Binary Sensor Networks Research Articles

Scalable Distributed State Estimation over Binary Sensor Networks with Energy Harvester

This paper deals with distributed state estimation problem for discrete time-varying systems over binary sensor networks, where every binary sensor is equipped with an energy harvester. The input of every binary sensor considers the randomly occurring missing measurements. The differences between the real and estimated inputs of binary sensor are employed to derive useful information in order to address the insufficient information for estimation purpose. The information from neighboring nodes is transmitted only if its energy level is positive, where a random variable is introduced to formulate the energy level. By means of the available information, distributed estimator is constructed for each binary sensor and the desirable performance constraints is given for the dynamic characteristics of estimation errors within a finite time horizon. Sufficient conditions are established for the existence of desired distribution estimation quantities through local performance analysis methods. Also, the desired distributed estimator gains are calculated recursively, which means the desirable scalability. Ultimately, the viability and efficiency of the distributed scheme are exhibited through a practical illustration.

Distributed Sequential State Estimation Over Binary Sensor Networks with Inaccurate Process Noise Covariance: A Variational Bayesian Framework

Distributed Sequential State Estimation Over Binary Sensor Networks with Inaccurate Process Noise Covariance: A Variational Bayesian Framework

Zonotopic Distributed Fusion Over Binary Sensor Networks With Bit Rate Allocation: A Coding-Decoding Approach.

In this article, the zonotopic distributed fusion estimation problem is investigated for a class of general nonlinear systems over binary sensor networks subject to unknown-but-bounded (UBB) noises. The network communication from nodes to the fusion center is confined to the limited bit rate. To alleviate the impact from less measurement information of the binary sensor, a modified innovation is constructed to improve the estimation accuracy. Then, a novel coding-decoding approach is proposed to ensure that the decoder has the ability to decode information from each node. Based on the matrix weighting fusion method, a distributed fusion algorithm is put forward under the zonotopic set-membership filtering framework, and the F -radius of the local zonotopic sets are derived and minimized by selecting the filtering gain parameters. Moreover, the bit rate allocation scheme and the weighting coefficients are determined by resolving two optimization problems. In addition, a sufficient condition is established to guarantee the uniform boundedness of the F -radius of the fused zonopotic. Finally, the ballistic object tracking systems is utilized to illustrate the availability of the presented algorithm.

Local Design of Distributed State Estimators for Linear Discrete Time-Varying Systems Over Binary Sensor Networks: A Set-Membership Approach

Local Design of Distributed State Estimators for Linear Discrete Time-Varying Systems Over Binary Sensor Networks: A Set-Membership Approach

A Recursive Matrix Inequality Approach to Distributed Filtering Over Binary Sensor Networks: Handling Amplify-and-Forward Relays

A Recursive Matrix Inequality Approach to Distributed Filtering Over Binary Sensor Networks: Handling Amplify-and-Forward Relays

Distributed $H_{\infty }$-Consensus Estimation for Random Parameter Systems Over Binary Sensor Networks: A Local Performance Analysis Method

This paper deals with the distributed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> -consensus estimation problem for a class of discrete time-varying random parameter systems over binary sensor networks, where the statistical information of the random parameter matrix is characterized by a generalized covariance matrix known a priori. As a binary sensor can only provide one bit of information according to a given threshold, an indicator variable is introduced so as to extract functional information (from the sensor output) that can be employed to estimate the system state. With the introduced indicator variable, a distributed estimator is constructed for each binary sensor with guaranteed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> -consensus performance constraint on the estimation error dynamics over a finite horizon. By means of a local performance analysis method, indicator-variable-dependent conditions are established for the existence of the desired distributed estimators whose gains are calculated by solving a set of recursive linear matrix inequalities. Finally, the applicability and effectiveness of the developed distributed estimation scheme are demonstrated through a numerical example.

Recursive Distributed Filtering for Time-Varying Systems Over Sensor Network via Rayleigh Fading Channels: Tackling Binary Measurements

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic systems over binary sensor networks with the Rayleigh fading channels. Both the system state and measurement are subject to random noises with known statistical information, where the distribution function of measurement noise is employed to extract the functional information for state estimation purposes. The communication between a sensor node and its neighboring ones is implemented over a Rayleigh fading channel. For each binary sensor, a distributed filter is constructed by virtue of the available information from itself and its neighboring nodes, and the overall filtering error dynamics is guaranteed to be exponentially ultimately bounded in the mean square sense. By resorting to a local performance analysis method, sufficient criteria are established for ensuring the existence of the desired distributed filter in terms of a set of recursive linear matrix inequalities. The desired filter parameters are recursively calculated on every node by solving an optimization problem at each time instant with the aim of improving the estimation accuracy. Finally, some comparative results are presented to demonstrate the applicability and effectiveness of the developed distributed filtering scheme.

