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With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

In the field of mobile computing, after the placement of sensor nodes, energy efficiency of data transmission in the sensor network is becoming a critical issue as the sensor nodes are battery-operated devices. Most of the proposed protocols implement clustering based methods in order to balance the energy consumption among the sensor nodes through data aggregation. In this paper, a new method named a two-stage RPSO-ACS based protocol is proposed to improve the energy efficiency and network operation quality in the data transmission process, prolonging the lifespan of the network. Firstly, in the process of cluster head (CH) selection, the resampling particle swarm optimization (RPSO) is introduced, which shows better precision and efficiency than particle swarm optimization (PSO) and genetic algorithm (GA). Secondly, after the selection of cluster head, instead of the single-hop transmission from the cluster heads to base station (BS), which is used in many papers, we adopt multi-hop transmission to save energy. The ant colony optimization (ACO) is used to select the relay nodes between the cluster heads and the base station for the purpose of minimizing the energy consumption in the transmission process. In addition, we determine the reasonable threshold based on the energy consumption curve of the transmission. In this way, the nodes within a radius of the threshold from BS communicate with BS directly to save energy. Experimental results show that the proposed methods achieve the goal of improving the energy efficiency and operation quality of the sensor network in comparison to traditional methods.

To save energy, wireless sensor networks often run in a low-duty-cycle mode, where the radios of sensor nodes are scheduled between ON and OFF states. For nodes to communicate with each other, low power listening (LPL) and low power probing (LPP) are two types of rendezvous mechanisms. Nodes with LPL or LPP rely on signal strength or probe packets to detect potential transmissions, and then keep the radio-ON for communications. Unfortunately, in co-existing environments, signal strength and probe packets are susceptible to interference, resulting in undesirable radio ON time when the signal strength of interference is above a threshold or a probe packet is interfered. To address the issue, we propose ZiSense, a low duty cycling mechanism resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the ZigBee signals and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with the state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

This paper considers malicious link detection in multi-hop wireless sensor networks (WSNs). Existing work on malicious link detection generally requires that the detection process being performed at the intermediate nodes, leading to considerable overhead in system design, as well as unstable detection accuracy due to limited resources and the uncertainty in the loyalty of the intermediate nodes themselves. In this paper, we propose an efficient and robust malicious link detection scheme by exploiting the statistics of packet delivery rates only at the base station. More specifically, first, we present a secure packet transmission protocol to ensure that except the base station, any intermediate nodes on the route cannot access the contents and routing paths of the packets. Second, we design a malicious link detection algorithm that can effectively detect the irregular dropout at every hop (or link) along the routing path. We prove that the proposed algorithm has guaranteed false alarm rate and low miss detection rate. Simulation results are provided to validate the proposed approaches.

Detecting and diagnosing anomalies in networked embedded systems like sensor networks is a very difficult task, due to the variable workloads and severe resource constraints. In this paper, we focus on how to aid bug diagnosis after the system has been deployed. We notice that most node-level debugging tools can provide detailed program information inside the node but fail to detect when and where a problem occurs in the network. On the other hand, most network-level diagnosis tools can effectively detect a problem from the network but fail to narrow down the problem within the node because they lack detailed program information. To close the gap, we propose D2, a new method for post-deployment anomaly detection and diagnosis in networked embedded systems by combining program profiling and symptom mining. D2 employs binary instrumentation to perform lightweight function count profiling. Based on the statistics, D2 uses PCA (Principal Component Analysis) based approach for automatically detecting network anomalies. Compared with previous methods, D2 is able to point programmers closer to the most likely causes by a novel approach combining statistical tests and program call graph analysis. We implement our method based on TinyOS 2.1.1 and evaluate its effectiveness by case studies in the development of a working sensor network. Results show that our method can aid programmers to diagnose problems quickly in real-world sensor network systems, and at the same time, incurs an acceptable overhead to the running system.

