While earables present a promising avenue for cardiac sensing, whether they may replace the stethoscope to perform heart sound (a.k.a. PCG) monitoring remains questionable. The latest effort attempts to generate PCG-like waveform out of in-ear audio collected via earphones, yet its data-driven approach does not seem to be grounded in the underlying physics. To this end, this paper introduces EarPCG, a system for continuous PCG monitoring leveraging physics-informed neural models. As opposed to the debatable belief that bone-conducted PCG appears within ear canal, EarPCG generates PCG waveforms from the (actually existing) photoplethysmography (PPG) waveforms conveyed via blood vessels. Arising from pressure variations induced by heartbeats, PPG can be mathematically described by a Partial Differential Equation (PDE). Therefore, solving this PDE inversely may reconstruct cardiac dynamics and in turn enable the generation of PCG waveforms with another PDE characterizing the pressure oscillations propagating through soft tissues. Pipelining the two PDE-solving neural models, EarPCG achieves accurate PCG monitoring from in-ear audio, while requiring minimal training. Our extensive experiments leveraging a custom-built prototype demonstrate the efficacy of our proposed system. Furthermore, we have conducted clinical trials, with clinicians reporting no perceptible difference between authentic PCG and the sounds reconstructed by EarPCG.

Millimeter-wave radar is a useful tool for Internet-of-Things (IoT) enabled indoor subject tracking, gesture recognition, or other human-computer interaction. However, existing proposals are post-processing techniques, depending on unreliable processed outcomes, often resulting in ghost points. In addition, current solutions mostly exploit wide-band (up to 4 GHz) mmWave radar. However, for indoor IoT applications, regulations limit the available bandwidth to a maximum of 250 MHz, rendering current solutions infeasible for practical deployment. In light of this, we propose a hybrid approach in a pre-processing manner. Our proposal differs from existing solutions that leverage posterior point clouds, but apply a processing algorithm to original range-Doppler profiles. Meanwhile, we leverage physical constraints to further smooth tracking trajectories. To enhance the network generalizability and tracking performance, we incorporate a curriculum learning strategy. We have implemented our solution on a commercial mmWave radar with 250 MHz bandwidth. Experimental results show that, in complex indoor multi-person scenarios, the average estimation errors of AoA, range, and speed are 0.1 rad, 0.4 m, and 0.17 m/s, respectively, with a tracking RMSE of 0.77m for multiple targets.

Many reconnaissance missions require a group of mobile terminals (such as soldiers, mobile robots, and unmanned boats) to jointly operate within a region which is far away from the command centre. When a critical event occurs and is detected by a terminal, it is often required for the terminal to upload some critical data to the command centre (or via the satellite). As the bandwidth of the upload link is usually low due to the long distance, uploading the critical data often has long latency. To reduce the latency, a feasible way is to utilize the nearby terminals’ idle uplinks to help with the upload process, which requires the terminal’s data to be disseminated to other terminals as soon as possible. This is a new dissemination problem because the data being disseminated is also partially being uploaded, which seems as a noveldata-leakingdissemination problem. To solve it, we propose LHDS (Low-latency Heterogeneous Dissemination Scheduling) scheme by transforming the problem into two special sub-problems, i.e., constructing a special degree-decreasing tree with maximum multichild nodes, and designing a leaking-sustained dissemination schedule for each subtree. Extensive simulation experiments have been conducted on LHDS as well as two heuristic algorithms (i.e.,DBOandS-GA) designed for baselines. The results show that LHDS scheme significantly outperforms theDBOandS-GAalgorithms in terms of total collaborative data uploading latency, with saving 41% and 42% latency on average, respectively.

Wireless Multi-Hop Networks (WMNs) have been widely applied in many industrial monitoring applications, where typically nodes deployed on machines sample the data periodically and the data then needs to be gathered to the sink node in time. Due to the complicated industrial environment, the channel capacity between neighboring nodes differs greatly from each other. This leads to a typical dilemma of radio communication module selection, i.e., choosing the wide-bandwidth module with short communication range or the long-range module with low bandwidth. Guaranteeing both the connectivity of the network and high network throughput is not easy. To address this issue, we equip the nodes with heterogeneous radio modules, leveraging their advantages to ensure both long-distance and high-bandwidth communication capabilities. The problem of efficiently scheduling the links between the nodes with multiple heterogeneous RF modules to minimize the data gathering delay has not yet been explored in existing research. In this paper, we propose a low-latency data collection method based on Deep Reinforcement Learning, with which nodes can autonomously appropriately schedule and assign their heterogeneous RF modules to their transmission/reception targets based on the current amount of their local data as well as that of their neighbors’ data. Extensive simulations are conducted, and the results show that the proposed method can achieve 10%-25% latency reduction compared to existing multi-radio approaches.

