Energy-efficient and privacy-preserving spatial range aggregation query processing in wireless sensor networks

Several applications of Wireless Sensor Networks (WSN) demand information only from sub-regions of the monitored area. The user establishes the region of interest (RoI) and requires the WSN to collect data only inside this region. This kind of query is called spatial query. The state of the art of spatial query processing considers, in general, that nodes do not fail. However, nodes can be destroyed, interference can deny the communication and nodes can go to sleep mode (turn off the radio in duty cycles) in order to save energy. This work proposes a fault-tolerant energy efficient in-network spatial query processing mechanism for WSN. The proposed mechanism employs failure detection algorithms in order to avoid failed neighbors when forwarding and processing spatial queries. Our contributions are lightweight and resilient because they operate on demand and avoid the use of routing tables.

Wireless sensor networks have been widely used in many applications, such as soil temperature monitoring for plant growth and abnormal event detection of industrial parameters. Among these applications, aggregate queries, such as SUM, COUNT, AVERAGE, MIN and MAX are often used to collect statistical data. Due to the low quality sensing devices or random environmental disturbances, sensor data are often noisy. Hence, the idea of moving average, which computes the average over consecutive aggregate data, is introduced to offset the effect. The high link loss rate, however, makes the result after averaging still inaccurate. To address this issue, we propose a PCM-based data transmission scheme to "make up" the possibly lost data. Specifically, we focus on obtaining robust aggregate results under high link loss rate. In order to reduce the communication traffic that dominates the energy consumption of the sensor network, we also design an intelligent path selection algorithm for our scheme. Our extensive simulation results have shown that this technique outperforms its counterparts under various sensor network conditions.

In wireless sensor networks, preserving privacy is more important and has attracted more attentions. Protecting data and sensor privacy while collecting and computing query results is a challenge. In cluster-based sensor networks, when a user queries a sensitive data, the adversaries can monitor original node or gain the data in cluster node. To deal with this problem, we propose a secure and efficient scheme for cluster-based query processing in wireless sensor networks. To preserve location privacy of sensors, we use anonymity method to confuse adversaries. To protect the sensitive data, we use prefix membership verification method to prevent adversaries from gaining sensitive messages collected by sensor nodes. And we analyze the security and communication cost. The results show that our scheme can efficiently protect privacy in query processing.

We address the problem of efficient in-network processing of hybrid spatial queries in Wireless Sensor Networks (WSN), where the data may correspond to different physical phenomena in different regions. We propose space and communication efficient schemes capable of correlating spatial dimension with different physical values. To trade-off (im)precision vs. energy consumption, the proposed schemes combine rank order statistics, regular sampling, and bitmap representation. We present a proof of concept implementation of the proposed methodology and quantify the benefits of our approach through simulations.

Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model. Aggregation queries on the data sampled at each sensor node are the main means to extract the abstract characteristics of the surrounding environment. However, the non-uniform distribution of the sensor nodes in the environment leads to inaccurate results generated by the aggregation queries. In this paper, we introduce "spatial aggregations" that take into consideration the spatial location of each measurement generated by the sensor nodes. We propose the use of spatial interpolation methods derived from the fields of spatial statistics and computational geometry to answer spatial aggregations. In particular, we study Spatial Moving Average (SMA), Voronoi Diagram and Triangulated Irregular Network (TIN). Investigating these methods for answering spatial average queries, we show that the average value on the data samples weighted by the area of the Voronoi cell of the corresponding sensor node, provides the best precision. Consequently, we introduce an algorithms to compute and maintain the accurate Voronoi cell at each sensor node while the location of the others arrive on data stream. We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network. To demonstrate the performance of in-network implementation of our aggregation operators, we have developed prototypes of two different approaches to distributed spatial aggregate processing.

Wireless sensor networks (WSN) have become a significant research challenge, attracting many researchers. This paper provides an overview of collaborative WSN, reviewing the algorithms, techniques and state-of-the-art developed so far. We discuss the research challenges and opportunities in this area. The major focus is given to cooperative signal and information processing in collaborative WSN in order to expose important constraints in wireless applications which require distributed computing, such as node localization, target detection and tracking. In this paper, we also present and discuss the applications of multi-agent systems into WSN as a core technology of cooperative information processing.

Data aggregation is the technique used togather the sensor data to reduce the duplicate data transmission. It is an effectual approach for Wireless Sensor Networks (WSNs) to save energy and prolong network life time. Existing Reliable Spatial Window Aggregation (RESA) query processing algorithm proposed to reduce packet loss rate during data communication and balance the load of nodes. The main problem in existing system is the single node act as the cluster head at every transmission so it takes more energy than the sensor nodes and the duplicate data can be stored at the cluster head. In order to address this problem, a Pattern Based Aggregation using query processing algorithm called PBA is proposed. PBA only requires each node to maintain location of its neighbours. According to the information it divides the query area into several sub regions followed by collection of sensor readings in each sub region. It is proposed to reduce the transmission of duplicate data during communication. An Oscilloscope measurement technique is proposed to measure the energy level of the sensor node.

