-duty-cycle wireless sensor networks in which nodes stay asleep most of time and wake up asynchronously. In this type of network,abroadcastingpacketisrarelyreceivedbymultiplenodes simultaneously, a unique constraining feature that makes existing solutions unsuitable. Combined with unreliable links, flooding in low-duty-cycle networks is a new challenging issue. In this paper, we introduce Opportunistic Flooding, a novel design tailored for low-duty-cycle networks with unreliable wireless links and predetermined working schedules. The key idea is to make probabilistic forwarding decisions at a sender based on the delay distribution of next-hop nodes. Only opportunistically early packets are forwarded using links outside the energy optimal tree to reduce the flooding delay and redundancy in transmission. To improve performance further, we propose a forwarder selection methodtoalleviatethehiddenterminalproblemandalink-qualitybased backoff method to resolve simultaneous forwarding operations. We evaluate Opportunistic Flooding with extensive simulation and a test-bed implementation consisting of 30 MicaZ nodes. Evaluation shows our design is close to the optimal performance achievable by oracle flooding designs. Compared with improved traditionalflooding,ourdesignachievessignificantlyshorterflooding delay while consuming only 20 % ∼ 60 % of the transmission energy invarious low-duty-cycle networksettings. Categories andSubject Descriptors

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 system's 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.

In road networks, sensors are deployed sparsely (hundreds of meters apart) to save costs. This makes the existing localization solutions based on the ranging be ineffective. To address this issue, this paper introduces an autonomous passive localization scheme, called APL. Our work is inspired by the fact that vehicles move along routes with a known map. Using binary vehicle-detection timestamps, we can obtain distance estimates between any pair of sensors on roadways to construct a virtual graph composed of sensor identifications (i.e., vertices) and distance estimates (i.e., edges). The virtual graph is then matched with the topology of road map, in order to identify where sensors are located in roadways. We evaluate our design outdoor in Minnesota roadways and show that our distance estimate method works well despite of traffic noises. In addition, we show that our localization scheme is effective in a road network with eighteen intersections, where we found no location matching error, even with a maximum sensor time synchronization error of 0.3 sec and the vehicle speed deviation of 10 km/h.

Sensor networks have been used in many surveillance systems, providing statistical information about monitored areas. Accurate counting information (e.g., the distribution of the total number of targets) is often important for decision making. As a complementary solution to double-counting in communication, this paper presents the first work that deals with double-counting in sensing for wireless sensor networks. The probability mass function (pmf) of target counts is derived first. This, however, is shown to be computationally prohibitive when a network becomes large. A partitioning algorithm is then designed to significantly reduce computation complexity with a certain loss in counting accuracy. Finally, two methods are proposed to compensate for the loss. To evaluate the design, we compare the derived probability mass function with ground truth obtained through exhaustive enumeration in small-scale networks. In large-scale networks, where pmf ground truth is not available, we compare the expected count with true target counts. We demonstrate that accurate counting within 1 ∼ 3% relative error can be achieved with orders of magnitude reduction in computation, compared with an exhaustive enumeration-based approach.