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

To guarantee reliability, bulk data dissemination relies on the negotiation scheme in which senders and receivers negotiate transmission schedule through a three-way handshake procedure. However, we find negotiation incurs a long dissemination time and seriously defers the network-wide convergence. On the other hand, the flooding approach, which is conventionally considered inefficient and energy-consuming, can facilitate bulk data dissemination if appropriately incorporated. This motivates us to pursue a delicate tradeoff between negotiation and flooding in the bulk data dissemination. We propose SurF (Survival of the Fittest), a bulk data dissemination protocol which adaptively adopts negotiation and leverages flooding opportunistically. SurF incorporates a time-reliability model to estimate the time efficiencies (flooding versus negotiation) and dynamically selects the fittest one to facilitate the dissemination process. We implement SurF in TinyOS 2.1.1 and evaluate its performance with 40 TelosB nodes. The results show that SurF, while retaining the dissemination reliability, reduces the dissemination time by 40 percent in average, compared with the state-of-the-art protocols.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g., overhearing or coordination based approaches, either cannot scale up with the system size, or suffer high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate-free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgment scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor testbed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.

Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy.

Duty cycling mode is widely adopted in wireless sensor networks to save energy. Existing duty-cycling protocols cannot well adapt to different data rates and dynamics, resulting in a high energy consumption in real networks. Improving those protocols may require global information or heavy computation and thus may not be practical, leading to many empirical parameters in real protocols. To fill the gap between the application requirement and protocol performance, in this paper, we analyze the energy consumption for duty cycled sensor networks with different data rates. Our analysis shows that existing protocols cannot lead to an efficient energy consumption in various scenarios. Based on the analysis, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. LAD can adaptively adjust the protocol parameters according to network conditions such as data rate and achieve an optimal energy efficiency. To make LAD practical in real network, we further pre-calculate optimal parameters offline and store them on sensor nodes, which significantly reduces the computation time. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2-40.1 percent compared with state-of-the-art protocols. Results based on data from a 1,200-node operational network further show the effectiveness and scalability of the design.

Prior work on anti-collision for radio frequency identification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers’ collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of tag collision, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above $6\times$ improvement on the identification throughput in a large-scale dense reader environment.

Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts sophisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95% for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%.

Emergency navigation is an emerging application of wireless sensor networks with significant research and social value. In order to ensure the safe and timely navigation of the evacuees, most of the existing works model navigation as a path-planning problem or movement decision support problem and adopt different metrics, such as the shortest route, the minimum exposure path, and the maximum safe distance. Without sufficient consideration of the dynamics of danger, the existing approaches are likely to cause users to move back and forth during navigation, known as oscillation. Frequent oscillations inevitably result in the user remaining in danger for a longer period of time, amplification of the user’s panic, and eventual decrease in the chances of survival. In this paper we take users’ oscillations in the dynamic environments into account and quantify the local success rate of navigation using a metric called ENO (Expected Number of Oscillations). We then propose OPEN, an oscillation-free navigation approach that minimizes the probability of oscillation and guarantees the success rate of emergency navigation. We implement OPEN and evaluate its performance through the trace from our system and extensive simulations. The results demonstrate that OPEN outperforms the current state-of-the-art approaches with respect to user safety and navigation efficiency.

How to shorten the time for channel waiting is critical to avoid network contention. Traditional MAC protocols with CSMA often assume that a transmission must be deferred if the channel is busy, so they focus more on the optimization of serial transmission performance. Recent advances in physical layer, however, allows a receiver to reengage onto a stronger incoming signal from an ongoing transmission or interference, and thus shows the potential of parallel transmissions. Indeed, even if the channel is busy, a node has opportunities to carry out a successful transmission. In this study, we propose opportunistic concurrency (OPC), a new MAC layer scheme, which enables sensor nodes to capture the opportunistic concurrency and carry out parallel transmissions instead of always waiting for a clear channel. Based on local concurrency map, which encodes the interactions among different links, OPC utilizes concurrency control algorithm to make transmission decision distributedly. Our experiments on a testbed consisting of 60 TelosB sensor motes identify the transmission opportunities in WSNs with OPC. Evaluation results show that OPC achieves a 17 percent reduction in packet latency, a 9.4 percent addition in throughput and a 10 percent reduction in power consumption compared with existing approaches.

