Gait recognition offers a non-intrusive biometric solution by identifying individuals through their walking patterns. Although discriminative models have achieved notable success in this domain, the full potential of generative models remains largely unexplored. In this paper, we introduce CoD², a novel framework that combines the data distribution modeling capabilities of diffusion models with the semantic representation learning strengths of discriminative models to extract robust gait features. We propose a Multi-level Conditional Control strategy that integrates both high-level identity-aware semantic conditions and low-level visual details. Specifically, the high-level condition, extracted by the discriminative extractor, guides the generation of identity-consistent gait sequences, while low-level visual details, such as appearance and motion, are preserved to enhance consistency. Moreover, the generated sequences facilitate the discriminative extractor's learning, enabling it to capture more comprehensive high-level semantic features. Extensive experiments on four datasets (SUSTech1K, CCPG, GREW, and Gait3D) demonstrate that CoD² achieves state-of-the-art performance and can be seamlessly integrated with existing discriminative methods, yielding consistent improvements.

Many real-world applications require generating a chronological report from an evolving document stream; Timeline Summarization (TLS) provides a standard testbed for this setting. While large language models (LLMs) improve event synthesis, most LLM-based TLS systems remain monolithic: they repeatedly process overlapping evidence and often mirror the corpus’ bursty reporting patterns, producing redundant timelines with temporal/topical imbalance and high cost. We propose MAS-TLS, a multi-agent framework that casts TLS as a newsroom-like collaboration. A master editor steers balanced coverage by allocating system-visible evidence with a coverage–diversity objective; specialist reporter agents independently draft time-anchored, evidence-grounded events while cross-reviewing to limit redundancy; an adjudication round reconciles competing drafts and consolidates duplicates into a global timeline; and a non-stationary Bayesian controller adaptively staffs agents under token/time budgets. Experiments on three benchmarks show that MAS-TLS improves semantic coverage and temporal grounding while substantially reducing token usage and latency.

Multi-agent LLM systems accumulate long, non-monotonic memories where claims are revised, retracted, and later resurfaced, making final aggregation a validity-adjudication problem rather than simple summarization. However, existing agent systems provide little support for final-stage validity adjudication, leaving the final aggregator to conflate conflicting versions and resurface outdated claims. We present EvoMem, a lightweight plug-in for post-retrieval evidence structuring that converts retrieved interaction traces into an evolutionary timeline: it pools backend evidence, selects salient timepoints under a fixed budget, and summarizes each timepoint to trace updates and suppress outdated answers. We also introduce EvoMemBench, a timeline-style benchmark with controlled revisions, retractions, and cross-topic noise. Across long-context agents, RAG base agents, and agentic memory frameworks, EvoMem consistently improves answer accuracy and reduces outdated errors with modest overhead.

Implicit discourse relation recognition (IDRR) aims to recognize the discourse relation between two text segments without explicit connective. Although prompt learning-based approaches have achieved significant success in the IDRR task, these methods typically require carefully designed prompt templates for specific tasks, as well as auxiliary tasks that have a positive relationship with the main task, to enhance the performance of prompt learning. Recently, in-context learning (ICL) lets the input be prefaced by explicit guiding demonstrations, which can provide richer contextual information and mitigate its reliance on complex prompts and derived tasks. In this paper, we propose a COntrastive-based IN-context learning with Bias Calibration (COINBC) which utilizes contrastive learning for in-context learning and distinguishes between positive and negative demonstration samples. Additionally, we apply a bias calibration model based on the k -nearest-neighbor ( k NN) calibration to mitigate the inherent biases in in-context learning. Experiments are conducted on the PDTB 3.0 corpus, and the results show that our proposed COINBC achieves new state-of-the-art performance for the IDRR task.

Personalized Federated Learning (PFL) has been widely adopted in Mobile Edge Computing (MEC) to enable tailored services without private user data ever leaving the devices. Previous efforts have illustrated that the weighted aggregation determined by the number of samples on clients will hurt the convergence of PFL. Furthermore, the over-personalization of PFL may result in model overfitting and lack of generalization capabilities. Therefore, in this paper, we propose novel quality/discrepancy-aware Multi-task Personalized Federated Learning (MPFL) for MEC to tackle these issues. Specifically, a number of personalized learning objectives of different clients in PFL are considered as multiple tasks. Both local private Batch Normalization (BN) and global shared BN layers are introduced into PFL as the specialized experts to better balance the personalization and generalization capabilities of the local models. Furthermore, the statistical discrepancies between such two BN layers and the model quality of clients are jointly taken into account to design aggregation weights for quick model aggregation with better performance gains. Extensive experiments conducted on three well-known datasets demonstrate that the proposed framework outperforms the state-of-the-art benchmarks in terms of convergence speed and learning accuracy.

In recommender systems, reranking is an important post-processing technique to reorder the items in a recommendation list. Recently, some LLM-based reranking approaches have been proposed to enjoy the semantic reasoning capability of a large language model. However, they are sensitive to the order of the input list and often incur large computational overheads. To address their limitations, we propose the APCR, an Anchor-based Pairwise Comparison for recommendation Reranking in this paper. It first leverages an LLM to conduct pairwise comparisons between those recommended items and an anchor and computes a kind of preference scores for producing a new LLM suggested list. We next propose a position-aware list reranking technique to reorder the items in the recommendation list by considering their positions in the LLM suggested list to output the final list. Experiments on real-world datasets show that our APCR outperforms the state-of-the-art LLM-based reranking techniques in terms of better list ranking performance.

Recent years have witnessed the proliferation of wireless energy transfer for Wireless Sensor Networks (WSNs), which are mainly used for data gathering in real-world applications. A number of studies have investigated mobile vehicle scheduling to charge sensor nodes via wireless Mobile Chargers (MCs). Unfortunately, most of them cannot parallelly charge all nodes in an intelligent manner with the global network attributes. Furthermore, the time-variable charging ignores the optimal data gathering, resulting in poor Joint Energy Replenishment and Data Gathering (JERDG). To fill this gap, this paper proposes a Federated Deep Reinforcement Learning (FDRL)-based JERDG (FERG) solution for WSNs. To this end, FERG first partitions the networks into a set of clusters to distribute the workload evenly among multiple MCs, and then designs an FDRL-based framework that incorporates various time-variant network attributes to determine the optimal schedule for charging and data gathering via multiple MCs and a base station (BS). The BS as the cloud server is responsible for global training of JERDG models, while multiple MCs will parallelly train local models to jointly charge energy-exhausted nodes and gather the data from all nodes in clusters. To reserve more personalized characteristics of each cluster, a density-based partial aggregation strategy is designed to train the global model. Furthermore, a reward-weighted update and selection solution is proposed to generate and exploit reference samples with high rewards. Simulation results obtained from various scenarios demonstrate that FERG significantly outperforms the state-of-the-art approaches in terms of network lifetime, energy efficiency and data collection latency.

Introducing a Knowledge Graph (KG) to facilitate a recommender system has become a tendency in recent years. Many existing methods leverage KGs to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KGs also may contribute to better user representation learning. To solve this issue, we propose a novel algorithm, the KIGR ( K nowledge-aware I nteraction G raph for R ecommendation), to mine user–item interactions via KGs for assisting user representation learning. Specifically, a user–item interaction is encoded by attentively summing up the relation embedding about the item in the KG. Then, an unsupervised learning method is used to group the user–item interactions into different latent types. Further, a user–item interaction graph is divided into several subgraphs, which is referred to as a Knowledge-aware Interaction Graph, making each subgraph only contain one latent type of interaction. Finally, user representation is the fusion of user interest embedding, which is learned on the knowledge-aware interaction graph, whereas item representation is learned on the KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the state-of-the-art algorithms.

Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document.Existing studies adopt a kind of identifying after learning paradigm, where events' representations are first learned and then used for the identification.Furthermore, they mainly focus on the causality existence, but ignore causal direction.In this paper, we take care of the causal direction and propose a new identifying while learning mode for the ECI task.We argue that a few causal relations can be easily identified with high confidence, and the directionality and structure of these identified causalities can be utilized to update events' representations for boosting next round of causality identification.To this end, this paper designs an iterative learning and identifying framework: In each iteration, we construct an event causality graph, on which events' causal structure representations are updated for boosting causal identification.Experiments on two public datasets show that our approach outperforms the state-of-theart algorithms in both evaluations for causality existence identification and direction identification. 1

Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events.Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often subject to the delicate design of multiple prompts and the positive correlations between the main task and derivate tasks.The in-context learning paradigm provides explicit guidance for label prediction in the prompt learning paradigm, alleviating its reliance on complex prompts and derivative tasks.However, it does not distinguish between positive and negative demonstrations for analogy learning.Motivated from such considerations, this paper proposes an In-Context Contrastive Learning (ICCL) model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations.Additionally, we apply contrastive learning to event pairs to better facilitate event causality identification.Our ICCL is evaluated on the widely used corpora, including the EventStoryLine and Causal-TimeBank, and results show significant performance improvements over the state-of-the-art algorithms. 1

Influence Maximization (IM) on online social networks is often modeled as an optimization problem: How to select the seed nodes to influence the most other nodes during a diffusion process? Recently some neural network-based methods have been proposed for the IM problem. Although they can outperform existing heuristic algorithms, some limitations are still yet to be addressed, such as ignoring the set collective diffusion capability and requiring huge computation to obtain training data labels. To overcome such limitations, we propose the IACD neural model for the IM problem, which consists of three modules: The first is to encode each node an individual activation capability based on its egnonet features; The second is to encode the cascade seclusion likelihood by a seed set; And the third is to encode the cascade expansion capability of a node if being included into a seed set. set. The three encoding modules are then fused to rank the most influential node to be a new seed, and the iteration proceeds till reaching the required seed number. Trained on a small synthetic network, the IACD model can be applied to large scale networks with diverse topological characteristics. Experiments are conducted on both synthetic and real-world networks, and results show that our IACD outperforms classical heuristic methods and recent neural models methods in terms of better influence capabilities in most experimented cases.

News recommendation (NR) is critical for helping users to navigate the vast amount of information on online news platforms. However, the key challenges of tackling the cold-start problem, comprehensively modeling user interests and accurately encoding news semantics in NR have not yet been effectively addressed by existing methods. In this paper, we propose to alleviate the cold-start problem by exploiting news popularity. We also argue that the browsing history of a user can be organized as a personalized hypergraph to augment user interest modeling. Furthermore, we suggest enriching the representation of a candidate news article by retrieving its similar news. Our proposed NR method, named P opularity P rediction with S emantic R etrieval (PPSR), features a retrieval-driven popularity predictor that leverages click records to predict news popularity by incorporating semantic retrieval. It also comprises a hypergraph-based user encoder that mines the rich and diverse relatedness among news in a user’s browsing history. In addition, it designs a semantic-enhanced news encoder that retrieves and utilizes similar news in the training set to enrich semantic encoding for candidate news. Experiments on real-world datasets show that our PPSR can outperform the state-of-the-art methods in terms of higher recommendation accuracy and can well mitigate both the user-level and news-level cold-start problems.

With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this article, we propose DALFRec, a fairness-aware recommendation algorithm based on user-side and item-side adversarial learning to mitigate the effects of sensitive information on both sides of the recommendation process. First, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.

Learning representations from pairwise comparisons has achieved significant success in various fields, including computer vision and information retrieval. In recommendation systems, collaborative filtering algorithms based on pairwise learning are also rooted in this approach. However, a major challenge in collaborative filtering is the lack of labels for negative instances in implicit feedback data, leading to the inclusion of false negatives among randomly selected instances. This issue causes biased optimization objectives and results in biased parameter estimation. In this paper, we propose a novel method to address learning biases arising from implicit feedback data and introduce a modified loss function for pairwise learning, called debiased pairwise loss (DPL). The core idea of DPL is to correct the biased probability estimates caused by false negatives, thereby adjusting the gradients to more closely approximate those of fully supervised data. Implementing DPL requires only a small modification to the existing codebase. Experimental studies on public datasets demonstrate the effectiveness of the proposed method.

Ideology detection (ID) is important for gaining insights about peoples' opinions and stances on our world and society, which can find many applications in politics, economics and social sciences.It is not uncommon that a piece of text can contain descriptions of various issues.It is also widely accepted that a person can take different ideological stances in different facets.However, existing datasets for the ID task only label a text as ideologically left-or right-leaning as a whole, regardless whether the text containing one or more different issues.Moreover, most prior work annotates texts from data resources with known ideological bias through distant supervision approaches, which may result in many false labels.With some theoretical help from social sciences, this work first designs an ideological schema containing five domains and twelve facets for a new multifaceted ideology detection (MID) task to provide a more complete and delicate description of ideology.We construct a MITweet dataset for the MID task, which contains 12,594 English Twitter posts, each annotated with a Relevance and an Ideology label for all twelve facets.We also design and test a few of strong baselines for the MID task under in-topic and cross-topic settings, which can serve as benchmarks for further research.

How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches to sample informative instances, discriminating false negative from true negative for unbiased negative sampling remains an unsolved problem. On the basis of our order relation analysis of negatives’ scores, we first derive the class conditional density of true negatives and that of false negatives. We next design a Bayesian classifier for negative classification, from which we define a model-agnostic posterior probability estimate of an instance being true negative as a quantitative negative signal measure. We also propose a Bayesian optimal sampling rule to sample high-quality negatives. The proposed Bayesian Negative Sampling (BNS) algorithm has a linear time complexity. Experimental studies validate the superiority of BNS over the peers in terms of better sampling quality and better recommendation performance.1

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.

Some recent news recommendation (NR) methods introduce a Pre-trained Language Model (PLM) to encode news representation by following the vanilla pre-train and fine-tune paradigm with carefully-designed recommendation-specific neural networks and objective functions. Due to the inconsistent task objective with that of PLM, we argue that their modeling paradigm has not well exploited the abundant semantic information and linguistic knowledge embedded in the pre-training process. Recently, the pre-train, prompt, and predict paradigm, called prompt learning, has achieved many successes in natural language processing domain. In this paper, we make the first trial of this new paradigm to develop a Prompt Learning for News Recommendation (Prompt4NR) framework, which transforms the task of predicting whether a user would click a candidate news as a cloze-style mask-prediction task. Specifically, we design a series of prompt templates, including discrete, continuous, and hybrid templates, and construct their corresponding answer spaces to examine the proposed Prompt4NR framework. Furthermore, we use the prompt ensembling to integrate predictions from multiple prompt templates. Extensive experiments on the MIND dataset validate the effectiveness of our Prompt4NR with a set of new benchmark results.

Incorporating knowledge graph as side information has become a new trend in recommendation systems. Recent studies regard items as entities of a knowledge graph and leverage graph neural networks to assist item encoding, yet by considering each relation type independently. However, relation types are often too many and sometimes one relation type involves too few entities. We argue that there may exist some latent relevance among relations in KG. It may not necessary nor effective to consider all relation types for item encoding. In this paper, we propose a VRKG4Rec model (Virtual Relational Knowledge Graphs for Recommendation), which clusters relations with latent relevance to generates virtual relations. Specifically, we first construct virtual relational graphs (VRKGs) by an unsupervised learning scheme. We also design a local weighted smoothing (LWS) mechanism for node encoding on VRKGs, which iteratively updates a node embedding only depending on the node itself and its neighbors, but involve no additional training parameters. LWS mechanism is also employed on a user-item bipartite graph for user representation learning, which utilizes item encodings with virtual relational knowledge to help train user representations. Experiment results on two public datasets validate that our VRKG4Rec model outperforms the state-of-the-art methods. The implementations are available at https://github.com/lulu0913/VRKG4Rec.

Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The network dismantling task focuses on how to efficiently select the least such critical nodes. Most existing approaches focus on measuring nodes’ importance from either functional or topological viewpoint. Different from theirs, we argue that nodes’ importance can be measured from both of the two complementary aspects: The functional importance can be based on the nodes’ competence in relaying network information; While the topological importance can be measured from nodes’ regional structural patterns. In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node diffusion competence and role significance. Specifically, we propose a graph diffusion neural network which emulates information diffusion for competence encoding; We divide nodes with similar egonet structural patterns into a few roles, and construct a role graph on which to encode node role significance. The DCRS converts and fuses the two encodings to output a final ranking score for selecting critical nodes. Experiments on both real-world networks and synthetic networks demonstrate that our scheme significantly outperforms the state-of-the-art competitors for its mostly requiring much fewer nodes to dismantle a network.

The fairness consideration has received increasing attention in artificial intelligence (AI), especially in the significant application, recommender system. One kind of fairness issue is to balance the exposure of popular items and less popular items. Existing works mainly compensate the less popular items when predicting their ratings. However, the compensation for the items may result in a loss of recommendation accuracy, as the compensated items may not match user’s preference. For this problem, we propose a multi-community clustering recommendation with fair decision fusion (MultiFDF) framework to compensate less popular items locally, which can reduce the negative impact on user’s preferred popular item. We first design an experiment and deliver a causal graph-based mathematical proof to demonstrate the feasibility of local compensation. It proves that there is a discrete tendency of ratings predicted by a recommendation algorithm and a recommendation algorithm gives a higher rating to the popular item than the less popular item. The MultiFDF consists of three parts, community exploration module, local recommendation module, and fair decision fusion module. The community exploration module outputs several communities for local recommendation module to generate local recommendation lists, respectively. The fair decision fusion module then computes the discrete ratings of items based on local recommendation lists and designs an edge reranking strategy based on their discrete ratings to obtain the final fair top-$N$recommendation list. To verify the superiority of our proposed MultiFDF, we conduct experiments on three real world datasets and the results demonstrate that MultiFDF can improve fairness at the cost of lower accuracy than the state-of the-art algorithms.

Can we employ one neural model to efficiently dismantle many complex yet unique networks? This article provides an affirmative answer. Diverse real-world systems can be abstracted as complex networks each consisting of many functional nodes and edges. Percolation theory has indicated that removing only a few vital nodes can cause the collapse of whole network. However, finding the least number of such vital nodes is a rather challenging task for large networks due to its NP-hardness. Previous studies have proposed many centrality measures and heuristic algorithms to tackle this network dismantling (ND) problem. Different from theirs, this article tries to approach the ND task by designing a neural model which can be trained from tiny synthetic networks but will be applied for various real-world networks. It seems a discouraging mission at first sight, as network sizes and topologies are quite different across distinct real-world networks. Nonetheless, this article initiates insightful efforts of designing and training a neural influence ranking model (NIRM). Experiments on fifteen real-world networks validate its effectiveness for its mostly requiring fewer vital nodes to dismantle a network, compared with the state-of-the-art competitors. The key to its success lies in that our NIRM can efficiently encode both local structural and global topological signals for ranking nodes, in addition to our innovative labelling method in training dataset construction.

Implicit Discourse Relation Recognition (IDRR) is to detect and classify relation sense between two text segments without an explicit connective. Vanilla pre-train and fine-tuning paradigm builds upon a Pre-trained Language Model (PLM) with a task-specific neural network. However, the task objective functions are often not in accordance with that of the PLM. Furthermore, this paradigm cannot well exploit some linguistic evidence embedded in the pre-training process. The recent pre-train, prompt, and predict paradigm selects appropriate prompts to reformulate downstream tasks, so as to utilizing the PLM itself for prediction. However, for its success applications, prompts, verbalizer as well as model training should still be carefully designed for different tasks. As the first trial of using this new paradigm for IDRR, this paper develops a Connective-cloze Prompt (ConnPrompt) to transform the relation prediction task as a connective-cloze task. Specifically, we design two styles of ConnPrompt template: Insert-cloze Prompt (ICP) and Prefix-cloze Prompt (PCP) and construct an answer space mapping to the relation senses based on the hierarchy sense tags and implicit connectives. Furthermore, we use a multi-prompt ensemble to fuse predictions from different prompting results. Experiments on the PDTB corpus show that our method significantly outperforms the state-of-the-art algorithms, even with fewer training data.

Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE).However, the existing prompt-tuning methods have not achieved satisfactory performance due to the lack of consideration of entity information.In this paper, we propose a bidirectional iterative prompt-tuning method for EAE, where the EAE task is treated as a clozestyle task to take full advantage of entity information and pre-trained language models (PLMs).Furthermore, our method explores event argument interactions by introducing the argument roles of contextual entities into prompt construction.Since template and verbalizer are two crucial components in a clozestyle prompt, we propose to utilize the role label semantic knowledge to construct a semantic verbalizer and design three kinds of templates for the EAE task.Experiments on the ACE 2005 English dataset with standard and low-resource settings show that the proposed method significantly outperforms the peer stateof-the-art methods.Our code is available at https://github.com/HustMinsLab/BIP.

It is important to obtain accurate indoor location information in the Wi-Fi fingerprinting localization system, which suffers from a lot of labour-power to construct the fingerprint database. Improving positioning accuracy with less manual labour is still an issue. In this paper, we propose a signal-physical Siamese neural network SPSNN, consisting of a signal distance matrix encoder and a physical distance matrix encoder to learn features. The main idea of the SPSNN is to use the physical feature to constrain the signal feature. Grid fingerprints are constructed using crowdsourcing samples. We compute the distance difference between the sample and different grid fingerprints in signal space to build the pictures as the input of the Siamese network for further feature extraction. In addition, we exploit the distance distribution information in physical space to modify and constrain the signal feature vector during the network training phase. The position prediction is given by the regression method. Experiments on-field measurements validate the effectiveness and robustness of the proposed model, and the optimal localization accuracy is less than 1 m.

Indoor localization based on Wi-Fi received signal strength (RSS) fingerprints has been widely studied in recent years, mainly focusing on how to improve localization accuracy in an independent way. Some studies propose to use additional hardware devices to measure the distance in between users to improve localization accuracy, but these methods suffer from high cost and low practicality. In order to solve this problem, an inter-user distance estimation algorithm iDE is proposed in this paper. We first construct user features based on Wi-Fi fingerprints, then train the random forest and nearest neighbor regressors to obtain inter-user distance estimates, and design a multi-layer perceptron to fuse them. We propose a multiuser collaborative localization MCLoc based on inter-user distance estimation. It takes the distance estimation from iDE as a soft constraint to optimize the user's location using gradient descent search. Experiments in real scenarios show that in terms of inter-user distance estimation, the iDE algorithm can reduce the error by 24.2% compared with the single-model algorithm; in terms of positioning performance, the MCLoc algorithm can reduce the localization error by 11.4% compared with the non-collaborative method.

Whether it be congestion control in cities or massive access in the Internet, these dynamic behaviors can be abstracted as flow demand between origin-destination node pairs (OD pairs) in the network. Links in complex systems are with notable heterogeneity, which means the volume of flow varies greatly, resulting in some critical links being more likely to congest under the flow attack, reducing the service capability of the system, and even causing network collapses. To explore how flow dynamics influences the network functionality and stability under congestion, we propose a link-based flow attack strategy that significantly degrades the service capability between OD pairs. In this approach, we first extract the routing paths and score the vulnerability of links between OD pairs, then an attack flow allocation rule based on critical links is designed to efficiently attack the target flow between OD pairs. Experiments on real-world networks show that the proposed strategy can quickly identify critical links and accurately attack the target flow. Besides, the proposed method provides positive and unique insights for defense strategy and network topology optimization.

