Yongxuan Lai

Energy-Efficient Fault-Tolerant Mechanism for Clustered Wireless Sensor Networks

Clustering is an effective topology control and communication protocol in wireless sensor networks (sensornets). However, the harsh deployed environments, the serious resource limitation of nodes, and the unbalanced workload among nodes make the clustered sensornets vulnerable to communication faults and errors, which undermine the usability of the network. So mechanisms to improve the robustness and fault-tolerance are highly required in real applications of sensornets. In this paper, a distributed fault-tolerant mechanism called CMATO (Cluster-Member-based fAult-TOlerant mechanism) for sensornets is proposed. It views the cluster as an individual whole and utilizes the monitoring of each other within the cluster to detect and recover from the faults in a quick and energy-efficient way. CMATO only needs the local knowledge of the network, relaxing the pre-deployment of the cluster heads and a k-dominating set (k>1) coverage assumptions. This advantage makes our mechanism flexible to be incorporated into various existing clustering schemes in sensornets. Furthermore, CMATO is able to deal with failures of multiple cluster heads, so it effectively recovers the nodes from the failures of multiple cluster heads and the failures of links within the cluster, gaining a much more robust and fault-tolerant sensornets. The simulation results show that our mechanism outperforms the existing cluster-head based fault-tolerant mechanism in both fault coverage and energy consumption.

Adaptive Data Gathering in Mobile Sensor Networks Using Speedy Mobile Elements

Data gathering is a key operator for applications in wireless sensor networks; yet it is also a challenging problem in mobile sensor networks when considering that all nodes are mobile and the communications among them are opportunistic. This paper proposes an efficient data gathering scheme called ADG that adopts speedy mobile elements as the mobile data collector and takes advantage of the movement patterns of the network. ADG first extracts the network meta-data at initial epochs, and calculates a set of proxy nodes based on the meta-data. Data gathering is then mapped into the Proxy node Time Slot Allocation (PTSA) problem that schedules the time slots and orders, according to which the data collector could gather the maximal amount of data within a limited period. Finally, the collector follows the schedule and picks up the sensed data from the proxy nodes through one hop of message transmissions. ADG learns the period when nodes are relatively stationary, so that the collector is able to pick up the data from them during the limited data gathering period. Moreover, proxy nodes and data gathering points could also be timely updated so that the collector could adapt to the change of node movements. Extensive experimental results show that the proposed scheme outperforms other data gathering schemes on the cost of message transmissions and the data gathering rate, especially under the constraint of limited data gathering period.

Research on Cloud Databases: Research on Cloud Databases

With the recent development of cloud computing, the importance of cloud databases has been widely acknowledged. Here, the features, influence and related products of cloud databases are first discussed. Then, research issues of cloud databases are presented in detail, which include data model, architecture, consistency, programming model, data security, performance optimization, benchmark, and so on. Finally, some future trends in this area are discussed.

PEJA: progressive energy-efficient join processing for sensor networks

Sensor networks are widely used in many applications to collaboratively collect information from the physical environment. In these applications, the exploration of the relationship and linkage of sensing data within multiple regions can be naturally expressed by joining tuples in these regions. However, the highly distributed and resource-constraint nature of the network makes join a challenging query. In this paper, we address the problem of processing join query among diffeerent regions progressively and energy-efficiently in sensor networks. The proposed algorithm PEJA (Progressive Energy-efficient Join Algorithm) adopts an event-driven strategy to output the joining results as soon as possible, and alleviates the storage shortage problem in the in-network nodes. It also installs filters in the joining regions to prune unmatchable tuples in the early processing phase, saving lots of unnecessary transmissions. Extensive experiments on both synthetic and real world data sets indicate that the PEJA scheme outperforms other join algorithms, and it is effective in reducing the number of transmissions and the delay of query results during the join processing.

Maintaining Internal Consistency of Report for Real-Time OLAP with Layer-Based View

Maintaining internal consistency of report is an important aspect in the field of real-time data warehouses. OLAP and Query tools were initially designed to operate on top of unchanging, static historical data. In a real-time environment, however, the results they produce are usually negatively influenced by data changes concurrent to query execution, which may result in some internal report inconsistency. In this paper, we propose a new method, called layer-based view approach, to appropriately and effectively maintain report data consistency. The core idea is to prevent the data involved in an OLAP query from being changed through using lock mechanism, and avoid the confliction between read and write operations with the help of layer mechanism. Our approach can effectively deal with report consistency issue, while at the same time avoiding the query contention between read and write operations under real-time OLAP environment.

