Queries In Wireless Sensor Networks Research Articles

Cluster-based multi-hop wake-up control for top-k query in wireless sensor networks

Cluster-based multi-hop wake-up control for top-k query in wireless sensor networks

In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges.

We investigate the in-network processing of a skyline join query in wireless sensor networks (WSNs). While much research was conducted on processing skyline queries in WSNs, skyline join queries were dealt with only in traditional centralized or distributed database environments. However, such techniques cannot be applied to WSNs. Carrying out join filtering, as well as skyline filtering using them in WSNs, is infeasible due to limited memory in senor nodes and to excessive energy consumption in wireless communications. In this paper, we propose a protocol to process a skyline join query in WSNs energy efficiently with only a small amount of memory in each sensor node. It uses a synopsis of skyline attribute value ranges, which is a very compact data structure. The range synopsis is used both in the search of anchor points for skyline filtering and in 2-way semijoins for join filtering. We describe the structure of a range synopsis and present our protocol. To optimize our protocol, we solve some optimization problems. Through implementation and a set of detailed simulations, we show the effectiveness of our protocol. The range synopsis is confirmed to be compact enough for our protocol to work well with the limited memory and energy in each sensor node. For the correlated and random distributions, our protocol significantly outperforms other possible protocols, confirming the effectiveness of an in-network skyline as well as the join filtering capabilities of our protocol.

Queries allocation in WSNs with fuzzy control system

AbstractProcessing queries in wireless sensor networks causes quality of service challenges such as data latency and validity. In fact, this paper is going to shed light on identifying the right trade‐off between latency and the freshness of the data. For this purpose, a fuzzy logic‐based decision‐making system that assigns incoming queries either to the sensor network, where the queries are waiting to be processed into a queue, or a central database which provides stored and potentially stale data or to a cache server which in turn provides acceptable latency and recent data, is provided. Thus, a new rule‐based system decision is introduced, that minimizes the request latency and returns the most recent data. In addition, a comparison between the approach and other work that is restricted to the database server and sensor network with no focus on the caching server is provided. This paper is going to carry on with the theory of Markov Chain which is more complicated compared to the fuzzy logic system. This approach is confirmed in the basis of an experience is developed with python.

ENMSJ : An Efficiency Filtering Technique using Bitmap Vectors for n-way Joins in Wireless Sensor Networks

In wireless sensor networks, join queries execution introduces a high energy consumption. While energy is an important factor for sensors survival, several techniques were developed to reduce it. Sensors energy is affected by the number of transferred messages whereas query is performed. The aim of the proposed techniques was then to decrease the communicated data volume. So, the exchanged data volume is soaring when joins are performed between many data tables. This joins type is called: n-way join query. In this paper, we present an efficiency technique to treat n-way join queries in wireless sensor networks. This technique is named: Enhanced N-way Mediated Semi-Join (ENMSJ). ENMSJ is an improvement of a precedent strategie that we proposed: N-way Mediated Semi-Join (NMSJ). ENMSJ uses bitmap tables to more reduce transferred messages quantity. We compared the two techniques to test their performance. Obtained results are very hopeful.

Video-Like Lossless Compression of Data Cube for Big Data Query in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) contain many sensor nodes which are placed in chosen spatial area to temporally monitor the environmental changes. As the sensor data is big, it should be well organized and stored in cloud servers to support efficient data query. In this paper, we first adopt the streamed sensor data as "data cubes" to enhance data compression by video-like lossless compression (VLLC). With layered tree structure of WSNs, compression can be done on the aggregation nodes of edge computing. Then, an algorithm is designed to well organize and store these VLLC data cubes into cloud servers to support cost-effect big data query with parallel processing. Our experiments are tested by real-world sensor data. Results show that our method can save 94% construction time and 79% storage space to achieve the same retrieval time in data query when compared with a well-known database MySQL

Content-Based Wake-Up for Top-k Query in Wireless Sensor Networks

This article proposes content-based wake-up control for top- k query in wireless sensor networks, where the sink attempts to collect information on top- k nodes or top- k values from sensor nodes employing wake-up receivers. The wake-up procedure is designed with a goal of waking up only the subset of nodes which have the relevant data observations to construct the desired top- k set. This prevents the sensors with less relevant data from waking up and wasting energy. Assuming p-persistent carrier sense multiple access (CSMA) as the medium access protocol, the proposed scheme is analyzed theoretically in terms of data collection delay and total energy consumption. The performance of the proposed wake-up control is examined over a wide variety of parameters with practical considerations and compared to the conventional identity (ID)-based wake-up (IDWu) such as unicast wake-up (UCWu) and broadcast wake-up (BCWu). The obtained numerical results show that the proposed CDCoWu outperforms IDWu in terms of data collection delay and energy consumption for the maximum ratio of top-data to number of sensor nodes ranging from 0.1 to 0.5 when the number of sensor nodes is equal to or more than 20.

