19,370 research outputs found
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
Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks
In this paper, we study the problem of processing continuous range queries in
a hierarchical wireless sensor network. Contrasted with the traditional
approach of building networks in a "flat" structure using sensor devices of the
same capability, the hierarchical approach deploys devices of higher capability
in a higher tier, i.e., a tier closer to the server. While query processing in
flat sensor networks has been widely studied, the study on query processing in
hierarchical sensor networks has been inadequate. In wireless sensor networks,
the main costs that should be considered are the energy for sending data and
the storage for storing queries. There is a trade-off between these two costs.
Based on this, we first propose a progressive processing method that
effectively processes a large number of continuous range queries in
hierarchical sensor networks. The proposed method uses the query merging
technique proposed by Xiang et al. as the basis and additionally considers the
trade-off between the two costs. More specifically, it works toward reducing
the storage cost at lower-tier nodes by merging more queries, and toward
reducing the energy cost at higher-tier nodes by merging fewer queries (thereby
reducing "false alarms"). We then present how to build a hierarchical sensor
network that is optimal with respect to the weighted sum of the two costs. It
allows for a cost-based systematic control of the trade-off based on the
relative importance between the storage and energy in a given network
environment and application. Experimental results show that the proposed method
achieves a near-optimal control between the storage and energy and reduces the
cost by 0.989~84.995 times compared with the cost achieved using the flat
(i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Power Aware Routing for Sensor Databases
Wireless sensor networks offer the potential to span and monitor large
geographical areas inexpensively. Sensor network databases like TinyDB are the
dominant architectures to extract and manage data in such networks. Since
sensors have significant power constraints (battery life), and high
communication costs, design of energy efficient communication algorithms is of
great importance. The data flow in a sensor database is very different from
data flow in an ordinary network and poses novel challenges in designing
efficient routing algorithms. In this work we explore the problem of energy
efficient routing for various different types of database queries and show that
in general, this problem is NP-complete. We give a constant factor
approximation algorithm for one class of query, and for other queries give
heuristic algorithms. We evaluate the efficiency of the proposed algorithms by
simulation and demonstrate their near optimal performance for various network
sizes
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