The ubiquity of WiFi infrastructure has motivated significant research into WiFi-based indoor positioning systems as practical alternatives to GNSS. While deep learning approaches show promise, existing models face three critical limitations: (1) inadequate modeling of long-range feature dependencies, (2) difficulty in correcting time-of-flight (ToF) offsets induced by device clock asynchrony, and (3) prohibitive computational costs for environmental adaptation through model retraining. This paper introduces SwinULoc, a novel U-shaped indoor positioning framework that synergizes Swin Transformer blocks with 2D CSI heatmap processing. Our architecture uniquely addresses these challenges through three key innovations: First, the integration of shifted window attention mechanisms enables effective learning of long-range signal correlations. Second, a multi-access-point fusion strategy enhanced with skip connections achieves precise ToF offset correction through cross-device pattern analysis and multi-scale feature integration. Third, a transfer learning paradigm reduces retraining costs by 75% compared to conventional approaches. Extensive evaluations demonstrate SwinULoc's superiority, achieving 70% higher positioning accuracy than state-of-the-art baselines while requiring only 1/4 of the training resources for new environments.

Indoor positioning holds significant application value in fields, such as smart homes and industrial IoT. However, CNN-based positioning methods do not effectively utilize the topological structure of positional relationships between access points and smart devices. This limitation hampers their ability to accurately model and interpret the spatial dependencies inherent in indoor environments. This article proposes a GLoc algorithm, which is the first to introduce graph neural networks (GNNs) into Wi-Fi-based indoor positioning. The method models the positioning environment as a graph structure consisting of location nodes and signal feature edges, leveraging GNNs to capture spatial dependencies and signal distribution characteristics between different locations. On a 144-m2 dataset, GLoc demonstrates excellent positioning accuracy, with a median accuracy of 23 cm, and 90% of the estimates within a 97-cm range. Compared to existing techniques, GLoc also exhibits stronger robustness and higher positioning accuracy in dynamic environments. This method opens new possibilities for advancing indoor positioning technology and may spur progress in related fields.

Hand gesture recognition (HGR) is essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., demanding multi-task ability to support newly arrived HGR tasks. In this paper, we propose the first IMU-vision based system hosted on wrist-worn devices to support multi-task HGR, denoted as MultiHGR. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gesture through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism. Since only the second task-related stage should be executed for each new task, MultiHGR could accommodate multiple tasks with significant reduced training cost and storage requirement. The evaluation results on three HGR tasks demonstrates that MultiHGR reduces 64.92% training time, and 24.04% storage as compared with traditional multimodal single-task models, and MultiHGR outperforms unimodal single-task models with 14.37%, 19.28%, and 31% improvements in these three tasks, respectively. As compared with state-of-the-art multimodal single-task model, MultiHGR achieves average 6.35% accuracy improvement, along with 65.74% training time reduction.

Although learning-based light field disparity estimation has achieved great progress in the most recent years, the performance of unsupervised light field learning is still hindered by occlusions and noises. By analyzing the overall strategy underlying the unsupervised methodology and the light field geometry implied in epipolar plane images (EPIs), we look beyond the photometric consistency assumption, and design an occlusion-aware unsupervised framework to deal with the situations of photometric consistency conflict. Specifically, we present a geometry-based light field occlusion modeling, which predicts a group of visibility masks and occlusion maps, respectively, by forward warping and backward EPI-line tracing. In order to learn better the noise- and occlusion-invariant representations of the light field, we propose two occlusion-aware unsupervised losses: occlusion-aware SSIM and statistics-based EPI loss. Experiment results demonstrate that our method can improve the estimation accuracy of light field depth over the occluded and noisy regions, and preserve the occlusion boundaries better.