In order to simultaneously achieve good energy efficiency and high packet delivery performance, a multihop forwarding scheme should generally involve three design elements: media access mechanism, link estimation scheme, and routing strategy. Disregarding the low-duty-cycle nature of media access often leads to overestimation of link quality. Neglecting the bursty loss characteristic of wireless links inevitably consumes much more energy than necessary and underutilizes wireless channels. The routing strategy, if not well tailored to the above two factors, results in poor packet delivery performance. In this paper, we propose $L^{2}$ , a practical design of data forwarding in low-duty-cycle wireless sensor networks. $L^{2}$ addresses link burstiness by employing multivariate Bernoulli link model. Further incorporated with synchronized rendezvous, $L^{2}$ enables sensor nodes to work in a lazy mode, keep their radios off most of the time, and realize highly reliable forwarding by scheduling very limited packet transmissions. We implement $L^{2}$ on a real sensor network testbed. The results demonstrate that $L^{2}$ outperforms state-of-the-art approaches in terms of energy efficiency and network yield.

Network diagnosis is crucial in managing a wireless sensor network (WSN) since many network-related faults, such as node and link failures, can easily happen. Diagnosis tools usually consist of two key components, information collection and root-cause deduction, while in most cases information collection process is independent with root-cause deduction. This results in either redundant information which might pose high communication burden on WSNs, or incomplete information for root-cause inference that leads false judgments. To address the issue, we propose DID, a directional diagnosis approach, in which the diagnosis information acquirement is guided by the fault inference process. Through several rounds of incremental information probing and fault reasoning, root causes of the network abnormalities with high credibility are deduced. We employ a node tracing scheme to reconstruct the topical topology of faulty regions and build the inference model accordingly. We implement the DID approach in our forest monitoring sensor network system, GreenOrbs. Experimental results validate the scalability and effectiveness of this design.

Traditional approaches for wireless sensor network diagnosis are mainly sink-based. They actively collect global evidences from sensor nodes to the sink so as to conduct centralized analysis at the powerful back-end. On the one hand, long distance proactive information retrieval incurs huge transmission overhead; On the other hand, due to the coupling effect between diagnosis component and the application itself, sink often fails to obtain complete and precise evidences from the network, especially for the problematic or critical parts. To avoid large overhead in evidence collection process, self-diagnosis injects fault inference modules into sensor nodes and let them make local decisions. Diagnosis results from single nodes, however, are generally inaccurate due to the narrow scope of system performances. Besides, existing self-diagnosis methods usually lead to inconsistent results from different inference processes. How to balance the workload among the sensor nodes in a diagnosis task is a critical issue. In this work, we present a new in-network diagnosis approach named Local-Diagnosis (LD2), which conducts the diagnosis process in a local area. LD2 achieves diagnosis decision through distributed evidence fusion operations. Each sensor node provides its own judgements and the evidences are fused within a local area based on the Dempster-Shafer theory, resulting in the consensus diagnosis report. We implement LD2 on TinyOS 2.1 and examine the performance on a 50 nodes indoor testbed.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m $^2$ , and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world's largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring future developments and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses for a complete stack of protocols at large scale. Specifically, very little is known about: 1) when, where, and under what kind of circumstances packet losses occur; 2) why packets are lost. As a step toward addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined causal graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power ad hoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments .

Existing approaches to diagnosing sensor networks are generally sink based, which rely on actively pulling state information from sensor nodes so as to conduct centralized analysis. First, sink-based tools incur huge communication overhead to the traffic-sensitive sensor networks. Second, due to the unreliable wireless communications, sink often obtains incomplete and suspicious information, leading to inaccurate judgments. Even worse, it is always more difficult to obtain state information from problematic or critical regions. To address the given issues, we present a novel self-diagnosis approach, which encourages each single sensor to join the fault decision process. We design a series of fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. Fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the diagnosis by transiting the detector's current state to a new state based on local evidences and then passing the detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs a final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100-node indoor testbed and conduct field studies in the GreenOrbs system, which is an operational sensor network with 330 nodes outdoor.

A critical function of wireless sensor networks (WSNs) is data gathering. One is often only interested in collecting a specific function of the sensor measurements at a sink node, rather than downloading all the raw data from all the sensors. In this paper, we study the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. We focus on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power limitation. We derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive divisible perfectly compressible functions and type-threshold divisible perfectly compressible functions, the aggregation capacities for random extended WSNs with ${\mbi {n}}$ nodes are of order $\Thetab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}} - {\bf 1}}}})$ and $\Theta ({{{{{({\bf log} {\mbi {n}})}^{ - \alphab /{\bf 2}}}} \over {{\bf log}{\bf log} {\mbi {n}}}}})$ , respectively, where $\alphab \gt {\bf 2}$ denotes the power attenuation exponent in the generalized physical model. Furthermore, we improve the aggregation throughput for general divisible perfectly compressible functions to $\Omegab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}}}}})$ by choosing $\Thetab ({\bf log}\; {\mbi {n}})$ sensors from a small region (relative to the whole region) as sink nodes.

