Showing papers by "Chunsheng Zhu published in 2011"

Implementing top-k query in duty-cycled wireless sensor networks

Abstract: Top-k query is a very useful and important query in wireless sensor networks (WSNs), aiming to find the k nodes with highest readings among the sensor nodes. In WSNs, there are generally two kinds of networks: always-on WSNs (AO-WSNs) in which sensors always keep awake and duty-cycled WSNs (DC-WSNs) where sensors dynamically sleep and wake. To the best of our knowledge, there are a lot of work about top-k query in AO-WSNs but little research has been done regarding top-k query in DC-WSNs. However, DC-WSN is a very practical network model in which energy consumption can be greatly saved. In this paper, we analyze the research issues when implementing top-k query in DC-WSNs and propose the DCDC-WSNs (DC-WSNs with data replication (DR) and connected k-neighborhood (CKN)) to implement top-k query. Further theoretical analysis and simulation results show that implementing top-k query in DCDC-WSNs can achieve the best tradeoff with respect to query data accessibility and query cost (total energy consumption, query response time), compared with AO-WSNs and DCC-WSNs (DC-WSNs with only CKN).

Sleep scheduling towards geographic routing in duty-cycled sensor networks with a mobile sink

Abstract: Focusing on achieving better geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) when there is a mobile sink, this paper proposes a geographic distance based connected-k neighborhood (GCKN) algorithm. The algorithm analysis and simulation results show that GCKN can obtain shorter length of the transmission paths explored by TPGF in duty-cycled mobile sink WSNs, compared with the original connected-k neighborhood (CKN).

Influence Strength Aware Diffusion Models for Dynamic Influence Maximization in Social Networks

Abstract: Social network plays a fundamental role as a medium for the spread of influence among its individuals. During the influence spreading process, one favorable goal is achieving influence maximization in social marketing. Thus diffusion model which identifies a set of individuals to initiate this spread so that more individuals can be triggered at last is very critical. However, to the best of our knowledge, all current diffusion models only consider the dynamics during diffusion and ignore the dynamically changing influence strength during information propagation. In this paper, taking account of both dynamics and influence strength during information diffusion, we propose two diffusion models in social networks for dynamic influence maximization. The first one is the Time-dependent Comprehensive Cascade (TCC) model, which considers that the activation probability between two individuals is dependent on previous activation trials. The second one is the Dynamic Variable Threshold (DVT) model, which considers that the activation threshold of an individual could be changed based on the individual's attitude towards the propagated information. Theoretical analysis show that our proposed two diffusion models are more practical compared with previous diffusion models.

Sleep scheduling towards geographic routing in duty-cycled sensor networks

Abstract: This paper focuses on improving the geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) and proposes a geographic routing oriented sleep scheduling (GSS) algorithm. The algorithm analysis and simulation results show that GSS can shorten the length of the first explored transmission path of TPGF, compared with the connected-k neighborhood (CKN) algorithm.

An Android Multimedia Framework Based on Gstreamer

Abstract: Android is a widely used operating system in mobile devices, due to that it is free, open source and easy-to-use. However, the multimedia processing ability of current android is quite limited, as the original android multimedia engine OpenCore cannot deal with lots of commonly used video (audio) formats. Recently, several approaches are proposed to enhance the multimedia processing ability and Gstreamer based method is supposed to own the best performance. However, the multimedia processing ability of current extension multimedia frameworks are still not good enough, which weakens the potential application prospect. In this paper, we provide another android multimedia framework based on Gstreamer. Extensive experiments show that our Gstreamer based framework can greatly improve the multimedia processing ability in terms of efficiency, compatibility, feasibility and universality.

Mitigating Radio Irregularity Impact: An RSSI Calibration Method for Range-Free Localization in Sensor Networks

Abstract: Range-Free algorithms, appealing to people for their cost-efficiency, suffer from the precision problem. Some methods try to combine received signal strength indication (RSSI) with range-free localization algorithms to improve the accuracy, but RSSI is sensitive to the radio irregularity. Based on the well known RIM model, we present a new method of RSSI calibration, namely MRIRC, to mitigate the impact of radio irregularity. MRIRC divides nodes within a continuous angle into groups with the same level of RSSI deviation. By doing this, given an irregular deviation input, MRIRC can get a maximum angle (worst case), which guarantees that the nodes in the same group are in the same level of radio irregularity, thereby improving the accuracy of the distance estimations. We conduct simulations for large-scale sensor networks, and the results show that MRIRC achieves superior performance over the other two typical Range-Free algorithms.