Session-based recommendation (SBR) is to predict the next item for an anonymous item sequence. Although many neural models have proven effectiveness in the SBR task, how to learn better items’ embeddings for neural models still remains a key challenge due to the anonymity of sessions and sparsity of users’ behaviors. This paper proposes a graph-based neural model, called Graph N eighborhood Routing and Random Walk (GNRRW), which learns two kinds of item embeddings for the SBR task. We first construct an item graph based on items’ co-occurrences in all sessions, on which we learn a local embedding and a global embedding for each item. For local embedding learning, we propose a novel neighborhood routing (NR) algorithm to exploit the compositive relations between an item and its neighbors. The NR algorithm has an excellent feature in that no additional parameters are needed in the training process. For global embedding learning, we propose a random walk-based approach to explore a kind of global relations between an item and representative items. Furthermore, we propose a switch-based shared gated recurrent unit (GRU) network to alternatively learn session local representation to make a local prediction, and learn session global representation to make a global prediction. Finally, we design a decision fusion mechanism to adaptively fuse both local and global predictions to output final items’ preference scores. Experiments on the public Yoochoose and Diginetica dataset validate the superiority of our GNRRW model over the state-of-the-art neural models.

The classic Hegselmann–Krause opinion dynamics model plays an important role in analyzing opinion evolution among people. However, it is not in line with the reality of interpersonal communication during the modern online social times, in which people are easy to exchange opinions with another one who agrees or disagrees with themselves on certain issues by social networks. In this paper, considering the effects of the one-sided versus two-sided presentation of a controversial issue, we improve the classic HK model and propose a novel information-type education-based Hegselmann–Krause model. In our work, we formulate rules of how agents’ opinions change and carry out simulation experiments on networks with different proportions of high-educated agents. Extensive experiments on artificially generated networks show that it is more difficult for someone to reach a consensus with another one who is within a group with a higher proportion of low-educated people than with a higher proportion of high-educated people, which verify the effectiveness of the proposed model.

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and lead to a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to February 10th, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. Based on the interaction between users, a communication topological network is constructed and the emotional direction is explored. We find that people had different sentiments between Chinese vaccine and those in other countries. The sentiment value might be affected by the number of daily news cases and deaths, the nature of key issues in the communication network. And revealing that the key nodes in the social network can produce emotional contagion to other nodes.

Can fingerprinting-based localization benefit from simultaneous observations from different users? This article provides an affirmative answer to this question. Existing fingerprinting schemes work in a kind of stand-alone mode, as they localize a user merely based on her own observations about surrounding environments. However, we argue the existence of scenarios where multiple users request for localization services simultaneously. If they are not far apart, their observations are actually distributed versions of the same environment, which can be exploited to calibrate underlying localization systems. In this article, we propose an online collaborative localization (OCLoc) scheme to boost received signal strength (RSS) fingerprinting in scenarios with multiple requesting users. The basic idea of OCLoc is based on the fact of distance attenuated radio propagations. For two users, their received signals from the same access point (AP) can be used to infer who is closer to this AP. We exploit such information for online collaboration and propose a series of operations to calibrate reference points (RPs) from distributed versions of radio attenuation observations. By integrating user calibration and fingerprint distance, we define a new ranking measure for reselecting and weighting candidate RPs to output the final location. Experiments on field measurements validate the effectiveness of such online collaborations in terms of greatly reduced localization errors.

Social emotion classification is to predict the distribution of different emotions evoked by an article among its readers. Prior studies have shown that document semantic and topical features can help improve classification performance. However, how to effectively extract and jointly exploit such features have not been well researched. In this paper, we propose an end-to-end topic-enhanced self-attention network (TESAN) that jointly encodes document semantics and extracts document topics. In particular, TESAN first constructs a neural topic model to learn topical information and generates a topic embedding for a document. We then propose a topic-enhanced self-attention mechanism to encode semantic and topical information into a document vector. Finally, a fusion gate is used to compose the document representation for emotion classification by integrating the document vector and the topic embedding. The entire TESAN is trained in an end-to-end manner. Experimental results on three public datasets reveal that TESAN outperforms the state-of-the-art schemes in terms of higher classification accuracy and higher average Pearson correlation coefficient. Furthermore, the TESAN is computation efficient and can generate more coherent topics.

Social emotion classification is to estimate the distribution of readers' emotion evoked by an article. In this paper, we design a new neural network model by encoding sentence syntactic dependency and document topical information into the document representation. We first use a dependency embedded recursive neural network to learn syntactic features for each sentence, and then use a gated recurrent unit to transform the sentences' vectors into a document vector. We also use a multi-layer perceptron to encode the topical information of a document into a topic vector. Finally, a gate layer is used to compose the document representation from the gated summation of the document vector and the topic vector. Experiment results on two public datasets indicate that our proposed model outperforms the state-of-the-art methods in terms of better average Pearson correlation coefficient and MicroF1 performance.

How to decompose a large global matrix into many small local matrices has been recently researched a lot for matrix approximation. However, the distance computation in matrix decomposition is a challenging issue, as no prior knowledge about the most appropriate feature vectors and distance measures are available. In this paper, we propose a novel scheme for local matrix construction without involving distance computation. The basic idea is based on the application of convergence probabilities of graph random walk. At first, a user-item bipartite graph is constructed from the global matrix. After performing random walk on the bipartite graph, we select some user-item pairs as anchors. Then another random walk with restart is applied to construct the local matrix for each anchor. Finally, the global matrix approximation is obtained by averaging the prediction results of local matrices. Our experiments on the four real-world datasets show that the proposed solution outperforms the state-of-the-art schemes in terms of lower prediction errors and higher coverage ratios.

The ever-growing Industrial Internet of Things (IoT) provides a powerful method to sense a series of critical industrial environments. This paper studies how to deploy the fixed number of IoT nodes so that the network lifetime is maximized in a sensing field with obstacles while guaranteeing the requirements of confident information coverage, network connectivity, energy efficiency, fault tolerance, and reliability. An IoT node deployment scheme based on an improved nature-inspired genetic algorithm is proposed to solve the defined constrained optimization problem. In the proposed IoT node deployment scheme, we utilize a population initialization based on the Delaunay triangulation to generate the better initial population, a chromosome modification operation to achieve both connectivity and coverage for each chromosome and a chromosome mirror-crossover operation to produce the better offsprings. Experimental results show that our deployment schema equips better performance in terms of longer network lifetime and comparable coverage ratio compared with the other four peer algorithms.

The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal. Up to now, as one of the most representative outdoor localization systems, the global positioning system has been widely used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure. At the same time, the location fingerprint-based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling. In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT. Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view. After that, by selecting an appropriate time interval to sample the independent and nonredundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined. Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks.

The emergence of coverage holes will dramatically degrade the quality of service of the industrial wireless sensor networks (IWSNs). Based on the novel confident information coverage (CIC) model, this work focuses on how to heal the CIC holes in hybrid IWSNs containing both static nodes and mobile nodes. We pinpoint the CIC hole healing (CICHH) problem with the goal of selecting and dispatching some randomly scattered mobile nodes to the CIC holes detected by the stationary nodes such that the CIC holes can be repaired and the CIC performance can be satisfied, and prove its NP-completeness. For handling the CICHH problem, we devise two energy-efficient heuristic solutions including a centralized CICHH algorithm and a distributed one. Both the proposed schemes aim at efficiently healing the CIC holes while minimizing the total moving energy consumption of the dispatched mobile nodes, or maximizing the mobile nodes' average remaining energy after movement, or minimizing the maximum mobile energy consumption of each dispatched mobile node. Experimental simulation results show the proposed schemes can energy-efficiently heal the CIC holes and outperform three peer algorithms in terms of energy efficiency and coverage ratio.