Accurate localization is crucial for wireless ad-hoc and sensor networks. Among the localization schemes, component-based approaches specialize in localization performance. By grouping nodes into increasingly large rigid components, component-based localization algorithms can properly conquer network sparseness and anchor sparseness. However, such design is sensitive to measurement errors. Existing robust localization methods focus on eliminating the positioning error of a single node. Indeed, a single node has two dimensions of freedom in 2D space and only suffers from one type of transformation: translation. As a rigid 2D structure, a component suffers from three possible transformations: translation, rotation, and reflection. A high degree of freedom brings about complicated cases of error productions and difficulties on error controlling. This study is the first work addressing how to deal with ranging noises for component-based methods. By exploiting a set of robust patterns, we present an Error-TOlerant Component-based algorithm (ETOC) that not only inherits the high-performance characteristic of component-based methods, but also achieves robustness of the result. We evaluate ETOC through a real-world sensor network consisting of 120 TelosB motes as well as extensive large-scale simulations. Experiment results show that, comparing with the-state-of-the-art designs, ETOC can work properly in sparse networks and provide more accurate localization results.

Due to its large scale and constrained communication radius, a wireless sensor network mostly relies on multi-hop transmissions to deliver a data packet along a sequence of nodes. It is of essential importance to measure the forwarding quality of multi-hop paths and such information shall be utilized in designing efficient routing strategies. Existing metrics like ETX, ETF mainly focus on quantifying the link performance in between the nodes while overlooking the forwarding capabilities inside the sensor nodes. The experience on manipulating GreenOrbs, a large-scale sensor network with 330 nodes, reveals that the quality of forwarding inside each sensor node is at the least an equally important factor that contributes to the path quality in data delivery. In this paper we propose QoF, Quality of Forwarding, a new metric which explores the performance in the gray zone inside a node left unattended in previous studies. By combining the QoF measurements within a node and over a link, we are able to comprehensively measure the intact path quality in designing efficient multi-hop routing protocols. We implement QoF and build a modified Collection Tree Protocol (CTP). We evaluate the data collection performance in a testbed consisting of 50 TelosB nodes, and compare it with the original CTP protocol. The experimental results show that our approach takes both transmission cost and forwarding reliability into consideration, thus achieving a high throughput for data collection.

We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wireless sensor networks. Elon reduces the transferred code size significantly by introducing the concept of replaceable component. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, it significantly prolongs the reprogrammable lifetime (i.e., the time period during which the sensor nodes can be reprogrammed) by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfers up to 120--389 times less information than Deluge, and 18--42 times less information than Stream. The software reboot mechanism that Elon applies reduces the rebooting cost by 50.4%--53.87% in terms of beacon packets, and 56.83% in terms of unsynchronized nodes. In addition, Elon prolongs the reprogrammable lifetime by a factor of 3.3.

Previous packet length optimizations for sensor networks often employ a fixed optimal length scheme, while in this study we present DPLC, a Dynamic Packet Length Control scheme. To make DPLC more efficient in terms of channel utilization, we incorporate a lightweight and accurate link estimation method. We further provide two easy-to-use services, i.e., small message aggregation and large message fragmentation, to facilitate upper-layer application programming. The implementation of DPLC based on TinyOS 2.1 is lightweight, with respect to computation, memory, and header overhead. Our experiments using a real indoor testbed running CTP show that DPLC achieves the best performance compared with previous works.

Due to slow advance in battery technology, power remains a bottleneck to limit wide applications of mobile ad hoc and wireless sensor networks. Among all extensive studies on minimizing power consumption, neighbor discovery is one of the fundamental components focusing on communication and access. This work surveys research literature on neighbor discovery protocols (NDPs). In general, they can be roughly classified by four underlying principles: randomness, over-half occupation, rotation-resistant intersection, and coprime cycles. The Birthday protocols act as representatives of NDPs using randomness, in which a node decides to listen, transmit, or sleep with probabilities. The original idea of over-half occupation is to be active over at least half of each period, though several refinements have been proposed to decrease its high duty cycle. Methods of rotation-resistant intersection formulate the problem of discovery using combinatorial characteristics of discrete time slots, and guarantee discovery at least once per period. Moreover, neighbor discovery can also be guaranteed within a worst-case bound, as shown by methods adopting coprime cycles. In this paper, we elaborate on these ideas and present several representative protocols, respectively. In particular, we give an integrative analysis of deterministic protocols via a generic framework. A qualitative comparison incorporating multiple criteria and a quantitative evaluation on energy efficiency are also included. Finally, we point out promising research directions towards energy-efficient neighbor discovery.