Online dating is an increasingly thriving business which boosts billion-dollar revenues and attracts users in the tens of millions. Despite its popularity, internet dating is not exempt from the concerns about privacy and trust posed by the revelation of potentially sensitive data as well as the exposure to self-reported (and hence potentially distorted) information. The increasing popularity of online dating networks leads to an increase in security concerns and challenges, as well as harmful actions and attacks, such as creating fake accounts, phishing on these networks. To maintain the safety of legitimate online dating users, it is critical to recognize and isolate criminal people as soon as possible. However, researchers concerning malicious user detection in dating social networks are merely a few. To address some key challenges in this space, we propose a trust-aware detection framework to detect malicious users based on different kinds of data from a real dating site. In particular, we develop a user trust model to distinguish between malicious and legitimate users. Furthermore, we propose a novel data-balancing method to improve the recall rate of malicious user detection. Extensive experiments have been conducted over real-world datasets. The results show that the proposed approach yields a precision of up to 59.16% and a recall rate of up to 73%, which is significantly higher than other baseline algorithms.

Determining whether nodes can be uniquely localized, called localizability detection, is a concomitant problem in network localization. Localizability detection under the traditional Non-Linear Localization (NLL) schema has been well explored, whereas localizability under the emerging Barycentric coordinate-based Linear Localization (BLL) schema has not been well investigated. Non-awareness of the node localizability in BLL may cause theoretically localizable nodes to converge to wrong locations because their locations are impacted by the wrong locations of the unlocalizable nodes through the iterative location propagation. In this article, the deficiency of existing localizability theories and algorithms in BLL is firstly investigated and then a necessary condition and a sufficient condition for BLL node localizability detection are proposed. Based on these two conditions, an efficient Iterative Maximum Flow (IMF) algorithm is designed to identify BLL localizable nodes, and only localizable nodes are selected to enable a Localizability Aware Barycentric Linear Localization (LABLL) algorithm, which can guarantee the locations of the localizable nodes converging correctly. The proposed IMF and LABLL algorithms are validated by both theoretical analysis and experimental evaluations.

The rapid popularization of mobile devices makes it more convenient and cost-efficient to collect synchronized WiFi received signal strength (RSS) and inertial measurement unit sequences by crowdsensing. The transition model has proven to be a promising unsupervised localization approach that captures the transition relationship between the change of RSS signal space and the change of physical space, alleviating the need of extra knowledge for creating radio map. However, it faces two essential challenges in real-world deployments. First, model coverage affects its locating performance, because a specific transition model only represents its local space. Second, the instability of RSS leads to a conflicting relationship between changes of two spaces because of the complex environment and the heterogeneous type of devices. To address these challenges, we propose Lightgbm-CTMM, a novel unsupervised localization framework. First, a clustering method is adopted to capture the expected relationship to ensure robust coverage. Second, direction filter is employed to guarantee that the change in signal space corresponds to the change in physical space. The feasibility and effectiveness of Lightgbm-CTMM are evaluated by extensive experiments, and the locating performance of Lightgbm-CTMM is better than that of conventional approaches. Moreover, Lightgbm-CTMM reduces the work on quality assessment of trajectories.

With the popularity of mobile devices and the emergence of various audio-editing tools, it becomes easier to produce and forge audio files. Many criminals will forge false audio information as evidence. Therefore, audio forensics technology becomes particularly important. Audio recording device identification technology, which can verify the authenticity and uniqueness of the evidence obtained, is one of the promising branches of audio forensics technology. In this article, a novel neural-network-based framework using the device noise feature is proposed to identify the source of recording according to the device traces generated by the device during the recording. We also propose a new neural network model RARS (Recognition of Audio Recording Source based on residual neural network). The proposed framework achieves state-of-the-art performance on MOBIPHONE, the only publicly available dataset in this field. Moreover, we build a new dataset based on the latest mobile phones and tablet devices. Our method achieves good performance on both the two datasets, which proves that our model has a certain degree of reusability and robustness.