Mean Shift-Based Multisource Localization Method in Wireless Binary Sensor Network

Source localization is one of the major research contents in the localization research of wireless sensor networks, which has attracted considerable attention for a long period. In recent years, the wireless binary sensor network (WBSN) has been widely used for source localization due to its high energy efficiency. A novel method which is based on WBSN for multiple source localization is presented in this paper. Firstly, the Neyman-Pearson criterion-based sensing model which takes into account the false alarms is utilized to identify the alarmed nodes. Secondly, the mean shift and hierarchical clustering method are performed on the alarmed nodes to obtain the cluster centers as the initial locations of signal sources. Finally, some voting matrices which can improve the localization accuracy are constructed to decide the location of each acoustic source. The simulation results demonstrate that the proposed method can provide a desirable performance superior to some traditional methods in accuracy and efficiency.

Detection of a recurrent static target using binary sensor networks

The authors address the problem of decentralised detection of a recurrent event produced by a static uncooperative target in a wireless network of spatially distributed sensors. The presence or absence of the target is determined by applying fusion rules on local binary decisions of individual sensors. Optimal fusion rules require complete statistical knowledge about local detection and false alarm probabilities; hence, in practice they are infeasible. Previous works, such as the counting rule test, scan statistics and the generalised likelihood ratio test, have suggested sub-optimal fusion rules based on the total number of detecting sensors. Neither method takes full advantage of the spatial or temporal correlation between adjacent sensors. In practice, the decisions of adjacent sensors are dependent both in space (as the target is static) and in time (as the event reoccurs). Herein, the authors propose an approach that capitalises on the temporal and spatial arrangement of the local decisions. Simulation examples demonstrate that the detection probability of the proposed spatio-temporal rule is significantly higher than that of the common methods, and also achieves a lower probability of false alarm. Moreover, real data results show that the proposed approach is robust to strong environmental noises.

Source Localization by a Binary Sensor Network in the Presence of Imperfection, Noise, and Outliers

In this paper, source localization by a network of primitive binary sensors under various imperfections are studied. Detailed analysis and mathematical modeling of imperfect binary sensors are presented. Imperfections include sensor failures of two types, drifting, uncertainty, and heterogeneity in binary sensor trigger thresholds, presence of noise, and nonradial symmetry of sensing ranges. Theoretical results, including asymptotical convergence, are established, in particular in the presence of substantial outliers due to sensor failure and large noise. Efficient numerical algorithms are proposed and simulated supporting the theoretical analysis.

Effective Self Adaptive Multiple Source Localization Technique by Primal Dual Interior Point Method in Binary Sensor Networks

Wireless sensor networks are creating a new era of pervasive computing applications, such as various monitoring and tracking system. The sensor network consists of so many tiny sensor nodes that have so many critical challenges, since they are battery operated and have limited processing capabilities. Binary sensor networks are modeled in a way that the sensor nodes can communicate with the only 1 b of information. One of the challenges in a binary sensor network is to localize the multiple sources. Very few works have been done considering this challenge. Localization failure may cause the whole system useless. We propose a multiple source localization method. We convert the localization problem into an optimization problem, and we solve that optimization problem using primal dual interior point method. Simulation results show that our proposed method provides better performance in every perspective compared with the existing works.

User-oriented seamless service handoff in the ubiquitous computing environment

Purpose User-oriented seamless service handoff is prosperous in the ubiquitous computing environment. This paper aims to present an open architecture for the user-oriented seamless service handoff across heterogeneous platforms. The migration of service environment is the key to implement the user-oriented service handoff. A distributed service handoff scheme based on the binary sensor network and context-aware middleware has been proposed. Design/methodology/approach To validate this scheme, a test-bed has been constructed in the lab environment. Findings The experimental results show that the proposed scheme can reduce the service handoff delay compared to the centralized scheme. Originality/value The experimental results show that the scheme can reduce the delay of service handoff compared to the traditional centralized scheme.

Decentralized detection for censored binary observations with statistical dependence

This paper analyzes the problem of distributed detection in a sensor network of binary sensors. In particular, statistical dependence between local decisions (at binary sensors) is assumed, and two complementary methods to save energy have been considered: censoring, to avoid some transmissions from sensors to fusion center, and a sleep and wake up random schedule at local sensors. The effect of possible failures in transmission has been also included, considering the probability of having a successful transmission from a sensor to the fusion center. In this scenario, the necessary statistical information has been identified, the optimal decision rule at the fusion center has been obtained, and some examples have been used to analyze the effect of statistical dependence in a simple network with two sensors.

Robust tracking of piecewise linear trajectories with binary sensor networks

We present a novel approach to the problem of tracking objects moving along smooth trajectories using a network of simple inexpensive binary sensors. Specifically, we consider object trajectories that can be well-approximated by piecewise linear curves and sensors that can only detect whether an object is in their sensing range. We start by considering objects moving along straight-line trajectories with an unknown speed and show that such objects can be tracked using the measurements of just three generically placed binary sensors whose sensing ranges intersect the trajectory. Next, we present an asymptotic analysis that shows that a trajectory consisting of a finite number of straight line segments can be recovered with high probability using an arbitrarily low spatial density of sensors in the limit when the area to be covered gets larger and larger. We also present efficient algorithms that effectively recover piecewise linear trajectories. Finally we present analysis and simulations to demonstrate the high tracking accuracy of our approach and its robustness to sensing errors.

