Lijuan Sun

Coverage-guaranteed sensor node deployment strategies for wireless sensor networks.

Deployment quality and cost are two conflicting aspects in wireless sensor networks. Random deployment, where the monitored field is covered by randomly and uniformly deployed sensor nodes, is an appropriate approach for large-scale network applications. However, their successful applications depend considerably on the deployment quality that uses the minimum number of sensors to achieve a desired coverage. Currently, the number of sensors required to meet the desired coverage is based on asymptotic analysis, which cannot meet deployment quality due to coverage overestimation in real applications. In this paper, we first investigate the coverage overestimation and address the challenge of designing coverage-guaranteed deployment strategies. To overcome this problem, we propose two deployment strategies, namely, the Expected-area Coverage Deployment (ECD) and BOundary Assistant Deployment (BOAD). The deployment quality of the two strategies is analyzed mathematically. Under the analysis, a lower bound on the number of deployed sensor nodes is given to satisfy the desired deployment quality. We justify the correctness of our analysis through rigorous proof, and validate the effectiveness of the two strategies through extensive simulation experiments. The simulation results show that both strategies alleviate the coverage overestimation significantly. In addition, we also evaluate two proposed strategies in the context of target detection application. The comparison results demonstrate that if the target appears at the boundary of monitored region in a given random deployment, the average intrusion distance of BOAD is considerably shorter than that of ECD with the same desired deployment quality. In contrast, ECD has better performance in terms of the average intrusion distance when the invasion of intruder is from the inside of monitored region.

R-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human With WiFi

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human–computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit–receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

Utility-aware data transmission scheme for delay tolerant networks

Social-based routing approaches in delay-tolerant networks have attracted widespread attention in recent years, which attempt to import social behaviors and relations in real scene for node mobility. However, most social-based schemes resort to users’ contact history and social relations that are dynamic, causing it so hard to establish stable relations between nodes. In this paper, we propose a utility-aware data transmission scheme which considers both internal property and external contact of nodes. Inspired by the concept of transfer station in real life, we set a central group and choose nodes for message forwarding, which have higher utility, i.e., enough energy, adequate cache, and more nodes encountered during the motivation. Two extensions are proposed also to further reduce the overhead. Simulation results demonstrate the increase in delivery ratio and decrease in overhead ratio, especially in large scale scenarios.

Surface Coverage Algorithm in Directional Sensor Networks for Three-Dimensional Complex Terrains

Coverage is an important issue in the area of wireless sensor networks, which reflects the monitoring quality of the sensor networks in scenes. Most sensor coverage research focuses on the ideal two-dimensional (2-D) plane and full three-dimensional (3-D) space. However, in many real-world applications, the target field is a 3-D complex surface, which makes conventional methods unsuitable. In this paper, we study the coverage problem in directional sensor networks for complex 3-D terrains, and design a new surface coverage algorithm. Based on a 3-D directional sensing model of nodes, this algorithm employs grid division, simulated annealing, and local optimum ideas to improve the area coverage ratio by optimizing the position coordinates and the deviation angles of the nodes, which results in coverage enhancement for complex 3-D terrains. We also conduct extensive simulations to evaluate the performance of our algorithms.

Novel congestion control approach in wireless multimedia sensor networks

Data generated in wireless multimedia sensor networks (WMSNs) may have different importance and it has been claimed that the network exert more efforts in servicing applications carrying more important information. Nevertheless, importance of packets cannot generally be accurately represented by a static priority value. This article presents a dynamic priority based congestion control (DPCC) approach that makes two major innovations in WMSNs. First, DPCC employs dynamic priority to represent packet importance. Second, it prioritizes the local traffic of motes near the base station when WMSN is highly congested. Simulation results confirm the superior performance of the proposed approach with respect to energy efficiency, loss probability and latency as well.

Noise-tolerant localization from incomplete range measurements for wireless sensor networks

Accurate and sufficient range measurements are essential for range-based localization in wireless sensor networks. However, noise and data missing are inevitable in distance ranging, which may degrade localization accuracy drastically. Existing localization approaches often degrade in terms of accuracy in the co-existence of incomplete and corrupted range measurements. To address this challenge, a noise-tolerant localization algorithm called NLIRM is presented. By utilizing the natural low rank property of Euclidean distance matrix, the reconstruction of partially sampled and noisy distance matrix is formulated as a norm-regularized matrix completion problem, where Gaussian noises and outliers are smoothed by Frobenius-norm and L1 norm regularization, respectively. As far as we are aware of, this is the first scheme that can recover the missing range measurements and explicitly sift Gaussian noise and outlier simultaneously. Simulation results demonstrate that, compared with traditional algorithms, NLIRM achieves better localization performance under the same experiment setting. In addition, our algorithm provides an accurate prediction of outlier positions, which is the prerequisite for malfunction diagnosis in WSN.

A Camera Nodes Correlation Model Based on 3D Sensing in Wireless Multimedia Sensor Networks

In wireless multimedia sensor networks, multiple camera sensor nodes generally are used for gaining enhanced observations of a certain area of interest. This brings on the visual information retrieved from adjacent camera nodes usually exhibits high levels of correlation. In this paper, first, based on the analysis of 3D directional sensing model of camera sensor nodes, a correlation model is proposed by measuring the intersection area of multiple camera nodes’ field of views. In this model, there is a asymmetrical relationship of the correlation between two camera nodes. Then, to farthest eliminate the data redundancy and use the node collaboration characteristic of wireless (multimedia) sensor networks, two kinds of cluster structure, camera sensor nodes cluster, and common sensor nodes cluster are established to cooperate on image processing and transmission tasks. A set of experiments are performed to investigate the proposed correlation coefficient. Further simulations based on a sample of monitoring a crossing by three correlative camera nodes show that the proposed network topology and image fusion and transmission scheme released the pressure of camera node greatly and reduce the network energy consumption of communication of the whole network efficiently.

Fuzzy Multilevel Image Thresholding Based on Modified Discrete Grey Wolf Optimizer and Local Information Aggregation

Fuzzy entropy and image thresholding are the most direct and effective methods for image segmentation. This paper, taking fuzzy Kapurs entropy as the optimal objective function, with modified discrete Grey wolf optimizer (GWO) as the tool, uses pseudotrapezoid-shaped to conduct fuzzy membership initialization so as to achieve image segmentation finally by means of local information aggregation. Experiment results show that the proposed fuzzy-based GWO and aggregation algorithm and fuzzy-based modified discrete GWO and aggregation (FMDGWOA) algorithm can search out the optimal thresholds effectively and accurately. In this paper, electro-magnetism optimization based on Kapurs entropy, standard GWO and fuzzy entropy-based differential evolution algorithm are experimentally compared with the proposed method, respectively. It shows that FMDGWOA enjoys obvious advantages in segmentation quality, objective function, and stability.

A type of healthcare system based on intelligent wireless sensor networks

Abstract A healthcare system based on wireless sensor networks (WSNs) is proposed to improve the quality of medical care in hospital or even at home. Structures of the wearable healthcare node, wireless multimedia node as well as the gateway in this system are described in detail. Moreover, a type of localization method for patients and an energy-efficient transmission strategy are put forward as well. Experimental results show that this system performs well on data transmission and information processing.

Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapurs entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.