This paper studies how to place the least number of sensor nodes to ensure confident information coverage in an area with obstacles, while maintaining the connectivity of the placed nodes. For this constrained optimization problem, we propose a sensor placement scheme based on an improved genetic algorithm with our new problem-specific operations, including the Delaunaytriangulation-based population initialization, the chromosome correction operation with new gene insertion and redundant gene removal to produce a valid chromosome ensuring both coverage and connectivity, the chromosome mirror-crossover operation to enable the production of better offsprings. Simulation results show that the proposed algorithm can find better placement schemes in terms of much fewer deployed nodes, compared with the peer algorithms.

Fingerprint-based indoor localization has been intensively researched in the last decade, yet the labor-intensive and time-consuming site survey for radio map construction has impeded its practical implementations. Recently, crowdsourcing has been promoted as a promising approach to exploit casually collected samples for radio map construction, however, such samples may be labelled with erroneous location information. In this paper, we study how to construct a radio map from crowdsourced samples with annotation errors. In the sample set for creating an offline fingerprint, we apply a new local density-based clustering technique to detect and remove samples being classified as outliers; Next, we propose an access point selection algorithm based on the sample signal variation to further refine each offline fingerprint structure. Field measurements and experiments are conducted and results validate the necessity of outlier removal and effectiveness of our proposed schemes. Comparing with the peer schemes, ours can achieve significant improvements of localization performance.

Event recommendation has become an important issue in event-based social networks (EBSN). In this paper, we study how to exploit diverse relations in an EBSN as well as individual history preferences to recommend preferred events. We first construct a hybrid graph consisting of different types of nodes to represent available entities in an EBSN. The graph uses explicit relations as edges to connect nodes of different types; while transferring implicit relations of event attributes to interconnect the event nodes. After executing the graph random walking, we obtain the candidate events with high convergency probabilities. We next extract a user preference from his attended events to further compute his interest similarities to his candidate events. The recommended event list is then obtained by combining the two similarity scores. Data sets from a real EBSN are used to examine the proposed scheme, and experiment results validate its superiority over peer schemes.

Indoor landmarks, like corners, staircases and etc, play an important role in crowdsourcing-based indoor localization systems. This paper studies the problem of indoor corner detection and matching from crowdsourced movement trajectories. For corner detection, we adopt a machine learning approach by training a corner detector with both time and frequency features. For corner matching, we first apply the multidimensional scaling technique for matrix dimensionality reduction and then propose an improved K-means algorithm to obtain an intermediate matching result for each feature dimension. We also propose a voting algorithm to obtain the final matching result for each corner sample based on its all intermediate dimension matching results. Experiment results show that the machine learning-based corner detection can achieve much better detection performance, compared with the existing algorithms based on signal change detection. For corner matching, the proposed scheme can achieve high matching accuracy and the constructed corner fingerprints can achieve the nearest distance with their respective reference corner fingerprints.

Recently, crowdsourcing has been promoted as a promising solution for fingerprint-based indoor localization systems. However, when using crowdsourced fingerprints to construct an indoor radio map, the following challenges should be first addressed: inaccurate location information, nonuniform spatial density, fingerprint dimension diversity and collection device diversity. In this paper, we propose a radio map construction scheme based on crowdsourced fingerprint splitting and fitting to deal with the first three challenges. Our objective is to construct a grid radio map from crowdsourced fingerprints. We propose to split a crowdsourced fingerprint into several neighboring grid cells with some probability. For each grid cell, we propose to first filter out some unimportant features when composing a grid cell fingerprint. Furthermore, for a grid cell with very few or non supporting crowdsourced fingerprints, we propose to construct its fingerprint by using the surface fitting technique. We conduct field measurements and experiments to examine the localization performance. Results show that the proposed scheme can achieve 1.48m mean localization error and is more robustness to location annotation errors.

How to construct barrier coverage efficiently is a critical problem for many wireless sensor network applications, such as boundary surveillance and intrusion detection. In this paper, we study the belt barrier coverage in bistatic radar sensor networks. Much different from the disk and sector coverage, the coverage area of bistatic radar is dependent on the distance between a pair of radar transmitter and receiver. To improve coverage quality, we require to construct a belt barrier with the breadth not smaller than a predefined threshold. Furthermore, the unit cost of a radar transmitter may be different from a receiver. The bistatic radar placement problem is to construct a belt barrier with the minimum total placement cost. To solve the minimum cost placement problem, we propose a line-based equipartition placement strategy such that all radars placed on a deployment line can form a barrier with some breadth and one or more such placement lines can form a belt barrier with the required breadth. We first study the barrier property of different placement patterns on one deployment line, and prove the structure property of the optimal placement sequence on one deployment line. When multiple deployment lines are needed for belt barrier construction, we propose algorithms to find out the number of deployment lines and the number of receivers in the optimal placement pattern on each deployment line to minimize the total placement cost. The efficiency of the proposed algorithm is also validated by our simulation results.

Finding the optimal placement pattern to provide complete area coverage is of great theoretical and practical significance. Most of the current studies on placement problem are based on the simplest disk coverage model. In this paper, we study the optimal placement pattern based on theconfident information coverage(CIC or$\Phi$-coverage) model[1], which is much more complicated than the disk coverage model. Based on the reconstruction theory, the CIC model takes into consideration of not only the collaboration of sensors for information processing but also the spatial correlation of physical phenomena. We first analyze$\Phi$-coverage of one sensor and two sensors, and then$\Phi$-coverage of$n$sensors for$n \ge 3$, where$n$sensors are deployed at$n$vertices of a regular$n$-sided polygon. We prove that the regular triangular lattice is the optimal placement pattern among all the placement patterns consisting of regular polygons. We also extend our analysis to acute cyclic polygons, and prove that the regular triangular lattice is still the optimal placement pattern among all the placement patterns consisting of acute cyclic polygons.

Coverage is one of the fundamental issues in wireless sensor networks, yet most of the current studies on coverage are based on the simplest disk coverage model. Based on the theory of field reconstruction, we proposed a novel coverage model called confident information coverage in our previous study. In this paper, based on the confident information coverage model, we study the critical sensor density to achieve complete coverage in randomly deployed sensor networks. We first use the average vacancy to measure the degree of coverage, and compute the average vacancy through the computation of the probability that an arbitrary point is not covered by randomly deployed sensors within its correlation range. We then propose a numerical computation method called discrete approximation algorithm to compute this probability, and prove that this probability is actually the limit of the output of the proposed algorithm. Furthermore, we derive the upper and lower bound for the average vacancy as a function of sensor density, which provides a useful insight for the critical sensor density to achieve complete coverage. The simulation results validate our theoretical analysis.

The exponential growth of wireless service has led to a great amount of energy consumption on base stations. In this paper, we envision that base stations of future cellular networks are heterogenous and can be powered by hybrid energy supplies: on-grid energy and green energy. And the unit cost of green energy can be much less than that of on-grid energy. We study the energy cost saving problem and propose an adaptive range expansion algorithm in such a green heterogeneous network. The key idea of our proposed algorithm is to adaptively set the biasing factor for each pico BS according to the estimated energy drain ratio to make more utilization of the green energy for energy cost saving. We compare the proposed algorithm with the max-RSRP algorithm and the conventional range expansion algorithm with an identical biasing factor for all pico BSs. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.

The fingerprinting technique based on received signal strength (RSS) has been intensively researched for indoor localization in the last decade. Instead of using discrete reference points to build a fingerprint database, this paper applies the surface fitting technique to construct RSS spatial distribution functions and proposes two location search methods to find the target location. We also propose to use subarea division and determination to improve the fitting accuracy and search efficiency. In the offline phase, we divide the whole indoor environment into several subareas, construct a fingerprint for each subarea, and build a RSS fitting function for each access point in each subarea. In the online phase, we first determine to which subarea a target belongs, and then search its location according to the proposed exhaustive location search or gradient descent based search algorithm. We conduct both simulations and field experiments to verify the proposed scheme. The experiment results show that for the same reference point granularity, the proposed scheme can achieve on average 10% to 22% localization accuracy improvements, compared with the classical nearest neighbor-based fingerprinting method.