Sampling-based approximate skyline query in sensor equipped IoT networks

The ever increasing requirements of data sensing applications result in the usage of IoT networks. These networks are often used for efficient data transfer. Wireless sensors are incorporated in the IoT networks to reduce the deployment and maintenance costs. Designing an energy efficient data aggregation method for sensor equipped IoT to process skyline query, is one of the most critical problems. In this paper, we propose two approximation algorithms to process the skyline query in wireless sensor networks. These two algorithms are uniform sampling based approximate skyline query and Bernoulli sampling-based approximate skyline query. Solid theoretical proofs are provided to confirm that the proposed algorithms can yield the required query results. Experiments conducted on actual datasets show that the two proposed algorithms have high performance in terms of energy consumption compared to the simple distributed algorithm.

SER: Secure and efficient retrieval for anonymous range query in wireless sensor networks

A two-tiered wireless sensor network, where storage nodes take charge of storing sensitive data and processing queries with respect to the sensing nodes and querists, incurs a security breach. This is because the important role of storage nodes may be (1) instructed by attackers to return fake and/or incomplete data in response to querists’ queries or (2) compromised by attackers to arbitrarily expose mass of valuable data or launch choice attacks to make authorized querists miss valuable data and disturb their decisions. Additionally, attackers may launch DoS attacks via wireless channels to storage nodes. To address the above issues, we propose a secure and efficient retrieval scheme for wireless sensor networks, namely SER, which mainly prevents attackers from gaining the valuable information stored on storage nodes, detects the compromised storage nodes when they misbehave, and efficiently verifies the querists’ privileges without knowing their identities. In addition to privacy and system analyses, we demonstrate the feasibility and efficiency of SER through experiments conducted on TelosB prototype sensor platform equipped with IEEE 802.15.4 TI wireless transceiver, and conduct comparisons with state-of-the-art methods.

On Efficient Processing of Continuous Reverse Skyline Queries in Wireless Sensor Networks

The reverse skyline query plays an important role in information searching applications. This paper deals with continuous reverse skyline queries in sensor networks, which retrieves reverse skylines as well as the set of nodes that reported them for continuous sampling epochs. Designing an energy-efficient approach to answer continuous reverse skyline queries is non-trivial because the reverse skyline query is not decomposable and a huge number of unqualified nodes need to report their sensor readings. In this paper, we develop a new algorithm that avoids transmission of updates from nodes that cannot influence the reverse skyline. We propose a data mapping scheme to estimate sensor readings and determine their dominance relationships without having to know the true values. We also theoretically analyze the properties for reverse skyline computation, and propose efficient pruning techniques while guaranteeing the correctness of the answer. An extensive experimental evaluation demonstrates the efficiency of our approach.

Distributed high-dimensional similarity search approach for large-scale wireless sensor networks

Similarity search in high-dimensional space has become increasingly important in many wireless sensor network applications. However, existing approaches to similarity search is based on the premise that sensed data are centralized to deal with, or sensed data are simple enough to be stored in a relational database. Different from the previous work, we propose a distributed approximate similarity search algorithm to retrieve similar high-dimensional sensed data for query in wireless sensor networks. First, the sensors are divided into several clusters using the distributed clustering method. Furthermore, the sink transmits the compressed hash code set to the cluster heads. Finally, the estimated similarity score is compared with a specified threshold to filter out irrelevant sensed data. Therefore, the higher search precision and energy efficiency can be achieved. Extensive simulation results show that the proposed algorithms provide significant performance gains in terms of precision and energy efficiency compared with the existing algorithms.

