ABSTRACT Virtual try‐on (VTON) technology enables the rapid creation of realistic try‐on experiences, which makes it highly valuable for the metaverse and e‐commerce. However, 2D VTON methods struggle to convey depth and immersion, while existing 3D methods require multi‐view garment images and face challenges in generating high‐fidelity garment textures. To address the aforementioned limitations, this paper proposes a panoramic Gaussian VTON framework guided solely by front‐and‐back garment information, named PG‐VTON, which uses an adapted local controllable diffusion model for generating virtual dressing effects in specific regions. Specifically, PG‐VTON adopts a coarse‐to‐fine architecture consisting of two stages. The coarse editing stage employs a local controllable diffusion model with a score distillation sampling (SDS) loss to generate coarse garment geometries with high‐level semantics. Meanwhile, the refinement stage applies the same diffusion model with a photometric loss not only to enhance garment details and reduce artifacts but also to correct unwanted noise and distortions introduced during the coarse stage, thereby effectively enhancing realism. To improve training efficiency, we further introduce a dynamic noise scheduling (DNS) strategy, which ensures stable training and high‐fidelity results. Experimental results demonstrate the superiority of our method, which achieves geometrically consistent and highly realistic 3D virtual try‐on generation.

In electroencephalogram (EEG) cognitive recognition research, the combined use of artificial neural networks (ANNs) and spiking neural networks (SNNs) plays an important role to realize different categories of recognition tasks. However, most of the existing studies focus on the unidirectional interaction between an ANN and a SNN, which may be overly dependent on the performance of ANNs or SNNs. Inspired by the symbiosis phenomenon in nature, in this study, we propose a general DNA-like Hybrid Symbiosis (DNA-HS) framework, which enables mutual learning between the ANN and the SNN generated by this ANN through parametric genetic algorithm and bidirectional interaction mechanism to enhance the optimization ability of the model parameters, resulting in a significant improvement of the performance of the DNA-HS framework in all aspects. By comparing with seven typical EEG cognitive recognition models, the performance of the seven hybrid network frameworks constructed using this method on different EEG-based cognitive recognition tasks are all improved to different degrees, verifying the effectiveness of the proposed method. This unified hybrid network framework similar to the DNA structure is expected to open up a new approach and form a new research paradigm for EEG-based cognitive recognition task.

Learning distinctive point-wise features is critical for low-overlap point cloud registration. Recently, it has achieved huge success in incorporating Transformer into point cloud feature representation, which usually adopts a self-attention module to learn intra-point-cloud features first, then utilizes a cross-attention module to perform feature exchange between input point clouds. The advantage of Transformer models mainly benefits from the use of self-attention to capture the global correlations in feature space. However, these global correlations may involve ambiguity for point cloud registration task, especially in indoor low-overlap scenarios, because the correlations with an extensive range of non-overlapping points may degrade the feature distinctiveness. To address this issue, we present PEAL, a Prior-embedded Explicit Attention Learning model. By incorporating prior knowledge into the learning process, the points are divided into two parts. One includes points lying in the putative overlapping region and the other includes points located in the putative non-overlapping region. Then PEAL explicitly learns one-way attention with the putative overlapping points. This simplistic design attains surprising performance, significantly relieving the aforementioned feature ambiguity. Our method improves the Registration Recall by 6+% on the challenging 3DLoMatch benchmark and achieves state-of-the-art performance on Feature Matching Recall, Inlier Ratio, and Registration Recall on both 3DMatch and 3DLoMatch.

Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this article, we propose a novel PLL approach named HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the desired model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, whereas the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data, for improving the learning model. Comprehensive ablation study demonstrates the effectiveness of our employed heterogeneous loss, and extensive experiments on both artificial and real-world datasets demonstrate that our method achieves superior or comparable performance against state-of-the-art methods.

