Qianqian Yang

Energy-Efficient Probabilistic Area Coverage in Wireless Sensor Networks

As the binary sensing model is a coarse approximation of reality, the probabilistic sensing model has been proposed as a more realistic model for characterizing the sensing region. A point is covered by sensor networks under the probabilistic sensing model if the joint sensing probability from multiple sensors is larger than a predefined threshold ε. Existing work has focused on probabilistic point coverage since it is extremely difficult to verify the coverage of a full continuous area (i.e., probabilistic area coverage). In this paper, we tackle such a challenging problem. We first study the sensing probabilities of two points with a distance of d and obtain the fundamental mathematical relationship between them. If the sensing probability of one point is larger than a certain value, the other is covered. Based on such a finding, we transform probabilistic area coverage into probabilistic point coverage, which greatly reduces the problem dimension. Then, we design the ε-full area coverage optimization (FCO) algorithm to select a subset of sensors to provide probabilistic area coverage dynamically so that the network lifetime can be prolonged as much as possible. We also theoretically derive the approximation ratio obtained by FCO to that by the optimal one. Finally, through extensive simulations, we demonstrate that FCO outperforms the state-of-the-art solutions significantly.

Energy-efficient probabilistic full coverage in wireless sensor networks

It is a common class of applications with wireless sensor network to provide full coverage to the region of interest (ROI), such as environment monitoring, military detection and agricultural observation. Existing literatures on full coverage are mostly based on the binary sensing model to simplify the problem. However, the results are far from the reality since binary sensing model as a coarse approximation is too conservative. The probabilistic sensing model has been proposed as a more realistic model to characterize the sensing region. In this paper, we introduce the concept of ε-full coverage based on probabilistic model, i.e., every point in ROI has at least a probability ε of being covered by sensors. We explore the mathematic relationship between the probabilities of two adjacent points being covered and transform ε-full coverage problem into point coverage problem. Then, we design ε-full coverage optimization (FCO) to select a subset of sensors to provide ε-full coverage dynamically so that the lifetime of network is prolonged. This algorithm outperforms the state-of-the-art solution significantly, which we have validated by simulations.

Energy-efficient area coverage in bistatic radar sensor networks

In this paper we study area coverage in bistatic radar sensor networks (BRSN), which is composed of a collection of transmitters and receivers. Coverage in BRSN is much more difficult than that in traditional sensor networks as the sensing area of a bistatic radar depends on the positions of its component transmitter and receiver, and is in general of an elliptical shape. We first investigate the geometrical relationship between the c-coverage area of a bistatic radar and the distance between its component transmitter and receiver, based on which we reduce the number of candidate bistatic radars from all transmitter-receiver pairs. Then we reduce the problem dimension by transforming the area coverage problem to point coverage problem by employing intersection point concept. Finally we propose an efficient algorithm to solve the Point Coverage Problem, which thus solves the area coverage problem. We perform extensive simulations to validate our analysis and the performance of the proposed algorithm.

Audience retention rate aware coded video caching

Users often do not watch an online video content in its entirety, and abort the video before it is completed. This is captured by the notion of audience retention rate, which indicates the portion of a video users watch on average. A decentralized coded caching scheme, called partial coded caching (PCC), is proposed here to take into account both the popularity, and the audience retention rate of the video files in a database. The achievable average delivery rate of PCC is characterised over all possible demand combinations. Two different cache allocation schemes, called the optimal cache allocation (OCA) and the popularity based cache allocation (PCA), are proposed to allocate cache capacities among the different chunks of video files. Numerical results validate that the proposed coded caching scheme, either with the OCA or the PCA, outperforms conventional uncoded caching, as well as the state-of-the-art coded caching schemes that consider only file popularities.

Decentralized Caching and Coded Delivery With Distinct Cache Capacities

Decentralized proactive caching and coded delivery is studied in a content delivery network, where each user is equipped with a cache memory, not necessarily of equal capacity. Cache memories are filled in advance during the off-peak traffic period in a decentralized manner, i.e., without the knowledge of the number of active users, their identities, or their particular demands. User demands are revealed during the peak traffic period, and are served simultaneously through an error-free shared link. The goal is to find the minimum delivery rate during the peak traffic period that is sufficient to satisfy all possible demand combinations. A group-based decentralized caching and coded delivery scheme is proposed, and it is shown to improve upon the state of the art in terms of the minimum required delivery rate when there are more users in the system than files. Numerical results indicate that the improvement is more significant as the cache capacities of the users become more skewed. A new lower bound on the delivery rate is also presented, which provides a tighter bound than the classical cut-set bound.

Variable-power scheduling for perpetual target coverage in energy harvesting wireless sensor networks

We study perpetual target coverage with an energy harvesting wireless sensor network (WSN) assuming that each sensor can modulate its sensing range by dynamically varying its operating power, e.g., radar sensors. In this variable-power scheduling scenario, we first consider the maximum network lifetime problem for battery-powered WSNs. The solution to this problem allows us to decide if a given energy harvesting WSN is capable of perpetual operation satisfying energy neutrality. Then, we formulate the energy efficient perpetual target coverage problem and prove its NP completeness. A polynomial algorithm is proposed, and its effectiveness is validated through extensive numerical simulations.

The multi-layer information bottleneck problem

The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variable, quantified by the mutual information. The hidden variables and the source can be arbitrarily correlated. The optimal trade-off between rates of relevance and compression (or complexity) is obtained through a singleletter characterization, referred to as the rate-relevance region. Conditions of successive refinabilty are given. Binary source with BSC hidden variables and binary source with BSC/BEC mixed hidden variables are both proved to be successively refinable. We further extend our result to Guassian models. A counterexample of successive refinability is also provided.

Towards optimal barrier coverage in wireless sensor and actor networks

Barrier coverage in sensor networks has attracted much attention in recent years. Existing results revealed that sensor mobility can remarkably improve the coverage performance of sensor networks. Considering the high manufacture cost of mobile sensors, in this paper we propose to tradeoff the barrier coverage performance and deployment budget by employing a wireless sensor and actor network (WSAN), wherein an actor is used to move static sensors around in order to enhance the barrier coverage performance. We first formulate the barrier coverage problem in WSAN and propose a new coverage metric to evaluate the barrier coverage performance. Then we design an efficient actor movement scheme, S-AMS, for the case where the number of monitoring points can be divided by the number of available sensors. By exploiting the actors mobility and clustering procedure, S-AMS is able to significantly improve barrier coverage. Based on the insight from S-AMS, we design G-AMS for the general case. We show that S-AMS achieves asymptotically optimal solution for the special case and G-AMS obtains close-to-optimal solution for the general case. Extensive simulations are conducted to demonstrate the performance of our proposed schemes.