Reliable wireless connections for fast-moving rail users based on a chained fog structure

Abstract Currently, 3G and 4G networks provide customers with high-speed wireless services almost everywhere. However, the wireless connection is often unstable and unreliable, especially for fast-moving end users (e.g., those on trains and buses). To investigate the severity of this problem, we conducted real experiments on fast-moving trains to investigate the quality of 3G connections. From the results, we found that 1) from the temporal perspective, the 3G connections were not stable and suffered from frequent disruptions of connectivity, and 2) from the spatial perspective, the connections that were established in different train compartments were largely independent. These two findings motivate us to propose a brand-new fog computing structure, which acts as an intermediate layer between the end users and the 3G infrastructure. This new fog structure introduces a series of mutually chained network gateways that are located in different compartments. This structure addresses the aforementioned problem of unstable connectivity and thus ensures reliable wireless service for fast-moving users, such as passengers on trains. We performed a series of theoretical and empirical analyses to evaluate the performance of the newly proposed structure. All of the experimental results suggest that our proposed fog structure greatly improves the reliability of wireless connections on fast-moving trains.

Data Delivery from WSNs to Cloud Based on a Fog Structure

Recent years, with the emerging technology of cloud computing, the powerful computing and storage capability of cloud computing injects new vitality into wireless sensor networks (WSNs) and motivates a series of new applications. However, the data delivery from WSNs to Cloud becomes a bottleneck because of the poor communication ability of WSNs, especially for delay-sensitive applications, which limits their further development and applications. To address this problem, we propose a fog structure which composes of multiple mobile sinks. Mobile sinks act as fog nodes to bridge the gap between WSNs and Cloud. They cooperate with each other to set up a multi-input multi-output (MIMO) network, aiming at maximizing the throughput and minimizing the transmission latency. The problem is proved to be NP-hard and we design an approximation algorithm to solve this problem with several provable properties. We compare our method to several traditional solutions. Extensive experimental results suggest that the proposed method significantly outperforms the traditional solutions.

Data gathering in opportunistic wireless sensor networks

The wireless sensor networks and opportunistic networks have nowadays presented a trend of technology convergence. On one hand, the nodes periodically sense the environment and continuously generate sensing data; on the other hand, the movements and sparse deployment of nodes usually lead to intermitted connected links and create some form of opportunistic communications. So it is a challenging problem to effectively collect the sensing data in opportunistic wireless sensor networks. In this paper, we propose an efficient data gathering algorithm based on location prediction in opportunistic wireless sensor networks. The algorithm first collects the network metadata such as history of node encounters and contact durations; then it creates a node contact graph, based on which predictive optimal data gathering locations are dynamically calculated and updated. Finally, the sink is controlled to move to these locations to collect sensing data, avoiding lots of unnecessary data exchanges and message transmissions. Extensive experimental results show that the proposed algorithm is effective to reduce the message transmissions and improve the data collection coverage rate.

LAMF: Framework for complex event processing in wireless sensor networks

In wireless sensor networks the nodes periodically sense the environment, and huge amount of raw data are injected into the network. In order to extract useful information from the network, yet at the same time to incur least energy consumption, complex event processing techniques are highly required. In this paper we propose a framework for complex event processing in wireless sensor networks. Our framework is based on the layered network, and transforms the complex events into sub-events that are suitable to be detected by nodes within regional areas. As a middleware, our framework is easy to extend, and various complex event processing algorithms could be embedded into the framework.

Energy-Efficient Robust Data-Centric Storage in Wireless Sensor Networks

In this paper, we propose a new Dynamic Data- centric Storage (DDS) mechanism in wireless sensor network. DDS, which is aware of the data distributions of the network, dynamically adjusts the mappings from sensor readings to the storage points to reduce the cost of storing these readings, as well as to balance the storage and workload in the network. Moreover, it takes advantage of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that the approach is more energy-efficient and robust than other data-centric schemes.