Chunyu Ai

Transforming Complete Coverage Algorithms to Partial Coverage Algorithms for Wireless Sensor Networks

The complete area coverage problem in Wireless Sensor Networks (WSNs) has been extensively studied in the literature. However, many applications do not require complete coverage all the time. For such applications, one effective method to save energy and prolong network lifetime is to partially cover the area. This method for prolonging network lifetime recently attracts much attention. However, due to the hardness of verifying the coverage ratio, all the existing centralized or distributed but nonparallel algorithms for partial coverage have very high time complexities. In this work, we propose a framework which can transform almost any existing complete coverage algorithm to a partial coverage one with any coverage ratio by running a complete coverage algorithm to find full coverage sets with virtual radii and converting the coverage sets to partial coverage sets via adjusting sensing radii. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

Sensor scheduling for p-percent coverage in wireless sensor networks

We study sensor scheduling problems of p-percent coverage in this paper and propose two scheduling algorithms to prolong network lifetime due to the fact that for some applications full coverage is not necessary and different subareas of the monitored area may have different coverage requirements. Centralized p-Percent Coverage Algorithm (CPCA) we proposed is a centralized algorithm which selects the least number of nodes to monitor p-percent of the monitored area. Distributed p-Percent Coverage Protocol (DPCP) we represented is a distributed algorithm which can determine a set of nodes in a distributed manner to cover p-percent of the monitored area. Both of the algorithms can guarantee network connectivity. The simulation results show that our algorithms can remarkably prolong network lifetime, have less than 5% un-required coverage for large networks, and employ nodes fairly for most cases.

Processing Area Queries in Wireless Sensor Networks

Area query processing is significant for various applications of wireless sensor networks. No previous study has specifically addressed this issue. We can adopt a naive method, which is to send all data to Base Station for centralized processing. However, this method wastes a large amount of energy for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the whole monitored area is partitioned into grids, and a gray code is used to represent a Grid ID (GID), which is a smart way to describe an area. Furthermore, a reporting tree is constructed to process merging areas and aggregations. Based on the properties of GIDs, useless data can be dropped and areas can be merged as early as possible. Incremental update is used to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is energy-efficient.

Delay-Bounded and Energy-Efficient Composite Event Monitoring in Heterogeneous Wireless Sensor Networks

Wireless sensor networks can be used for event warning applications. Till date, in most of the proposed schemes, the raw or aggregated sensed data are periodically sent to a data consuming center. However, with those schemes, the occurrence of an emergency event such as a fire is hardly reported timely, which is a strict requirement for event warning applications. In wireless sensor networks, it is also highly desired to conserve energy so that network lifetime can be maximized. Furthermore, to ensure the quality of surveillance, some applications require that if an event occurs, it needs to be detected by at least k sensors, where k is a user-defined parameter. In this work, we examine the Timely Energy-efficient k-Watching Event Monitoring (TEKWEM) problem and propose a scheme, which involves an event detection model and a warning delivery model, for monitoring composite events and delivering warnings to users. Theoretical analysis and simulation results are shown to validate the proposed scheme.

DSF - A Distributed Security Framework for heterogeneous wireless sensor networks

Wireless sensor networks (WSNs) have many applications that handle sensitive information such as surveillance, reconnaissance, and target tracking. Therefore, a WSN deployed in a hostile region should be resilient to attacks. The current approach to defending against malicious threats is to develop and deploy a specific defense mechanism for a specific attack. However, the problem with this traditional approach to defending sensor networks is that the solution for the jamming attack does not defend against other attacks (e.g., sybil, selective forwarding, and wormhole attacks). In reality, one cannot know a priori what type of attack an adversary will launch. Also, given the resource constraints of sensor nodes, the current defense mechanisms cannot be simply combined on the node to provide a complete solution. This work addresses the challenges with the traditional approach to securing sensor networks and presents a collaborative framework (the Distributed Security Framework -DSF) that can defend against all known attacks. The framework is extensible, therefore, as new attacks are discovered they can also be defended against. The DSF leverages existing defense mechanisms created by researchers. These defense mechanisms are distributed in such a way that they can, collectively, provide comprehensive defense to the network. The efficacy of the DSF is determined using simulations for scenarios consisting of multiple stationary and multiple mobile attackers. The simulation results show that though the DSF consumes more energy than single defense schemes, it can significantly enhance the network security even when the network is under multiple types of attacks.

