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Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.

In this paper we are interested in the task of searching and tracking multiple moving targets in a bounded surveillance area with a group of autonomous mobile agents. More specifically, we assume that targets can appear and disappear at random times inside the surveillance region and their positions are random and unknown. The agents have a limited sensing range, and due to sensor imperfections they receive noisy measurements from the targets. In this work we utilize the theory of random finite sets (RFS) to capture the uncertainty in the time-varying number of targets and their states and we propose a decision and control framework, in which the mode of operation (i.e. search or track) as well as the mobility control action for each agent, at each time instance, are determined so that the collective goal of searching and tracking is achieved. Extensive simulation results demonstrate the effectiveness and performance of the proposed solution. 253 262

The monitoring of the indoor air quality against the presence of contaminant sources is of paramount importance since it directly influences the occupants’ comfort, productivity, health and safety. Various approaches exist in the literature for monitoring and controlling the building environment. However, there is an identified lack of automated tools for evaluating, testing and comparing such approaches. This paper presents COMOB (COntaminat MOnitoring in Buildings), a developed MATLAB toolbox for sensor placement and contaminant monitoring in buildings. Specifically, the COMOB toolbox realizes an offline sensor placement procedure for positioning the contaminant sensors inside the building, such that the harmful effects due to a possible airborne contaminant event are minimized. In addition, an online contaminant monitoring approach is also implemented for detecting and isolating contaminant events in the building interior. Besides the aforementioned algorithms, the modularity of the COMOB toolbox facilitates the implementationof various other algorithms related to air-quality monitoring in buildings. In particular, the COMOB toolbox is able to generate multiple airflow and contaminant concentration data-sets by varying the building parameters; thus enabling the comprehensive and systematic evaluation and testing of the developed algorithms. Finally, the functionalities of the COMOB toolbox are demonstrated through a case-study corresponding to a real building model. COMOB is released under an open-source license, and is available at https://github.com/KIOS-Research/COMOB-Toolbox.

Networks of smart cameras, equipped with on-board processing and communication infrastructure, are increasingly being deployed in a variety of different application fields, such as security and surveillance, traffic monitoring, industrial monitoring, and critical infrastructure protection. The task(s) that a network of smart cameras executes in these applications, e.g., activity monitoring and object identification, can be severely degraded due to errors in the detection module. However, in most cases, higher level tasks and decision making processes in smart camera networks (SCNs) assume ideal detection capabilities for the cameras, which is often not the case due to the probabilistic nature of the detection process, especially for low-cost cameras with limited capabilities. Realizing that it is necessary to introduce robustness in the decision process, this paper presents results toward uncertainty-aware SCNs. Specifically, we introduce a flexible uncertainty model that can be used to characterize the detection behavior in a camera network. We also show how to utilize the model to formulate detection-aware optimization algorithms that can be used to reconfigure the network in order to improve the overall detection efficiency and thus increase the effective number of detected targets. We evaluate our proposed model and algorithms using a network of Raspberry-Pi-based smart cameras that reconfigure in order to improve the detection performance based on the position of targets in the area. The experimental results in the laboratory as well as in a human monitoring application and extensive simulation results indicate that the proposed solutions are able to improve the robustness and reliability of SCNs. 28 5 1197 1211

The effect of traffic congestion within an urban region can be eliminated by curbing the number of vehicles that use the congested part of the transport network. To do so, earlier work by the authors introduced a novel route-reservation architecture that manages the vehicle departure times and makes the appropriate routing decisions so that vehicles arrive at their destination at the earliest possible time avoiding the congested parts of the network Menelaou et al. (2017a,b). Under the proposed route-reservation architecture, the future state of each road segment is predicted based on the received reservations and assuming that all vehicles travel at a constant speed which is set equal with the free-flow speed. Even within a homogeneous region, this assumption is not always valid which can lead to non accurate estimation of the time that a vehicle occupies a road segment which in turn affects the accuracy of the overall architecture. Therefore, the key objective of this work is to investigate different prediction schemes that improve the prediction-accuracy of vehicles travel times within each road segment. In this paper we explore two prediction methods, an Exponential Moving Average (EMA) and a Multiple Linear Regression (MLR) method. The performance of the two prediction schemes is also presented. Finally, realistic simulation results across an urban region of San Francisco demonstrate the gains that can be achieved applying the proposed prediction methods. 51 9 470 475

Detection of faults is a topic of high importance because it increases robot dependability, a requirement for the wide acceptance of service robots in domestic environments. This work takes a model-based approach for detecting and identifying actuator faults on differential-drive mobile robots in an indoor environment. An error-bound is calculated between the estimated and measured robot states which is constantly adapted based on the current state and input signals. A fault is detected when the estimation error is outside this bound. The model parameters are learned by the robot using an adaptive law, after the robot deployment in the target environment. Model uncertainties have an important impact on the fault detection performance, and are dealt with by considering the uncertainty bounds in the bound calculations. This ensures no false alarms occur when the uncertainty remains bounded during normal operation. Furthermore an extension to the method is proposed that addresses the problem of detecting small faults. The method is experimentally validated on a iRobot Roomba autonomous robot.

