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Articles 301 - 330 of 511
Full-Text Articles in Computer Engineering
Ambient Rendezvous: Energy Efficient Neighbor Discovery Via Acoustic Sensing, Keyu Wang, Zheng Yang, Zimu Zhou, Yunhao Liu, Lionel M. Ni
Ambient Rendezvous: Energy Efficient Neighbor Discovery Via Acoustic Sensing, Keyu Wang, Zheng Yang, Zimu Zhou, Yunhao Liu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
The continual proliferation of mobile devices has stimulated the development of opportunistic encounter-based networking and has spurred a myriad of proximity-based mobile applications. A primary cornerstone of such applications is to discover neighboring devices effectively and efficiently. Despite extensive protocol optimization, current neighbor discovery modalities mainly rely on radio interfaces, whose energy and wake up delay required to initiate, configure and operate these protocols hamper practical applicability. Unlike conventional schemes that actively emit radio tones, we exploit ubiquitous audio events to discover neighbors passively. The rationale is that spatially adjacent neighbors tend to share similar ambient acoustic environments. We propose …
Matchmaking Game Players On Public Transport, Nairan Zhang, Youngki Lee, Rajesh Krishna Balan
Matchmaking Game Players On Public Transport, Nairan Zhang, Youngki Lee, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
This paper extends our recent work, called GameOn, which presented a system for allowing public transport commuters to engage in multiplayer games with fellow commuters traveling on the same bus or train. An important challenge for GameOn is to group players with reliable connections into the same game. In this case, the meaning of reliability has two dimensions. First, the network connectivity (TCP, UDP etc.) should be robust. Second, the players should be collocated with each other for a sufficiently long duration so that a game session will not be terminated by players leaving the public transport modality such as …
Mining Patterns Of Unsatisfiable Constraints To Detect Infeasible Paths, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar
Mining Patterns Of Unsatisfiable Constraints To Detect Infeasible Paths, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar
Research Collection School Of Computing and Information Systems
Detection of infeasible paths is required in many areas including test coverage analysis, test case generation, security vulnerability analysis, etc. Existing approaches typically use static analysis coupled with symbolic evaluation, heuristics, or path-pattern analysis. This paper is related to these approaches but with a different objective. It is to analyze code of real systems to build patterns of unsatisfiable constraints in infeasible paths. The resulting patterns can be used to detect infeasible paths without the use of constraint solver and evaluation of function calls involved, thus improving scalability. The patterns can be built gradually. Evaluation of the proposed approach shows …
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Research Collection School Of Computing and Information Systems
The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and incremental adaptation can integrate domain knowledge and RL. Specifically, symbol-based domain knowledge is translated into numeric patterns before inserting into the self-organizing neural networks. To ensure effective use of domain knowledge, we present an analysis of how the inserted knowledge is used by …
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results …
Exploring Discriminative Features For Anomaly Detection In Public Spaces, Shriguru Nayak, Archan Misra, Kasthuri Jeyarajah, Philips Kokoh Prasetyo, Ee-Peng Lim
Exploring Discriminative Features For Anomaly Detection In Public Spaces, Shriguru Nayak, Archan Misra, Kasthuri Jeyarajah, Philips Kokoh Prasetyo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Context data, collected either from mobile devices or from user-generated social media content, can help identify abnormal behavioural patterns in public spaces (e.g., shopping malls, college campuses or downtown city areas). Spatiotemporal analysis of such data streams provides a compelling new approach towards automatically creating real-time urban situational awareness, especially about events that are unanticipated or that evolve very rapidly. In this work, we use real-life datasets collected via SMU's LiveLabs testbed or via SMU's Palanteer software, to explore various discriminative features (both spatial and temporal - e.g., occupancy volumes, rate of change in topic{specific tweets or probabilistic distribution of …
Click-Boosting Multi-Modality Graph-Based Reranking For Image Search, Xiaopeng Yang, Yongdong Zhang, Ting Yao, Chong-Wah Ngo, Tao Mei
Click-Boosting Multi-Modality Graph-Based Reranking For Image Search, Xiaopeng Yang, Yongdong Zhang, Ting Yao, Chong-Wah Ngo, Tao Mei
Research Collection School Of Computing and Information Systems
Image reranking is an effective way for improving the retrieval performance of keyword-based image search engines. A fundamental issue underlying the success of existing image reranking approaches is the ability in identifying potentially useful recurrent patterns from the initial search results. Ideally, these patterns can be leveraged to upgrade the ranks of visually similar images, which are also likely to be relevant. The challenge, nevertheless, originates from the fact that keyword-based queries are used to be ambiguous, resulting in difficulty in predicting the search intention. Mining useful patterns without understanding query is risky, and may lead to incorrect judgment in …
Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong
Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong
Research Collection School Of Computing and Information Systems
The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. …
Online Learning On Incremental Distance Metric For Person Re-Identification, Yuke Sun, Hong Liu, Qianru Sun
Online Learning On Incremental Distance Metric For Person Re-Identification, Yuke Sun, Hong Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Person re-identification is to match persons appearing across non-overlapping cameras. The matching is challenging due to visual ambiguities and disparities of human bodies. Most previous distance metrics are learned by off-line and supervised approaches. However, they are not practical in real-world applications in which online data comes in without any label. In this paper, a novel online learning approach on incremental distance metric, OL-IDM, is proposed. The approach firstly modifies Self-Organizing Incremental Neural Network (SOINN) using Mahalanobis distance metric to cluster incoming data into neural nodes. Such metric maximizes the likelihood of a true image pair matches with a smaller …
