A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN

Consistent data aggregate retrieval for sensor network systems.

Lee Lok Hang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 87-93).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Sensors and Sensor Networks --- p.3Chapter 1.2 --- Sensor Network Deployment --- p.7Chapter 1.3 --- Motivations --- p.7Chapter 1.4 --- Contributions --- p.9Chapter 1.5 --- Thesis Organization --- p.10Chapter 2 --- Literature Review --- p.11Chapter 2.1 --- Data Cube --- p.11Chapter 2.2 --- Data Aggregation in Sensor Networks --- p.12Chapter 2.2.1 --- Hierarchical Data Aggregation --- p.13Chapter 2.2.2 --- Gossip-based Aggregation --- p.13Chapter 2.2.3 --- Hierarchical Gossip Aggregation --- p.13Chapter 2.3 --- GAF Algorithm --- p.14Chapter 2.4 --- Concurrency Control --- p.17Chapter 2.4.1 --- Two-phase Locking --- p.17Chapter 2.4.2 --- Timestamp Ordering --- p.18Chapter 3 --- Building Distributed Data Cubes in Sensor Network --- p.20Chapter 3.1 --- Aggregation Operators --- p.21Chapter 3.2 --- Distributed Prefix (PS) Sum Data Cube --- p.22Chapter 3.2.1 --- Prefix Sum (PS) Data Cube --- p.22Chapter 3.2.2 --- Notations --- p.24Chapter 3.2.3 --- Querying a PS Data Cube --- p.25Chapter 3.2.4 --- Building Distributed PS Data Cube --- p.27Chapter 3.2.5 --- Time Bounds --- p.32Chapter 3.2.6 --- Fast Aggregate Queries on Multiple Regions --- p.37Chapter 3.2.7 --- Simulation Results --- p.43Chapter 3.3 --- Distributed Local Prefix Sum (LPS) Data Cube --- p.50Chapter 3.3.1 --- Local Prefix Sum Data Cube --- p.52Chapter 3.3.2 --- Notations --- p.55Chapter 3.3.3 --- Querying an LPS Data Cube --- p.56Chapter 3.3.4 --- Building Distributed LPS Data Cube --- p.61Chapter 3.3.5 --- Time Bounds --- p.63Chapter 3.3.6 --- Fast Aggregate Queries on Multiple Regions --- p.67Chapter 3.3.7 --- Simulation Results --- p.68Chapter 3.3.8 --- Distributed PS Data Cube Vs Distributed LPS Data Cube --- p.74Chapter 4 --- Concurrency Control and Consistency in Sensor Networks --- p.76Chapter 4.1 --- Data Inconsistency in Sensor Networks --- p.76Chapter 4.2 --- Traditional Concurrency Control Protocols and Sensor Networks --- p.80Chapter 4.3 --- The Consistent Retrieval of Data from Distributed Data Cubes --- p.81Chapter 5 --- Conclusions --- p.85References --- p.87Appendix --- p.94A Publications --- p.9

A Review of the Enviro-Net Project

Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.Comment: v2: 29 pages, 5 figures, reflects changes addressing reviewers' comments v1: 38 pages, 8 figure

Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks

Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067

Synchronous Relaying Of Sensor Data

In this paper we have put forth a novel methodology to relay data obtained by inbuilt sensors of smart phones in real time to remote database followed by fetching of this data . Smart phones are becoming very common and they are laced with a number of sensors that can not only be used in native applications but can also be sent to external nodes to be used by third parties for application and service development