On search sets of expanding ring search in wireless networks

We focus on the problem of finding the best search set for expanding ring search (ERS) in wireless networks. ERS is widely used to locate randomly selected destinations or information in wireless networks such as wireless sensor networks In ERS, controlled flooding is employed to search for the destinations in a region limited by a time-to-live (TTL) before the searched region is expanded. The performance of such ERS schemes depends largely on the search set, the set of TTL values that are used sequentially to search for one destination. Using a cost function of searched area size, we identify, through analysis and numerical calculations, the optimum search set for the scenarios where the source is at the center of a circular region and the destination is randomly chosen within the entire network. When the location of the source node and the destination node are both randomly distributed, we provide an almost-optimal search set. This search set guarantees the search cost to be at most 1% higher than the minimum search cost, when the network radius is relatively large

Adaptive Square-Shaped Trajectory-Based Service Location Protocol in Wireless Sensor Networks

In this paper we propose an adaptive square-shaped trajectory (ASST)-based service location method to ensure load scalability in wireless sensor networks. This first establishes a square-shaped trajectory over the nodes that surround a target point computed by the hash function and any user can access it, using the hash. Both the width and the size of the trajectory are dynamically adjustable, depending on the number of queries made to the service information on the trajectory. The number of sensor nodes on the trajectory varies in proportion to the changing trajectory shape, allowing high loads to be distributed around the hot spot area

Optimizing Data Replication for Expanding Ring-based Queries in Wireless Sensor Networks

Abstract — We consider the problem of optimizing the number of replicas for event information in wireless sensor networks, when queries are disseminated using expanding rings. We obtain closed-form approximations for the expected energy costs of search, as well as replication. Using these expressions we derive the replication strategies that minimize the expected total energy cost consisting of search and replication costs, both with and without storage constraints. In both cases, we find that events should be replicated with a frequency that is proportional to the square root of their query rates. We validate our analysis and optimization through a set of realistic simulations that incorporate non-idealities including deployment boundary effects and lossy wireless links. I

PERFORMANCE ANALYSIS AND OPTIMIZATION OF QUERY-BASED WIRELESS SENSOR NETWORKS

This dissertation is concerned with the modeling, analysis, and optimization of large-scale, query-based wireless sensor networks (WSNs). It addresses issues related to the time sensitivity of information retrieval and dissemination, network lifetime maximization, and optimal clustering of sensor nodes in mobile WSNs. First, a queueing-theoretic framework is proposed to evaluate the performance of such networks whose nodes detect and advertise significant events that are useful for only a limited time; queries generated by sensor nodes are also time-limited. The main performance parameter is the steady state proportion of generated queries that fail to be answered on time. A scalable approximation for this parameter is first derived assuming the transmission range of sensors is unlimited. Subsequently, the proportion of failed queries is approximated using a finite transmission range. The latter approximation is remarkably accurate, even when key model assumptions related to event and query lifetime distributions and network topology are violated. Second, optimization models are proposed to maximize the lifetime of a query-based WSN by selecting the transmission range for all of the sensor nodes, the resource replication level (or time-to-live counter) and the active/sleep schedule of nodes, subject to connectivity and quality-of-service constraints. An improved lower bound is provided for the minimum transmission range needed to ensure no network nodes are isolated with high probability. The optimization models select the optimal operating parameters in each period of a finite planning horizon, and computational results indicate that the maximum lifetime can be significantly extended by adjusting the key operating parameters as sensors fail over time due to energy depletion. Finally, optimization models are proposed to maximize the demand coverage and minimize the costs of locating, and relocating, cluster heads in mobile WSNs. In these models, the locations of mobile sensor nodes evolve randomly so that each sensor must be optimally assigned to a cluster head during each period of a finite planning horizon. Additionally, these models prescribe the optimal times at which to update the sensor locations to improve coverage. Computational experiments illustrate the usefulness of dynamically updating cluster head locations and sensor location information over time

Energy-Efficient Querying of Wireless Sensor Networks

Due to the distributed nature of information collection in wireless sensor networks and the inherent limitations of the component devices, the ability to store, locate, and retrieve data and services with minimum energy expenditure is a critical network function. Additionally, effective search protocols must scale efficiently and consume a minimum of network energy and memory reserves. A novel search protocol, the Trajectory-based Selective Broadcast Query protocol, is proposed. An analytical model of the protocol is derived, and an optimization model is formulated. Based on the results of analysis and simulation, the protocol is shown to reduce the expected total network energy expenditure by 45.5 percent to 75 percent compared to current methods. This research also derives an enhanced analytical node model of random walk search protocols for networks with limited-lifetime resources and time-constrained queries. An optimization program is developed to minimize the expected total energy expenditure while simultaneously ensuring the proportion of failed queries does not exceed a specified threshold. Finally, the ability of the analytical node model to predict the performance of random walk search protocols in large-population networks is established through extensive simulation experiments. It is shown that the model provides a reliable estimate of optimum search algorithm parameters

Data centric storage framework for an intelligent wireless sensor network

In the last decade research into Wireless Sensor Networks (WSN) has triggered extensive growth in flexible and previously difficult to achieve scientific activities carried out in the most demanding and often remote areas of the world. This success has provoked research into new WSN related challenges including finding techniques for data management, analysis, and how to gather information from large, diverse, distributed and heterogeneous data sets. The shift in focus to research into a scalable, accessible and sustainable intelligent sensor networks reflects the ongoing improvements made in the design, development, deployment and operation of WSNs. However, one of the key and prime pre-requisites of an intelligent network is to have the ability of in-network data storage and processing which is referred to as Data Centric Storage (DCS). This research project has successfully proposed, developed and implemented a comprehensive DCS framework for WSN. Range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven the research focus. The architecture of the deployed network, referred to as Disk Based Data Centric Storage (DBDCS), was inspired by the magnetic disk storage platter consisting of tracks and sectors. The core contributions made in this research can be summarized as: a) An optimally synchronized routing algorithm, referred to Sector Based Distance (SBD) routing for the DBDCS architecture; b) DCS Metric based Similarity Searching (DCSMSS) with the realization of three exemplar queries – Range query, K-nearest neighbor query (KNN) and Skyline query; and c) A Decentralized Distributed Erasure Coding (DDEC) algorithm that achieves a similar level of reliability with less redundancy. SBD achieves high power efficiency whilst reducing updates and query traffic, end-to-end delay, and collisions. In order to guarantee reliability and minimizing end-to-end latency, a simple Grid Coloring Algorithm (GCA) is used to derive the time division multiple access (TDMA) schedules. The GCA uses a slot reuse concept to minimize the TDMA frame length. A performance evaluation was conducted with simulation results showing that SBD achieves a throughput enhancement by a factor of two, extension of network life time by 30%, and reduced end-to-end latency. DCSMSS takes advantage of a vector distance index, called iDistance, transforming the issue of similarity searching into the problem of an interval search in one dimension. DCSMSS balances the load across the network and provides efficient similarity searching in terms of three types of queries – range query, k-query and skyline query. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. DDEC encoded the acquired information into n fragments and disseminated across n nodes inside a sector so that the original source packets can be recovered from any k surviving nodes. A lost fragment can also be regenerated from any d helper nodes. DDEC was evaluated against 3-Way Replication using different performance matrices. The results have highlighted that the use of erasure encoding in network storage can provide the desired level of data availability at a smaller memory overhead when compared to replication