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Such events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. This places the problem of contaminant event monitoring in the fault diagnosis framework of detection and isolation. A main contribution of this work is the development of the state-space method, based on multi-zone building models, that enables the use of advanced fault diagnosis tools for contaminant event monitoring. Specifically, in this paper, we develop estimator schemes with adaptive thresholds for the detection and isolation of a single contaminant source under conditions of noise and modeling uncertainty. We demonstrate our proposed formulation using a 2-zone illustration example and a more realistic 14-zone building setting.

This work describes a model-based approach for contamination event detection in water distribution systems using chlorine mea- surements. The proposed method considers the known chlorine input injection signals, and uses multiple Monte-Carlo simulations which run in parallel to the real system, in order to compute at each time step, bounds of the expected chlorine concentration at the different chlorine sensing locations. The sensor measurements are then compared with the estimated bounds and according to a certain event logic, an event alarm flag is raised when these bounds are exceeded. The methodology is applied on a realistic benchmark network, taking into account uncertainties in the hydraulic dynamics.

Received signal strength RSS fingerprinting is an attractive solution for indoor positioning using Wireless Local Area Network WLAN due to the wide availability of WLAN access points and the ease of monitoring RSS measurements on WLAN-enabled mobile devices. Fingerprinting systems rely on a radiomap collected using a reference device inside the localisation area; however, a major limitation is that the quality of the location information can be degraded if the user carries a different device. This is because diverse devices tend to report the RSS values very differently for a variety of reasons. To ensure compatibility with the existing radiomap, we propose a self-calibration method that attains a good mapping between the reference and user devices using RSS histograms. We do so by relating the RSS histogram of the reference device, which is deduced from the radiomap, and the RSS histogram of the user device, which is updated concurrently with positioning. Unlike other approaches, our calibration method does not require any user intervention, e.g. collecting calibration data using the new device prior to positioning. Experimental results with five smartphones in a real indoor environment demonstrate the effectiveness of the proposed method and indicate that it is more robust to device diversity compared with other calibration methods in the literature.

Visual Sensor Networks (VSNs) exploit the processing and communication capabilities of modern smart cameras to handle a variety of applications such as security and surveillance and critical infrastructure protection. The performance of various tasks in such applications, such as activity recognition, tracking, etc., can be severely affected by the detection module especially when considering low-cost embedded smart cameras with limited processing capabilities. Hence, this paper presents research towards the development of optimization algorithms and decision making solutions to improve the detection performance of such VSNs. Specifically, it introduces a probabilistic detection model that can be used to characterize the detection capabilities of cameras, and shows how it can be used to reconfigure VSNs. Experimental as well as simulation results indicate that the proposed solution is able to effectively improve the robustness and overall detection performance of VSNs.

Intelligent buildings are responsible for ensuring the indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events due to accidental or malicious actions. An emerging environmental control application is monitoring the intelligent buildings against the presence of such events, by incorporating various sensing technologies and distributed detection and isolation algorithms. The needed simplicity, the improved scalability and fault tolerance are some of the main reasons for choosing distributed approaches over centralized ones. Hence, the effective partitioning of buildings into smaller sections for contaminant detection and isolation approaches is of great importance. In this paper, we present a heuristic algorithm for partitioning the building into smaller sections. The proposed algorithm is based on matrix clustering techniques and groups the building zones in order to form the different sections while ensuring (i) maximum decoupling between the various sections, (ii) strong connectivity between the zones of a section and (iii) fairness with respect to the number of allocated zones. The partitioning is achieved in near real time, providing the ability of repartitioning and adapting to the dynamic nature of the airflows. The main contribution of this work is the automatic partitioning of the building into sections, which enables the distributed simulation, modeling, analysis and management of the intelligent building while ensuring the effective detection and isolation of contaminants in the building interior.