Nowadays, public bike-sharing systems are broadly adopted and deployed in many major cities, however, as public facilities, bicycles will be prone to damage and need to be replaced frequently, which results in high system maintenance costs. One of the root causes of bicycle damages is the serious load-unbalance of bicycle usage. In this paper, we propose a hybrid bicycle allocation strategy for bicycle lifetime optimization, which can effectively reduce the degree of imbalance of system load. First, we analyze and verify the load-unbalance status of bicycle usage in the existing bike-sharing system. Then, a hybrid bicycle allocation strategy is proposed, which is evaluated on Washington D.C. bike-sharing system. Furthermore, according to a bicycle lifetime model based on Weibull distribution, the proposed bicycle allocation strategy could significantly cut down the percentage of the bicycles need to be replaced in a certain period of time.

Performance of the Kalman filter (KF) is degraded when dealing with nonlinear dynamic systems. For a kind of nonlinear biomechatronics system, a fifth-degree ensemble iterated cubature square-root information filter (EsFICIF), which can effectively improve estimation performance, is proposed by combing many estimation schemes. Moreover, the associated multisensor fusion is deeply studied based on this proposed nonlinear filter in this paper. That is, four classic nonlinear fusion methods, which include augmented measurements fusion, weighted measurements fusion, sequential filtering fusion, and distributed filtering fusion, are compared on estimation performance. The motivation of this paper is to extend the work on estimation performance comparison of nonlinear fusion methods based on the conventional extended KF and to validate some basic conclusions existed in the traditional linear data fusion theory based on the proposed EsFICIF. The estimation accuracies of the four nonlinear fusion methods are compared and the exchanging property of measurements update order is also discussed. It is observed that, when the measurement properties are identical, the estimation accuracies of augmented measurements fusion, weighted measurements fusion, and distributed feedback fusion are equivalent, while the sequential filtering fusion does not hold. Furthermore, the exchanging property of the measurements update order of the sequential filtering fusion can no longer be guaranteed. These results further show some basic conclusions existed in linear fusion theory are no longer valid for nonlinear systems and the conclusions based on the EKF are still available for more complex nonlinear filters. Finally, numerical examples are provided to validate the results given in this paper.

As an important supporting technology, floor localization in multi-floor buildings plays significant roles in many indoor Location Based Service (LBS) applications such as the fire emergency response and the floor-based precise advertising. While the majority of Received-Signal-Strength (RSS)-fingerprint-based wireless indoor localization approaches suffer from the labor-intensive and time-consuming site-survey and the low localization accuracy, barometer-based floor localization is another promising direction due to the increasing availability of the barometer-sensor-equipped smartphones. This paper is the first indoor localization work that exploits the combination of Wi-Fi RSS and barometric pressure for accurate floor localization. Compared with an art-of-the-state algorithm, B-Loc, the highlight of the proposed Bar Fi approach is that it does not need all client smartphones but only low percentage of them equipped with barometer sensors. Using crowd sourcing, Bar Fi eliminates the need of war-driving of site-survey and prior knowledge about both the Wi-Fi infrastructure and the floor plans of buildings. The key novelty of Bar Fi is a two-phase clustering method proposed to train the RSS fingerprint floor map with the aid of barometer, which consists of a barometer-based hierarchical clustering phase and a Wi-Fi-based K-Means clustering phase. The real-world evaluation shows Bar Fi achieves satisfying performance that its accuracy reaches 96.3% when the proportion of smartphones equipped with barometer sensors is 12% out of the total.

To support the sustainable operation of wireless sensor networks using limited energy, duty cycling is a promising solution. However, it is a challenge to guarantee each node communicating with its neighbors under duty cycle when the network is asynchronous. The challenge becomes bigger when nodes’ duty cycles are required to be adjusted separately according to their demands to save energy and achieve high channel utilization. Existing low power listening- and contention-based protocols are not energy-efficient and cannot ensure high channel utility. Additionally, synchronization-based media access control (MAC) protocols suffer from extra energy consumption and low synchronization precision. This paper proposes a localized and on-demand (LOD) duty cycling scheme based on a specifically designed semi-quorum system. LOD can adjust duty cycle of each node adaptively according to its demand so as to avoid channel contention, consequently achieving high channel utilization. This allows the fairness for channel access within asynchronous sensor networks. Extensive experiments are conducted on a real test-bed of 100 TelosB nodes to evaluate the performance of LOD. As compared with B-MAC, LOD substantially reduces contention for channel access and the energy consumption, thus improving the network throughput significantly.