As a classic data processing tool, Principal Component Analysis (PCA) has been widely applied in various data analysis applications. To mitigate the high computational complexity of PCA on big data, distributed PCA methods have been extensively studied, which disperse the computational tasks across multiple computation units while guaranteeing the accuracy. For the scenarios of distributed PCA in wireless networks, as the data is originally dispersed across different locations, it is further required to reduce the communication cost of distributed PCA in networks, which however has been seldom studied. Reducing the communication cost of distributed PCA in wireless networks requires not only appropriately partitioning the computation of PCA, ensuring accuracy, but also effectively assigning the partitioned computations and routing strategies to the nodes. In this paper, we propose CD-PCA, a communication-efficient distributed PCA (CD-PCA) scheme. This scheme implements a transmission-benefit equipartition strategy for the network to facilitate high-accuracy distributed computation and designs novel routing strategies for nodes to execute the distributed PCA within each partitioned region. Extensive simulation results demonstrate that the proposed CD-PCA scheme can reduce transmission costs by over 30% on average compared to related methods and baseline approaches.

As one of the most preferred forms of Human-Computer Interaction (HCI) nowadays, speech-based HCI enables people to communicate verbally with machines, leveraging technologies such as speech recognition and speech synthesis. Current paradigm of speech-based HCI focus on the content of speech only, failing to comprehend deeper pointing information in voice interaction. In particular, when encountering scenarios with multiple smart voice devices around, if people intend to interact with a certain device, the lack of extra pointing information (like the role played by the direction of eye gaze) would cause unintended response from the other devices, resulting in poor interaction experience during HCI. Hence, an interesting problem is: Is it possible for the devices to be aware of the orientation of human voice with only the acoustic speech signals? There is little research studying this topic, except for very a few primary works with much room for improvement. The main challenge of this study lies in capturing the concealed orientation information embedded within the speech signal, while simultaneously maintaining the scheme’s practicality and high precision. In this paper, we propose Oriennet, for identifying the orientation of human voice. With a series of features intentionally designed in view of the indoor voice propagation model and mouth radiation pattern, as well as the application of attention mechanism, Oriennet achieve 95% accuracy in terms of judging whether people are facing the device or not. Even for the fine-grained task of classifying people’s specific orientation from 8 different directions, our work achieved an accuracy of 74%, far outperforming the existed works. We have validated the robustness of Oriennet under various conditions (noisy environment; different people, rooms, languages, locations; fewer microphones), demonstrating its promising applicability in real-life scenarios.

Feature pyramid representations have been widely adopted in the object detection literature for better handling of variations in scale, which provide abundant information from various spatial levels for classification and localization sub-tasks. We find that inter sub-task feature disentanglement and intra sub-task feature re-fusion are crucial for final prediction performance, but are hard to be achieved simultaneously considering the computational efficiency. We find this issue can be addressed by delicate module design. In this paper, we propose an Efficient Task-specific Feature Re-fusion (ETFR) module to mitigate the dilemma. ETFR disentangles inter sub-task features, reduces the output channels of multi-scale features based on their importance and re-fuses intra sub-task features via concatenation operation. As a plug-and-play module, ETFR can remarkably and consistently improve the well-established and highly-optimized object detection and instance segmentation methods, such as RetinaNet, FCOS, BlendMask and CondInst, with neglectable extra computation cost. Extensive experiments demonstrate that ETFR has good generalization ability on various changeling datasets, including COCO, LVIS and Cityscapes.