Emerging computer vision applications of smart camera networks (SCNs) often require that the network cameras operate under limited or unreliable power sources. Therefore in order to extend the SCN lifetime it is important to manage the energy consumption of the cameras which is related to the workload of the vision tasks they perform. Hence, by assigning vision tasks to cameras in an energy-aware manner it is possible to extend the network lifetime. In this letter, we address this problem by proposing a market-based solution where cameras bid for tasks using an adaptive utility function. The early results for different SCN configurations and scenarios indicate that the proposed methodology can increase network lifetime. &nbsp

Wireless sensor networks (WSNs) is a relatively new technology that has been proposed for several applications including wide area monitoring. Such applications may include stationary or mobile sensor platforms or they may include several stationary and some mobile-robotic sensor nodes that can move in the area in order to achieve certain objectives, e.g., monitor areas that are not adequately covered or assist in the transfer of data to prevent the energy depletion of certain critical nodes. Such networks that consist of both stationary and mobile nodes are referred to as mixed WSNs. This paper presents the development of an experimental testbed for mixed WSNs consisting of stationary and mobile sensor nodes that collaborate to improve the sensing coverage and event detection of the network in a given deployment area. The paper describes the hardware and infrastructure of the testbed as well as a case study for coverage control that was investigated using the testbed. We point out that the developed testbed can be used for the evaluation and validation of different algorithms for coverage control that involve collaboration between stationary and mobile sensors to improve the WSN's monitoring capabilities. In addition, it can also be used to investigate other objectives as well as other concepts (e.g., network control).

This paper presents a design methodology and some analytical results for distributed sensor fault detection and isolation (SFDI) of a class of nonlinear uncertain systems. The proposed architecture is based on the design of local SFDI modules, which monitor the healthy operation of a set of sensors and aim to detect the faults in this set, using a dedicated nonlinear observer scheme. The multiple sensor fault isolation procedure is further enhanced by deriving a combinatorial decision logic that processes information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of its robustness with respect to the modeling uncertainties and conditions for ensuring fault detectability and isolability.

Traffic state estimation is an important problem with significant applications in advanced traveler information systems, transportation management and traffic control. Nonetheless, the often faulty nature of measurement sensors, especially inductive loop detectors, hinders reliable state estimation. This work proposes a systematic, model-based, network-wide, moving-horizon approach for fault-tolerant traffic state estimation. By exploiting information redundancy and fault sparsity, it achieves reliable estimation and simultaneously detects, isolates and corrects measurements from periods of faulty sensor behavior. The approach is examined in relation to the Asymmetric Cell Transmission Model, a popular and powerful macroscopic first-order traffic flow model. In the absence of any faults, the proposed approach achieves similar results with other state-of-the-art estimation approaches while it can achieve better estimation performance when some sensors are faulty. It is further demonstrated that the developed approach can successfully handle multiple faults of different types.

Motivated by the problem of Quality of Service (QoS) provisioning, this paper investigates a system with two classes of traffic and addresses the problem of dynamically controlling the buffer threshold used with the threshold policy, such that an objective function is optimized. For the analysis, a Stochastic Fluid Model (SFM) is used to derive unbiased sensitivity estimators of various metrics (workload, loss, throughput and packet expiration) with respect to the buffer threshold. These estimators are computed using information obtained from the sample path of the ''real'' discrete event system. Thus, one can use these estimators together with stochastic approximation techniques in order to maintain the system at a near-optimum point despite any changes in the incoming traffic or the transmission capacity of the network.

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is Subtract on Negative Add on Positive (SNAP), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding \pm 1 contributions from the sensor nodes depending on their alarm state (positive or negative). This simple estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations. © 2009 IEEE.

Statistical Terminal Assisted Mobile Positioning (STAMP) is a methodology that improves the accuracy of existing positioning techniques by exploiting measurements collected at the terminal side. STAMP is setting a unified positioning framework, in which different types of raw network related measurements are employed by multiple positioning techniques in order to derive coarse position estimates. Subsequently, statistical processing is performed to further increase accuracy. STAMP is complementary to satellite positioning systems, while the proposed architecture is highly applicable to User Plane location architectures. Due to its open and modular architecture, new positioning algorithms and post processing techniques can be added in the localization chain, thus supporting effectively a wide variety of Location Based Services (LBS). The implementation of STAMP in a prototype focuses particularly on Quality of Position issues and compatibility with currently available and up-coming standards and communication protocols. Preliminary results using actual network measurements, in both Greece and Finland, reveal the efficiency of STAMP.

The often faulty nature of measurement sensors hinders reliable traffic state estimation, affecting in this way various transportation operations such as traffic control and in-car navigation. This work proposes a systematic, model-based, online and network-wide approach to achieve robust and good quality state estimation in the presence of sensor faults. The approach is comprised of three stages aiming at 1) identifying the level of faulty behavior of each sensor using a novel fault-tolerant optimization algorithm, 2) isolating faults, and 3) improving state estimation performance by adaptively compensating sensor faults and resolving the state estimation problem. The approach is examined in the context of the Asymmetric Cell Transmission Model for freeway traffic density estimation. Simulation results demonstrate the effectiveness of the proposed fault-adaptive approach, yielding estimation performance very close to the one obtained with healthy measurements, irrespective of the fault magnitude. It is further illustrated that the developed fault-tolerant optimization algorithm can simultaneously identify different types of faults from multiple sensors.