RiPPAS: A Ring-Based Privacy-Preserving Aggregation Scheme in Wireless Sensor Networks

Recently, data privacy in wireless sensor networks (WSNs) has been paid increased attention. The characteristics of WSNs determine that users’ queries are mainly aggregation queries. In this paper, the problem of processing aggregation queries in WSNs with data privacy preservation is investigated. A Ring-based Privacy-Preserving Aggregation Scheme (RiPPAS) is proposed. RiPPAS adopts ring structure to perform aggregation. It uses pseudonym mechanism for anonymous communication and uses homomorphic encryption technique to add noise to the data easily to be disclosed. RiPPAS can handle both queries and queries, while the existing privacy-preserving aggregation methods can only deal with queries. For processing queries, compared with the existing methods, RiPPAS has advantages in the aspects of privacy preservation and communication efficiency, which can be proved by theoretical analysis and simulation results. For processing queries, RiPPAS provides effective privacy preservation and has low communication overhead.

An energy-efficient query based on variable region for large-scale smart grid

The state-of-the-art query techniques in power grid monitoring systems focus on querying history data, which typically introduces an unwanted lag when the systems try to discover emergency situations. The monitoring data of large-scale smart grids are massive, dynamic and highly dimensional, so global query, the method widely adopted in continuous queries in Wireless Sensor Networks (WSN), is rendered not suitable for its high energy consumption. The situation is even worse with increasing application complexity. We propose an energy-efficient query technique for large-scale smart grids based on variable regions. This method can query an arbitrary region based on variable physical windows, and optimizes data retrieve paths by a key nodes selection strategy. According to the characteristics of sensing data, we introduce an efficient filter into the each query subtree to keep non-skyline data from being retrieved. Experimental results show that our method can efficiently return the overview situation of any query region. Compared to TAG and ESA, the average query efficiency of our approach is improved by 79% and 46%, respectively; the total energy consumption of regional query is decreased by 82% and 50%, respectively.

Geometry-Based Distributed Spatial Skyline Queries in Wireless Sensor Networks

Algorithms for skyline querying based on wireless sensor networks (WSNs) have been widely used in the field of environmental monitoring. Because of the multi-dimensional nature of the problem of monitoring spatial position, traditional skyline query strategies cause enormous computational costs and energy consumption. To ensure the efficient use of sensor energy, a geometry-based distributed spatial query strategy (GDSSky) is proposed in this paper. Firstly, the paper presents a geometry-based region partition strategy. It uses the skyline area reduction method based on the convex hull vertices, to quickly query the spatial skyline data related to a specific query area, and proposes a regional partition strategy based on the triangulation method, to implement distributed queries in each sub-region and reduce the comparison times between nodes. Secondly, a sub-region clustering strategy is designed to group the data inside into clusters for parallel queries that can save time. Finally, the paper presents a distributed query strategy based on the data node tree to traverse all adjacent sensors’ monitoring locations. It conducts spatial skyline queries for spatial skyline data that have been obtained and not found respectively, so as to realize the parallel queries. A large number of simulation results shows that GDSSky can quickly return the places which are nearer to query locations and have larger pollution capacity, and significantly reduce the WSN energy consumption.

SDAMQ: Secure Data Aggregation for Multiple Queries in Wireless Sensor Networks

Wireless Sensor Network consists of severely energy constrained sensor nodes and are susceptible to security attacks due to broadcast communication model. It is necessary to optimize the transmission of packets to reduce the energy consumption. In addition data has to be encrypted in order to overcome the attack from the compromising nodes. We propose Secure Data Aggregation for Multiple Queries (SDAMQ) in Wireless Sensor Networks where multiple aggregate queries from the sink are authenticated and distributed to the sensor nodes. The sensor nodes respond by aggregating data belonging to multiple coexisting queries into a single packet, there by reducing the transmission cost. The intermediary nodes aggregate the encrypted data using additively homomorphic encryption. Thus authenticated query propagation combined with homomorphic encryption provide secure data aggregation at low energy consumption. Simulation results shows that SDAMQ provides better performance.