Localization is an enabling technique for many sensor network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically nonlocalizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable. However, the existing methods are considered to be coarse-grained, since they equally deal with localizable and nonlocalizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, localizability-aided localization (LAL), which basically consists of three phases: node localizability testing, structure analysis, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all network adjustments made by LAL are purposefully selected. Experiment and simulation results show that LAL effectively guides the adjustment while makes it efficient in terms of the number of added edges and affected nodes.

Wireless sensor networks (WSNs) are developed in an accelerated pace during the past years. Motivated by the need of closely monitoring the forest and urban environments and accurately measuring carbon sequestration and emission, we launched two long-term large-scale WSNs: "GreenOrbs" in 2009 and "CitySee" in 2011, respectively. This paper presents our recent advances and experience on how to implement the two WSNs with stable and high performance. Some research directions are also discussed based on our observations and measurements.

Coverage is a fundamental problem in wireless sensor networks (WSNs). From both economic and applicable concerns, designers always would like to provide guaranteed QoS of coverage of WSNs. In this paper, we address two path-coverage problems in WSNs, maximum k-support path coverage (a.k.a. best case coverage) and minimum k-breach path coverage (a.k.a. worst case coverage), in which every point on the desired resultant path is covered by at least k sensors simultaneously while optimizing certain objectives. We present two polynomial-time approaches to find optimal solutions for both maximum k-support coverage problem and minimum k-breach coverage problem. The time complexity of both algorithms are O(k2n log n), where n is the number of deployed sensor nodes and k is the coverage degree. In addition, a number of properties of kth-nearest point Voronoi diagram are presented, which is new to the literature.

In wireless sensor networks (WSNs), diagnosis is a crucial and challenging task due to the distributed nature and stringent resources. Most previous approaches are supervised, relying on a-priori knowledge of network faults. Our experience with GreenOrbs, a long-term large-scale WSN system, reveals the need of diagnosis in an agnostic manner. Specifically, in addition to predefined faults (i.e., with known types and symptoms), silent failures that are unknown beforehand, account for a large fraction of network performance degradation. Currently, there is no effective solution for silent failures because they are often diverse and highly system-related. In this paper, we propose Agnostic Diagnosis (AD), an online lightweight failure detection approach. AD is motivated by the fact that the system metrics (e.g., radio-on time, number of packets transmitted) of sensor nodes usually exhibit certain correlation patterns. Violations of such patterns indicate potential silent failures. We implement AD on a working WSN consisting of 330 nodes. Our experimental results demonstrate the advantages of AD to discover silent failures, effectively expanding the capacity and scope of WSN diagnosis.

Sensor networks are deemed suitable for large-scale deployments in the wild for a variety of applications. In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large-scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large-scale sensor networks. 1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. 2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. 3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained and thus better adapted to the wild environment when building a large-scale sensor network.

We present R2, an incremental reprogramming approach using relocatable code, to improve program similarity for efficient incremental reprogramming in networked embedded systems. R2 achieves a higher degree of similarity than existing approaches by mitigating effects of both function shifts and data shifts. R2 adopts a content-aware differencing algorithm to generate small delta files for efficient dissemination. Besides, it makes efficient use of memory and does not degrade program quality. We implement R2 based on TinyOS 2.1 and demonstrate its advantages through detailed analysis of TinyOS examples. We also present case studies on the software programs of a large-scale sensor system GreenOrbs. Results show that R2 reduces the dissemination cost by approximately 65 percent compared to state-of-the-art network reprogramming approach Deluge, and reduces the dissemination cost by approximately 20 percent compared to Zephyr and Hermes the latest works on incremental reprogramming.