Visual Sensor Networks (VSNs) exploit the processing and communication capabilities of modern smart cameras to handle a variety of applications such as security and surveillance, industrial monitoring, and critical infrastructure protection. The performance of VSNs can be severely degraded because of errors in the detection module. As a result, the performance of the higher-level application such as activity recognition, tracking, etc., also suffers due to the fact that in most cases the decision making process in VSNs assumes ideal detection capabilities for the cameras. Realizing that it is necessary to introduce robustness in the decision process this paper presents results towards uncertainty-aware VSNs. Specifically, we introduce a flexible uncertainty model that can be used to study the behaviour of missed detections in a camera network. We also show how to utilize the model to develop uncertainty-aware coordination and decision making solutions to improve the efficiency of VSNs. Our experimental results in an active vision application indicate that the proposed solution is able to improve the robustness and reliability of VSNs.

The Internet of Things (IoT) is poised to deliver connected automation for monitoring, optimization, and control of processes and systems in sectors such as the automotive, healthcare and security. In this way, the IoT promises to make better use of the available resources, enable real-time interaction, and support innovative applications. Under this regime, the present work details an event-triggering technique (ETT) for efficient and effective tracking of road vehicles in the realm of intelligent transportation systems. The technique assumes that both the local and remote host follow a predetermined mobility model to approximate the actual vehicle location. Upon detection of deviation from this model, the local host triggers an update event to resynchronize the remote host. Within the proposed ETT, both the mobility model, as well as the detection thresholds are calculated solely based on sensory data, making the whole system both scalable and robust to dynamic changes. Experimental results demonstrate the many-fold improvements in efficiency of the proposed solution compared to traditional periodic tracking. These improvements are also validated in practice through an implementation on an operating bus route service.

To monitor water quality, utilities typically employ periodic manual sampling. However, when a contamination event occurs, it may require days before it is detected. To enhance monitoring, utilities employ sensors which monitor various water quality parameters. A common approach is the use of chlorine sensors for monitoring chlorine residuals at different locations in the network, in order to determine whether a contamination event has occurred. Unfortunately, due to significant variability in water demands, as well as the effect of hydraulic and quality control actions, the disinfectant residual at the sensor location may fluctuate significantly in time, and therefore, model-free event detection algorithms may not be able to detect certain contamination events, or they may cause false alarms. This work extends the work in [1] by proposing a model-based method for contamination event detection using real-time concentration lower-bound estimations as well as multi-level thresholds, for enhancing detection and reducing detection delay while minimizing false positive alarms.

International audience; This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoningbased decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

Emerging Camera Sensor Networks (CSN) leverage the collaboration, processing and communication capabilities of modern cameras to handle a wide variety of security and safety-critical tasks, including target tracking. However, the performance of CSN in terms of tracking accuracy can be severely degraded when faults occur. Faults may be caused by unpredictable software errors (e.g., in the image processing, feature extraction and data association modules), hardware malfunctions (e.g., in the camera mechanical parts or lens), or as the result of a malicious attack. We propose a decentralized CSN-based system for tracking multiple targets that mitigates the effect of faulty cameras. According to our findings, the proposed solution can well handle faulty camera observations and is able to reliably track a number of targets that may change over time.

International audience; This paper proposes a distributed methodology for detecting and isolating multiple sensor faults in interconnected cyber-physical systems. The distributed sensor fault detection and isolation process is conducted in the cyber superstratum, in two levels. The first-level diagnosis is based on the design of monitoring agents, where every agent is dedicated to a corresponding interconnected subsystem. The monitoring agent is designed to isolate multiple sensor faults occurring in the sensor set of the physical part, while it is allowed to exchange information with its neighboring monitoring agents. The secondlevel diagnosis is realized by applying a global decision logic designed to isolate multiple sensor faults that may propagate in the cyber superstratum through the exchange of information between monitoring agents. The decision making process, executed in both levels of diagnosis, relies on a multiple sensor fault combinatorial logic and diagnostic reasoning. The performance of the proposed methodology is analyzed with respect to the sensor fault propagation effects and the distributed sensor fault detectability.