p-Percent Coverage in Wireless Sensor Networks

Due to resource constraint of WSNs, it may be unnecessary or impossible to provide full coverage in many applications. Instead, partial coverage is enough to satisfy user requirements. Meanwhile, by applying partial coverage, network lifetime can be prolonged remarkably which is a primary goal of WSNs. In this paper, we investigate the p-Percent Coverage Problemwhich only requires that p% of the whole area to be monitored at any time and the Connected p-Percent Coverage Problemwhich enforces connectivity in addition. We propose two algorithms. One is pPCAwhich is a greedy algorithm to solve the p-Percent Coverage Problem. The other is CpPCA-CDS, which is a total distributed algorithm based on Connected Dominating Set to address Connected p-Percent Coverage Problem. The Sensing Void Distance after using CpPCA-CDScan be bounded by a constant. Theoretical analysis as well as simulation results are provided to evaluate our algorithms.

An urban area-oriented traffic information query strategy in VANETs

Traffic information query in Vehicular Ad Hoc Network has various significant applications. Real-time traffic information can provide support for users to choose an optimal route according to current traffic situation. In this paper, we propose an urban area-oriented traffic information query processing mechanism, which can acquire the realtime traffic information of multiple paths from source to destination in relatively fast and accurate manner, and help users to determine an optimal route. The proposed mechanism includes two key algorithms - query dissemination and processing, and routing results backward to query requester. The query processing algorithm determines the scope of each query, so that a vehicle can query and collect data within a certain efficient scope to avoid returning overwhelmed large amount results. For queried vehicles, returning results to the moving query requester is a dynamic routing problem. We proposed a position predicting method to estimate the current location of the requester according to the information stored in the query packet. Simulation results show that the proposed strategy can improve the efficiency of data transmission, and the returned query results is effective for choosing an optimal route.

Privacy protection on sliding window of data streams

In many applications, transaction data arrive in the form of high speed data streams. These data contain a lot of information about customers that needs to be carefully managed to protect customerspsila privacy. In this paper, we consider the problem of preserving customerpsilas privacy on the sliding window of transaction data streams. This problem is challenging because sliding window is updated frequently and rapidly. We propose a novel approach, SWAF (sliding window anonymization framework), to solve this problem by continuously facilitating k-anonymity on the sliding window. Three advantages make SWAF practical: (1) Small processing time for each tuple of data steam. (2) Small memory requirement. (3) Both privacy protection and utility of anonymized sliding window are carefully considered. Theoretical analysis and experimental results show that SWAF is efficient and effective.

Data Estimation in Sensor Networks Using Physical and Statistical Methodologies

Wireless sensor networks (WSNs) are employed in many applications in order to collect data. One key challenge is to minimize energy consumption to prolong network lifetime. A scheme of making some nodes asleep and estimating their values according to the other active nodespsila readings has been proved energy-efficient. For the purpose of improving the precision of estimation, we propose two powerful estimation models, data estimation using physical model (DEPM) and data estimation using statistical model (DESM). DEPM estimates the values of sleeping nodes by the physical characteristics of sensed attributes, while DESM estimates the values through the spatial and temporal correlations of the nodes. Experimental results on real sensor networks show that the proposed techniques provide accurate estimations and conserve energy efficiently.

Data Aggregation Scheduling in Probabilistic Wireless Networks with Cognitive Radio Capability

Transitional Region Phenomenon leads to the existence of lossy links in wireless networks, which results in a transmission between two users who are theoretically connected under the Deterministic Network Model cannot be guaranteed. Therefore, we focus on a more practical network model - Probabilistic Network Model (PNM) which can better characterize the lossy links in wireless networks. To be specific, we focus on the investigation of accelerating data aggregation process in probabilistic wireless networks with the cognitive radio technology. By involving cognitive radio technology, users in the wireless networks can seek extra transmission opportunity if other spectrum resource is available. Otherwise, the data aggregation process still can be done on the default working spectrum. Particularly, we are interested in the time efficient data aggregation scheduling problem. In this work, a two phase scheduling algorithm is proposed. The first phase is finding an efficient routing structure considering the speciality of the network model under investigation. In the second phase, a dynamic scheduling algorithm is introduced. Theoretical analysis is provided to estimate the lower latency bound for the scheduling algorithm, followed by the experimental simulation verification.