A Novel Two-Tier Cooperative Caching Mechanism for the Optimization of Multi-Attribute Periodic Queries in Wireless Sensor Networks

Wireless sensor networks, serving as an important interface between physical environments and computational systems, have been used extensively for supporting domain applications, where multiple-attribute sensory data are queried from the network continuously and periodically. Usually, certain sensory data may not vary significantly within a certain time duration for certain applications. In this setting, sensory data gathered at a certain time slot can be used for answering concurrent queries and may be reused for answering the forthcoming queries when the variation of these data is within a certain threshold. To address this challenge, a popularity-based cooperative caching mechanism is proposed in this article, where the popularity of sensory data is calculated according to the queries issued in recent time slots. This popularity reflects the possibility that sensory data are interested in the forthcoming queries. Generally, sensory data with the highest popularity are cached at the sink node, while sensory data that may not be interested in the forthcoming queries are cached in the head nodes of divided grid cells. Leveraging these cooperatively cached sensory data, queries are answered through composing these two-tier cached data. Experimental evaluation shows that this approach can reduce the network communication cost significantly and increase the network capability.

Random and Directed Walk-Based Top- Queries in Wireless Sensor Networks

In wireless sensor networks, filter-based top-k query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors’ readings and declines in the overall range of all the readings. In this work, a random walk-based top-k query approach called RWTQ and a directed walk-based top-k query approach called DWTQ are proposed. At the beginning of a top-k query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the “right” way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime.

In-network processing of an iceberg join query in wireless sensor networks based on 2-way fragment semijoins.

We investigate the in-network processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result. Processing such a join involves the value matching for the join predicate as well as the checking of the cardinality constraint for the iceberg threshold. In the previous scheme, the value matching is carried out as the main task for filtering non-joinable tuples while the iceberg threshold is treated as an additional constraint. We take an alternative approach, meeting the cardinality constraint first and matching values next. In this approach, with a logical fragmentation of the join operand relations on the aggregate counts of the joining attribute values, the optimal sequence of 2-way fragment semijoins is generated, where each fragment semijoin employs a Bloom filter as a synopsis of the joining attribute values. This sequence filters non-joinable tuples in an energy-efficient way in WSNs. Through implementation and a set of detailed experiments, we show that our alternative approach considerably outperforms the previous one.

Insights of Top- <inline-formula> <tex-math notation="TeX">$k$</tex-math> </inline-formula> Query in Duty-Cycled Wireless Sensor Networks

Aiming at finding the $k$ nodes with the highest readings among sensor nodes, top- $k$ query is a very important and useful query in wireless sensor networks (WSNs). Current studies about top- $k$ query in WSNs mainly focus on always-on WSNs (A-WSNs) where sensors always stay awake and little research pays attention to top- $k$ query in duty-cycled WSNs (D-WSNs) in which sensors are dynamically awake and asleep to conserve energy. In this paper, we originally analyze the research issues when implementing top- $k$ query in D-WSNs and then propose the DRC-WSNs [D-WSNs with data replication (DR) and connected $k_{c}$ -neighborhood (CKN)] scheme for performing top- $k$ query. Extensive theoretical analysis and simulation results provide the following two new insights regarding top- $k$ query in D-WSNs: 1) Implementing top- $k$ query in DRC-WSNs can achieve the best tradeoff with respect to query data accessibility and query cost (total energy consumption and query response time), compared with implementing top- $k$ query in DC-WSNs (D-WSNs with only CKN) and A-WSNs; and 2) the query data accessibility and the query cost of DRC-WSNs, DC-WSNs, and A-WSNs can be tuned by varying the number of neighbor nodes that the data are replicated to during DR, the $k_{c}$ in CKN, and the total sensor nodes number.

Distributed Database Management Techniques for Wireless Sensor Networks

In sensor networks, the large amount of data generated by sensors greatly influences the lifetime of the network. To manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.

Alternative Tuples Based Probabilistic Skyline Query Processing in Wireless Sensor Networks

As uncertainty is the inherent character of sensing data, the processing and optimization techniques for Probabilistic Skyline (PS) in wireless sensor networks (WSNs) are investigated. It can be proved that PS is not decomposable after analyzing its properties, so in-network aggregation techniques cannot be used directly to improve the performance. In this paper, an efficient algorithm, called Distributed Processing of Probabilistic Skyline (DPPS) query in WSNs, is proposed. The algorithm divides the sensing data into candidate data (CD), irrelevant data (ID), and relevant data (RD). The ID in each sensor node can be filtered directly to reduce data transmissions cost, since, only according to both CD and RD, PS result can be correctly obtained on the base station. Experimental results show that the proposed algorithm can effectively reduce data transmissions by filtering the unnecessary data and greatly prolong the lifetime of WSNs.