Recent advances in low power networked embedded systems and electrochemical/optical water quality sensors have enabled the generation of low-cost, low-power water quality sensor nodes. These sensor nodes leverage the idea of wireless sensor networks for water quality monitoring in drinking water distribution systems, where large numbers of spatially distributed sensor nodes operate cooperatively to monitor and detect contamination events in a timely manner. However, such approach it is expected to suffer from false alarm errors and missed detection errors due to sensor inaccuracies and modeling uncertainties. This paper aims at developing algorithms for network-wide fusion for processing such inaccurate sensor data in order to minimize detection errors and improve contamination detection probability by correlating imperfect sensor decisions in space and time, taking advantage of the large scale deployment and the drinking water distribution system topology. A simulation example of a simple water distribution system is used to illustrate the proposed methodology and provide intuition through a comparative study.

Camera Sensor Networks (CSNs) have a large and diverse application spectrum ranging from security and safety-critical applications, to industrial monitoring, and augmented reality. Cameras in such networks are equipped with real-time multitasking processors and communication infrastructure, which enables them to perform various computer vision tasks in a distributed and collaborative manner. In many cases, the cameras in the network operate under limited or unreliable power sources. Therefore in order to extend the CSN lifetime it is important to manage the energy consumption of the cameras, which is related to the workload of the vision tasks they perform. Hence by managing and assigning vision tasks to cameras in an energy-aware manner it is possible to extend the network lifetime. In this paper we address this problem by proposing a distributed market-based solution where cameras bid for tasks using an energy-aware utility function. An additional novelty of the proposed solution is that as the cameras can adapt their bidding strategy based on their remaining energy levels. The results for different CSN configurations and setups show that the proposed methodology can increase network lifetime by 10%-30% while improving the number of dynamic and static tasks being monitored by 30-50%.

Camera Sensor Networks (CSN) are becoming increasingly popular in a variety of security and safety-critical applications including public space surveillance, monitoring of attack-sensitive facilities, and critical infrastructure protection. Cameras in such networks are equipped with high-resolution visual sensors and on-board processors, while featuring wireless communication capabilities. These features enable the execution of various tasks, such as area coverage, activity recognition and target tracking, in a cooperative fashion. However, the performance of CSN may be compromised when faults occur, either due to unintentional software and hardware faults or as the result of a malicious attack. Paving the way for fault tolerance in CSN-based target tracking, we introduce a flexible fault model that can be used to generate different types of erroneous behaviour, thus simulating realistic faults in CSN. We also propose a fault-tolerant decentralized solution for tracking a target that passes through the area monitored by the CSN. Our simulation results indicate that the proposed solution is able to track the target reliably despite the presence of faults.

In this work we present the problem of contamination event detection in drinking water distribution networks using real-time learning approaches and a model-based event detection scheme. By using chlorine concentration measurements a contamination event detection algorithm is developed with the aim of detecting the occurrence of contaminant injection into a water tank by monitoring the change of the chlorine concentration. The proposed methodology is comprised of two steps: a) learn in real-time the unknown chlorine reaction dynamics using a Radial-Basis Function network; b) activate a contamination-event detection methodology which uses an adaptive detection threshold. A contamination event detection alarm is activated when the magnitude of the estimation error exceeds the detection threshold. To demonstrate the proposed methodology realistic case study is evaluated with Monte-Carlo simulations of contamination events of different magnitudes occurrence times and environmental characteristics. The results demonstrate the improvement in the contamination event detection performance when using the real-time learning approach.

This paper presents a low cost and holistic approach to the water quality monitoring problem for drinking water distribution systems as well as for consumer sites. Our approach is based on the development of low cost sensor nodes for real time and in-pipe monitoring and assessment of water quality on the fly. The main sensor node consists of several in-pipe electrochemical and optical sensors and emphasis is given on low cost, lightweight implementation, and reliable long time operation. Such implementation is suitable for large scale deployments enabling a sensor network approach for providing spatiotemporally rich data to water consumers, water companies, and authorities. Extensive literature and market research are performed to identify low cost sensors that can reliably monitor several parameters, which can be used to infer the water quality. Based on selected parameters, a sensor array is developed along with several microsystems for analog signal conditioning, processing, logging, and remote presentation of data. Finally, algorithms for fusing online multisensor measurements at local level are developed to assess the water contamination risk. Experiments are performed to evaluate and validate these algorithms on intentional contamination events of various concentrations of escherichia coli bacteria and heavy metals (arsenic). Experimental results indicate that this inexpensive system is capable of detecting these high impact contaminants at fairly low concentrations. The results demonstrate that this system satisfies the online, in-pipe, low deployment-operation cost, and good detection accuracy criteria of an ideal early warning system.

