- Plainsboro, New Jersey, United States
Jie Gao
Rutgers, The State University of New Jersey, Computer Science, Faculty Member
- SUNY: Stony Brook University, Computer Science, Faculty Memberadd
- I am a Professor of Computer Science at Rutgers University.edit
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In this paper we study the following problem: given a set of m sensors that collectively cover a set of n target points with heterogeneous coverage requirements (target j needs to be covered every fj slots), how to schedule the sensor... more
In this paper we study the following problem: given a set of m sensors that collectively cover a set of n target points with heterogeneous coverage requirements (target j needs to be covered every fj slots), how to schedule the sensor duty cycles such that all coverage requirements are satisfied and the maximum number of sensors turned on at any time slot is minimized. The problem models varied real-world applications in which sensing tasks exhibit high discrepancy in coverage requirements — critical locations often need to be covered much more frequently. We provide multiple algorithms with best approximation ratio of O (log n + log m) for the maximum number of sensors to turn on, and bi-criteria algorithm with (α, β)-approximation factors with high probability, where the number of sensors turned on is an α = O(δ(log (n) + log(m))/β)-approximation of the optimal (satisfying all requirements) and the coverage requirement is a β-approximation; δ is the approximation ratio achievable ...
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Research Interests: Computer Science, Wireless Sensor Networks, Clustering Algorithms, Space Exploration, Kinetic Theory, and 13 moreWireless Network, Efficient Algorithm for ECG Coding, Sensor Network, Mobile Robot, Data Structures, Mobile Agents, Communication Channels, Mobile Agent, Steiner Minimal Tree, Real Time Monitoring, Sensor nodes, OOB (Out Of Band, and Approximate solution
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Research Interests: Computer Science, Algorithms, Distributed Computing, Computer Graphics, Design, and 15 moreComputer Networks, Theory, Ad Hoc Networks, Sensor networks, Protocols, Algorithm Design, Sensor Network, Rare Event, Wireless Sensor, Sparse Data, Sensor Networks, Sensor nodes, Information Aggregation, Cumulant, and Data Duplication
Several aspects of managing a sensor network (e.g., motion planning for data mules, serial data fusion and inference) benefit once the network is linearized to a path. The linearization is often achieved by constructing a space filling... more
Several aspects of managing a sensor network (e.g., motion planning for data mules, serial data fusion and inference) benefit once the network is linearized to a path. The linearization is often achieved by constructing a space filling curve in the domain. However, existing methods cannot handle networks distributed on surfaces of complex topology. This paper presents a novel method for generating space filling curves for 3D sensor networks that are distributed densely on some two-dimensional geometric surface. Our algorithm is completely distributed and constructs a path which gets uniformly, progressively denser as it becomes longer. We analyze the algorithm mathematically and prove that the curve we obtain is dense. Our method is based on the Hodge decomposition theorem and uses holomorphic differentials on Riemann surfaces. The underlying high genus surface is conformally mapped to a union of flat tori and then a proportionally-dense space filling curve on this union is construc...
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Research Interests: Harmonic Analysis, Partial Differential Equations, Distributed Computing, Computer Networks, Wireless Sensor Networks, and 13 moreQueueing theory, System Design, Navigation, Sensor Network, Data Structures, PARTIAL DIFFERENTIAL EQUATION, Robustness, Load Balance, Tree Structure, Wireless Sensor Network, Harmonic Function, Range Query, and Algebraic Structure
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Research Interests: Computational Geometry, Graph Theory, Distributed Algorithms, Routing, Wireless Sensor Networks, and 11 moreResource Allocation, Face, Wireless networks, Greedy Algorithms, Optimization, Wireless Network, Three Dimensional, Load Balance, Wireless Sensor Network, Sensor nodes, and Distributed Algorithm
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Research Interests: Linear Programming, Localization, Low Noise Measurement Systems, Sensor networks, Convergence Rate, and 8 moreLocalization in underwater sensor network, Wireless Sensor Network Secure Localization, Sensor Networks, Noise Measurement, Wireless Sensor Network, LINEAR PROGRAM, Sensor nodes, and Distributed Algorithm
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A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information, and has broad... more
A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information, and has broad applica- tions. In this paper, we generalize the traditional Bloom filter to Weighted Bloom Filter, which incorporates the information on the query frequencies and the membership likelihood of the elements into its optimal design. It has been widely observed that in many applications, some popular elements are queried much more often than the others. The traditional Bloom filter for data sets with irregular query patterns and non-uniform membership likelihood can be further optimized. We derive the optimal configuration of the Bloom filter with query-frequency and membership- likelihood information, and show that the adapted Bloom filter always outperforms the traditional Bloom filter. Under reasonable frequency models such as the step distribution or the...
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This paper surveys the use of geometric methods for wireless sensor networks. The close relationship of sensor nodes with their embedded physical space imposes a unique geometric character on such systems. The physical locations of the... more
This paper surveys the use of geometric methods for wireless sensor networks. The close relationship of sensor nodes with their embedded physical space imposes a unique geometric character on such systems. The physical locations of the sensor nodes greatly impact on system design in all aspects, from low-level networking and organization to high-level information processing and applications. This paper reviews work in the past 10 years on topics such as network localization, geometric routing, information discovery, data-centric routing and topology discovery.
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Social behaviors and choices spread through interactions and may lead to a cascading behavior. Understanding how such social cascades spread in a network is crucial for many applications ranging from viral marketing to political... more
Social behaviors and choices spread through interactions and may lead to a cascading behavior. Understanding how such social cascades spread in a network is crucial for many applications ranging from viral marketing to political campaigns. The behavior of cascade depends crucially on the model of cascade or social influence and the topological structure of the social network.
In this paper we study the general threshold model of cascades which are parameterized by a distribution over the natural numbers, in which the collective influence from infected neighbors, once beyond the threshold of an individual u, will trigger the infection of u. By varying the choice of the distribution, the general threshold model can model cascades with and without the submodular property. In fact, the general threshold model captures many previously studied cascade models as special cases, including the independent cascade model, the linear threshold model, and k-complex contagions.
We provide both analytical and experimental results for how cascades from a general threshold model spread in a general growing network model, which contains preferential attachment models as special cases. We show that if we choose the initial seeds as the early arriving nodes, the contagion can spread to a good fraction of the network and this fraction crucially depends on the fixed points of a function derived only from the specified distribution. We also show, using a coauthorship network derived from DBLP databases and the Stanford web network, that our theoretical results can be used to predict the infection rate up to a decent degree of accuracy, while the configuration model does the job poorly.
In this paper we study the general threshold model of cascades which are parameterized by a distribution over the natural numbers, in which the collective influence from infected neighbors, once beyond the threshold of an individual u, will trigger the infection of u. By varying the choice of the distribution, the general threshold model can model cascades with and without the submodular property. In fact, the general threshold model captures many previously studied cascade models as special cases, including the independent cascade model, the linear threshold model, and k-complex contagions.
We provide both analytical and experimental results for how cascades from a general threshold model spread in a general growing network model, which contains preferential attachment models as special cases. We show that if we choose the initial seeds as the early arriving nodes, the contagion can spread to a good fraction of the network and this fraction crucially depends on the fixed points of a function derived only from the specified distribution. We also show, using a coauthorship network derived from DBLP databases and the Stanford web network, that our theoretical results can be used to predict the infection rate up to a decent degree of accuracy, while the configuration model does the job poorly.