Shili Xiang

Two-Tier Multiple Query Optimization for Sensor Networks

When there are multiple queries posed to the resource-constrained wireless sensor network, it is critical to process them efficiently. In this paper, we propose a two-tier multiple query optimization (TTMQO) scheme. The first tier, called base station optimization, adopts a cost-based approach to rewrite a set of queries into an optimized set that shares the commonality and eliminates the redundancy among the queries in the original set. The optimized queries are then injected into the wireless sensor network. In the second tier, called in-network optimization, our scheme efficiently delivers query results by taking advantage of the broadcast nature of the radio channel and sharing the sensor readings among similar queries over time and space at a finer granularity. Our experimental results indicate that our proposed TTMQO scheme offers significant improvements over the traditional single query optimization technique.

Sensor relocation for emergent data acquisition in sparse mobile sensor networks

In this paper, we study the problem of sensor relocation for emergent data acquisition (initiated by a base station) in sparse mobile sensor networks. We propose a distributed scheme called BRIDGE that relocates mobile sensors to fulfill an emergent data acquisition task with the objective to minimize the task completion time. BRIDGE gradually finds a sensor that is close to the task location and relocates that sensor to the task location, and at the same time relocates some other sensors to connect that sensor to the base station. BRIDGE exploits the encountered sensors during relocation, and handles the challenges caused by intermittent connections. Our extensive performance study shows the effectiveness of our proposed scheme.

Multiple Query Optimization for Wireless Sensor Networks

Our goal is to design a light-weight but effective scheme to support multiple data acquisition and aggregation queries in a wireless sensor network, in order to minimize the number of radio transmissions. Apart from being much more powerful than sensor nodes, the base station is also the interface of a wireless sensor network. Thus, we use the base station as a filter to reduce duplicate data accesses from the sensor network, and as a screen to hide the query dynamics as much as possible. We design a two-tier optimization scheme, base station optimization and in-network optimization.

Query Allocation in Wireless Sensor Networks with Multiple Base Stations

To support large-scale sensor network applications, in terms of both network size and query population, an infrastructure with multiple base stations is necessary. In this paper, we optimize the allocation of queries among multiple base stations, in order to minimize the total data communication cost among the sensors. We first examine the query allocation problem in a static context, where a two-phase semi-greedy allocation framework is designed to exploit the sharing among queries. Also, we investigate dynamic environments with frequent query arrivals and terminations and propose adaptive query migration algorithms. Finally, extensive experiments are conducted to compare the proposed techniques with existing works. The experimental results show the effectiveness of our proposed query allocation schemes.

Similarity-aware query allocation in sensor networks with multiple base stations

In this paper, we consider a large scale sensor network comprising multiple, say K, base stations and a large number of wireless sensors. Such an infrastructure is expected to be more energy efficient and scale well with the size of the sensor nodes. To support a large number of queries, we examine the problem of allocating queries across the base stations to minimize the total data communication cost among the sensors. In particular, we examine similarity-aware techniques that exploit the similarities among queries when allocating queries, so that queries that require data from a common set of sensor nodes are allocated to the same base stations. We first approximate the problem of allocating queries to K base stations as a max-K-cut problem, and adapts an existing solution to our context. However, the scheme only works in a static context, where all queries are known in advance. In order to operate in a dynamic environment with frequent query arrivals and termination, we further propose a novel similarity-aware strategy that allocates queries to base stations one at a time. We also propose several heuristics to order a batch of queries for incremental allocation. We conducted experiments to evaluate our proposed schemes, and our results show that our similarity-aware query allocation schemes can effectively exploit the sharing among queries to greatly reduce the communication cost.

Privacy Preservation in Streaming Data Collection

Big data management and analysis has become a hot topic in academic and industrial research. In fact, a large portion of big data in service today are initially streaming data. To preserve the privacy of such data that are collected from data streams, the most efficient way is to control the process of data collection according to corresponding privacy polices. In this paper, we design a framework to support data stream management with privacy-preserving capabilities. In particular, we focus on two premier principles of data privacy, limited disclosure and limited collection. With these two principles guaranteed, the archived data will not necessarily be checked for privacy protection, before analysis and other operations can be done.

Optimizing Multiple Data Acquisition Queries in Sparse Mobile Sensor Networks

In mobile sensor networks (MSNs), it is common for the base station to issue {\em data acquisition} queries requesting for data to be sensed from specific regions of the data space. Such kind of queries are especially important in MSNs for reconnaissance and disaster rescue applications. In this paper, we investigate how multiple data acquisition queries can be answered quickly in sparse mobile sensor networks. Because of the sparseness and mobility, the number of sensors is limited, the connection is intermittent and the topology is unpredictable. To effectively handle the above challenges, we design distributed schemes where mobile sensors strategically relocate themselves to proper locations to collaboratively facilitate efficient query processing and enable sharing over space and time. We first propose a novel scheme, {\em Dynamic}, that enables queries to share resources at runtime while sensors are greedily relocated to benefit the processing of each query. We also design another scheme, {\em aMST}, that optimizes a batch of queries as a whole and utilizes a Minimum Steiner Tree to guide the execution of all queries in the batch. In addition, a parameter is defined to guide the selection of the most appropriate scheme to adapt to the environment. Our extensive performance study shows the effectiveness of our proposed schemes.