This paper investigates the use of a Wireless Sensor Network for localizing and tracking multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development and analysis of a low-complexity, distributed, real-time algorithm that uses the binary observations of the sensors for identifying, localizing, and tracking multiple targets in a fault tolerant way. Specifically, our results indicate that the proposed algorithm retains its performance in tracking accuracy in the presence of noise and faults, even when a large percentage of sensor nodes (25 percent) report erroneous observations.

The provision of accurate and reliable localization and tracking information for a target moving inside a binary Wireless Sensor Network (WSN) is quite challenging, especially when sensor failures due to hardware and/or software malfunctions or adversary attacks are considered. Most tracking algorithms assume fault-free scenarios and exploit all binary sensor observations, thus their accuracy may degrade when faults are present in the field. Spatiotemporal information available while the target is traversing the sensor field can be used not only for tracking the target, but also for detecting certain types of faults that appear highly correlated both in time and space. Our main contribution is ftTRACK, a target tracking architecture that is resilient to sensor faults and consists of three main components, namely the sensor health-state estimator, a fault-tolerant localization algorithm, and a location smoothing component. The key idea in the ftTRACK architecture lies in the sensor health-state estimator that leverages spatiotemporal information from previous estimation steps to intelligently choose which sensors to employ in the localization and tracking tasks. Simulation results indicate that ftTRACK maintains a high level of tracking accuracy, even when a large number of sensors fail.

In this demo, we present an efficient hybrid indoor positioning solution that uses multi-sensory location-oriented observations, including WiFi, accelerometer, gyroscope and digital compass data, that are widely available on Android smartphones1. Our system mainly comprises three building blocks, namely the WiFi Fingerprinting, the Inertial Measurement Unit (IMU) Positioning and the Location Fusion components. The WiFi Fingerprinting module relies on existing WiFi infrastructure and exploits Received Signal Strength (RSS) values from neighboring Access Points (AP) to infer the unknown user location. Specifically, it utilizes a number of RSS fingerprints collected a priori to build the so-called radiomap. Subsequently, the WiFi-based location is estimated with a state-of-the-art algorithm that exploits the currently measured fingerprint and fingerprints in the radiomap [1]. The IMU Positioning module performs multi-dimensional (i.e., 3-axis accelerometer, gyroscope and digital compass) motion sensor fusion for calculating the user orientation in real-time and implements an in-house pedometer algorithm for pedestrian trajectory tracking. An interesting feature in our implementation is the use of raw magnetic data to detect magnetic anomalies, which are common inside buildings, e.g. due to power cables, electrical appliances or metal surfaces, in order to refine orientation. Moreover, a map-matching submodule performs error correction in order to handle inaccurate IMU location estimates (e.g., showing a user passing through a wall or moving into a restricted area). Finally, the WiFi-based and IMU-based location estimates and associated uncertainties are provided as inputs to the Location Fusion module that implements the hybridization scheme by means of a particle filter. Thus, our prototype system delivers a smooth final location estimate that is consistent with the actual travelled path see Fig. 1. We will demonstrate the real-time positioning capabilities of our hybrid system by allowing attendees to carry an Android smartphone running our tracking application and viewing their current location on a floorplan map, while walking around the demo area. In this interactive scenario, the participants will be able to appreciate the potential of our indoor geolocation system, which is reliable and attains a localization error below 3 meters through the integration and optimization of diverse technologies, while our software may run on any commercial Android smartphone. 503 504 Sponsors: ACM SIGMOBILE Conference code: 98247 Cited By :8

Intelligent buildings are beginning to utilize sensor networks for monitoring and protecting indoor air quality against contamination events. This paper presents a methodology for determining where to install such sensors. In particular, a multi-objective optimization problem is formulated for minimizing the sensor cost, the average and the worst-case impact damage corresponding to a set of contamination event scenarios. Each contamination scenario is comprised of parameters characterized by some given probability distribution. Based on these distributions, a set of representative contamination scenarios is constructed through grid and random sampling, and the overall impact of each scenario is computed, thus providing a solution to the sensor placement problem. The proposed methodology is illustrated by two case studies, a simple building with five rooms and a realistic building with 14 rooms.

We consider the problem of target (event source) tracking using a binary Wireless Sensor Network (WSN). For this problem, a WSN consisting of sensors that can detect the presence of a target in an area around them, should fuse the information received by the individual sensors in order to localize and track the target. This is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors during target tracking may lead to high estimation errors. Since failure of individual sensors is unavoidable, there is a need to estimate the health state of each sensor in order to ignore those sensors that are considered as faulty. The contribution of this work is the investigation of three different algorithms for estimating the sensors' health state simultaneously with target tracking.