This paper studies the sensor placement problem for ensuring complete coverage in an area with obstacles. Instead of using the simplistic disk coverage model, we adopt our recently proposed confident information coverage model for field attribute monitoring applications. We propose a node placement algorithm based on iterative Delaunay triangulation, which is to first obtain Delaunay triangles for some initial seed nodes. Among all Delaunay triangles, we propose algorithms to find a valid one yet with the largest coverage hole for placing a new node. The Delaunay triangulation process is then repeated, until all the Delaunday triangles can be completely covered. Simulation results show that our algorithm has comparable performance in terms of the number of placed nodes, compared with a peer algorithm based on a grid approach to discretize the continuous field. However, our algorithm can truly achieve complete coverage yet with significantly smaller computation time.

The ever increasing data demand has led to the significant increase of energy consumption in cellular networks. In this paper, we study the problem of energy cost saving in heterogenous cellular networks with hybrid energy supplies. Owing to the diversities of mobile traffic and renewable energy, the energy cost saving problem involves both temporal and spatial dimensional optimization. We decompose the whole problem into three sub-problems and correspondingly our proposed solution is divided into three parts: First, we obtain average estimated energy consumption profiles for all base stations; Second, we allocate the green energy for each base station to minimize its energy cost based on its estimated energy consumption profile; Third, given the allocated green energy in the current slot, we perform the user association to minimize the total energy cost of the network. Simulation results demonstrate that our proposed algorithm can significantly reduce the total energy cost.

An important issue in Wireless Sensor Networks (WSNs) is to maximize the network lifetime while guaranteeing the desired coverage requirement. Recent studies have demonstrated that using rechargeable sensor nodes with energy harvesting capability has a great potential to extend the network lifetime. However, due to the high cost of rechargeable nodes, equipping every node with an energy harvesting unit is not practical in a large scale WSN. In this paper, we study the problem of maximizing the network lifetime of a hybrid WSN consisting of both common nodes and rechargeable nodes. Furthermore, we consider a new confident information coverage model which is more efficient for environment monitoring applications. We propose a novel priority-based greedy scheduling (PGS) algorithm to schedule the sensor nodes into a series of set covers that are activated sequentially, each satisfying the required coverage. Also, it can effectively exploit the advantages of using collaboration among sensors for the coverage requirement. Our simulations validate that the PGS algorithm can provide substantial performance improvement as compared to other peer algorithms.

The optimal node placement for a very large plane without boundary effect has been proven to be the regular triangular-lattice pattern in 1939. However, the regular triangular-lattice placement may not be optimal in a long belt with an upper and lower boundary. This paper proposes an optimal node deployment pattern to minimize the number of nodes for completely covering a long belt. The optimal pattern uses shifted node strips for belt coverage, and we compute the best node distance, strip offset, and strip distance for different belt heights. Mathematical analysis are provided to prove its optimality in terms of the minimum node density for belt coverage. Numerical computations are used to show its superiority, compared with other well-known placement patterns and our previously proposed equipartition placement.

Network lifetime maximization with guaranteed coverage is an important issue in wireless sensor networks. Based on our recently proposed confident information coverage (CIC) model, this paper studies the multi-modal confident information coverage (M2CIC) problem. Assuming that each node is equipped with different types of sensors, the objective is to schedule the multi-modal sensors' activity, such that the confident information coverage for each sensing modality can be guaranteed while the network lifetime can be maximized. We model the M2CIC problem as a multi-modal set cover problem (M2SC) and prove its NP-completeness. For solving the M2SC problem, we design two energy-efficient heuristics including a centralized one and a distributed one. In the proposed algorithms, different modal sensors are organized into a family of set covers, each of which can provide confident information coverage for all the monitored physical phenomena. Simulation results show that both the proposed algorithms can efficiently prolong the network lifetime and outperform two classical peer algorithms in terms of the extended network lifetime.

Nowadays, the fast development of wireless mesh networks have led to a significant growth of energy consumption. Green mesh networks consisting of rechargeable routers supplied by renewable energy sources such as solar power have become a cost-effective alternative solution. In this paper, we study the rechargeable router placement problem and formulate it as an optimization problem to minimize the number of deployed routers while ensuring system Quality of Service (QoS) constraints, including users' traffic demand, routers' energy consumption, network failure rate and traffic fairness among users. To assign users to appropriate routers, we first propose two cell association algorithms, namely, Nearest Cell Association Algorithm (NCA) and Proportional Fairness cell Association Algorithm (PFCA). The former only considers the system level efficiency, which may cause unfairness among different users. The later tries to achieve a balance between the network performance and the user fairness. We then design two heuristic placement algorithms embedded with the proposed cell association methods to find approximate solutions for the rechargeable router placement problem. Simulation results show that compared with the optimal placement achieved by exhaustive search, ours can achieve good performance while with greatly reduced computation complexity. Furthermore, the proposed PFCA algorithm can guarantee user fairness with a increase of placement cost.

Coverage is an important performance metric in wireless sensor networks, which reflects how well a sensor field is monitored by the deployed sensors. This paper studies the sensor placement problem for ensuring network coverage. Instead of using the simplistic disk coverage model, we adopt our recently proposed confident information coverage model for field attribute monitoring applications. The sensor field is divided into grid cells each with equal area, and sensors can be only placed at the center of each grid cell. Our objective is to place the least number of sensors to provide confident information coverage for all the grid points. We first formulate the sensor placement problem as a constrained optimization problem and use the exhaustive search algorithm to find the optimal solution. To reduce computation complexity, we also propose two greedy algorithms to find the approximate solutions. The simulation results show that the performance of the two proposed algorithms is close to the optimal solution obtained by the exhaustive search algorithm yet with significantly reduced computation complexity.

Subarea localization has been recently proposed to locate a mobile device within a certain subarea delimitated by the overlapping ranges of monitoring nodes. In our previous work, we have proposed a divide-and-cover node placement for complete coverage of a long belt, but the subarea localization has not been considered in the design of such a placement. This paper studies the subarea localization performance for the divide-and-cover deployment. We first obtain a formula to compute the mean subarea localization error of the whole belt and then analyze the optimal placement parameter to minimize the subarea localization error. Theoretical analysis show that compared with other node placement including the well-known regular triangular-lattice placement, the proposed scheme can achieve a lower mean localization error. Field experiments validate the effectiveness of the proposed scheme.

In the area of performance analysis of wireless networks, one critical issue is the hidden terminal problem, which is considered as one of the severest reasons for the degradation of network performance. In this paper, we incorporate reactive jamming scheme with distributed coordination function (DCF) in IEEE 802.11 based wireless local area networks (WLANs) to improve network throughput in the presence of hidden terminals. In the proposed protocol, we schedule access point (AP) to broadcast jamming signal reactively to hinder the simultaneous transmission of hidden terminals. Both analytical and numerical results show that our reactive jamming based protocol can constantly improve WLAN throughput for a wide range of conditions, compared with the traditional RTS/CTS.

Many applications in wireless sensor networks have strict coverage accuracy requirements and need to operate as long as possible. In this paper, based on the new confident information coverage model proposed in our previous study (Wang et al., 2012), we design a novel sensor scheduling algorithm to prolong the network lifetime. This algorithm organizes the sensors into a maximal number of set covers, each capable of providing required coverage. The task of reconstructing the physical phenomena will be accomplished only by the sensors from one working set cover, while all the other sensors are in sleep mode. And we rotate the working set cover to prolong the network lifetime. Our simulation results show that the proposed algorithm outperforms two typical peer algorithms in terms of longer network lifetime.