Energy-efficient and secure wireless communications have recently earned tremendous interests due to economic, environmental, and military concerns. This paper investigates the tradeoff between the secrecy throughput and the energy efficiency in cognitive radio networks (CRNs), where primary and secondary users with different priorities of spectrum access can either interfere or cooperate with each other. To gain an understanding of the intricate effects that system parameters have on underlay network's performance, we exclusively focus on characterizing several key aspects that may have potential impacts on secure underlay CRNs, including the transmission power, the number of interfering users, and the designed interference resistance coefficient. Based on the obtained analytical results, we further propose a cooperative spectrum sharing paradigm to improve both the secrecy throughput and the energy efficiency of primary users. The main idea is that primary users allow secondary users to simultaneously access the licensed spectrum and in return, the secondary transmitter acts as both a relay for primary transmissions and a friendly jammer against eavesdropping, in case the primary transmission fails. Both theoretical and numerical results reveal that: (i) When the interference from secondary transmitters is small, there is an optimal transmission power that maximizes the secrecy throughput for primary users compared to CRNs without the security issue. (ii) When the interference from secondary transmitters is large, the secrecy throughput increases with the transmission power for primary users. (iii) The transmission power that maximizes the energy efficiency is smaller than that maximizes the secrecy throughput for primary users. (iv) The number of interfering users has a slight impact on the secrecy throughput and the energy efficiency of primary users due to the secondary power control. (v) The proposed cooperative paradigm is an efficient approach to boost both the secrecy throughput and the energy efficiency of primary users compared with the traditional non-cooperative spectrum sharing, and provides an alternative method to compensate for the interference caused by secondary users.

This paper presents BlueAer, the first three-dimensional (3D) spatial-temporal fine particulate matter (PM2.5) monitoring system, which is designed to understand urban PM2.5concentration distribution in a fine-grained level. For cost-efficient data collection, vast amount of 3D samples are collected by limited mobile carriers with built-in low-cost sensors. A 3D probabilistic concentration estimated method (3D-PCEM) is proposed to infer PM2.5concentration for undetected area, so that the accuracy of BlueAer is ensured. A prototype system of BlueAer has been implemented and worked throughout a year in a 64km2testing area with a population of 400,000 in Hangzhou, China. Experimental data has verified that BlueAer can achieve good performance in terms of stability as well as a fine grained 3D distribution of PM2.5concentration. The inference accuracy of 3D-PCEM is enhanced by 15.4% and 41.0%, comparing to Gaussian Process (GP) and Artificial Network (ANN) respectively. BlueAer can easily be extended for a larger scale and applied city-wise. Besides, our findings can help ordinary citizens better understand their immediate air quality and serve as a framework towards detailed national-wise real-time pollution management.

The purpose of this paper is to present a novel localization scheme for sensor networks with large scale deployment with sparse anchor nodes. In most wireless sensor networks, sensed data are location-aware as they are only useful with the location information. However, localization is not a simple task according to the financial cost and technical limitations. The deployment environment also affect the accuracy of the localization. Many solutions have been introduced, but the performance is still not satisfactory or the cost of hardware device and computation complex are too high. In our proposed scheme, fewer anchor nodes are required to reduce the cost. With the help of modified Floyd algorithm and MDS-based Optimal Anchor Nodes Selection algorithm, good performance can be achieved in such sparse anchor situation. The analytical discussions and simulation results both prove these improvements.

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.

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.

Finding a maximum-weighted independent set of links is a fundamental problem in wireless networking and has broad applications in various wireless link scheduling problems. Under protocol interference model, it is NP-hard even when all nodes have uniform (and fixed) interference radii and the positions of all nodes are available. On one hand, it admits a polynomial-time approximation scheme (PTAS). In other words, for any fixed ε > 0, it has a polynomial-time (depending on ε) (1 + ε)-approximation algorithm. However, such PTAS is of theoretical interest only and is quite infeasible practically. On the other hand, only with the uniform interference radii is a simple (greedy) constant-approximation algorithm known. For the arbitrary interference radii, fast constant-approximation algorithms are still missing. In this paper, we present a number of fast and simple approximation algorithms under the general protocol interference model. When applied to the plane geometric variants of the protocol interference model, these algorithms produce constant-approximate solutions efficiently.

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.