Sensor networks are unattended deeply distributed systems whose database schema can be conceptualized using the relational model. Aggregation queries on the data sampled at each sensor node are the main means to extract the abstract characteristics of the surrounding environment. However, the non-uniform distribution of the sensor nodes in the environment leads to inaccurate results generated by the aggregation queries. In this paper, we introduce “spatial aggregations” that take into consideration the spatial location of each measurement generated by the sensor nodes. We propose the use of spatial interpolation methods derived from the fields of spatial statistics and computational geometry to answer spatial aggregations. In particular, we study Spatial Moving Average (SMA), Voronoi Diagram and Triangulated Irregular Network (TIN). Investigating these methods for answering spatial average queries, we show that the average value on the data samples weighted by the area of the Voronoi cell of the corresponding sensor node, provides the best precision. Consequently, we introduce an algorithm to compute and maintain the accurate Voronoi cell at each sensor node while the location of the others arrive on data stream. We also propose AVC-SW, a novel algorithm to approximate this Voronoi cell over a sliding window that supports dynamism in the sensor network. To demonstrate the performance of in-network implementation of our aggregation operators, we have developed prototypes of two different approaches to distributed spatial aggregate processing.

Optimizing in-network aggregate queries in wireless sensor networks for energy saving

Wireless sensor networks (WSNs) have been proposed for a large variety of applications. As the number of applications of sensor networks continue to grow, the number of users in sensor networks increases as well. Consequently, it is not uncommon that base station often needs to process multiple queries simultaneously. Furthermore, these queries often need to collect data from some particular sets of sensors such as the sensors in a hot spot.To reduce the communication cost of multiple query processing in WSNs, this paper proposes a new optimization technique based on similarities among multiple queries. Given a set of queries, Q, the proposed scheme constructs a set of shared intermediate views (SIVs) from Q. Each SIV identifies a set of shared data among queries in Q. The SIVs, are processed only once, but reused by at least two queries in Q. The queries in Q, are rewritten into a different set of queries, Q ′. The collected sensor data from Q ′ and SIVs, are aggregated and returned as the processing results for the original set of queries in Q. The simulation results show that the proposed technique can effectively reduce the communication cost of multiple query processing in WSNs.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkData TransmissionQuery ProcessingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Sensor networks are unattended deeply distributed systems whose schema can be conceptualized using the relational model. Aggregation queries on the data sampled at each sensor node are the main means to extract the abstract characteristics of the surrounding environment. However, the non-uniform distribution of the sensor nodes in the environment leads to inaccurate results generated by the aggregation queries. In this paper, we introduce aggregations that take into consideration the distribution of the values generated by the sensor nodes. We propose the use of spatial interpolation methods derived from the fields of spatial statistics and computational geometry to answer spatial aggregations. In particular, we study Spatial Moving Average (SMA), Voronoi Diagram and Triangulated Irregular Network (TIN). Investigating these methods for answering spatial average queries, we show that the average value on the data samples weighted by the area of the Voronoi cell of the corresponding sensor node, provides the best precision. Consequently, we introduce an incremental algorithm to compute and maintain the Voronoi cell at each sensor node. To demonstrate the performance of in-network implementation of our algorithm, we have developed prototypes of two different approaches to distributed spatial aggregate processing.

Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda?, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda? distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda? is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda? employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects' historical trajectories. (3) Panda? is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda? deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda? provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda? in evaluating a massive volume of predictive queries over large numbers of moving objects.

Event detection and notification is a common task in a wireless sensor networks (WSN). Efficient data aggregation and minimization of energy consumption are the great research challenges in WSN. In WSN, aggregated event information is more important than individual event information for energy saving and reliability. Proximity queries or query approximation can be used to reduce the complexity of data aggregation and energy consumption. This paper presents an efficient and scalable hybrid framework for processing spatial and temporal proximity queries in WSN which we call STP. STP builds tree structure with less overhead, and reduces the event propagation cost through proximity queries. STP reduces energy consumption by reducing the number of aggregator nodes, which ultimately increases the network life time. STP eliminates the unnecessary aggregation of events using a tunable temporal proximity threshold. We compare STP's performance with another spatial query processing method and we show that, STP performs better.

Over the past decades, hardware and software technologies for wireless sensor networks (WSNs) have significantly progressed, and WSNs are widely used in various areas including Internet of Things (IoT). In general, existing WSNs are mainly used for applications that require delay-tolerance and low-computation due to the poor resources of traditional sensor nodes in WSNs. However, compared to the traditional sensor nodes, today’s devices for WSNs have more powerful resource. Thus, sensor nodes these days not only conduct sensing and transmitting data to servers but also are able to process many operations, so more diverse applications can be applied to WSNs. Especially, many applications using audio data have been proposed because audio is one of the most widely used data types, and many mobile devices already have a built-in microphone. However, many of the applications have a requirement that heavy-operations should be done by a tight deadline, so it is difficult for a single node in WSNs to run relatively heavy applications by itself. In this paper, to overcome this limitation of WSNs, we propose a new emerging system, HeaLow, a cooperative computing system for heavy-computation and low-latency processing in WSNs. We designed HeaLow and carried out the practical implementation on real devices. We confirmed the effectiveness of HeaLow through various experiments using the real devices and simulations. Using HeaLow, nodes in WSNs are able to perform heavy-computation processes while satisfying a completion time requirement.

In wireless sensor network, many sensor nodes are distributed in the field. They communicate the sensing data each other and forward it to sink. Routing protocols, which define the delivery methods of sending data, affect to the lifetime of sensor network. This paper proposes RTAF that is a routing-tree construction algorithm of sensor nodes by a single flooding process in wireless sensor network. A routing tree is constructed by selecting a parent node using the forward-direction flooding packet and gathering children nodes using the reverse-direction flooding packet. In this process, a node with much energy becomes the parent node. And the routing tree is periodically reconstructed in order to distribute the loads of parent nodes. The proposed algorithm compared performance with Modified-LEACH using NS2 network simulation tool. The simulation result shows that the proposed algorithm constructs a routing-tree faster and reduced 40-80% in routing-tree construction packet.