CitySee, an environment monitoring system with 1196 sensor nodes and 4 mesh nodes in an urban area, is mainly motivated by the needs of precise carbon emission measurement and real-time surveillance for CO2 management in cities. Being one of the largest working wireless sensor networks, CitySee faces several challenges such as hardware design, software development, platforms, network protocols, and, most important, satisfactory services to users. We share some early lessons learned from this project, illustrate the potential benefits and risks of current solutions, and discuss the possible extensions of CitySee applications.

We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any preknowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces nontrivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics.

Extracting planar graph from network topologies is of great importance for efficient protocol design in wireless ad hoc and sensor networks. Previous techniques of planar topology extraction are often based on ideal assumptions, such as UDG communication model and accurate node location measurements. To make these protocols work effectively in practice, we need extract a planar topology in a location-free and distributed manner with small stretch factors. The planar topologies constructed by current location-free methods often have large stretch factors. In this paper, we present a fine-grained and location-free network planarization method under ρ-quasi-UDG communication model with ρ≥1/√2. Compared with existing location-free planarization approaches, our method can extract a provably connected planar graph, called topological planar simplification (TPS), from the connectivity graph in a fine-grained manner using local connectivity information. We evaluate our design through extensive simulations and compare with the state-of-the-art approaches. The simulation results show that our method produces high-quality planar graphs with a small stretch factor in practical large-scale networks.

Accurate self-localization is a key enabling technique for many pervasive applications. Existing approaches, most of which are multilateration based, often suffer ambiguities, resulting in huge localization errors. To address this problem, previous approaches discard those positioning results with possible flip ambiguities, trading the localization performance for the result robustness. However, the high false positive rate of flip prediction incorrectly rejects many reliable location estimates. By exploiting the characteristics of flip ambiguity, which causes either huge or zero error, we propose the concept of optimistic localization and design an algorithm, OFA, that employs a global consistency check and a location correction phase in the localization process. We analyze the performance gain and cost of OFA, and further evaluate this design through extensive simulations. The results show that OFA obtains robustness with extremely low performance cost, so as to reduce the requirement on the average degree from 25 to 10 for robustly localizing a network.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600 m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

Knowing accurate positions of nodes in wireless ad hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality, is often employed in existing approaches. Our study shows that triangle inequality has many limitations, which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles-based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results show that our design significantly improves the localization accuracy by wisely rejecting outliers.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.

Recent advances in the application field increasingly demand the use of wireless camera sensor networks (WCSNs), for which localization is a crucial task to enable various location-based services. Most of the existing localization approaches for WCSNs are essentially interactive, i.e., require the interaction among the nodes throughout the localization process. As a result, they are costly to realize in practice, vulnerable to sniffer attacks, inefficient in energy consumption and computation. In this paper, we propose LISTEN, a noninteractive localization approach. Using LISTEN, every camera sensor node only needs to silently listen to the beacon signals from a mobile beacon node and capture a few images until determining its own location. We design the movement trajectory of the mobile beacon node, which guarantees to locate all the nodes successfully. We have implemented LISTEN and evaluated it through extensive experiments. Both the analytical and experimental results demonstrate that it is accurate, cost-efficient, and especially suitable for WCSNs that consist of low-end camera sensors.

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

The networked application environment has motivated the development of multitasking operating systems for sensor networks and other low-power electronic devices, but their multitasking capability is severely limited because traditional stack management techniques perform poorly on small-memory systems without virtual memory support. In this paper, we show that combining binary translation and a new kernel runtime can lead to efficient OS designs on resource constrained platforms. We introduce SenSmart, a multitasking OS for sensor networks, and present new OS design techniques for supporting preemptive multitask scheduling, memory isolation, and adaptive stack management. Our solution provides memory isolation and automatic stack relocation on usual sensornet platforms. The adaptive stack management frees programmers from the burden of estimating tasks' stack usage, yet it enables SenSmart to schedule and run more tasks than other multitasking OSes for sensor networks. We have implemented SenSmart on MICA2/MICAz motes. Evaluation shows that SenSmart has a significantly better capability in managing concurrent tasks than other sensornet operating systems.