Existing work on flooding in wireless sensor networks (WSNs) mainly focuses on single-packet problem, while the work on sequential multipacket problem is surprisingly little. This paper proposes OppCode, a new opportunistic network-coding-based flooding architecture for multipacket dissemination in WSNs, where both unreliable and correlated links commonly exist. Instead of flooding a single packet each time, each node encodes multiple native packets chosen from a specific fixed-size page to an encoded packet and then rebroadcasts it further. The key idea consists of two parts. One is opportunistically coding decision, in which each node grasps every possible coding opportunity greedily to maximize its aggregate coding gain of all neighbors based on the probabilistic estimation of packets each neighbor already has. The other is paged collective acknowledgements (ACKs), in which one rebroadcast acts as not only an implicit ACK of successful disseminations of all packets in the entire page for the sender, but also probabilistic ACK to update page-scale per-packet coverage estimations for its neighbors in a batch. Experiments based on extensive simulations and 21-node testbed show that OppCode significantly increases performance of multipacket flooding in terms of reliability, transmission overhead, delay, and load balance.

In wireless sensor networks (WSNs), existing work on flooding mainly focuses on single-packet problem, while work in sequential multi-packet problem is surprisingly little. This paper proposes OppCode, a new opportunistic network-coding based flooding architecture for multi-packet dissemination in WSNs, where both unreliable and correlated links commonly exist. Instead of flooding a single packet each time, each node encodes multiple native packets chosen from a specific fixed-size page to an encoded packet, and then rebroadcasts it further. The key idea consists of two parts: one is opportunistically coding decision, in which each node grasps every possible coding opportunity greedily to conduct an encode-and-forward operation in order to maximize its total (or aggregate) coding gain of all neighbors based on the probabilistic estimations of packets each neighbor already has (i.e., coverage), the other is paged collective acknowledgements (ACKs), in which one rebroadcast that arrives at a receiver acts as not only an implicit ACK of successful disseminations of all packets in the entire page for the sender, but also probabilistic ACK to update page-scale per-packet coverage estimations for its neighbors in a batch. We evaluate our design using extensive simulations and on a 20-node WSNs testbed, and show that OppCode largely increases performance of multi-packet flooding compared with state-of-the-art solutions, especially when links are highly unreliable and correlated with each other. The gains vary from a few percent to several folds depending on the network density, link conditions, coverage threshold and page size.

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.

In this paper, we study the dynamic node activation schedule for the utility based coverage problem in solar-powered wireless sensor networks. We assume that the utility achieved by a WSN for coverage service is a sub modular function over the set of sensors that will provide the service. We first present an integer programming formulation with sub modular objective functions. We then present an efficient simple greedy hill-climbing algorithm such that the achieved average utility of the computed schedule is at least $1/2$ times that achieved by the optimal schedule. To the best of our knowledge, this is the first polynomial time algorithm that can ensure a good constant approximation of the achieved utility for multi-target coverage problem. We conduct extensive evaluations to study the performances of our proposed aggregation scheduling algorithm on real testbed. Our evaluation results corroborate our theoretical analysis.

Many TDMA- and CSMA-based protocols try to obtain fair channel access and to increase channel utilization. It is still challenging and crucial in Wire less Sensor Networks (WSNs), especially when the time synchronization cannot be well guaranteed and consumes much extra energy. This paper presents a localized and on demand scheme ADC to adaptively adjust duty cycle based on quorum systems. ADC takes advantages of TDMA and CSMA and guarantees that (1) each node can fairly access channel based on its demand; (2) channel utilization can be increased by reducing competition for channel access among neighboring nodes; (3) every node has at least one rendezvous active time slot with each of its neighboring nodes even under asynchronization. The latency bound of data aggregation is analyzed under ADC to show that ADC can bound the latency under both synchronization and asynchronization. We conduct extensive experiments in TinyOS on a real test-bed with TelosB nodes to evaluate the performance of ADC. Comparing with B-MAC, ADC substantially reduces the contention for channel access and energy consumption, and improves network throughput.

