Siyao Cheng

Curve Query Processing in Wireless Sensor Networks

Most existing query processing algorithms for wireless sensor networks (WSNs) can only deal with discrete values. However, since the monitored environment always changes continuously with time, discrete values cannot describe the environment accurately and, hence, may not satisfy a variety of query requirements, such as the queries of the maximal, minimal, and inflection points. It is, therefore, of great interest to introduce new queries capable of processing time-continuous data. This paper investigates curve query processing for WSNs as curve is an effective way to represent continuous sensed data. Specifically, a sensed curve derivation algorithm to support curve query processing in WSNs is first proposed. Then, the aggregation operation is employed as an example to illustrate curve query processing. The corresponding accurate and approximate aggregation algorithms are devised accordingly. We demonstrate that the energy cost of the approximate aggregation algorithm is optimal, provided that the required precision is satisfied. The theoretical analysis and experimental results indicate that the proposed algorithms can achieve high performance in terms of accuracy and energy efficiency.

Approximate aggregation for tracking quantiles and range countings in wireless sensor networks

We consider the problem of tracking quantiles and range countings in wireless sensor networks. The quantiles and range countings are two important aggregations to characterize a data distribution. Let S ( t ) = ( d 1 , ? , d n ) denote the multi-set of sensory data that have arrived until time t, which is a sequence of data orderly collected by nodes s 1 , s 2 , ? , s k . One of our goals is to continuously track ?-approximate ?-quantiles ( 0 ? ? ? 1 ) of S ( t ) for all ?s with efficient total communication cost and balanced individual communication cost. The other goal is to track ( ? , ? ) -approximate range countings satisfying the requirement of arbitrary precision specified by different users. In this paper, a deterministic tracking algorithm based on a dynamic binary tree is proposed to track ?-approximate ?-quantiles, whose total communication cost is O ( k / ? ? log ? n ? log 2 ? ( 1 / ? ) ) , where k is the number of the nodes in a network, n is the total number of the data, and ? is the user-specified approximation error. For range countings, a Bernoulli sampling based algorithm is proposed to track ( ? , ? ) -approximate range countings, whose total communication cost is O ( 2 ? 2 ln ? 2 1 - 1 - ? + n c ) , where ? is the user-specified error probability, n c is the number of clusters.

Approximate Physical World Reconstruction Algorithms in Sensor Networks

To observe the complicated physical world, the sensors in a network sense and sample the data from the physical world. Currently, most existing works use the Equi-Frequency Sampling (EFS) methods or EFS based methods for data acquisition. However, the accuracy of EFS and EFS based methods cannot be guaranteed in practice since the physical world keeps changing continuously, and these methods do not effectively support reconstruction of the monitored physical world. To overcome the shortages of EFS and EFS based methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ε)-approximation to the physical world for any ε ≥ 0. Two physical-world-aware data acquisition algorithms are proposed. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and the given ε. The thorough analysis on the performances of the algorithms are also provided. It is proven that the error bounds of the algorithms are O(ε) and the complexities of the algorithms are O(1/(ε1/4)). Based on the new data acquisition algorithms, an algorithm for reconstructing the physical world is proposed and analyzed. The theoretical analysis and experimental results show that the proposed algorithms have high performances on the aspects of accuracy and energy consumption.

Drawing dominant dataset from big sensory data in wireless sensor networks

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks. The scale of the sensory data in many applications has already exceeds several petabytes annually, which is beyond the computation and transmission capabilities of the conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we define the concept of e-dominant dataset, which is only a small data set and can represent the vast information carried by big sensory data with the information loss rate being less than e, where e can be arbitrarily small. We prove that drawing the minimum e-dominant dataset is polynomial time solvable and provide a centralized algorithm with 0(n3) time complexity. Furthermore, a distributed algorithm with constant complexity (O(l)) is also designed. It is shown that the result returned by the distributed algorithm can satisfy the e requirement with a near optimal size. Finally, the extensive real experiment results and simulation results are carried out. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.

O(ε)-Approximation to physical world by sensor networks

To observe the complicate physical world by a WSN, the sensors in the WSN senses and samples the data from the physical world. Currently, most of the existing work use equi-frequency sampling methods (EFS) or EFS based sampling methods for data acquisition in sensor networks. However, the accuracies of EFS and EFS based sampling methods cannot be guaranteed in practice since the physical world usually varies continuously, and these methods does not support reconstructing of the monitored physical world. To overcome the shortages of EFS and EFS based sampling methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ϵ)-approximation to the physical world for any ϵ ≥ 0. Two physical-world-aware data acquisition algorithms based on Hermit and Spline interpolation are proposed in the paper. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and given c. The thorough analysis on the performance of the algorithms are also provided, including the accuracies, the smooth of the outputted curves, the error bounds for computing first and second derivatives, the number of the sampling times and complexities of the algorithms. It is proven that the error bounds of the algorithms are O(ϵ) and the complexities of the algorithms are O(1/ϵ1/4). Based on the new data acquisition algorithms, an algorithm for reconstructing physical world is also proposed and analyzed. The theoretical analysis and experimental results show that all the proposed algorithms have high performance in items of accuracy and energy consumption.