Tracking of a source (target) which is moving inside a binary Wireless Sensor Network (WSN) is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors to locate and track a target may lead to high estimation errors, therefore, there is a need to develop fault tolerant localization algorithms which perform well even when a percentage of the sensors report erroneous observations. Alternatively, one may fuse information from neighboring sensors in order to determine the health state of each sensor, and subsequently use only healthy sensors in the localization and tracking process. Our contribution is the development of an architecture which combines the sensor health state estimation together with fault tolerant localization algorithms that leads to more robust target tracking in binary WSNs. Simulation results indicate that the proposed approach is resilient to various types of faults.

This work proposes the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to tackle with the problem using different devices. In addition to the fact that RSS data collection might be a tedious and time-consuming process, the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a very suitable and efficient solution to tackle this problem. Moreover, in traditional fingerprint-based methods, the underlying radiomap is restricted to the mobile device for which the radiomap has been created. To overcome this limitation, we propose the use of linear data transformation to match the characteristics of various devices. We address both challenges by using 3D RT-generated radiomaps and highlight the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device.

In this demonstration paper, we present an indoor positioning system developed for Android smartphones, coined Airplace. To infer the unknown user location we rely on ubiquitous WLANs and exploit Received Signal Strength (RSS) values from neighboring Access Points (AP) that are constantly monitored by the mobile devices under normal operation. Our system follows a mobile-based network-assisted architecture to eliminate the communication overhead and respect user privacy. In a typical scenario, when a user walks inside a building a smartphone client conducts a single communication with our Distribution Server to receive the RSS radiomap and is then able to position itself independently using the observed RSS values. Moreover, we have implemented an Android application to facilitate the collection of RSS values by users that may contribute their data to our system for constructing and updating the radiomap through crowdsourcing1. We will demonstrate the real-time positioning capabilities of the system during the conference by allowing attendees to carry an Android tablet in order to view their position on a floorplan map, while walking around inside the demo area (interactive scenario). Moreover, we will illustrate how to evaluate the performance of different positioning algorithms using profiled data in a trace-driven scenario. Our objective is to highlight the effectiveness and applicability of our system and at the same time the participants will be able to appreciate the potential of indoor location-oriented services and applications. © 2012 IEEE. 312 315 Sponsors: IBM MICS Indian Institute of Technology Microsoft Research Bell Labs Conference code: 94301 Cited By :30

Fingerprint-based positioning methods that utilise WLAN received signal strength (RSS) measurements are becoming increasingly popular for inferring terminal location in indoor environments. They are very attractive mainly because of the low deployment cost, as they exploit the ubiquity of WLAN access points (AP) and the worldwide availability of mobile devices featuring WLAN adapters for wireless connectivity. They provide reliable and accurate location estimates, however their performance can be compromised when faults occur in the overall positioning system. Such faults are injected in different ways, including the removal or relocation of one or several APs, and are caused either accidentally or deliberately due to malicious attacks. These faults corrupt the RSS fingerprints and may lead to accuracy degradation, unless countermeasures are taken. In this work, we focus on positioning under faults and construct detection schemes using fault indicators that are applicable to standard fingerprint-based methods and achieve high fault detection rates. In addition, we incorporate our detection mechanisms into hybrid positioning algorithms that greatly improve fault tolerance in the presence of different types of faults. Finally, we present experimental results to verify the effectiveness of the proposed approach

We study the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to make the overall process device-independent. RSS data collection might be a tedious and time-consuming process and also the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a more attractive and efficient way to generate radiomaps. Moreover, traditional fingerprint-based methods lead to radiomaps which are restricted to the device used to generate the radiomap and fail to provide acceptable performance when different devices are considered. We address both challenges by exploiting 3D RT-generated radiomaps and using linear data transformation to match the characteristics of various devices. We evaluate the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device.

The increasing demand for indoor location-based services has motivated the development of positioning methods that exploit the existing wireless network infrastructure. Accuracy is an important requirement, however fault tolerance is also highly desirable in case of failures or malicious attacks. We investigate the fault tolerance of fingerprint-based methods under a variety of fault or attack scenarios. We study the Subtract on Negative Add on Positive (SNAP) algorithm and modify it appropriately for the WLAN setup. Our results indicate that SNAP achieves adequate accuracy with very low computational complexity and exhibits smoother performance degradation in the presence of faults compared to other methods. © 2011 IEEE.

This paper investigates the use of a sensor network for localizing and tracking a moving target using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate a number of misbehaving nodes. SNAP (Subtract on Negative Add on Positive), is a simple event localization algorithm designed for WSNs applications that exhibits this fault tolerant behavior. The main contribution of this paper is to combine the decentralized implementation of SNAP with Kalman Filter techniques for tracking the movement of a target. This efficient tracking procedure provides fairly accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations. © 2011 IEEE.