We present TelosCAM, a networking system that integrates wireless module nodes (such as TelosB nodes) with legacy surveillance cameras to provide storage-efficient and privacy-aware services of accurate, real time tracking and identifying of the burglar who stole the property. In our system, a property owner will have a wireless module node (called secondary module) attached to the property that s/he wants to protect. The secondary wireless module node will not store any personal information about the owner, nor any specific information about the property to be protected. Each user of the system will also have a unique wireless module node (called primary module) that contains some security information about the user, thus should be privately held by the user and be kept to the user always. Once a tracking process is triggered in privacy preserving manner, the secondary module will start sending out the alarm signal periodically. The alarm signal will be captured by some surveillance wireless module, integrated with existing surveillance cameras. Using the trajectory information provided by the secondary wireless module node, and the videos captured by the surveillance cameras, our system will then automatically pinpoint a burglar (e.g., a person or a car) that is more likely to carry the stolen property. Our extensive evaluation of the system shows that we can find the burglars with surprisingly high accuracy under various experiment settings, with significantly reduced storage-requirement of the legacy video surveillance system. It also can help the police to catch the burglars more efficiently by providing critical images or videos containing the burglars.

In wireless sensor networks, most of existing data aggregation scheduling methods try to aggregate the data from all the nodes at all time-instances. It is neither energy efficient nor practical because of the link unreliability and spatio-temporal data correlation. This paper proposes a lossy data aggregation scheme to allow estimated aggregation at the root by selectively letting some nodes sample at some time slots. Firstly, all nodes sample data synchronously and the error between the real value and estimated one is guaranteed to being bounded respectively with and without the link unreliability. And the error bound is analyzed when the confidence is given a priori. Then we also design an algorithm to assign the confidence level among the parents such that each parent can calculate the minimum number of needed leaves based on the assigned confidence level. Secondly, all nodes sample data asynchronously, under which we analyze the probability that the error could be bounded under a given confidence level. Then a new algorithm is designed to implement data aggregation under a synchronization. We also present the experiment based on a real test-bed to evaluate our schemes.

Although there are a lot of synchronization protocols in WSN, almost all of them face the same problem, that is, synchronization overhead has not been well controlled. The root of this problem is that they have adopted the same basic communication model- pairwise communication model. Increasing communication overhead in synchronization shortens the lifetime of the system and limits the wide application of the WSN. This paper proposes the Clapping and Broadcasting Synchronization (CBS) for sensor network, which is especially designed for large-scale sensor networks with low communication overhea and high synchronization accuracy. On the one hand, the proposed synchronization reduces communication overhead dramatically by utilizing "broadcaster-receiver" communication model. The basic idea of "broadcaster-receiver" is using broadcasting rather than pairwise communication to accomplish synchronization. On the other hand, the initial offset of lock soft clock can be successfully eliminated by the operation of clapping nodes, which can indeed bring benefits in terms of the synchronization accuracy. The advantage in communication overhead is obviously. We prove that the communication overhead after the completion of initialization is close to (W i=1 1 min(Ti ) ), which means the system just need send (W i=1 1 min(Ti ) ) messages to perform synchronization once. And that is close to the minimum message number that let all nodes in the network receive a message. Its communication overhead is at least 50 percent of FTSP or less. The gap between them will increase dramatically with the increase of the network. Additionally, feasibility and reliability of the CBS have been verified in the experiment. The CBS was implemented on the TelosB platform to reach the real parameters of sensor nodes. And the simulation in large-scale was carried out under MATLAB. In Single-hop case, around 80% synchronization errors are bounded in 20?s, and the average per-hop synchronization error in large-scale was in the microsecond range. That means comparing with the existing famous protocol like TPSN or FTSP, the CBS significantly reduce the communication overhead without sacrificing synchronization accuracy. The advantage in communication overhead is obviously. We prove that the communication overhead after the completion of initialization is close to (Σi=1W1/min(Ti) which means the system just need send (Σi=1W1/min(Ti) messages to perform synchronization once. And that is close to the minimum message number that let all nodes in the network receive a message. Its communication overhead is at least 50 percent of FTSP or less. The gap between them will increase dramatically with the increase of the network. Additionally, feasibility and reliability of the CBS have been verified in the experiment. The CBS was implemented on the TelosB platform to reach the real parameters of sensor nodes. And the simulation in large-scale was carried out under MAT LAB. In Single-hop case, around 80% synchronization errors are bounded in 20μs, and the average per-hop synchronization error in large-scale was in the microsecond range. That means comparing with the existing famous protocol like TPSN or FTSP, the CBS significantly reduce the communication overhead without sacrificing synchronization accuracy.