Acoustic-based respiration sensing is promising due to its ubiquitous device support and great freedom in signal design. However, existing proposals often either fail to function properly when a target is non-static or is under multipath interference, or address it in an algorithmic manner. To this end, in this paper, we propose CORA, a COntinuous RespirAtion monitoring system using purely analytical signal processing methods. CORA is the first approach that achieves physical separation between motion artifacts and respiration, other than existing algorithmic solutions, and hence can obtain results that are closer to ground truth. CORA leverages the edges of Orthogonal Time Frequency Space signals in monitoring motion states and addressing multipath interference. The ability to tackle these challenges can help to compensate motion-induced artifacts for FMCW-based sensing techniques, enabling continuous respiration monitoring even in non-static scenarios. To achieve high-quality compensation, a pipeline of signal processing techniques is proposed, including robust moving target tracking, accurate frequency bin selection, and effective phase denoising. Unlike existing deep learning-based approaches, CORA is explainable and is readily deployable, without sophisticated adaptation or exhausted training processes. We have implemented a system prototype and evaluated its performance. Experiment results demonstrate a median error of 0.86 respiration per minute.

With the wide adoption of versatile IoT devices, device providers may desire to locate users around those devices to plan context-aware intelligence, which may improve the quality of daily life. As most IoT devices are WiFi enabled, the WiFi-based indoor positioning system is supposed to achieve this future scene. However, the state-of-the-art WiFi indoor positioning systems face challenges when being practically deployed as they may have to tradeoff between, say accuracy and computational overhead. In light of this, this article mainly introduces PLP-Track, a practical lightweight passive indoor tracking system based on channel state information (CSI) fingerprints. To settle the low granularity of fingerprints in distinguishing different positions, we propose a fingerprint preprocessing algorithm based on unsupervised learning and incorporate this algorithm with a state-space model to enable lightweight real-time tracking. Our implementation and evaluation of commodity WiFi devices demonstrate that PLP-Track can achieve indoor localization with high accuracy and low-computation cost.

In many domain-specific monitoring applications of wireless sensor networks (WSNs), such as structural health monitoring (SHM), volcano tomography, and machine diagnosis, all the raw data in WSNs are required to be gathered to the sink where a specialized centralized algorithm is then executed to extract some global features or model parameters. To reduce the large-scale raw data transmission while guaranteeing the global feature quality, there are two kinds of solutions: one is in-network processing, which generally needs to distribute the centralized computation of feature extraction into networks. Another solution is compressive sensing (CS) followed with the feature extraction (called feature CS in this article). An interesting question is: for in-network processing and feature CS, which kind of solutions is more cost efficient to accomplish the task of feature extraction? This question is seldom studied. To answer it, we take the case of SHM with WSNs along with the classic feature extraction algorithm, i.e., the Eigen-system realization algorithm (ERA), and appropriately design two novel routes for in-network processing and feature CS, respectively. Both theoretical analysis of the two solutions’transmission cost and numerous simulations have been conducted. Based on the comparison results, we summarize some guidelines on the solution choice for different kinds of WSNs for SHM. In addition, we find that, instead of guaranteeing the quality of raw data reconstructed, CS with guaranteeing the quality of feature extracted is usually more meaningful and cost efficient.

Convolutional neural networks (CNNs) have shown attractive performance for stereo matching. However, spatially shared convolution weights of CNN-based methods usually face a dilemma that the convolution weights suitable for aggregating contextual information in smooth regions often blur local matching details of textured regions and vice versa. This paper tries to find a way out of the dilemma via a novel region separable stereo matching (RSSM) method, which is universally applicable to CNN stereo models based on 4D cost volumes and can greatly improve the accuracy and efficiency of existing models. The key idea of our method is to automatically group image pixels into regions according to the gradients, and then construct and process the respective cost volume of each region separately. To perform cost aggregation, we propose a two-stage network consisted of regional grouping aggregation (RGA) and regional fusion aggregation (RFA). In RGA, convolutions are grouped in channel-wise, and each group of convolutions learn dedicated weights for the corresponding region via regional supervision. Through RGA, each group of convolutions can extract the most representative features from the corresponding region. In RFA, we combine matching clues of all convolution groups from RGA to output the final prediction map. We further extend the idea of regional grouping to feature extraction and modify the skip connection in aggregation networks to better adapt our method to stereo matching models. Experimental results on five public datasets show that our method can significantly improve several state-of-the-art 3D CNN based stereo models.

Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention weights from correlation clues to suppress redundant information and enhance matching-related information in the concatenation volume. To generate reliable attention weights, we propose multi-level adaptive patch matching to improve the distinctiveness of the matching cost at different disparities even for textureless regions. The proposed cost volume is named attention concatenation volume (ACV) which can be seamlessly embedded into most stereo matching networks, the resulting networks can use a more lightweight aggregation network and meanwhile achieve higher accuracy, e.g. using only 1/25 parameters of the aggregation network can achieve higher accuracy for GwcNet. Furthermore, we design a highly accurate network (ACVNet) based on our ACV, which achieves state-of-the-art performance on several benchmarks. The code is available at https://github.com/gangweiX/ACVNet.

Precise estimation of optical flow, stereo depth and camera motion are important for the real-world 3D scene understanding and visual perception. Since the three tasks are tightly coupled with the inherent 3D geometric constraints, current studies have demonstrated that the three tasks can be improved through jointly optimizing geometric loss functions of several individual networks. In this paper, we show that effective feature-level collaboration of the networks for the three respective tasks could achieve much greater performance improvement for all three tasks than only loss-level joint optimization. Specifically, we propose a single network to combine and improve the three tasks. The network extracts the features of two consecutive stereo images, and simultaneously estimates optical flow, stereo depth and camera motion. The whole network mainly contains four parts: (I) a feature-sharing encoder to extract features of input images, which can enhance features’ representation ability; (II) a pooled decoder to estimate both optical flow and stereo depth; (III) a camera pose estimation module which fuses optical flow and stereo depth information; (IV) a cost volume complement module to improve the performance of optical flow in static and occluded regions. Our method achieves state-of-the-art performance among the joint unsupervised methods, including optical flow and stereo depth estimation on KITTI 2012 and 2015 benchmarks, and camera motion estimation on KITTI VO dataset.

Pyroelectric infrared (PIR) sensors are considered to be promising devices for device-free localization due to its advantages of low cost, energy efficiency, and the immunity from multi-path fading. However, most of the existing PIR-based localization systems only utilize the binary information of PIR sensors and therefore require a large number of carefully deployed PIR sensors. A few works directly map the raw data of PIR sensors to one's location using machine learning approaches. However, these data-driven approaches require abundant training data and suffer from environmental change. In this paper, we propose PIRATES, a PIR-based device-free localization system based on the raw data of PIR sensors. The key of PIRATES is to extract a new type of location information called azimuth change. The extraction of the azimuth change relies on the physical properties of PIR sensors. Therefore, no abundant training data are needed and the system is robust to environmental change. Through experiments, we demonstrate that PIRATES can achieve higher localization accuracy than the state-of-the-art approaches. In addition, the information of the azimuth change can be easily incorporated with other information of PIR signals (e.g. amplitude) to improve the localization accuracy.

Internet of things (IOT) is emerging as sensing paradigms in many domain-specific monitoring applications in smart cities, such as structural health monitoring (SHM) and smart grid monitoring. Due to the large size of the monitoring objects (e.g., civil structure or the power grid), plenty of sensors need to be deployed and organized to be a large scale of multihop wireless sensor networks (WSNs), which tends to have quite high transmission cost. In-network processing is an efficient way to reduce the transmission cost in WSNs. However, implementing in-network processing for above domain-specific monitoring usually requires to losslessly distribute a dedicate domain-specific algorithm into WSNs, which is much different from most existing in-network processing works. This paper conducts a case study of a classic centralized SHM algorithm, i.e., eigensystem realization algorithm (ERA), and shows how to losslessly and optimally in-network process ERA, especially the typical feature extraction method, i.e., that is singular value decomposition (SVD) therein, in a WSN. Based on whether the intermediate data can be processed together or not by sensor nodes, we respectively implement tree-based in-network processing of SVD and chain-based in-network processing of SVD in WSNs. We prove that using an appropriate shallow light tree as routes for tree-based in-network processing of SVD, can achieve the approximation ratio ${\text{1}}+\sqrt{2}$ (in terms of transmission cost), while for the chain-based in-network processing of SVD, we design two efficient heuristic algorithms for searching the optimal routes. Extensive simulation results validate the efficiency of these proposed schemes that are customized for SVD-based IOT applications.