Human Action Classification Based On Sequential Bag-Of-Words Model, Hong Liu, Qiaoduo Zhang, Qianru Sun
Human Action Classification Based On Sequential Bag-Of-Words Model, Hong Liu, Qiaoduo Zhang, Qianru Sun
Research Collection School Of Computing and Information Systems
Recently, approaches utilizing spatial-temporal features have achieved great success in human action classification. However, they typically rely on bag-of-words (BoWs) model, and ignore the spatial and temporal structure information of visual words, bringing ambiguities among similar actions. In this paper, we present a novel approach called sequential BoWs for efficient human action classification. It captures temporal sequential structure by segmenting the entire action into sub-actions. Each sub-action has a tiny movement within a narrow range of action. Then the sequential BoWs are created, in which each sub-action is assigned with a certain weight and salience to highlight the distinguishing sections. …
Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong
Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong
Research Collection School Of Computing and Information Systems
Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of …
Organizing Video Search Results To Adapted Semantic Hierarchies For Topic-Based Browsing, Jiajun Wang, Yu-Gang Jiang, Qiang Wang, Kuiyuan Yang, Chong-Wah Ngo
Organizing Video Search Results To Adapted Semantic Hierarchies For Topic-Based Browsing, Jiajun Wang, Yu-Gang Jiang, Qiang Wang, Kuiyuan Yang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Organizing video search results into semantically structured hierarchies can greatly improve the efficiency of browsing complex query topics. Traditional hierarchical clustering techniques are inadequate since they lack the ability to generate semantically interpretable structures. In this paper, we introduce an approach to organize video search results to an adapted semantic hierarchy. As many hot search topics such as celebrities and famous cities have Wikipedia pages where hierarchical topic structures are available, we start from the Wikipedia hierarchies and adjust the structures according to the characteristics of the returned videos from a search engine. Ordinary clustering based on textual information of …
Vireo @ Trecvid 2014: Instance Search And Semantic Indexing, Wei Zhang, Hao Zhang, Ting Yao, Yijie Lu, Jingjing Chen, Chong-Wah Ngo
Vireo @ Trecvid 2014: Instance Search And Semantic Indexing, Wei Zhang, Hao Zhang, Ting Yao, Yijie Lu, Jingjing Chen, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper summarizes the following two tasks participated by VIREO group: instance search and semantic indexing. We will present our approaches and analyze the results obtained in TRECVID 2014 benchmark evaluation
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Network simulation is an essential tool for the design and evaluation of wireless network protocols, and realistic channel modeling is essential for meaningful analysis. Recently, several network protocols have demonstrated substantial network performance improvements by exploiting the capture effect, but existing models of the capture effect are still not adequate for protocol simulation and analysis. Physical-level models that calculate the signal-to-interference-plus-noise ratio (SINR) for every incoming bit are too slow to be used for large-scale or long-term networking experiments, and link-level models such as those currently used by the NS2 simulator do not accurately predict protocol performance. In this article, …
Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo
Click-Through-Based Subspace Learning For Image Search, Yingwei Pan, Ting Yao, Xinmei Tian, Houqiang Li, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
One of the fundamental problems in image search is to rank image documents according to a given textual query. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of comparing textual keywords with visual image content. Image search engines therefore highly depend on the surrounding texts, which are often noisy or too few to accurately describe the image content. Second, ranking functions are trained on query-image pairs labeled by human labelers, making the annotation intellectually expensive and thus cannot be scaled up. We demonstrate that the above two fundamental challenges …
Action Classification By Exploring Directional Co-Occurrence Of Weighted Stips, Mengyuan Liu, Hong Liu, Qianru Sun
Action Classification By Exploring Directional Co-Occurrence Of Weighted Stips, Mengyuan Liu, Hong Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Human action recognition is challenging mainly due to intro-variety, inter-ambiguity and clutter backgrounds in real videos. Bag-of-visual words model utilizes spatio-temporal interest points(STIPs), and represents action by the distribution of points which ignores visual context among points. To add more contextual information, we propose a method by encoding spatio-temporal distribution of weighted pairwise points. First, STIPs are extracted from an action sequence and clustered into visual words. Then, each word is weighted in both temporal and spatial domains to capture the relationships with other words. Finally, the directional relationships between co-occurrence pairwise words are used to encode visual contexts. We …
Distributed Event Detection In Sensor Networks Under Random Spatial Deployment, P. Zhang, G. Peters, I. Nevat, G. Xiao, Hwee-Pink Tan
Distributed Event Detection In Sensor Networks Under Random Spatial Deployment, P. Zhang, G. Peters, I. Nevat, G. Xiao, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
We present a novel event detection algorithm in sensor networks for the case where the sensors are randomly deployed in space. In particular we consider a random sensors deployment according to a Homogeneous Finite Binomial Point Process. We first derive the optimal event detection decision rule. We then develop a novel algorithm to evaluate the intractable marginal likelihood based on the Gram-Charlier series expansion. We evaluate our algorithms through extensive Monte Carlo simulations. Simulation results present the detection and false alarm rates for different system parameters such as number of sensors deployed, deployment region size etc.
