Optimized query routing trees for wireless sensor networks

In order to process continuous queries over Wireless Sensor Networks (WSNs), sensors are typically organized in a Query Routing Tree (denoted as T) that provides each sensor with a path over which query results can be transmitted to the querying node. We found that current methods deployed in predominant data acquisition systems construct T in a sub-optimal manner which leads to significant waste of energy. In particular, since T is constructed in an ad hoc manner there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. Additionally, current methods only provide a topological-based method, rather than a query-based method, to define the interval during which a sensing device should enable its transceiver in order to collect the query results from its children. We found that this imposes an order of magnitude increase in energy consumption. In this paper we present MicroPulse+, a novel framework for minimizing the consumption of energy during data acquisition in WSNs. MicroPulse+ continuously optimizes the operation of T by eliminating data transmission and data reception inefficiencies using a collection of in-network algorithms. In particular, MicroPulse+ introduces: (i) the Workload-Aware Routing Tree (WART) algorithm, which is established on profiling recent data acquisition activity and on identifying the bottlenecks using an in-network execution of the critical path method; and (ii) the Energy-driven Tree Construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions. We show through micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that MicroPulse+ provides significant energy reductions under a variety of conditions thus prolonging the longevity of a wireless sensor network

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

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

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

Data Aggregation and Cross-layer Design in WSNs

Over the past few years, advances in electrical engineering have allowed electronic devices to shrink in both size and cost. It has become possible to incorporate environmental sensors into a single device with a microprocessor and memory to interpret the data and wireless transceivers to communicate the data. These sensor nodes have become small and cheap enough that they can be distributed in very large numbers into the area to be monitored and can be considered disposable. Once deployed, these sensor nodes should be able to self-organize themselves into a usable network. These wireless sensor networks, or WSNs, differ from other ad hoc networks mainly in the way that they are used. For example, in ad hoc networks of personal computers, messages are addressed from one PC to another. If a message cannot be routed, the network has failed. In WSNs, data about the environment is requested by the data sink. If any or multiple sensor nodes can return an informative response to this request, the network has succeeded. A network that is viewed in terms of the data it can deliver as opposed to the individual devices that make it up has been termed a data-centric network [26]. The individual sensor nodes may fail to respond to a query, or even die, as long as the final result is valid. The network is only considered useless when no usable data can be delivered. In this thesis, we focus on two aspects. The first is data aggregation with accurate timing control. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Because wireless communication consumes a major amount of the limited battery power for these sensor nodes, we propose to limit the amount of data transmitted by combining redundant and complimentary data as much as possible in order to transmit smaller and fewer messages. By using mathematical models and computer simulations, we will show that our aggregation-focused protocol does, indeed, extend system lifetime. Our secondary focus is a study of cross-layer design. We argue that the extremely specialized use of WSNs should convince us not to adhere to the traditional OSI networking model. Through our experiments, we will show that significant energy savings are possible when a custom cross-layer communication model is used

Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art