Wireless charging technology is considered as a promising solution to address the energy limitation problem for wireless sensor networks (WSNs). In scenarios where the deployed chargers are static, we generally require a number of chargers to work simultaneously. However, due to the radio interference among different wireless chargers, scheduling these chargers is generally necessary. This scheduling problem is challenging since each charger's charging utility cannot be calculated independently due to the nonlinear superposition charging effect caused by radio interference. In this paper, based on the concurrent charging model, we formulate the concurrent charging scheduling problem (CCSP) with the objective of quickly fully charging all the sensor nodes. After proving the NP-hardness of CCSP, we propose two efficient greedy algorithms, and give the approximation ratio of one of them. Both the two greedy algorithms' performances are very close to that of a well-designed genetic algorithm (GA) which performs almost as well as a brute force algorithm at small network and charger scale. However, the running time of the two greedy algorithms is far lower than that of the GA. We conduct extensive simulations and specially implemented a testbed for wireless chargers. The results verified the good performance of the proposed algorithms.

In many domain-specific monitoring applications of wireless sensor networks (WSNs), such as structural health monitoring, volcano tomography, and machine diagnosis, the raw data in WSNs are required to be losslessly gathered to the sink, where a specialized centralized algorithm is then executed to extract some global features or model parameters. To reduce the large raw data transmission, in-network processing is usually employed. However, different from most existing in-network processing works that pre-assume some common computation/aggregation functions, in-network processing of a given centralized algorithm requires exact partitioning of the algorithm first and then appropriately assigning the partitioned computations into WSNs. We call this lossless in-network processing, which has not been studied much. Lossless in-network processing raises two questions: 1) what pattern should a centralized algorithm be partitioned into so that the partitioned computations can be flexibly assigned into a WSN with arbitrary topology? and 2) for each partition pattern, how should efficient routing for the resource-limited sensor nodes be designed? These two questions can be referred to as a topology-constrained computation partition problem and a computation-constrained routing design problem, respectively. In this paper, we first introduce some general patterns on the topology-constrained computation partition. Then, with the computation constraints in the patterns, we present a series of novel routing schemes customized for different cases of computation results. The work in this paper can also serve as a guideline for distributed computing of big data, where the data spreads in a large network.

In complex terrain where mobile chargers hardly move around, a feasible solution to charge wireless sensor networks (WSNs) is using multiple fixed chargers to charge WSNs concurrently with relative long distance. Due to the radio interference in the concurrent charging, it is needed to schedule the chargers so as to facilitate each sensor node to harvest sufficient energy quickly. The challenge lies that each charger's charging utility cannot be calculated (or even defined) independently due to the nonlinear superposition charging effect caused by the radio interference. In this paper, we model the concurrent radio charging, and formulate the concurrent charging scheduling problem (CCSP) whose objective is to design a scheduling algorithm for the chargers so as to minimize the time spent on charging each sensor node with at least energy E. We prove that CCSP is NP-hard, and propose a greedy algorithm based on submodular set cover problem. We also propose a genetic algorithm for CCSP. Simulation results show that the performance of the greedy CCSP algorithm is comparable to that of the genetic algorithm.

Wireless sensor networks (WSNs) have been considered to be the next generation paradigm of structural health monitoring (SHM) systems due to the low cost, high scalability and ease of deployment. Due to the intrinsically energy-intensive nature of the sensor nodes in SHM application, it is highly preferable that they can be divided into subsets and take turns to monitor the condition of a structure. This approach is generally called as `coverage-preserving scheduling' and has been widely adopted in existing WSN applications. The problem of partitioning the nodes into subsets is generally called as the 'maximum lifetime coverage problem (MLCP)'. However, existing solutions to the MLCP cannot be directly applied to SHM application. As compared to other WSN applications, we cannot define a specific coverage area independently for each sensor node in SHM, which is however the basic assumption in all existing solutions to the MLCP. In this paper, we proposed two approaches to solve the MLCP in SHM. The performance of the methods is demonstrated through both extensive simulations and real experiments.

Non-invasive human sensing based on radio signals has attracted numerous research interests in recent years. Previous work mainly focused on detecting the presence of a person or identifying human gestures and activities. In this paper, we show that with off-the-shelf WiFi devices, fine-grained respiration information of a person under different sleeping positions can be extracted successfully. We do this by introducing a breath monitoring system based on WiFi signals. This system adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, the rhythmic patterns associated with respiration and abrupt changes due to the body movement are identified. Compared to existing respiration monitoring systems that usually require special devices attached to human body, this system is completely contactless. In addition, different from many vision-based sleep monitoring systems, it is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that our system can reliably track a person's respiration reliably in different sleeping postures.

