14966701
A Deterministic approach to evaluate path loss exponents in large-scale outdoor 802.11 WLANs
Accurate network planning is critical with deployments of large outdoor wireless networks. Propagation and path loss considerations are key factors in predicting performance of wireless networks. We are currently studying large-scale wireless networks for US railway environments. In this study, both theoretical and measurement based propagation models indicate that the average received signal power decreased logarithmically with distance. The average large-scale path loss is expressed as a function of distance between the transmitter and the receiver by using a path loss exponent n and the close-in reference distance d0. However it is very important to select a suitable space reference distance that is appropriate for a specific propagation environment. In this paper, a deterministic approach is proposed to select an appropriate reference distance for a given propagation environment. Also a Minimum Mean Square Error (MMSE) based method is introduced to compute path loss exponents. Furthermore the comparison between the site specific measurement data collected from our testbed and the estimated values verifies the accuracy of our proposed method.
14028781
Study of an energy efficient multi rate scheme for wireless sensor network MAC protocol
Real-time Digital Signal Processing (DSP) applications in wireless sensor network are often multi-rate in nature. These multi rate attributes provide additional significant opportunities in network design for achieving higher energy efficiency, besides traditional approaches of saving energy. In this paper, we propose a novel multi rate sensor network scheme to take advantage of power scaling for saving energy consumption. It can be easily synthesized into existing wireless sensor network MAC protocols, providing multi rate service for upper layer, and achieving communication energy efficiency. By monitoring a desirable BER threshold as the critical system parameter, the quality of received multi-rate data is assured. Based on this BER and the corresponding modulation scheme, the SNR at receiver end can be derived. The transmission power can be supplied optimally according to this SNR and the channel attenuation between sender and receiver. This dynamic power scaling scheme can lead to optimal energy efficiency since it demands that only a necessary amount of communication energy be consumed for multi-rate DSP applications. Our simulation shows that this multi data rate scheme with dynamic power scaling achieves energy saving significantly
16514948
Interplay Between Routing and Distributed Source Coding in Wireless Sensor Network
Recent advances in distributed source coding (DSC) for mission-driven wireless sensor networks (WSN) are related to the coding for multiple correlated sensors in applications such as real time target tracking and environment monitoring. The characteristic of these DSC applications provides significant potential opportunities in the associated sensor network to utilize multirate transmissions for enhancing network performance. In this paper, we study a methodology for interplay optimization between routing and DSC in WSN, and propose a novel multirate based routing scheme for mission-driven DSC applications that considerably extends network lifetime. The proposed scheme adopts the rate assignment based on the residual energy, and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate constraint, information precision requirement, and the energy constraints in the network for DSC. Simulation results show that this multirate based routing scheme achieves significantly longer network lifetime compared to other existing research in WSN for DSC applications.
11683146
Collaborative Image Transmissions Based on Region and Path Diversity in Wireless Sensor Network
Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor networks (WSN). The large size image transmissions cause bottlenecks in WSN due to the limited energy resources and network capacity. In this paper, we propose a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on transmission path diversities, which achieves minimal energy consumption, balanced sensor lifetime and required image quality. This optimization scheme not only allows each image sensor to transmit optimal fractions of the overlapped images through appropriate transmission paths in energy-efficient way, but also provides unequal protection on the overlap image regions through path selections and resource allocations to achieve good transmission image quality. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the network lifetime extension, image distortion reduction, and energy efficiency.
21523302
Image Rate Based Cross Layer Optimizations for Image Delivery in Wireless Sensor Networks
8653895
The Implementation of an Energy Balanced Routing Protocol with Dynamic Power Scaling in TinyOS
In this paper, we study an energy balanced routing protocol based on dynamic power scaling techniques for wireless sensor networks. In the proposed protocol, each node maintains a list of neighboring nodes and their corresponding statistical residual energy information. The energy balanced routing distributes the levels of residue energy evenly throughout the network, while the dynamic power scaling scheme achieves further energy savings by adjusting transmission power to meet signal strength requirement at the receiver. In order to demonstrate the increased energy efficiency of the proposed protocol, we have conducted an experimental study. The results of our experimental study clearly show that the proposed energy balanced routing scheme with dynamic power improves energy efficiency while achieving energy balance as well
14320982
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.
14792668
Personalization as a service: the architecture and a case study
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
14212804
A Broadcasting Retransmission Approach Based on Random Linear Network Coding
In wireless broadcasting retransmission, any node of multi-nodes requests the retransmission of information packets. This approach always needs large amounts of broadcasting packets. This paper presents a novel retransmission approach (RLNCBR) in wireless broadcasting networks based on random linear network coding. Firstly, source node will send linear coding packets based on the largest amounts of lost packets in received nodes. Then, received nodes will decode lost packets with network coding theory. Mathematic analysis reveals that our approach can ensure the solvability in the received nodes, and have optimal performance in wireless retransmission. Simulation results indicate that compared with existing approach, the approach in this paper can effectively reduce the average number of transmissions and advance the transmission efficiency.
62759024
Fast Query over Encrypted Character Data in Database
34121335
Data-Aware Clustering Hierarchy for Wireless Sensor Networks
In recent years, the wireless sensor network (WSN) is employed a wide range of applications. But existing communication protocols for WSN ignore the characteristics of collected data and set routes only according to the mutual distance and residual energy of sensors. In this paper we propose a Data-Aware Clustering Hierarchy (DACH), which organizes the sensors based on both distance information and data distribution in the network Furthermore, we also present a multi-granularity query processing method based on DACH, which can estimate the query result more efficiently. Our empirical study shows that DACH has higher energy efficiency than Low-Energy Adaptive Clustering Hierarchy (LEACH), and the multi-granularity query processing method based on DACH brings more accurate results than a random access system using same cost of energy.