Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the gamma value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data.
In the literature, most of the optimal sensor placement studies identify the sentinels' location via the framework of the budgeted maximum coverage problem (BMCP) @cite_0 , which aims to maximize a specific objective by finding a set of components with the minimum budget (the budget is often defined as the set size). Examples include the timely detection of contaminated water @cite_22 and early detection of the outbreak of Weblogs @cite_8 . As BMCP is NP-hard, heuristic algorithms like sub-modular maximization are often employed. Note that designing the objective functions of BMCP relies heavily on the known interaction structure among components. When the underlying interaction structure cannot be observed directly, such as the social media network in charge of information diffusion and the social contact network for disease spread, the current methods proposed within the BMCP framework cannot be used @cite_23 .
@cite_0 @cite_22 @cite_23 @cite_8
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The problem of deploying sensors in a large water distribution network is considered, in order to detect the malicious introduction of contaminants. It is shown that a large class of realistic objective functions—such as reduction of detection time and the population protected from consuming contaminated water—exhibits an important diminishing returns effect called submodularity. The submodularity of these objectives is exploited in order to design efficient placement algorithms with provable performance guarantees. The algorithms presented in this paper do not rely on mixed integer programming, and scale well to networks of arbitrary size. The problem instances considered in the approach presented in this paper are orders of magnitude (a factor of 72) larger than the largest problems solved in the literature. It is shown how the method presented here can be extended to multicriteria optimization, selecting placements robust to sensor failures and optimizing minimax criteria. Extensive empirical evidence ... Given a water distribution network, where should we place sensors toquickly detect contaminants? Or, which blogs should we read to avoid missing important stories?. These seemingly different problems share common structure: Outbreak detection can be modeled as selecting nodes (sensor locations, blogs) in a network, in order to detect the spreading of a virus or information asquickly as possible. We present a general methodology for near optimal sensor placement in these and related problems. We demonstrate that many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of "submodularity". We exploit submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm. We also derive online bounds on the quality of the placements obtained by any algorithm. Our algorithms and bounds also handle cases where nodes (sensor locations, blogs) have different costs. We evaluate our approach on several large real-world problems,including a model of a water distribution network from the EPA, andreal blog data. The obtained sensor placements are provably near optimal, providing a constant fraction of the optimal solution. We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude. We also show how the approach leads to deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions. Abstract In this paper, a strength-based optimum signal detection scheme for binary concentration-encoded molecular communication (CEMC) system has been presented. In CEMC, a single type of information molecule is assumed to carry the information from the transmitting nanomachine (TN), through the propagation medium, to the receiving nanomachine (RN) in the form of received concentration of information molecules at the location of the RN. We consider a pair of nanomachines communicating by means of on–off keying (OOK) transmission protocol in a three-dimensional ideal (i.e. free) diffusion-based unbounded propagation environment. First, based on stochastic chemical kinetics of the reaction events between ligand molecules and receptors, we develop a mathematical receiver model of strength-based detection scheme for OOK CEMC system. Using an analytical approach, we explain the receiver operating characteristic (ROC) curves of the receiver thus developed. Finally, we propose a variable threshold -based detection scheme and explain its communication range and rate dependent characteristics. We show that it provides an improvement in the communication ranges compared to fixed threshold -based detection scheme. (Part of this paper has been peer-reviewed and published in BWCCA-2012 conference in Victoria, BC, 12–14 November, 2012 [20] .) One of the research issues in wireless sensor networks (WSNs) is how to efficiently deploy sensors to cover an area. In this paper, we solve the k-coverage sensor deployment problem to achieve multi-level coverage of an area I. We consider two sub-problems: k-coverage placement and distributed dispatch problems. The placement problem asks how to determine the minimum number of sensors required and their locations in I to guarantee that I is k-covered and the network is connected; the dispatch problem asks how to schedule mobile sensors to move to the designated locations according to the result computed by the placement strategy such that the energy consumption due to movement is minimized. Our solutions to the placement problem consider both the binary and probabilistic sensing models, and allow an arbitrary relationship between the communication distance and sensing distance of sensors. For the dispatch problem, we propose a competition-based and a pattern-based schemes. The former allows mobile sensors to bid for their closest locations, while the latter allows sensors to derive the target locations on their own. Our proposed schemes are efficient in terms of the number of sensors required and are distributed in nature. Simulation results are presented to verify their effectiveness.