A Self-Adaptive Particle Swarm Optimization Based Multiple Source Localization Algorithm in Binary Sensor Networks

With the development of wireless communication and sensor techniques, source localization based on sensor network is getting more attention. However, fewer works investigate the multiple source localization for binary sensor network. In this paper, a self-adaptive particle swarm optimization based multiple source localization method is proposed. A detection model based on Neyman-Pearson criterion is introduced. Then the maximum likelihood estimator is employed to establish the objective function which is used to estimate the location of sources. Therefore, the multiple-source localization problem is transformed into optimization problem. In order to improve the ability of global search of particle swarm optimization, the self-adaptive particle swarm optimization is used to solve this problem. Various simulations have been conducted, and the results show that the proposed method owns higher localization accuracy in comparison with other methods.

Extracting patterns of behavior from a network of binary sensors

According to the World Health Organization and the US Department of Health, older adults are one of the most rapidly expanding population segments of many developed countries. As a result, significant research in the area of assistive technologies for the elderly has been undertaken to help manage this growing demographic segment. Our contribution to this effort is based on leveraging environmental sensors to track occupants for extended durations in their homes in order to extract and track various behavioral patterns in order to identify atypical activities. To aid in our detection of behavioral patterns, we extract additional intermediate indicators of mobility and behavior such as estimates of an occupant’s position as well as other parameters such as guest visitations. In our initial research, we did not associate these instances of atypical behaviors with any specific health events. Our primary goal was to see if it was possible to detect atypical behaviors using only the limited data that can be extracted using passive infrared sensors. We compared the days when atypical patterns were detected with the similar analysis of a human annotated activity log and determined that there is a strong correlation between patterns extracted from our derived behavioral indicators and those from the annotated activities. We also compared our systems result’s across multiple datasets to verify that our approach could be applied to different sensor configurations and occupants.

Achievable accuracy in Gaussian plume parameter estimation using a network of binary sensors

The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength) and environmental parameters (wind speed, direction, atmospheric stability conditions). The paper presents a theoretical analysis of the best achievable accuracy in estimation of Gaussian plume parameters in the context of a continuous point-source release and using a binary sensor network for acquisition of measurements. The problem is relevant for automatic localisation of atmospheric pollutants with applications in public health and defence. The theoretical bounds of achievable accuracy provide a guideline for sensor network deployment and its performance under various environmental conditions. The bounds are compared with empirical errors obtained using a Markov chain Monte Carlo (MCMC) parameter estimation technique.

Concurrent activation events based trajectory propagation in smart environments

In the past 10 years, the concept of smart homes and buildings has become progressively mainstream with the introduction of several commercial solutions and available sensor technologies. One of the most important aspects of a smart home system is sensing, which is typically leveraged to observe, understand and predict the daily behavior of the occupant. This sensing of a smart home’s occupant extends to personal location estimation, behavior detection, and activity monitoring. However, there is always a trade off between the amount of information that can be sensed and the loss of privacy of the occupant. Cameras coupled with state of the art computer vision algorithms can provide much more nuanced and rich information than binary sensors, like motion sensors, door sensors and so on. Unfortunately, cameras are not well tolerated by people in their homes and other private areas for privacy protection reasons. In this article, an intensive study was performed on a dataset gathered in a smart home test bed equipped with binary motion sensors. Leveraging our previous works in a concurrent activation model that describes an occupant’s mobility along with the newly defined continuity property of a occupant’s mobility, we propose a bipartite sensor graph with intersection sensor nodes that models a occupant’s high level mobility. We then developed a trajectory propagation algorithm, which can generate an occupant’s most probable trajectories and locations. We show that by using the concurrent activation events of a binary motion sensor network we can extract useful high level details of an occupant’s behavior.

Tracking pedestrians across multiple microcells based on successive Bayesian estimations.

We propose a method for tracking multiple pedestrians using a binary sensor network. In our proposed method, sensor nodes are composed of pairs of binary sensors and placed at specific points, referred to as gates, where pedestrians temporarily change their movement characteristics, such as doors, stairs, and elevators, to detect pedestrian arrival and departure events. Tracking pedestrians in each subregion divided by gates, referred to as microcells, is conducted by matching the pedestrian gate arrival and gate departure events using a Bayesian estimation-based method. To improve accuracy of pedestrian tracking, estimated pedestrian velocity and its reliability in a microcell are used for trajectory estimation in the succeeding microcell. Through simulation experiments, we show that the accuracy of pedestrian tracking using our proposed method is improved by up to 35% compared to the conventional method.

Track Estimation with Binary Derivative Observations

We focus in the work presented here on in the estimation of a target trajectory defined by whether a time constant parameter is a simple stochastic process or a random walk with binary observations. The binary observations come from binary derivative sensors, that is, the target is getting closer or moving away. Such a binary observation has a time property that will be used to ensure the quality of the velocity estimation, through single index model or classification for the constant velocity movement. In the second part of this work we present a new algorithm for target tracking within a binary sensor network when the target trajectory is assumed to be modelled by a random walk. For a given target this algorithm provides an estimation of its velocity and its position. The greatest improvements are made through a position correction and velocity analysis.