Name-Face Association In Web Videos: A Large-Scale Dataset, Baselines, And Open Issues, Zhi-Neng Chen, Chong-Wah Ngo, Wei Zhang, Juan Cao, Yu-Gang Jiang
Name-Face Association In Web Videos: A Large-Scale Dataset, Baselines, And Open Issues, Zhi-Neng Chen, Chong-Wah Ngo, Wei Zhang, Juan Cao, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Associating faces appearing in Web videos with names presented in the surrounding context is an important task in many applications. However, the problem is not well investigated particularly under large-scale realistic scenario, mainly due to the scarcity of dataset constructed in such circumstance. In this paper, we introduce a Web video dataset of celebrities, named WebV-Cele, for name-face association. The dataset consists of 75 073 Internet videos of over 4 000 hours, covering 2 427 celebrities and 649 001 faces. This is, to our knowledge, the most comprehensive dataset for this problem. We describe the details of dataset construction, discuss …
Click-Through-Based Cross-View Learning For Image Search, Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui
Click-Through-Based Cross-View Learning For Image Search, Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui
Research Collection School Of Computing and Information Systems
One of the fundamental problems in image search is to rank image documents according to a given textual query. Existing search engines highly depend on surrounding texts for ranking images, or leverage the query-image pairs annotated by human labelers to train a series of ranking functions. However, there are two major limitations: 1) the surrounding texts are often noisy or too few to accurately describe the image content, and 2) the human annotations are resourcefully expensive and thus cannot be scaled up. We demonstrate in this paper that the above two fundamental challenges can be mitigated by jointly exploring the …
Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu
Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu
Research Collection School Of Computing and Information Systems
Influential user can play a crucial role in online social networks. This paper documents an empirical study aiming at exploring the effects of influential users in the context of music social network. To achieve this goal, music diffusion graph is developed to model how information propagates over network. We also propose a heuristic method to measure users' influences. Using the real data from Last. fm, our empirical test demonstrates key effects of influential users and reveals limitations of existing influence identification/characterization schemes.
Creating Autonomous Adaptive Agents In A Real-Time First-Person Shooter Computer Game, Di Wang, Ah-Hwee Tan
Creating Autonomous Adaptive Agents In A Real-Time First-Person Shooter Computer Game, Di Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without …
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee-Peng Lim
Lifetime Lexical Variation In Social Media, Lizi Liao, Jing Jiang, Ying Ding, Heyan Huang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
As the rapid growth of online social media attracts a large number of Internet users, the large volume of content generated by these users also provides us with an opportunity to study the lexical variation of people of different ages. In this paper, we present a latent variable model that jointly models the lexical content of tweets and Twitter users' ages. Our model inherently assumes that a topic has not only a word distribution but also an age distribution. We propose a Gibbs-EM algorithm to perform inference on our model. Empirical evaluation shows that our model can learn meaningful age-specific …
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Placing Videos On A Semantic Hierarchy For Search Result Navigation, Song Tan, Yu-Gang Jiang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Organizing video search results in a list view is widely adopted by current commercial search engines, which cannot support efficient browsing for complex search topics that have multiple semantic facets. In this article, we propose to organize video search results in a highly structured way. Specifically, videos are placed on a semantic hierarchy that accurately organizes various facets of a given search topic. To pick the most suitable videos for each node of the hierarchy, we define and utilize three important criteria: relevance, uniqueness, and diversity. Extensive evaluations on a large YouTube video dataset demonstrate the effectiveness of our approach.