A DataSpace Support Platform (DSSP) is a self-sustained and self-managed system which needs to support uncertainty among its mediated schemas and its schema mappings. Some approaches for managing such uncertainty by assigning probabilities and reliability degrees to schema mappings have been proposed. Unfortunately, the number of mappings self-generated by a DSSP is usually too large and among those possible mappings, some might be totally correct and others partially correct. Therefore, providing probabilities or reliability degrees to the mappings is necessary but not sufficient to resolve uncertainty among them. This paper proposes a stepper-based approach called pos-mapping to managing reliable mappings using possibility theory. Instead of choosing a threshold for managing the reliable mappings, pos-mapping approach orders and divides the set of reliable mappings into subsets of possibility distributions and assigns to each of these subsets a recursive possibility degree function. The recursiveness of the possibility degree function leads to an incremental management of the possibility distributions. Experimental results show that our system is more efficient than the existing systems and the accuracy of the results increases with the number of reliable schemas in the DSSP.

For wireless link scheduling in multi-channel multi-radio wireless networks aiming at maximizing (concurrent) multi-flow, constant-approximation algorithms have recently been developed in [11]. However, the running time of those algorithms grows quickly with the number of radios per node (at least in the sixth order) and the number of channels (at least in the cubic order). Such poor scalability stems intrinsically from the exploding size of the fine-grained network representation upon which those algorithms are built. In this paper, we introduce a new structure, termed as concise conflict graph, on the node-level links directly. Such structure succinctly captures the essential advantage of multiple radios and multiple channels. By exploring and exploiting the rich structural properties of the concise conflict graphs, we are able to develop fast and scalable link scheduling algorithms for either minimizing the communication latency or maximizing the (concurrent) multi-flow. These algorithms have running time growing linearly in both the number of radios per node and the number of channels, while not sacrificing the approximation bounds.

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.

This paper addresses the joint selection and power assignment of a largest set of given links which can communicate successfully at the same time under the physical interference model in the duplex (i.e. bidirectional) mode. For the special setting in which all nodes have unlimited maximum transmission power, Halldorsson and Mitra [5] developed an approximation algorithm with a huge constant approximation bound. For the general setting in which all nodes have bounded maximum transmission power, the existence of constant approximation algorithm remains open. In this paper, we resolve this open problem by developing an approximation algorithm which not only works for the general setting of bounded maximum transmission power, but also has a much smaller constant approximation bound.

Reconfigurable computing makes use of the reconfiguration capability of modern FPGAs (Field-Programmable Gate Arrays), and can reform the computing functions of systems at runtime. In fact, the tasks in reconfigurable systems are hybrid tasks which consist of software tasks and hardware tasks, and the task model of hybrid tasks is the limited parallel model. Aiming at the interference time analysis of tasks under the limited parallel model, this paper presents a new calculation method called ion algorithm (IA). Although our current research is not involved in response time of tasks because the critical scheduling instant is unknown, this paper provides a fast and comprehensible method for calculating interference time among hybrid tasks, which is more general and can be applied to the research on response time of hybrid tasks. Evaluation results show that this method has acceptable time and space complexity and the limited parallel model is more complex than conventional ones.

The purpose of this paper is to represent a wind powered wireless sensor network system and introduce a novel transmission power control scheme based on remaining energy level and energy harvesting status to extend the lifetime of WSNs. Energy constraint has always been one of the most significant problems of wireless sensor networks along with the development. Many methods have been introduced to solve this problem, basically in two aspects: energy management and energy harvesting. In this paper, a sensor network system has been developed which uses wind power as energy harvesting resource and ultra-capacitor as energy storage. By analyzing the power recharging, leakage and energy consumption rate, a novel Energy Level based Transmission Power Control scheme (EL-TPC) is produced. In EL-TPC scheme, the transmission power is classified into three levels which correspond to specified communication requirements. By adapting the nodes' operation pattern, hierarchical network architecture can be formed, which prioritizes the use of high energy level, fast charging and leaking nodes to save the energy of uncharged nodes. The scheme is implemented in a Building Surface mounted, Wind Power collected Wireless Sensor Network system called BSWPWSN, which aims to monitoring the usage pattern of air conditioners and the outdoor temperature. The results show that EL-TPC scheme can significantly balance the energy consumption in different nodes and extend the entire network lifetime. The overall energy level of the network keeps a dynamic balance during the experiment, which indicates that the network will not lose effect due to energy constraint.