Fault-tolerant event detection (FTED), whose objective is to correctly detect events of interest in the presence of faulty nodes, remains to be one of the hot research areas in wireless sensor networks (WSNs). However, many recently emerged `domain-specific' applications of WSNs, such as structural health monitoring (SHM) and volcano monitoring, have shown some distinct features from traditional WSN applications. For example, data collected each time from sensor nodes is not a scalar but a long dynamic data sequence. In addition, detecting an event in these applications generally requires low-level collaboration of multiple sensors. As a consequence, existing FTED schemes usually cannot work well in these applications. In this paper, we realize FTED in a typical domain-specific application of WSNs: SHM. The main contribution of this work is I-FUND, a faulty node detection algorithm that takes feature vectors as input and can even handle the `element mismatch problem' where comparable elements in vectors are located at unknown different positions. In addition, I-FUND adopts adaptive stop criterion identified from data and turns out to be reliable even when a large percentage of the sensor nodes report erroneous observations. The effectiveness of the proposed scheme is demonstrated through both simulations and real experiments.

In critical event (e.g., fire or gas) monitoring applications of wireless sensor networks (WSNs), convex hull of the event region is an efficient tool in handling the usual tasks like event report, routes reconstruction and human motion planning. Existing works on estimating convex hull of event region usually require location information of sensor nodes, which needs high communication cost or hardware cost. In this paper, to avoid the requirement of location information, we define topological convex hull (T-convex hull) which presents the convex contour of an event region directly with a route passing by nodes, and hence becomes more efficient in handling the above tasks. To obtain the T-convex hull of event region in the absence of locations, we propose a low-weight (in terms of computation and storage resource requirement) distributed algorithm, with which sensor nodes just need to count the hop counts from some nodes. The communication cost of the algorithm is also low and independent of the network size. Comprehensive and largescale simulations are conducted, showing the effectiveness and much lower communication cost of the proposed algorithm, compared with related method.

Due to the low cost and ease of deployment, wireless sensor networks (WSNs) are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered structural health monitoring (SHM) systems. Different from other applications of WSNs such as environmental monitoring, SHM applications are much more data intensive and it is not feasible to stream the raw data back to the server due to the severe bandwidth and energy limitations of low-power sensor networks. In-network processing is a promising approach to address this problem but designing distributed versions for the sophisticated SHM algorithms is much more challenging because SHM algorithms are computationally intensive, and involve data-level collaboration of multiple sensors. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a few distributed ERAs suitable for WSNs. In particular, we first design a method to incrementally calculate the ERA and then propose three schemes upon which the incremental ERA can be carried out along an Hamiltonian path, along a path in the minimum connected dominating set (MCDS) and along the shortest path tree (SPT). The efficacy of these schemes are demonstrated and compared through both simulation experiment. We believe the proposed schemes can also serve as a guideline when applying WSNs for other applications like SHM which are also data-intensive and involve sophisticated signal processing of collected information.

Pervasive computing environments (PvCE) are embedded with interconnected smart devices which provide users with services desired. To meet requirements of users, smart devices with different kinds of functions may need to be associated together to provide the service described in the user requirement, which is called service composition. As the service composition environment may be dynamic and large scale, centralized service composition algorithm is usually inefficient due to message cost. On the other hand, a decentralized approach, which employs pre-determined coordinators to search and compose service, may have high cost as well. In this paper, we discuss a localized approach for service composition based on the Ubiquitous Interacting Object (UIO) model we have proposed earlier. UIO is an abstraction of physical devices in PvCE with ability to find and collaborate with other devices through exposing their capabilities as services. In our localized service composition algorithm (LASEC), UIOs collaborate with each other in a bottom-up, localized manner to compose required service without requiring global knowledge. To solve the problem of blind compositions in LASEC, we propose a novel mechanism called Alien-information-based Acknowledging (A-Ack), in which a UIO decides on collaborating with another UIO only after obtaining some additional information from the collaboration candidate. Specifically, this information refers to ability of a given UIO to compose another part of the service. Proposed LASEC is message-efficient and quality-guaranteed. Extensive simulations of LASEC as well as existing decentralized and pull-based centralized algorithms have been conducted. The results show the relatively low communication cost and composition time of LASEC. Moreover, we demonstrate feasibility of our approach with a prototype implementation.