A majority of localization approaches for wireless sensor networks rely on the measurements of internode distance. Errors are inevitable in distance measurements, and we observe that a small number of outliers can drastically degrade localization accuracy. To deal with noisy and outlier ranging results, a straightforward method, known as triangle inequality, has often been employed in previous studies. However, triangle inequality has its own limitations that make it far from accurate and reliable. In this paper, we first analyze how much information is needed to identify outlier measurements. Applying the rigidity theory, we propose the concept of verifiable edges and derive the conditions for an edge to be verifiable. On this basis, we design a localization approach with outlier detection, which explicitly eliminates ranges with large errors before location computation. Considering the entire network, we define verifiable graphs in which all edges are verifiable. If a wireless network meets the requirements of graph verifiability, it is not only localizable but outlier resistant as well. Extensive simulations are conducted to examine the effectiveness of the proposed approach. The results show remarkable improvement in location accuracy by sifting outliers.

Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is not always entirely localizable. Theoretical analyses also suggest that, in most cases, it is unlikely that all nodes in a network are localizable, although a (large) portion of the nodes can be uniquely located. Existing studies merely examine whether or not a network is localizable as a whole; yet two fundamental questions remain unaddressed: First, given a network configuration, whether or not a specific node is localizable? Second, how many nodes in a network can be located and which are them? In this study, we analyze the limitation of previous works and propose a novel concept of node localizability. By deriving the necessary and sufficient conditions for node localizability, for the first time, it is possible to analyze how many nodes one can expect to locate in sparsely or moderately connected networks. To validate this design, we implement our solution on a real-world system and the experimental results show that node localizability provides useful guidelines for network deployment and other location-based services.

Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings.

Wireless sensor networks (WSNs) have recently gained a great deal of attention as a topic of research, with a wide range of applications being explored. Bulk data dissemination is a basic building block for sensor network applications. The problem of designing efficient bulk data dissemination protocols has been addressed in a number of recent studies. The problem of accurately analyzing the performance of these protocols, however, has not been addressed sufficiently in the literature. In this work, we show a way of accurately analyzing the performance of bulk data dissemination protocols in WSNs. Our model can be applied to practical network topologies by use of the shortest propagation path. Our model is accurate by considering topological information, impact of contention, and impact of pipelining. We validate the analytical results through testbeds and detailed simulations. Results show that the analytical results fit well with the testbed results and simulation results. Further, we demonstrate that the analytical results can be used to aid protocol design for performance optimizations, e.g., page size tuning for shortening the completion time.

Wormhole attack is a severe threat to wireless ad hoc and sensor networks. Most existing countermeasures either require specialized hardware devices or make strong assumptions on the network in order to capture the specific (partial) symptom induced by wormholes. Those requirements and assumptions limit the applicability of previous approaches. In this work, we present our attempt to understand the impact and inevitable symptom of wormholes and develop distributed detection methods by making as few restrictions and assumptions as possible. We fundamentally analyze the wormhole problem using a topology methodology, and propose an effective distributed approach, which relies solely on network connectivity information, without any requirements on special hardware devices or any rigorous assumptions on network properties. We rigorously prove the correctness of this design in continuous geometric domains and extend it into discrete domains. We evaluate its performance through extensive simulations.

The development of a modern sensor network is difficult because of the long-term unattended operation mode, diverse application requirements, and stringent resource constraints. To address these issues, we present SenSpire OS, a predictable, flexible, and efficient operating system for wireless sensor networks. We improve system predictability by two-phase interrupt servicing and predictable thread synchronization; we achieve system flexibility by providing a hybrid model for both event-driven programming and multithreaded programming; we retain system efficiency by employing stack sharing and modular design. Moreover, we have designed a three-layer networking stack and an object-oriented programming language (CSpire) to enhance system usability and programming convenience. Having implemented SenSpire OS on three most commonly used sensor node platforms, we evaluate its performance extensively. Results show that SenSpire OS ensures predictable system performance, provides a flexible hybrid model for application programming, and is efficient in resource utilization.

Localization is crucial for wireless ad hoc and sensor networks. As the distance-measurement ranges are often less than the communication ranges for many ranging systems, most communication-dense wireless networks are localization-sparse. Consequently, existing algorithms fail to provide accurate localization supports. In order to address this issue, by introducing the concept of component, we group nodes into components so that nodes are able to better share ranging and anchor knowledge. Operating on the granularity of components, our design, CALL, relaxes two essential restrictions in localization: the node ordering and the anchor distribution. Compared to previous designs, CALL is proven to be able to locate the same number of nodes using the least information. We evaluate the effectiveness of CALL through extensive simulations. The results show that CALL locates 90% nodes in a network with average degree 7.5 and 5% anchors, which outperforms the state-of-the-art design Sweeps by about 40%.