We propose a neuro-symbolic approach for learning causal complex event models from multi-source data, integrating causal discovery and temporal logic. Given resource constraints, we employ signal-level fusion by averaging the data from different antennas of the same WiFi receiver, followed by downsampling to reduce computational overhead. We consider a dataset of WiFi Channel State Information capturing human activities alongside video data from which we extract atomic symbolic activities such as “moving the upper arm.” The extracted symbolic information is processed through LPCMCI (Latent PCMCI). This causal discovery method extends PCMCI (Peter and Clark Momentary Conditional Independence) to handle latent dependencies across multiple time steps while mitigating false discoveries due to auto-correlations. The resulting causal structure is then translated into a temporal logic formula, which serves as a symbolic constraint in a neuro-symbolic learning pipeline. To efficiently process and learn from these structured constraints under resource limitations, we leverage Spiking Neural Networks, which offer energy-efficient computation while preserving temporal dynamics.

In this work, we analyze limited knowledge about likelihoods in traditional fusion as an uncertain Naïve Bayesian network whose conditional probabilities are known within posterior Dirichlet distributions reflected of limited training data. In prior work, we showed that inferences from these uncertain networks are confidently precise despite finite training data as the number of features goes to infinity for a uniform prior. The inference is usually correct except for pathological cases that diminish as the training data size goes to infinity. This work extends the analysis by studying how various priors affect asymptotic inference when the priors do or do not match the generative process for the conditional probabilities. Furthermore, the work analyzes how the prior affects the convergence rate of the inference.

Evaluating uncertainty in large language model (LLM) reasoning is challenging due to their vast parameter space, abstract knowledge representation, and limited transparency regarding training data. While normative formalisms, such as deductive logic, clearly define sound reasoning in the absence of uncertainty, reasoning under uncertainty admits multiple approaches, including probabilistic reasoning (e.g. Bayesian), belief function reasoning (e.g. Dempster-Shafer), or fuzzy logic, to name a few. This paper examines how LLMs handle uncertainty by analyzing outcomes based on an atomic fusion and reasoning problem. We establish a point of reference using the simplest of fusion topologies to facilitate transparency and understanding of how LLMs align with established theories. The reasoning approaches of different LLMs with varying complexities are compared to established normative frameworks, providing insights into which formalism best aligns with LLM reasoning and assessing its soundness and consistency. A deviation function for assessment is developed, and the results indicate that the tested LLMs' reasoning under uncertainty does not consistently align with established theories, even for the simplest information fusion topologies. These preliminary results form the basis for further investigations and LLM refinements.

Federated Learning (FL) offers a promising approach for collaborative model training in healthcare while preserving data privacy. However, existing FL methods often fall short in addressing two critical challenges: client-level fairness and compounded uncertainty from data heterogeneity and privacy-preserving mechanisms. We propose fair-LDP, a fairness-aware Local Differential Privacy framework that promotes fairness and privacy via uncertainty-guided aggregation in federated healthcare AI. fair-LDP leverages evidential neural networks (ENNs) to quantify predictive uncertainty and introduces a novel strategy that uses uncertainty-driven local differential privacy to guide fairness-aware updates while preserving data privacy. This ensures equitable performance across clients with varying data quality while mitigating the influence of unreliable or outlier updates. fair-LDP incorporates an adaptive mechanism that adjusts each client's privacy budget based on model performance, balancing fairness, privacy, and accuracy. We evaluate fair-LDP on real-world healthcare datasets under both IID and non-IID settings. Our experimental results show that it consistently outperforms state-of-the-art fairness-aware and privacy-preserving FL baselines, with no added computational overhead, while maintaining privacy guarantees comparable to homomorphic encryption and secure multiparty computation. By integrating uncertainty modeling, fairness-aware aggregation, and adaptive local differential privacy, fair-LDP provides a practical and principled solution for responsible, equitable, and privacy-preserving federated learning in healthcare.

The Competitive Influence Maximization (CIM) problem involves entities competing to maximize influence in online social networks (OSNs). While Deep Reinforcement Learning (DRL) methods have shown promise, most assume binary user opinions and overlook behavioral factors. We introduce DRIM, a novel DRL-based CIM framework using Subjective Logic (SL) to incorporate user preferences and uncertainty, optimizing seed selection to spread true information while countering false information. DRIM’s Uncertainty-based Opinion Model (UOM) provides a realistic representation of user opinions. Results demonstrate that UOM maintains over 80% true influence against advanced misinformation, and DRIM outperforms state-of-the-art methods by up to 45% in influence and 77% in speed. DRIM also excels in limited-resource scenarios, networks with 10% invisibility, and when users are inclined to doubt true information.

Teams of embodied AI-enabled agents are critical for applications in extreme and highly dynamic environments. Developing robust controllers for such agents requires a deep understanding of the challenges encountered when attempting to coordinate and synchronize their individual perception-cognition-communication-action (PCCA) loops for team-wide mission objectives. We introduce a framework to explore the coordination of the PCCA loops across multiple agents in a new simulated physical environment designed to explore collaboration in each PCCA stage. This environment tasks teams of agents with the correct disposal of dangerous objects in an area and forces careful coordination of sensing, communication, movement, and manipulation actions by providing spatially-bounded communication, incorporating situations that require concerted effort by groups of agents, and introducing uncertainty into agents’ sensing capabilities. We provide a set of heuristic controllers, an offline oracle model, and an initial exploration of a Reward Machine-based controller that learns its policies from training. Together these approaches serve to provide insights into the complexity of the multi-agent PCCA loop coordination problem. The multiagent PCCA simulation environment, which supports AI and human-controlled agents, and the code for various agent controllers are available at https://github.com/nesl/AI-Collab.

Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body’s interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly.

We study the problem of Quickest Change Detection (QCD) in a complex networked system consisting of a set of heterogeneous agents that sequentially feed information to a central fusion center. At any unknown deterministic time, a persistent anomaly occurs, causing the distribution of observations from an unknown distinguishable subset of agents to simultaneously change from a nominal (pre-change) distribution to an anomalous (post-change) distribution, and the goal of the fusion center is to detect the change as quickly as possible subject to a false alarm constraint. Traditionally, various fusion rules have been proposed that assume that the distributions at each agent are either completely known or unknown and are locally solved using the Cumulative Sum (CuSum) and Generalized Likelihood Ratio (GLR) statistics, respectively. When an agent has access to training data, the Uncertain Likelihood Ratio (ULR) test generalizes distributional assumptions using uncertain distributions. However, the ULR has not been implemented for network change detection. This paper empirically studies incorporating the ULR statistics into the existing fusion rules for QCD and compares the average detection delay. Our results show that the ULR test can improve the average detection delay over the GLR tests using certain fusion techniques, while approaching the detection delay of the CuSum tests as the training data increases. Our results provide insights into future theoretical analysis to improve network QCD with imprecise knowledge of the distributions.

Likelihood fusion is a special case of Bayesian networks known as naïve Bayes. It is well known that as the number of observations goes to infinity with known likelihoods, the aleatoric uncertainty of the queried (or parent) variable goes to zero, and furthermore, the declared values are guaranteed to match the ground truth. This work considers the case that the conditional probabilities are learned with limited training data leading to uncertain likelihoods, and second-order probabilistic reasoning is incorporated to characterize the aleatoric and epistemic uncertainty. Remarkably, it is shown that both the aleatoric and epistemic uncertainty goes to zero despite limited knowledge of the likelihoods. The rate of convergence is dictated by a quasi-divergence value that is related to the Kullback-Liebler (KL) divergence. However, the quasi-divergence can be negative leading to false declarations. This paper investigates when false declarations can emerge and shows how such cases diminish as the amount of training data for the likelihoods increases.

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and Evidential Deep Learning for its ability to sense out-of-distribution activities. We verify that the fused data processed by different antennas of the same Wi-Fi receiver results in increased accuracy of human activity recognition compared with the most recent benchmarks, while still being informative when facing out-of-distribution samples and enabling semantic interpretation of latent variables in terms of physical phenomena. The results of this paper are a first contribution toward the ultimate goal of providing a flexible, semantic characterisation of black-swan events, i.e., events for which we have limited to no training data.

The shifted Dirichlet distribution is utilized to extend the definition of subjective opinions to include upper and lower bounds on belief and base rates. The characteristics of the bounded subjective opinions are examined and contrasted with unbounded subjective opinions.

Efficient second-order probabilistic inference in uncertain Bayesian networks was recently introduced. However, such second -order inference methods presume training over complete training data. While the expectation-maximization framework is well-established for learning Bayesian network parameters for incomplete training data, the framework does not determine the covariance of the parameters. This paper introduces two methods to compute the covariances for the parameters of Bayesian networks or Markov random fields due to incomplete data for two-node networks. The first method computes the covariances directly from the posterior distribution of parameters, and the second method more efficiently estimates the covariances from the Fisher information matrix. Finally, the implications and effectiveness of these covariances is theoretically and empirically evaluated.

This paper introduces an active-learning-based truth estimator for social networks, such as Twitter, that enhances estimation accuracy significantly by requesting a well-selected (small) fraction of data to be labeled. Data assessment and truth discovery from arbitrary open online sources are a hard problem due to uncertainty regarding source reliability. Multiple truth finding systems were developed to solve this problem. Their accuracy is limited by the noisy nature of the data, where distortions, fabrications, omissions, and duplication are introduced. This paper presents a semi-supervised truth estimator for social networks, in which a portion of inputs are carefully selected to be reliably verified. The challenge is to find the subset of observations to verify that would maximally enhance the overall fact-finding accuracy. This work extends previous passive approaches to recursive truth estimation, as well as semi-supervised approaches where the estimator has no control over the choice of data to be labeled. Results show that by optimally selecting claims to be verified, we improve estimated accuracy by 12% over unsupervised baseline, and by 5% over previous semi-supervised approaches.

In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight-i.e., accurate and deep understanding of a situation derived from uncertain and often sparse data and collective foresight-i.e., the ability to predict what will happen in the future. When it comes to complex scenarios, the need for a distributed CSU naturally emerges, as a single monolithic approach not only is unfeasible: it is also undesirable. We therefore propose a principled, critical analysis of AI techniques that can support specific tasks for CSU to derive guidelines for designing distributed systems for CSU.

This work analyzes the impact of correlated propositions when estimating the reporting behavior of information sources. These behavior estimates are critical for fusion, and traditional methods assume the propositions are statistically independent. A new source behavior estimation methods is presented that accounts for statistical dependencies between the training propositions. Simulations seem to indicate that the potential performance gains for accounting for the correlations is small relative to the increased computational complexity. One may conclude that the traditional independence assumption in source behavior estimation methods is reasonable even in cases where it is actually violated.

Data cube is a cornerstone architecture in multidimensional analysis of structured datasets. It is highly desirable to conduct multidimensional analysis on text corpora with cube structures for various text-intensive applications in healthcare, business intelligence, and social media analysis. However, one bottleneck to constructing text cube is to automatically put millions of documents into the right cube cells so that quality multidimensional analysis can be conducted afterwards-it is too expensive to allocate documents manually or rely on massively labeled data. We propose Doc2Cube, a method that constructs a text cube from a given text corpus in an unsupervised way. Initially, only the label names (e.g., USA, China) of each dimension (e.g., location) are provided instead of any labeled data. Doc2Cube leverages label names as weak supervision signals and iteratively performs joint embedding of labels, terms, and documents to uncover their semantic similarities. To generate joint embeddings that are discriminative for cube construction, Doc2Cube learns dimension-tailored document representations by selectively focusing on terms that are highly label-indicative in each dimension. Furthermore, Doc2Cube alleviates label sparsity by propagating the information from label names to other terms and enriching the labeled term set. Our experiments on real data demonstrate the superiority of Doc2Cube over existing methods.

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet. Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications. Therefore, we are motivated to propose a novel embedding learning framework-ASPEM-to preserve the semantic information in HINs based on multiple aspects. Instead of preserving information of the network in one semantic space, ASPEM encapsulates information regarding each aspect individually. In order to select aspects for embedding purpose, we further devise a solution for ASPEM based on dataset-wide statistics. To corroborate the efficacy of ASPEM, we conducted experiments on two real-words datasets with two types of applications-classification and link prediction. Experiment results demonstrate that ASPEM can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.

The real-time discovery of local events (e.g., protests, disasters) has been widely recognized as a fundamental socioeconomic task. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from massive geo-tagged tweet streams in real time remains challenging. To bridge the gap, we propose a method for effective and real-time local event detection from geo-tagged tweet streams. Our method, named G eo B urst+ , first leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract similar tweets to form candidate events. G eo B urst+ further summarizes the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. Better still, as the query window shifts, G eo B urst+ is capable of updating the event list with little time cost, thus achieving continuous monitoring of the stream. We used crowdsourcing to evaluate G eo B urst+ on two million-scale datasets and found it significantly more effective than existing methods while being orders of magnitude faster.

As a widely observable social effect, influence diffusion refers to a process where innovations, trends, awareness, etc. spread across the network via the social impact among individuals. Motivated by such social effect, the concept of influence maximization is coined, where the goal is to select a bounded number of the most influential nodes (seed nodes) from a social network so that they can jointly trigger the maximal influence diffusion. A rich body of research in this area is performed under statistical diffusion models with provable submodularity, which essentially simplifies the problem as the optimal result can be approximated by the simple greedy search. When the diffusion models are non-submodular, however, the research community mostly focuses on how to bound/approximate them by tractable submodular functions. In other words, there is still a lack of efficient methods that can directly resolve non-submodular influence maximization problems. In this regard, we fill the gap by proposing seed selection strategies using network graphical properties in a generalized non-submodular threshold-based model, called influence barricade model. Under this model, we first establish theories to reveal graphical conditions that ensure the network generated by node removals has the same optimal seed set as that in the original network. We then exploit these theoretical conditions to develop efficient algorithms by strategically removing less-important nodes and selecting seeds only in the remaining network. To the best of our knowledge, this is the first graph-based approach that directly tackles non-submodular influence maximization. Evaluations on both synthetic and real-world Facebook/Twitter datasets confirm the superior efficiency of the proposed algorithms, which are orders of magnitude faster than benchmarks for large networks.

This work documents our investigation of multiple target tracking filters in proximity sensor networks when the target power levels are not known. The challenge is that when the targets are close, it is hard to determine if the sensor reports are the results of a loud target or multiple quiet targets. Given the binary measurements:1 for detection of targets and 0 for nondetection of targets, the works studies the feasibility of using the joint multitarget particle filter to estimate the number of targets, and at the same time estimate the 5D target states including the positions, velocities and power levels.

Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction.

In real-world applications, objects of multiple types are interconnected, formingHeterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to someeventand the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework calledHyperEdge-BasedEmbedding(Hebe) to learn object embeddings with events in heterogeneous information networks, where ahyperedgeencompasses the objects participating in one event. TheHebeframework models the proximity among objects in each event with two methods: (1) predicting a target object given other participating objects in the event, and (2) predicting if the event can be observed given all the participating objects. Since each hyperedge encapsulates more information of a given event,Hebeis robust to data sparseness and noise. In addition,Hebeis scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.

We study the possibility of predicting a social protest (planned, or unplanned) based on social media messaging. We consider the process called mobilization, described in the literature as the precursor of participation. Mobilization includes four stages: being sympathetic to the cause, being aware of the movement, motivation to take part and ability to participate. We suggest that expressions of mobilization in communications of individuals may be used to predict the approaching protest. We have utilized several Natural Language Processing techniques to create a methodology to identify mobilization in social media communication. Results of experimentation with Twitter data collected before and during the 2015 Baltimore events and the information on actual protests taken from news media show a correlation over time between volume of Twitter communications related to mobilization and occurrences of protest at certain geographical locations. We conclude with discussion of possible theoretical explanations and practical applications of these results.

In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.

The real-time discovery of local events (e.g., protests, crimes, disasters) is of great importance to various applications, such as crime monitoring, disaster alarming, and activity recommendation. While this task was nearly impossible years ago due to the lack of timely and reliable data sources, the recent explosive growth in geo-tagged tweet data brings new opportunities to it. That said, how to extract quality local events from geo-tagged tweet streams in real time remains largely unsolved so far.

Truth discovery is an important technique for enabling reliable crowdsourcing applications. It aims to automatically discover the truths from possibly conflicting crowdsourced claims. Most existing truth discovery approaches focus oncategoricalapplications, such as image classification. They use the accuracy, i.e., rate of exactly correct claims, to capture the reliability of participants. As a consequence, they are not effective for truth discovery inquantitativeapplications, such as percentage annotation and object counting, where similarity rather than exact matching between crowdsourced claims and latent truths should be considered. In this paper, we propose two unsupervised Quantitative Truth Finders (QTFs) for truth discovery in quantitative crowdsourcing applications. One QTF explores an additive model and the other explores a multiplicative model to capture different relationships between crowdsourced claims and latent truths in different classes of quantitative tasks. These QTFs naturally incorporate the similarity between variables. Moreover, they use the bias and the confidence instead of the accuracy to capture participants’ abilities in quantity estimation. These QTFs are thus capable of accurately discovering quantitative truths in particular domains. Through extensive experiments, we demonstrate that these QTFs outperform other state-of-the-art approaches for truth discovery in quantitative crowdsourcing applications and they are also quite efficient.

Networked data often consists of interconnected multi-typed nodes and links. A common assumption behind such heterogeneity is the shared clustering structure. However, existing network clustering approaches over-simplify the heterogeneity by either treating nodes or links in a homogeneous fashion, resulting in massive loss of information. In addition, these studies are more or less restricted to specific network schemas or applications, losing generality. In this paper, we introduce a flexible model to explain the process of forming heterogeneous links based on shared clustering information of heterogeneous nodes. Specifically, we categorize the link generation process into binary and weighted cases and model them respectively. We show these two cases can be seamlessly integrated into a unified model. We propose to maximize a joint log-likelihood function to infer the model efficiently with Expectation Maximization (EM) algorithms. Experiments on real-world networked data sets demonstrate the effectiveness and flexibility of the proposed method in fully capturing the dual heterogeneity of both nodes and links.

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model (Grempt), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.

Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns.

A Cyber-Physical System (CPS) integrates physical (i.e., sensor) devices with cyber (i.e., informational) components to form a situation-aware system that responds intelligently to dynamic changes in real-world. It has wide application to scenarios of traffic control, environment monitoring and battlefield surveillance. This study investigates the specific problem of intruder mining in CPS: With a large number of sensors deployed in a designated area, the task is real time detection of intruders who enter the area, based on untrustworthy data. We propose a method called IntruMine to detect and verify the intruders. IntruMine constructs monitoring graphs to model the relationships between sensors and possible intruders, and computes the position and energy of each intruder with the link information from these monitoring graphs. Finally, a confidence rating is calculated for each potential detection, reducing false positives in the results. IntruMine is a generalized approach. Two classical methods of intruder detection can be seen as special cases of IntruMine under certain conditions. We conduct extensive experiments to evaluate the performance of IntruMine on both synthetic and real datasets and the experimental results show that IntruMine has better effectiveness and efficiency than existing methods.

Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlations can reveal important relationships between different events. Unfortunately, correlation relationships on graph structures are not well studied and cannot be captured by traditional measures. In this work, we design a novel measure for assessing two-event structural correlations on graphs. Given the occurrences of two events, we choose uniformly a sample of "reference nodes" from the vicinity of all event nodes and employ the Kendall's τ rank correlation measure to compute the average concordance of event density changes. Significance can be efficiently assessed by τ's nice property of being asymptotically normal under the null hypothesis. In order to compute the measure in large scale networks, we develop a scalable framework using different sampling strategies. The complexity of these strategies is analyzed. Experiments on real graph datasets with both synthetic and real events demonstrate that the proposed framework is not only efficacious, but also efficient and scalable.

Assessing the quality of sensor-originated information is key for the effective and predictable operation sensor-enabled computerized applications. However, with increasing uncertainties due to alternative deployment scenarios, operational realities, and sensing resource use, it becomes very challenging in designing replicable software solutions that can be easily reused in a number of occasions with minimal customization. Leveraging semantic sensor web technologies, this paper presents an ontology-based design framework for organizing a library of quality of information (QoI) analysis algorithms specific to a data source, and interfacing to a library containing computational algorithms assessing quality of information. The ontology-based framework is broad enough to allow easy accommodation of new computational algorithms that domain experts may provide to the library as needed to reflect specific deployment, operational, sensing realities.

Prior to committing personnel to investigate a building or suspicious site such as a cave, it is imperative to determine the importance and current danger of the site. To this end, sensors on a robotic platform can interrogate the site prior to sending in personnel. This paper investigates methods to exploit multiple sensor modalities in order to automatically 1) detect human presence, and 2) detect human infrastructure and recent human activity. The paper describes 10 experimental scenarios to support these two tasks, demonstrates what type of inference each modality can make, and shows how to fuse the information from all sensors. Experimental results are also provided for the detection of the presence of humans.

In this paper we investigate the k-connectivity threshold of distributed dense ad hoc heterogeneous wireless sensor network architecture. We consider the situation when sensors are deployed in the surveillance area according to a uniform distribution perturbed by a Gaussian noise. We derive analytically the minimum detection range which guarantees an emerging structure in the network, namely the connectivity, which becomes larger and larger as the number of sensors in the network increase. This allows the target track to be propagated almost surely throughout the network using the minimum possible amount of prime energy. We report the results of some simulation experiments which further support the theoretical results.

This paper compares two different approaches for sensor selection for distributed tracking: 1) The Frisbee method, and 2) Global Node Selection (GNS). The Frisbee method is based on the proximity of the nodes to the predicted location of the target; GNS is based on minimizing the unbiased Cramer Rao lower bound (CRLB). Both theoretical and experimental results indicate that the Frisbee method is as effective as GNS. Furthermore, the Frisbee method is attractive due to its very light computational load.