Geometric routing or geo-routing has been shown as a promising approach to scalable routing in sensor networks. Despite its success in 2-D networks, very few designs are available for 3-D networks that can ensure short routes using only small per-node state, without incurring high load imbalance on the nodes. In this paper, we propose a novel addressing and routing scheme, i.e., OnionMap, for 3-D sensor networks that achieve the above goals, using solely connectivity information and at a linear message cost. The key idea is to decompose a 3-D network into a set of connected layers, which are then mapped to a set of concentric sphere structures (similar to an onion). On each sphere, a discrete Ricci flow method is used to assign each node a set of coordinates that permits purely greedy routing within that sphere; across the different spheres, a layer alignment algorithm helps rotate and scale the spheres, to form a coherent global coordinate system that guides global routing. Theoretical analysis and simulation show OnionMap's advantages over state-of-the-art solutions in path stretch, per-node storage, and load balance.

Co-existing in the unlicensed ISM band, ZigBee transmissions can be significantly interfered by WiFi. Although several approaches recently are proposed to enable ZigBee transmission under WiFi interference, the ZigBee throughput still decreases to zero when WiFi throughput (generated by D-ITG) is over 8Mbps. In this paper, we propose a real-time (<; 5ms) adaptive transmission (RAT) scheme to efficiently adapt forward error-correction coding (FEC) on ZigBee devices in dynamic WiFi environment. We find that sizes of WiFi frames well follow the power law distribution model. With the model, corruption in ZigBee packets can be estimated to some extent, thus facilitating ZigBee device to choose a suitable FEC coding to maximize the throughput. Extensive experimental results show that, compared with existing works, RAT achieves significant performance improvement of ZigBee transmissions in WiFi environment with different traffic load. Particularly, the ZigBee throughput of RAT can be about 10kbps when the WiFi throughput is 8Mbps.

Wireless sensor networks (WSNs) are generally used to monitor, in an area, certain phenomena which can be events or targets that users are interested. To extend the system lifetime, a widely used technique is `Energy-Efficient Coverage-Preserving Scheduling(EECPS)', in which at any time, only part of the nodes are activated to fulfill the function. To determine which nodes should be activated at a certain time is the key for the EECPS and this problem has been studied extensively. Existing solutions are based on the assumption that each node has a fixed coverage area, and once the event/target occurs in this area, it can be detected by this sensor. However, this coverage model is not always valid. In some applications such as structural health monitoring (SHM) and volcano monitoring, to fulfill a required function always requires low level collaboration from multiple sensors. The coverage area for individual sensor node therefore cannot be defined explicitly since single sensor is not able to fulfill the function alone, even it is close to the event or target to be monitored. In this paper, using an example of SHM, we illustrate how to support EECPS in some special applications of WSNs. We re-define the `coverage' and based on the new coverage model, two methods are proposed to partition the deployed sensor nodes into qualified cover sets such that the system lifetime can be maximized by letting these sets work by turns. The performance of the methods is demonstrated through extensive simulation and experiment.

In chain-type wireless sensor networks (CWSNs), sensor nodes closer to the base station (BS) need to receive and transmit more packets, resulting in more energy consumption and shorter lifetime. Due to the friability of the CWSN's topology, the short lifetime of nodes closer to the BS usually limits the CWSN lifetime. To enhance the CWSN lifetime, in this paper, we propose a novel sensor nodes deployment strategy. With a non-uniform deployment method and an alternative duty mode, the proposed strategy can balance the energy consumption of sensor nodes in CWSNs. Hence, the CWSN lifetime can be effectively prolonged. Numerical experiments have been conducted, showing better performance of the proposed strategy than existing methods.

Due to the high propagation delay and limited bandwidth of underwater acoustic channels, the design of media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) faces great challenges and opportunities. In this paper, we propose a novel handshaking-based MAC protocol, called as sender and receiver concurrent reservation (SRCR) protocol, for multi-hop UASNs. The key idea of the proposed protocol is to adopt a concurrent reservation mechanism to allow the sender's neighbors and the receiver's neighbors to transmit packets during the communication between the sender and the receiver without collision. Simulation results have confirmed that the proposed protocol can offer good performance of both throughput and delay in multi-hop UASNs with high channel utilization.