Accurate and reliable location estimates using wireless networks are important for enabling indoor location oriented services and applications, such as in-building guidance and asset tracking. Providing adequate level of accuracy in case of faults or attacks to the positioning system is equally significant, thus our main interest is on the fault tolerance of positioning methods, rather than the absolute accuracy in the fault-free case. We introduce several fault models to capture the effect of failures in the wireless infrastructure or malicious attacks and discuss how these models can simulate the corruption of signal strength values during positioning. The models are used to investigate the fault tolerance of positioning methods and evaluate them in terms of their performance degradation as the percentage of corrupted signal strength measurements increases. Experimental results using our fault models are also presented. © IEEE.

Provision of accurate and reliable location estimates is the key issue for the proliferation of indoor location oriented services and applications. Our positioning method is based on Radial Basis Function (RBF) networks and we exploit Received Signal Strength (RSS) measurements from several WLAN Access Points (AP). We incorporate the RSS covariance matrix into the estimation method and couple that with a methodology that indicates which APs can be ignored during positioning without sacrificing accuracy. We evaluate the RBF method in a real-life setup, and experimental results suggest that the proposed approach performs well. (14 refs.)

This paper investigates the use of a Wireless Sensor Network for detecting and tracking the location of multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development of a simple and decentralized algorithm that uses the binary observations of the sensors for tracking multiple targets in a fault-tolerant way. Furthermore, tracking is performed in real-time by the alarmed sensor nodes that are elected as leaders, utilizing only information from their neighbors. ©2009 IEEE.

This paper presents an efficient distributed collaboration scheme for a team of autonomous mobile sensor nodes which enables them to navigate through a sparse sensor network with stationary nodes searching for events and improving area coverage. The mobile sensor nodes have limited communication and sensing ranges and autonomously plan their trajectories in order to enhance the probability of event detection. The main objective of this work is to investigate collaboration schemes between the sensor nodes such that each mobile samples areas not covered by the stationary or other mobile nodes. The aim is to reduce the amount of information that needs to be exchanged between nodes without significant loss of performance (in terms of area coverage).

One of the main applications of Wireless Sensor Networks (WSNs) is area monitoring (e.g., environmental monitoring). In such problems, it is desirable to maximize the area coverage which can be achieved by appropriately positioning the sensors (if possible) and/or by increasing the detection range of the sensors. This paper considers the latter. The emphasis is on pairs of closely spaced sensors that can collaborate in order to increase their collective area coverage. The main contribution of this work is to investigate collaborative detection schemes between a pair of sensor nodes and show that the area coverage achieved by each scheme depends on the distance between the two sensors. For closely spaced sensors, we propose the Enhanced Covariance Detector (ECD) that combines the energy and the covariance information from the two nodes by utilizing two different thresholds (one for the energy test statistic and another for the covariance).

Monitoring a large area with stationary sensor networks requires a very large number of nodes which with current technology implies a prohibitive cost. The motivation of this work is to develop an architecture where a set of mobile sensors will collaborate with the stationary sensors in order to reliably detect and locate an event. The main idea of this collaborative architecture is that the mobile sensors should sample the areas that are least covered (monitored) by the stationary sensors. Furthermore, when stationary sensors have a “suspicion” that an event may have occurred, they report it to a mobile sensor that can move closer to the suspected area and can confirm whether the event has occurred or not. An important component of the proposed architecture is that the mobile nodes autonomously decide their path based on local information (their own beliefs and measurements as well as information collected from the stationary sensors in a neighborhood around them). We believe that this approach is appropriate in the context of wireless sensor networks since it is not feasible to have an accurate global view of the state of the environment.

This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal which propagates over a large region. In this context, we assume that the sensors make binary observations and report the event if the measured signal at their location is above a threshold; otherwise they remain silent. Based on the sensor binary beliefs we use 4 different estimators to localize the event: CE (Centroid Estimator), ML (Maximum Likelihood), SNAP (Subtract on Negative Add on Positive) and AP (Add on Positive). The main contribution of this paper is the fault tolerance analysis of the proposed estimators. Furthermore, the analysis shows that SNAP is the most fault tolerant of all estimators considered. ©2008 AACC.

This paper investigates the benefits of spatial correlation for the problem of coverage in Wireless Sensor Networks (WSN). Specifically, it studies two detectors, the Mean Detector (MD) and the Covariance Detector (CD) and compares their coverage performance for various scenarios. The main contribution of this paper is to show that one can exploit the possible correlation between measurements of neighboring sensor nodes in order to achieve significantly better coverage. Moreover, the results of this paper have direct implications on the topology of the network. Our results indicate that for the CD the best placement would be pairs of sensor nodes placed on a grid configuration. © 2007 IEEE.