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.

In this paper, we study two tightly coupled topics in online social networks (OSN): relationship classification and information propagation. The links in a social network often reflect social relationships among users. In this work, we first investigate identifying the relationships among social network users based on certain social network property and limited pre-known information. Social networks have been widely used for online marketing. A critical step is the propagation maximization by choosing a small set of seeds for marketing. Based on the social relationships learned in the first step, we show how to exploit these relationships to maximize the marketing efficacy. We evaluate our approach on large scale real-world data from Renren network, showing that the performances of our relationship classification and propagation maximization algorithm are pretty good in practice.

Coverage quality is one critical metric to evaluate the Quality of Service (QoS) provided by wireless sensor net works. In this paper, we address maximum support coverage problem (a.k.a. best case coverage) in wireless sensor networks. Most of the existing work assume that the coverage degree is 1, i.e. every point on the resultant path should fall within the sensing range of at least one sensor node. Here we study the k -coverage problem, in which every point on the resultant path is covered by at least k sensors while optimizing certain objectives. We present tackle this problem under both centralized and distributed setting. The time complexity is bounded by O(k2n log n) where n is the number of deployed sensor nodes. To the best of our knowledge, this is the first work that presents polynomial time algorithms that find optimal k-support paths for a general k.

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.

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time-frequency power spectrum activity. The spectral GC as well as other Granger-like measures are shown to generate misleading results. The proposed new causality measures may have wide potential applications in economics and neuroscience.

In wireless sensor and actor networks (WSANs), a set of static sensor nodes and a set of (mobile) actor nodes form a network that performs distributed sensing and actuation tasks. In [1], Abbasi et al. presented DARA, a Distributed Actor Recovery Algorithm, which restores the connectivity of the interactor network by efficiently relocating some mobile actors when failure of an actor happens. To restore 1 and 2-connectivity of the network, two algorithms are developed in [1]. Their basic idea is to find the smallest set of actors that needs to be repositioned to restore the required level of connectivity, with the objective to minimize the movement overhead of relocation. Here, we show that the algorithms proposed in [1] will not work smoothly in all scenarios as claimed and give counterexamples for some algorithms and theorems proposed in [1]. We then present a general actor relocation problem and propose methods that will work correctly for several subsets of the problems. Specifically, our method does result in an optimum movement strategy with minimum movement overhead for the problems studied in [1].

The benefits of using mobile sink to prolong sensor network lifetime have been well recognized. However, few provably theoretical results remain are developed due to the complexity caused by time-dependent network topology. In this work, we investigate the optimum routing strategy for the static sensor network. We further propose a number of motion stratifies for the mobile sink(s) to gather real time data from static sensor network, with the objective to maximize the network lifetime. Specially, we consider a more realistic model where the moving speed and path for mobile sinks are constrained. Our extensive experiments show that our scheme can significantly prolong entire network lifetime and reduce delivery delay.

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.

A novel architecture used in the Ad Hoc networks is designed for building a well communication network quickly and reliably without any support of base installation. The system including hardware and software, called NASA, is also designed for high data rate transmission. The system architecture of NASA takes collaborative design of hardware and software. Therefore, it is completely open and doesn't base on any operating system. NASA is designed for distributed networks and every NASA node could communicate with each other coequally. The paper discusses how to build the hardware platform of NASA, the cross layer implementation of the MAC and routing protocol. It has been proved that the proposed system is quite efficient and has low data transmission delay. In our experiments, the highest data transfer rate of NASA can be up to 1Mbps with 120 meters.