Barrier coverage is an important topic in wireless sensor networks. When sensors are randomly deployed, barrier gaps may occur if the number of deployed sensors is not large enough or some sensors start malfunctioning or run out of energy. How to efficiently mend these barrier gaps is an important research issue. In this paper, we study the gap mending problem in a hybrid sensor network which consists of both stationary and mobile sensors with adjustable sensing ranges. We propose two gap mending schemes: the min-max scheme and the max-lifetime scheme. The first is to minimize the maximal energy consumption to move sensors, and the second is to maximize the lifetime of barrier coverage after mending all gaps. Simulation results show that the min-max scheme can achieve a lower maximal moving distance and the max-lifetime scheme can efficiently extend the barrier lifetime.

Coverage is an important issue in many wireless networks. In this paper, we address the problem of node placement for ensuring complete coverage in a long belt scenario and propose a novel placement approach to minimize the number of nodes needed. In our work, each node is assumed to be able to cover a disk area centered at itself with a fixed radius, then a divide-and-cover node placement method is proposed. In the proposed method, a long belt is divided into some sub-belts (if necessary), and then a string of nodes are placed parallel to the long side of each sub-belt to completely cover the sub-belt. We then determine the optimal distance between two adjacent nodes in a string and the number of such strings to minimize the number of nodes for complete belt coverage. Theoretical proofs and analysis show that compared with other node placement including the well-known regular triangular-lattice placement, the proposed method can achieve lower node density in some cases when the belt height is not very large. A combination of the proposed method and the triangular-lattice placement is then proposed, and the optimal ranges of the belt height for their respective applications to achieve the lowest node density are computed.

Indoor localization is important to many location based applications and services. Many indoor localization methods have been proposed and they can be roughly categorized into two groups: One is based on the distance estimation between a target point and the Access Points (APs); and the other is based on the Received Signal Strength (RSS) fingerprint map. However, the two approaches all assume that the locations of Access Points (APs) are known beforehand. In this paper, we consider a scenario that the locations of APs are not known, and we use measured RSS (Received Signal Strength) at some location-known reference points and their geographical information to estimate virtual APs' parameters. Then those parameters are used to locate new user by optimization method. Experiments show that the position accuracy of the proposed method approaches that of RADAR with less effort.

Energy-efficient barrier coverage is an important issue in wireless sensor networks. In this paper we study the problem of how to maximize the lifetime of a barrier, where sensors have adjustable sensing ranges. In our approach, each node is divided into some virtual sub-nodes according to the available sensing ranges. For small-scale sensor networks, we construct a barrier coverage graph where a link exists between two sub-nodes in two different nodes, if their sensing ranges overlap. We propose to use a linear programming optimization method based on the exhaustive search of all possible barriers in the constructed graph to find the optimal barriers and their respective operation times. For large-scale sensor networks, we propose two distributed heuristics: one is to randomly select, from its neighboring sub-nodes, a next sub-node to construct barriers; another is to greedily select a next sub- node to best match the lifetime of the barrier constructed before choosing this sub-node. Simulation results show that compared with the randomized one, the greedy scheme can achieve longer lifetime and lower message overhead.

Range-free localisation methods are suitable for large scale wireless ad hoc and sensor networks because of their less-demanding hardware requirements. Many existing connectivity- or hop-count-based range-free localisation methods suffer from the hop-distance ambiguity problem where a node has a same distance estimation to all of its one-hop neighbours. In this study, the authors define a new measure, called regulated neighbourhood distance (RND), to address this problem by relating the proximity of two neighbours to their neighbour partitions. Furthermore, the authors propose a new RND-based range-free localisation method, and the simulation results show that it outperforms two existing peer algorithms in uniform and non-uniform network deployments.

Abstract Broadcast is a fundamental operation in wireless ad hoc and sensor networks. This paper proposes a simple and efficient broadcast protocol, called Broadcast based on Layered Diffusion (BLD), which is a stateless broadcast protocol with very low message overheads and very low computation requirements. In BLD, nodes do not need to exchange neighborhood information for building a broadcast backbone, but make their rebroadcast decisions locally. The design idea of BLD is to emulate the triangular tessellation for complete area coverage of the network field. It does not require nodes' location information for such tessellation emulation but only exploits the hop count information for each node to make local rebroadcast decision and timing. Simulation results show the effectiveness and efficiency of the BLD protocol and its robustness to localization errors and transmission errors. Copyright © 2009 John Wiley & Sons, Ltd.

With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.

Localization is an important research problem in WSNs (wireless sensor networks) and many WSN localization algorithms have been proposed in the literature. For a single sensor node whose anchors' positions are known, the location estimation error lower bound can be computed by using the CRLB (Cramer-Rao Lower Bound). However, it is still unclear to the research community what the localization error lower bound is from a network point of view. In this paper, we derive a lower bound of the expected localization error for a network whose sensor nodes and anchors are randomly distributed according to a Poisson point process. We show that the lower bound of the expected localization error for a network is a function of the anchor density and the variance of anchor-to-sensor distance measurements.

In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges are how to select cluster heads and active nodes. In this paper, we propose a coverage-aware clustering protocol. In the proposed protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results show that the network coverage lifetime can be significantly improved compared with an existing protocol.

Localization is one of the most important research issues in wireless sensor networks (WSNs). Hop-count based localization has been proposed as a cost-effective alternative to the expensive hardware-based localization schemes. In this paper, we propose a new method to estimate distances between anchors and nodes based on hop-count information. Localization is then achieved using the estimated distances. Simulation results show that the performance of our proposed algorithm is much better than that of the DV-hop algorithm in terms of the node-to- anchor distance estimation and localization accuracy, and the improvement is proportional to the node density.

Abstract In this paper, we study the problem of scheduling sensor activity to cover a set of targets with known locations such that all targets can be monitored all the time and the network can operate as long as possible. A solution to this scheduling problem is to partition all sensors into some sensor covers such that each cover can monitor all targets and the covers are activated sequentially. In this paper, we propose to provide information coverage instead of the conventional sensing disk coverage for target. The notion of information coverage is based on estimation theory to exploit the collaborative nature of geographically distributed sensors. Due to the use of information coverage, a target that is not within the sensing disk of any single sensor can still be considered to be monitored (information covered) by the cooperation of more than one sensor. This change of the problem settings complicates the solutions compared to that by using a disk coverage model. We first define the target information coverage (TIC) problem and prove its NP‐completeness. We then propose a heuristic to approximately solve our problem. Simulation results show that our heuristic is better than an existing algorithm and is close to the upper bound when only the sensing disk coverage model is used. Furthermore, simulation results also show that the network lifetime can be significantly improved by using the notion of information coverage compared with that by using the conventional definition of sensing disk coverage. Copyright © 2008 John Wiley & Sons, Ltd.

The rapid developments in wireless communications, embedded systems and MEMS sensor technologies have boosted the research and development on large-scale wireless sensor networks (WSNs). WSNs have very broad application areas such as security surveillance, environmental monitoring and healthcare monitoring. The paper presents a novel simulation framework called WSN middleware service modules simulation platform (WISDOM). The WISDOM simulator is written in Java and it uses the recursive porous agent simulation toolkit (Repast)O as the simulation engine to perform discrete event-driven simulations. WISDOM was used in WSN research (Wang et al.,2007) to simulate and verify middleware services for routing, sensing activity scheduling, group formation and management, target detection and tracking, and collaborative classification and fusion in a wireless sensor network. The versatility and performance of WISDOM for middleware service protocol development and evaluation have proven to be valuable.

In recent years, wireless sensor networks have received great interest with rapid developments in both hardware and software aspects. However, there is always a problem with limited energy supply for the tiny sensor node. In wireless imaging sensor network in which imaging devices are utilized, the bottleneck of energy consumption efficiency is even more serious. The main activities of such imaging networks are related to image data transmission over wireless channel. Therefore it is necessary to seek a good balance between the image quality and energy conservation. We propose an energy-efficient control scheme based on recent dynamic range coding techniques. The simulation results are presented with emphasis on prolonging the sensor network lifetime.

High dynamic range (HDR) images require a higher number of bits per color channel than traditional images. This brings about problems to storage and transmission, especially in distributed wireless sensor networks in which transmission capability and energy consumption are critical issues. The paper proposes a structure of compressing multiple dynamic range images, which is called multiple dynamic range (MDR) coding. MDR imaging system is attractive due to the capability of reconstructing lower dynamic range images from partial bitstream. MDR coding shows its advantages in terms of energy efficiency in distributed wireless sensor networks. The efficacy of MDR coding is also illustrated by presenting the results of encoding a series of synthetic and natural MDR images

Target tracking and localization are important applications in wireless sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the lower bound of sensor density to guarantee a bounded localization error over the sensing field. We first convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered at the sensor. Our results show that the disk coverage model requires 4 times more sensors for localization compared to detection applications. We then introduce the idea of sector coverage to tighten the lower bound. The lower bound derived through sector coverage is 2 times less than through disk coverage. A distributed sector coverage algorithm is then proposed in this paper. Compared to disk coverage, sector coverage requires more computations. However, it provides more accurate density estimations than the disk model. Numerical evaluations show that the density bound derived through our sector coverage model is tight.

In this paper we consider the coverage problem for target detection applications in wireless sensor networks. Unlike conventional coverage problems which assume sensing regions are disks around sensors, we define the sensing region according to detection constraints in terms of false alarm probability and missing probability. We show that exploiting cooperation between sensors can extend the overall sensing region while maintain the same constraints on false alarm probability and missing probability. We then propose an energy efficient cooperative detection scheme and study the trade-offs on energy consumption between cooperative and non-cooperative schemes. The cooperative scheme can use half the number of sensors to monitor the whole fleld compared to disk model in networks deployed on grids. We also study the communication overheads incurred by the co-operative scheme, and show that only cooperation between limited number of nearby sensors is profitable in terms of energy consumption. In our simulations on randomly deployed networks, cooperation reduces the number of sensors to cover the area by 30% and nearly doubles the number of disjoint sensor sets where each can fully cover the area. Appropriately trading off energy consumption with coverage extension, our cooperative detection scheme can increase the network lifetime by nearly 70%.

Coverage is an important performance metric for many applications such as surveillance in Wireless Sensor Networks (WSNs). Coverage control is used to select as few active nodes as possible from all deployed sensor nodes such that sufficient coverage of the monitored area can be guaranteed, while reducing the energy consumption of each individual sensor node to prolong the network lifetime. This paper presents a distributed and localized technique called the Layered Diffusion based Coverage Control (LDCC). The LDCC protocol does not require information on the node location coordinates when selecting active nodes. Instead, it exploits the hop count information, which is easily obtained in a WSN, to select active sensor nodes. Furthermore, the LDCC protocol is very simple and does not require any sophisticated computation such as distance or covered area computation. Our simulation results show that the LDCC protocol achieves high coverage ratio while incurring very low message overhead compared with other existing protocols.

This paper studies the problem of how to maximize the network lifetime while preserving network coverage for an energy constrained wireless sensor network. We consider network coverage from an our recently proposed information coverage model [1] other than the conventional sensing disk model. The lifetime maximization problem is modeled as a nonlinear programming problem and is shown NP-Complete. We then propose a family of greedy algorithms to allocate sensors different roles such that different sensors may consume different amount of energies in different intervals to prolong network lifetime while still guaranteeing application requirements. Simulation results suggest that the algorithm with the best balancing between communication energy consumption and area coverage requirement has the highest network lifetime.

Coverage is an important issue in wireless sensor networks. The most commonly used coverage model in the literature defines a point to be covered if its Euclidian distance to at least one sensor is less than a fixed threshold. This is a conservative definition of coverage which implicitly assumes that each sensor makes a decision independent of other sensors in the field. Sensors can cooperate to make an accurate estimation, even if any single sensor is unable to do so. We have previously proposed a new notion of information coverage and investigated its properties. In this paper, we study sensor density requirements for complete information coverage of a field with random sensor deployment. We provide an upper bound on the probability that an arbitrary point in a randomly deployed sensor field is not information covered and find the relationship between the sensor density and the average field vacancy. Simulation results validate our theoretical analysis and show that significant savings in terms of sensor density for complete coverage can be achieved with information coverage.

Target tracking and localization are important applications in wireless sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the necessary sensor density which can guarantee a localization error bound over the sensing field. In the first method, we convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered around the sensor. Our results show that the disk coverage model requires 4 times more sensors for tracking compared to detection applications. We then introduce the idea of sector coverage, which can satisfy the same coverage conditions with 2 times less sensors over the disk coverage approach. This shows that conventional disk coverage model is insufficient for tracking applications, since it overestimates the sensor density by two times. Simulation results show that the network density requirements derived through sector coverage are close to the actual need for target tracking applications.

An important application of wireless sensor networks is to perform the monitoring missions, for example, to monitor some targets of interests at all times. Sensors are often equipped with non-rechargeable batteries with limited energy and energy saving is a critical aspect for wireless sensor networks. If a target is monitored simultaneously by serval sensors, some of them can be switched off to save energy without causing mission failure and by which their operational times as well as the network lifetime can be prolonged. In this paper, we study the problem of scheduling sensor activity to cover a set of targets with known locations such that all targets can be monitored all the time and the network can operate as long as possible. A solution to this scheduling problem is to partition all sensors into sensor covers such that each cover can monitor all targets and the covers are activated successively. In this paper, we propose to use the notion of information coverage which is based on the estimation theory to exploit the collaborative nature of wireless sensor networks, instead of using the conventional definition of coverage. Due to the use of information coverage, a target that is not within the sensing disk of any single sensor can still be considered to be monitored (information covered) by the cooperation of more than one sensor.

In this paper, we study resource allocation policies for data services in a CDMA-based network. We present a detailed packet level resource allocation scheme that consists of different sorting strategies for backlogged flows and an exhaustive data rate allocation algorithm that satisfies the optimal power allocation constraint. The sorting strategies and the rate allocation algorithm determine the order that a user receives resources and the resource share that a user can receive. We present three sorting strategies, which may characterize the relationship between fairness among users and system utilization efficiency. We compare the performance of the sorting strategies via simulations and our investigations include the achievable system capacity, fairness among users and delay performance.

A data user may request different transmission rates during its connection holding time to reduce its resource usage cost but still meeting QoS requirements. In this paper, the procedure of the resource request for a data user is formulated as a Markov decision process in which the user requests different transmission rates based on its buffer occupancy. The objective is to find an optimal policy (if it exists) that minimizes the expected average cost per unit time. We prove that a stationary optimal policy is monotone in the buffer occupancy for our problem. Numerical examples are provided comparing the performance of the optimal policies with some other policies.

In the paper, we investigate and evaluate a radio resource allocation strategy for non-realtime data services in a single cell UMTS uplink. We focus on a packet level allocation strategy, which guarantees certain fairness levels. Non-realtime data services normally have average throughput requirement, which can be achieved either by way of continuous transmission with a low instantaneous data rate or by way of a higher instantaneous data rate over a shorter time. We find that it is energy-efficient by transmitting with high instantaneous data rate based on variable spreading factor operation. Furthermore, based on an average throughput based fairness criterion, we propose a fair resource allocation scheme that is easy to implement. Simulations are used to evaluate the proposed scheme.

IP overheads are large and need to be compressed when used in UMTS to save radio resources. A robust header compression (ROHC) scheme has been proposed to provide high compression efficiency and high robustness. We study the performance of ROHC and investigate how to adjust some compression parameters that are recommended as implementation-dependent according to the ROHC specification. Based on our study, we propose to employ the power control mechanism already in the UMTS architecture to prevent additional packet losses in the worst cases of consecutive packet losses exceeding the sliding window width. The proposed augmentation can be seen as a network assisted feedback mechanism for ROHC.