In this paper, we focus on critical event monitoring in wireless sensor networks (WSNs), where only a small number of packets need to be transmitted most of the time. When a critical event occurs, an alarm message should be broadcast to the entire network as soon as possible. To prolong the network lifetime, some sleep scheduling methods are always employed in WSNs, resulting in significant broadcasting delay, especially in large scale WSNs. In this paper, we propose a novel sleep scheduling method to reduce the delay of alarm broadcasting from any sensor node in WSNs. Specifically, we design two determined traffic paths for the transmission of alarm message, and level-by-level offset based wake-up pattern according to the paths, respectively. When a critical event occurs, an alarm is quickly transmitted along one of the traffic paths to a center node, and then it is immediately broadcast by the center node along another path without collision. Therefore, two of the big contributions are that the broadcasting delay is independent of the density of nodes and its energy consumption is ultra low. Exactly, the upper bound of the broadcasting delay is only 3D+2L, where D is the maximum hop of nodes to the center node, L is the length of sleeping duty cycle, and the unit is the size of time slot. Extensive simulations are conducted to evaluate these notable performances of the proposed method compared with existing works.

In this paper, we propose a novel distributed navigation algorithm for people to escape from critical event region in wireless sensor networks (WSNs). Unlike existing works, the scenario discussed in the paper has no goal or exit as guidance, leading to a big challenge for the navigation problem. To solve it, our proposed navigation algorithm computes the convex hull of the event region just by some topological methods. With the reference of the convex hull, people can be easily navigated out of the event region. Both the computation complexity and communication overhead of the proposed algorithm are very low, as it only needs to flood two shortest path trees in a limited area around the event region with a distance [L/2π] + 1 to the event boundary, where L is the length of the boundary. Conducted simulations have verified the effectiveness and scalability of the proposed algorithm.

In this paper, a novel spectrum sharing scheme is proposed to improve the achievable traffic load of secondary users (SUs) under grade of service (GoS) constraints in cognitive wireless networks with heterogeneous traffic. The key idea of the proposed scheme is to introduce preemptive priority and buffering mechanism for real-time traffic and non-real-time traffic, respectively, according to their different delay characteristics. The proposed scheme can reduce the forced termination probability and the blocking probability for heterogeneous calls simultaneously. Numerical results show that the proposed scheme can effectively improve the achievable traffic load of SUs under GoS constraints.

In mission-critical applications such as battlefield reconnaissance or industrial safety and security, a large number of sensor nodes are deployed in a large area to detect and report event related information to the end-users. When a critical event in the monitoring region is detected by a node, alarm should be broadcast to all the other nodes in the neighborhood. This effect is shown in Figure 1.

In the paper, we design a navigation system based on sensor network to guide a robot to walk out of event region. The navigation system does not require any exit or locations.

In most application scenarios of wireless sensor networks (WSN), sensor nodes are usually deployed randomly and do not have any knowledge about the network environment or even their ID's at the initial stage of their operations. In this paper, we address the clustering problems with a newly deployed multi-hop WSN where most existing clustering algorithms can hardly be used due to the absence of MAC link connections among the nodes. We propose an effective clustering algorithm based on a random contention model without the prior knowledge of the network and the ID's of nodes. Computer simulations have been used to show the effectiveness of the algorithm with a relatively low complexity if compared with existing schemes.

Abstract Long propagation delay is one of the most important characteristics in underwater wireless sensor networks (UWSNs) and poses a great challenge for medium access control (MAC) protocol design, especially for contention‐based MAC protocols due to intolerable delay caused by unpredictable retransmissions in UWSNs. Recently, some MAC protocols for UWSNs have been suggested in the literature, and most of them are based on random access with their capabilities to compensate propagation delay. However, two issues should still be resolved in these protocols: (a) they need to make measurements to realize duty cycle synchronization and (b) packet collisions exist, which not only reduce the throughput (thus increasing delays) but also waste energy. In this paper, we propose a novel MAC protocol explicitly designed for UWSNs, which makes the best use of the propagation delay to resolve collision problem and reduce overhead of control‐packet to save energy. The proposed MAC protocol can assure that the number of retransmissions is not more than one. To validate the analytical results, simulations have been conducted to show that the proposed MAC protocol can offer a low energy consumption while avoiding collisions in UWSNs. Copyright © 2008 John Wiley & Sons, Ltd.