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 sensor networks(WSNs) have been increasingly available for large-scale applications in which energy efficiency is an important performance measure. These applications include environmental monitoring and structure monitoring which demand multifarious data. Driven by the energy limitation nature of WSNs lots of research works have been done in aspects such as nodes deployment, routing protocol, topology control, data reduction, sleep scheduling, etc. However, heterogeneous, i.e. hybrid sensor nodes are combined together into semantic sensor networks to provide large-scale applications with content rich information. In this paper, we discuss the potential of energy efficiency that semantization could bring to sensor networks. First we have an overview of some related work and then address current approaches of energy conservation in WSNs as well as how semantization can contribute in each aspect of saving energy. Finally a recommendatory architecture of semantic sensor network is proposed. Semantization will be a promising solution to improve energy efficiency together with system performance.

We study efficient data collection in wireless sensor networks. We present efficient distributed algorithms with approximately the minimum delay, or the minimum number of messages to be sent by all nodes, or the minimum total energy costs by all nodes. We analytically prove that all proposed methods are either optimum or within constants factor of the optimum. We then investigate the possibility of designing one universal method such that the delay, the messages sent by nodes, and the total energy costs by all nodes are all optimum or within constants factor of optimum. Given a method A for data collection let ρT, ρM, and ρEbe the approximation ratios of A in terms of time complexity, message complexity, and energy complexity respectively. We show that, for data collection, there are networks of n nodes and maximum degree Δ, such that ρMρE= Ω(Δ) for any algorithm.

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.

Efficient aggregation of data collected by sensors is crucial for a successful application of wireless sensor networks (WSNs). Both minimizing the energy cost and reducing the time duration (or called latency) of data aggregation have been extensively studied for WSNs. Algorithms with theoretical performance guarantees are only known under the protocol interference model, or graph-based interference models generally. In this paper, we study the problem of designing time efficient aggregation algorithm under the physical interference model. To the best of our knowledge, no algorithms with theoretical performance guarantees are known for this problem in the literature. We propose an efficient algorithm that produces a data aggregation tree and a collision-free aggregation schedule. We theoretically prove that the latency of our aggregation schedule is bounded by O(R+Δ) time-slots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. In addition, we derive the lower-bound of latency for any aggregation scheduling algorithm under the physical interference model. We show that the latency achieved by our algorithm asymptotically matches the lower-bound for random wireless networks. Our extensive simulation results corroborate our theoretical analysis.

In this paper, we study efficient data collection and aggregation problem in wireless sensor networks. We first propose efficient distributed algorithms for data collection problem with approximately the minimum delay, or the minimum number of messages to be sent by all wireless nodes, or the minimum total energy consumption by all wireless nodes respectively. For example, given an algorithm A for data collection, let ¿T, ¿M, and ¿Ebe the approximation ratio of A in terms of time complexity, message complexity, and energy complexity respectively. We then show that, for data collection, there are networks of n nodes and maximum degree ¿, such that ¿M¿E= ¿(¿) for any algorithm. In addition, we analytically proved that all our proposed methods are either optimum or within constants factor of the optimum. We further present the message, energy, time complexity and studied the complexity tradeoffs for data aggregation problem.

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.

Fault tolerant is critical to data aggregation in wireless sensor networks. This paper mainly studies how to maintain a fault tolerant data aggregation tree efficiently. Our goal is to design an amendment strategy that minimizes the number of nodes affected by the failed nodes. Since not all networks are reparable, we mainly study the k-hop (k ¿ 1)reparable data aggregation tree amendment problem. When anode is out of service, we say the network is k-hop reparableif it satisfies the following conditions: all of the node's children nodes can find at least one alternate path within at most k-hops neighbors of the corrupted node. Nevertheless, not all nodes within its (k-1) hops can find their alternate paths. We first present a sorting and searching algorithm to facilitate the tree amendment process when we construct the aggregation tree. We then propose a local aggregation tree amendment strategy. The amendment algorithm only affects a very limited number of nodes around the corrupted node, and it is transparent to the other nodes. The proposed amendment algorithm does not change current aggregation tree structure nor the scheduling of other nodes. Theoretical analysis show that the maximum number of nodes affected by the corruption, the message and time complexity of our proposed tree amendment algorithm are all at most O(k). The results show that our proposed local amendment strategy is efficient with respect to both time complexity and message complexity.

Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.

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.