Sew-Ing A Simple Endorsement Web To Incentivize Trustworthy Participatory Sensing, T. Luo, S. Kanhere, Hwee-Pink Tan
Sew-Ing A Simple Endorsement Web To Incentivize Trustworthy Participatory Sensing, T. Luo, S. Kanhere, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Two crucial issues to the success of participatory sensing are (a) how to incentivize the large crowd of mobile users to participate and (b) how to ensure the sensing data to be trustworthy. While they are traditionally being studied separately in the literature, this paper proposes a Simple Endorsement Web (SEW) to address both issues in a synergistic manner. The key idea is (a) introducing a social concept called nepotism into participatory sensing, by linking mobile users into a social “web of participants” with endorsement relations, and (b) overlaying this network with investment-like economic implications. The social and economic layers …
Learning Directional Co-Occurrence For Human Action Classification, Hong Liu, Mengyuan Liu, Qianru Sun
Learning Directional Co-Occurrence For Human Action Classification, Hong Liu, Mengyuan Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Spatio-temporal interest point (STIP) based methods have shown promising results for human action classification. However, state-of-art works typically utilize bag-of-visual words (BoVW), which focuses on the statistical distribution of features but ignores their inherent structural relationships. To solve this problem, a descriptor, namely directional pair-wise feature (DPF), is proposed to encode the mutual direction information between pairwise words, aiming at adding more spatial discriminant to BoVW. Firstly, STIP features are extracted and classified into a set of labeled words. Then in each frame, the DPF is constructed for every pair of words with different labels, according to their assigned directional …
Robust Tracking With Discriminative Ranking Middle-Level Patches, Hong Liu, Zilin Liang, Qianru Sun
Robust Tracking With Discriminative Ranking Middle-Level Patches, Hong Liu, Zilin Liang, Qianru Sun
Research Collection School Of Computing and Information Systems
The appearance model has been shown to be essential for robust visual tracking since it is the basic criterion to locating targets in video sequences. Though existing tracking-by-detection algorithms have shown to be greatly promising, they still suffer from the drift problem, which is caused by updating appearance models. In this paper, we propose a new appearance model composed of ranking middle-level patches to capture more object distinctiveness than traditional tracking-by-detection models. Targets and backgrounds are represented by both low-level bottom-up features and high-level top-down patches, which can compensate each other. Bottom-up features are defined at the pixel level, and …
Qos-Aware Data Transmission And Wireless Energy Transfer: Performance Modeling And Optimization, D. Niyato, P. Wang, W. L. Yeow, Hwee-Pink Tan
Qos-Aware Data Transmission And Wireless Energy Transfer: Performance Modeling And Optimization, D. Niyato, P. Wang, W. L. Yeow, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
With wireless energy transfer, a mobile node can operate perpetually without having a wired connection to charge its battery. In this paper, we present a quality of service (QoS) aware data transmission and wireless energy transfer for the mobile node. The node can request for wireless energy transfer or transmit a packet when the node is in a coverage area of an access point. The node supports service differentiation for different type of traffic (i.e., low and high priority data). To meet the QoS requirement of each traffic type, we present the performance modeling and optimization framework. The objective is …
Where Am I? : Studying Users’ Indoor Navigation Location Needs, Kartik Muralidharan, Archan Misra, Rajesh Krishna Balan
Where Am I? : Studying Users’ Indoor Navigation Location Needs, Kartik Muralidharan, Archan Misra, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Location has emerged as the single-most important context whilst building pervasive mobile applications. Several mobile applications have appeared that use location to provide a host of services such as location-specific advertising as well as navigation. As a result, the key challenge of positioning techniques has been to provide the most precise location of the user (device) and much effort has been put in computing this fine grained location in indoor environments. This is under the assumption that highly accurate location is crucial for all indoor services. To understand the location accuracy, that should prove sufficient, for users to navigate to …
A Lightweight And Robust Interference Mitigation Scheme For Wireless Body Sensor Networks In Realistic Environments, S. Liang, Y. Ge, S. Jiang, Hwee-Pink Tan
A Lightweight And Robust Interference Mitigation Scheme For Wireless Body Sensor Networks In Realistic Environments, S. Liang, Y. Ge, S. Jiang, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In dense deployments of Wireless Body Sensor Networks (WBSNs), inter-user interference significantly degrades network performance when multiple WBSN users stay in a small area such as hospitals. Due to the high dynamics of the environments, it is usually hard to schedule the transmissions of multiple WBSNs without a central controller. In this paper, we propose a robust and lightweight interference mitigation scheme for realistic WBSN systems through adaptive channel hopping. A WBSN testbed is set up to investigate the impact of inter-user interference on various performance metrics when different severity levels of interference are present. Based on the measurement results, …
Los And Nlos Classification For Underwater Acoustic Localization, Roee Diamant, Hwee-Pink Tan, Lutz Lampe
Los And Nlos Classification For Underwater Acoustic Localization, Roee Diamant, Hwee-Pink Tan, Lutz Lampe
Research Collection School Of Computing and Information Systems
The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects …