We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon, as opposed to just instantaneous location. We formulate such task assignment as an optimization problem, that seeks to maximize the total payoff from all assigned tasks, subject to a maximum bound on the detour (from the expected path) that a worker will experience to complete her assigned tasks. We develop credible computationally-efficient heuristics to address this optimization problem (whose exact solution requires solving a complex integer linear program), and show, via simulations with realistic topologies and commuting patterns, that a specific heuristic (called Greedy-ILS) increases the fraction of assigned tasks by more than 20%, and reduces the average detour overhead by more than 60%, compared to the current decentralized approach.

The developing regions of the world contain most of the human population and the planet's natural resources, and hence are particularly important to the study of sustainability. Despite some difficult problems in such places, a period of enormous technology-driven change has created new opportunities to address poor management of resources and improve human well-being.

The capacitated P-median problem (CPMP) is an NPcomplete problem which investigates the problem of partitioning a set of N nodes into M different disjoint clusters, each represented by a certain node that is designed as concentrator. The NM nodes that are not chosen as concentrators are referred as terminals. The partitioning of the initial N nodes must be performed in such a way that a measure of total distance between the terminals and their corresponding concentrators is minimized. In addition, a capacity constraint imposed on the concentrators must be met, in order to obtain feasible solutions to the problem [1-4]. A direct application of the CPMP is in the context of communication networks deployment, where a set of terminals in the network must be assigned to the corresponding concentrator in order to compose access networks that satisfy the rate requirements of such terminals [5]. In this context, most of the efforts so far has focused on the topological design of communication networks (e.g. Wireless Sensor Networks (WSN), backbone networks or mobile networks [6-8]) since many of the processes involved in such networks can be approached as a CPMP problem, e.g.

Failure detection in telecommunication networks is a vital task. So far, several supervised and unsupervised solutions have been provided for discovering failures in such networks. Among them unsupervised approaches has attracted more attention since no label data is required. Often, network devices are not able to provide information about the type of failure. In such cases the type of failure is not known in advance and the unsupervised setting is more appropriate for diagnosis. Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the anomaly detection literature and can be applied to matrix data (e.g. Users-Features). However, one of the important properties of network data is their temporal sequential nature. So considering the interaction of dimensions over a third dimension, such as time, may provide us better insights into the nature of network failures. In this paper we demonstrate the power of three-way analysis to detect events and anomalies in time-evolving network data.

The Semantic Sensor Web (SSW) will eventually revolutionize how we perceive and query information about the physical world. Currently, there is an ongoing effort to develop a searchable web of things that sense and control the world. As this new internet of things expands, there will be an explosion of available raw data that may not always be reachable by users. Bridging this gap between what the user wants and the information collected and represented by embedded devices is a critical issue. In order to really maximize the benefit of such a web of networked sensing devices, new semantic approaches that can infer and predict additional information about the sensors and their context need to be developed. This paper proposes a content enrichment approach that uses sensor and context data as features to predict new meta-tags that can further identify relevant categorizations for the embedded devices and the physical data they collect. Specifically, machine learning classification and regression techniques are used to predict semantic tags for each embedded system context. Results of the 10-fold cross validation analysis and feature ranking are presented and discussed.

This chapter overviews a recently introduced network-based model of combinatorial landscapes: Local Optima Networks (LON). The model compresses the information given by the whole search space into a smaller mathematical object that is a graph having as vertices the local optima and as edges the possible weighted transitions between them. Two definitions of edges have been proposed: basin-transition and escape-edges, which capture relevant topological features of the underlying search spaces. This network model brings a new set of metrics to characterize the structure of combinatorial landscapes, those associated with the science of complex networks. These metrics are described, and results are presented of local optima network extraction and analysis for two selected combinatorial landscapes: NK landscapes and the quadratic assignment problem. Network features are found to correlate with and even predict the performance of heuristic search algorithms operating on these problems.

This article establishes the performance of stochastic blockmodels in addressing the co-clustering problem of partitioning a binary array into subsets, assuming only that the data are generated by a nonparametric process satisfying the condition of separate exchangeability. We provide oracle inequalities with rate of convergence $\mathcal{O}_P(n^{-1/4})$ corresponding to profile likelihood maximization and mean-square error minimization, and show that the blockmodel can be interpreted in this setting as an optimal piecewise-constant approximation to the generative nonparametric model. We also show for large sample sizes that the detection of co-clusters in such data indicates with high probability the existence of co-clusters of equal size and asymptotically equivalent connectivity in the underlying generative process.

Statistical analysis of social networks and other relational data is becoming an increasingly important problem as the scope and availability of network data increases. Network data--such as the friendships in a social network--is often dynamic in a sense that relations between entities rise and decay over time. A fundamental problem in the analysis of such dynamic network data is to extract a summary of the common structure and the dynamics of the underlying relations between entities. Accurate models of structure and dynamics of network data have many applications. They allow us to predict missing relationships [20, 21, 23], recommend potential new relations [2], identify clusters and groups of nodes [1, 29], forecast future links [4, 9, 11, 24], and even predict group growth and longevity [15]. Here we present a new approach to modeling network dynamics by considering time-evolving interactions between groups of nodes as well as the arrival and departure dynamics of individual nodes to these groups. We develop a dynamic network model, Dynamic Multi-group Membership Graph Model, that identifies the birth and death of individual groups as well as the dynamics of node joining and leaving groups in order to explain changes in the underlying network linking structure. Our nonparametric model considers an infinite number of latent groups, where each node can belong to multiple groups simultaneously. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model.

Recently, an increasing number of mobile users are eagerly using the cellular network in data applications. In particular, multimedia downloads generated by Internet-capable smart phones and other portable devices has been widely recognized as the major source for strains in cellular networks, to a degree where service quality for all users is significantly impacted. In this paper we explore the novel concept of proactive content caching using evolutionary algorithms inspired by the inherent predictability of the mobile user behavior. Users can then use the cached version of the content in order to achieve a better user experience and reduce the peak-to-average ratio in mobile networks, especially during peak hours of the day. Finally, we confirm the merits of the proposed scheduler using real data traces of different user's requests and Wi-Fi availability. The results after applying the proposed scheduling algorithm show that up to 70% of the user content requests can be fulfilled i.e. the content were successfully cached before request. We also observe that proposed scheduler outperforms a baseline scheduler based on simulated annealing.

Relational data-like graphs, networks, and matrices-is often dynamic, where the relational structure evolves over time. A fundamental problem in the analysis of time-varying network data is to extract a summary of the common structure and the dynamics of the underlying relations between the entities. Here we build on the intuition that changes in the network structure are driven by the dynamics at the level of groups of nodes. We propose a nonparametric multi-group membership model for dynamic networks. Our model contains three main components: We model the birth and death of individual groups with respect to the dynamics of the network structure via a distance dependent Indian Buffet Process. We capture the evolution of individual node group memberships via a Factorial Hidden Markov model. And, we explain the dynamics of the network structure by explicitly modeling the connectivity structure of groups. We demonstrate our model's capability of identifying the dynamics of latent groups in a number of different types of network data. Experimental results show that our model provides improved predictive performance over existing dynamic network models on future network forecasting and missing link prediction.