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 non-interactive 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. The experimental results demonstrate that it is accurate, efficient, and suitable for WCSNs that consist of low-end camera sensors.

Wireless mesh networks (WMN) provide network access for mobile users. Therefore, most traffic flows in WMNs are to and from the wired networks. Some mesh nodes, called Gateways connect directly to the wired networks, through which mesh clients can access the resources that reside on the wired networks. However, there are usually only a few of Gateways in a WMN. The packet processing ability of every wireless node is limited. As a result, traffic loads of mesh nodes affect greatly the network performance. In this paper, we put forward a queuing based traffic model for WMNs. In the model, both Gateways and mesh nodes at the largest hop count from the gateways are regarded as service stations with infinite capacity, whereas the other mesh nodes are modeled as service stations with finite capacity. The model also takes into account impacts of interference. We then analyze the network throughput, average packet loss and packet delay on each hop nodes using the proposed traffic model. Results show that the proposed model is accurate in modeling the characteristics of traffic loads in WMNs.

In wireless sensor networks, in-network data aggregation is an efficient way to reduce energy consumption in network. However, most of the existing data aggregation scheduling methods try to aggregate the data from all the nodes at all time-instances. It is neither energy efficient nor practical because of the link unreliability and spatial and temporal data correlations. In this paper, we propose anew data aggregation paradigm which allows estimated aggregation at the sink node. In our scheme, we will selectively let some nodes sample data and aggregate them to the sink node. Two different cases will be studied. Firstly, we assume that the links are reliable and the error between the value obtained from the data of all nodes and that from the data of sampled nodes is bounded. We give detailed analysis on the error bound when the confidence is given a priori. Secondly, we assume that the links are unreliable with a given probability and obtain that the error is still bounded under a given confidence when the probability of link unreliability is not too high or the success probability of retransmission is high enough. We also study how to assign the confidence level among the root nodes such that each root node can calculate the minimum number of sampling leaf nodes based on corresponding confidence level. Through analyzing, we show that it can surely save energy to adopt our method when the link is reliable. When the link is not reliable, the energy still can be saved if the success probability of retransmission is high enough.

In this work, we study indoor passive tracking problem in wireless sensor networks (WSNs), in which we assume the target being tracked is "clean", i.e., there is no any equipment carried by the target and the tracking procedure is considered to be passive. We design, implement and test our tracking methods in a WSN testbed consisting of 40 wireless sensor nodes and one base station (laptop).

Using solar panels to power wireless sensor nodes is feasible in most of WSNs applications. We present an efficient solar-charging system and a remote energy-profile monitoring system which can monitor the dynamic charging procedure of wireless sensor nodes in different environments. We design and implement dynamic routing policies according to the current available energy of nodes for WSNs powered by solar panels.

This paper describes a basic coverage issue, and proposes a scheme named Grid Scan which is applied to calculate the basic coverage rate with arbitrary sensing radius of each node. Based on Grid Scan, a re-deployment approach is suggested to meet any k-covered rate in some region according to application requirements. The objective of our re-deployment scheme is to get equivalent coverage rate using less number of sensor nodes or to achieve higher coverage rate with the same number of sensor nodes. The results of simulation experiments support that Grid Scan based re-deployment is more effective to cover monitored area than random spread.