(ε, δ)-Approximate Aggregation Algorithms in Dynamic Sensor Networks

Aggregation operations are important in WSN applications. Since large numbers of applications only require approximate aggregation results rather than the exact ones, some approximate aggregation algorithms have been proposed to save energy. However, the error bounds of these algorithms are fixed and it is impossible to adjust the error bounds automatically, so they cannot meet the requirement of arbitrary precision required by various users. Thus, a uniform sampling-based algorithm was proposed by the authors of this paper to satisfy arbitrary precision requirement. Unfortunately, this uniform sampling-based algorithm is only suitable for static sensor networks. To overcome the shortcoming of the uniform sampling-based algorithm, this paper proposes four Bernoulli sampling-based and distributed approximate aggregation algorithms to process the snapshot and continuous aggregation queries in dynamic sensor networks. Theoretical analysis and experimental results show that the proposed algorithms have high performance in terms of accuracy and energy consumption.

Approximate Aggregation for Tracking Quantiles in Wireless Sensor Networks

We consider the problem of tracking quantiles in wireless sensor networks with efficient communication cost. Compared with the algebraic aggregations such as Sum, Count, or Average, holistic aggregations such as quantiles can better characterize data distribution. Let \(S(t) = (d_1, \ldots , d_n)\) be the multi-set of sensory data that have arrived until time \(t\) in the entire network, which is a sequence of data orderly collected by nodes \(s_1, s_2, \ldots , s_k\). The goal is to continuously track \(\epsilon \)-approximate \(\phi \)-quantiles \((0 \le \phi \le 1)\) of \(S(t)\) at the sink for all \(\phi \)’s with efficient total communication cost and balanced individual communication cost. In this paper, a deterministic tracking algorithm based on a dynamic binary tree is proposed to track \(\epsilon \)-approximate \(\phi \)-quantiles \((0 \le \phi \le 1)\) in wireless sensor networks, whose total communication cost is \(O(k / \epsilon \cdot \log n \cdot \log ^2 (1 / \epsilon ))\), where \(k\) is the number of the nodes in a network, \(n\) is the total number of the data items, and \(\epsilon \) is the required approximation error.

Adaptive connected dominating set discovering algorithm in energy-harvest sensor networks

A Wireless Sensor Network consists of a number of sensors. The energy of each sensor is limited which limits network lifetime. There are many existing energy efficiency algorithms to prolong network lifetime. Basically, there are two kinds of methods. One is energy-efficiency management, such as duty-cycling using virtual-backbones. The other one is energy provision, such as energy harvest from the environment. In this paper, we introduce a new problem, CDSEH, to combine these two methods together. We also propose a new standard to define the network lifetime of a WSN. We prove that the CDSEH problem is NP-Complete and propose two approximate algorithms accordingly. Extensive simulation results are shown to validate the performance of our algorithms.

Exploring Connected Dominating Sets in Energy Harvest Networks

Duty-cycle scheduling is an effective way to balance energy consumptions and prolong network lifetime of wireless sensor networks (WSNs), which usually requires a connected dominating set (CDS) to guarantee network connectivity and coverage. Therefore, the problem of finding the largest number of CDSs is important for WSNs. The previous works always assume all the nodes are non-rechargeable. However, WSNs are now taking advantages of rechargeable nodes to become energy harvest networks (EHNs). To find the largest number of CDSs then becomes completely different. This is the first work to investigate, how to identify the largest number of CDSs in EHNs to prolong network lifetime. The investigated novel problems are proved to be NP-Complete and we propose four approximate algorithms, accordingly. Both the solid theoretical analysis and the extensive simulations are performed to evaluate our algorithms.

Energy-Efficient Algorithm for Multicasting in Duty-Cycled Sensor Networks.

Multicasting is a fundamental network service for one-to-many communications in wireless sensor networks. However, when the sensor nodes work in an asynchronous duty-cycled way, the sender may need to transmit the same message several times to one group of its neighboring nodes, which complicates the minimum energy multicasting problem. Thus, in this paper, we study the problem of minimum energy multicasting with adjusted power (the MEMAP problem) in the duty-cycled sensor networks, and we prove it to be NP-hard. To solve such a problem, the concept of an auxiliary graph is proposed to integrate the scheduling problem of the transmitting power and transmitting time slot and the constructing problem of the minimum multicast tree in MEMAP, and a greedy algorithm is proposed to construct such a graph. Based on the proposed auxiliary graph, an approximate scheduling and constructing algorithm with an approximation ratio of 4lnK is proposed, where K is the number of destination nodes. Finally, the theoretical analysis and experimental results verify the efficiency of the proposed algorithm in terms of the energy cost and transmission redundancy.