Secure and Efficient Cluster-Based Range Query Processing in Wireless Sensor Networks

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.

The existing privacy-preserving aggregation query processing methods in sensor networks rely on pre-established network topology and require all nodes in the network to participate in query processing. Maintaining the topology results in a large amount of energy overhead, and in many cases, the user is interested only in the aggregated query results of some areas in the network, and thus, the participation of the entire network node is not necessary. Aiming to solve this problem, this article proposes a spatial range aggregation query algorithm for a dynamic sensor network with privacy protection (energy-efficient privacy-preserving data aggregation). The algorithm does not rely on the pre-established topology but considers only the query area that the user is interested in, abandoning all nodes to participate in distributing the query messages while gathering the sensory data in the query range. To protect node data privacy, Shamir’s secret sharing technology is used to prevent internal attackers from stealing the sensitive data of the surrounding nodes. The analysis and experimental results show that the proposed algorithm outperforms the existing algorithms in terms of energy and privacy protection.

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.

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.

Wireless Sensor Networks (WSNs) are densely and largely deployed in a variety of environments to sense real-world events. Many approaches have been proposed for collecting sensory data in any wireless sensor networks in a flexible, reliable, and efficient manner. In this study, we design an efficient mobile agent-based model for data gathering in wireless sensor networks to achieve energy and timely collection of sensory information in the target scene. The current focus in wireless sensor networks is placed on energy optimization during data gathering, processing and transmission. Mobile Agents (MAs) as a piece of a program have attracted growing research interest that travels the network from node to node to compute the local data to get useful information globally. Mobile agent’s features such as autonomy, social ability, learning, and more significantly, mobility makes it a chosen technology for information processing in wireless sensor networks and other resource constrained computing environments. The nodes of the sensors are modelled and represented by the states of a Markov chain. The nodes are further grouped into clusters to save the energy of the nodes farther away from the base station. Keywords: Mobile Agents, Sensor Nodes, Wireless Sensor Networks, Clustering. DOI: https://doi.org/10.18267/j.aip.84

One of the main objectives of query processing in wireless sensor networks is to achieve high resource efficiency. In this paper, we focus on scheduling multiple queries which may have temporal and spatial overlapping in querying operations. Our purpose is to schedule the operations on the sensor nodes so as to save the total cost by maximizing the overlapping in time among the queries. We propose an algorithm, called RSQS, which allows the newly arrived query to best share the operations with existing queries. The simulation results showed that our proposed approach can be used to effectively reduce the amount of sensory data transmitted in the network. This leads to energy and bandwidth saving in wireless sensor network.

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.

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.

Join processing in wireless sensor networks is a challenging problem. Current solutions are not involved in the join operation among tuples of the latest sampling periods. In this article, we proposed a continuous Single attribute Join Queries within latest sampling Periods (SJQP) for wireless sensor networks. The main idea of our filter-based framework is to discard non-matching tuples, and our scheme can guarantee the result is correct independent of the filters. Experiments based on real-world sensor data show that our method performs close to a theoretical optimum and consistently outperforms the centralized join algorithm.

Wireless sensor networks which are consisted of limited sensing, processing and communication equipments have prominent role in nowadays real-life applications. Efficient robust routing is a key feature in information processing in wireless sensor networks, especially in the presence of high-loss regions which occur due to harsh environment, hardware or software faults. In this paper, we propose a novel routing algorithm for high loss regions which have message loss above a given threshold. We solve theoretically the problem of near-optimal routing in a weighted region. Furthermore, Simulation experiments confirm the validity of proposed algorithm with high degree of accuracy.

Data processing in wireless sensor networks often relies on high-speed data stream input, but at the same time is inherently constrained by limited resource availability. Thus, energy efficiency and good resource management are vital for in-network processing techniques. We propose enabling resource-awareness for in-network processing algorithms by means of a resource monitoring component and designed a corresponding framework. As proof of concept, we implement an online clustering algorithm, which uses the resource monitor to adapt to resource availability, on the Sun SPOT sensor nodes from Sun Microsystem. We refer to this adaptive clustering algorithm as extended resource-aware cluster (ERA-cluster). Finally, we report on the outcome of several experiments to evaluate the validity of our approach in terms of resource adaptiveness and accuracy of the ERA-cluster. Results show that ERA-cluster can effectively adapt to resource availability while maintaining acceptable level of accuracy.

Data aggregation is an important process for wireless sensor networks (WSNs). The important aspects with WSNs are to increase the lifetime and to reserve the battery power of sensor nodes. The energy of nodes is mainly utilized in the process of sensing and transmitting the data. Clustering is one of the processes in which the total WSNs are divided into the form of groups and each group has a cluster head (CH) and advantage of doing this is decrease of energy consumption and reduce the data collision. The blockchain technology is utilized in WSN by so many ways for example security, data storing, node recovery etc. because the role of blocks in blockchain is same as the role of node in WSN. In this paper, we propose a cluster-based data aggregation framework for wireless sensor networks based on the blockchain technique. The proposed scheme is based on cluster head (CH) selection based on energy, and nodes communicate with each other via the shortest path. Further, the proposed scheme is also using the concept of blockchain technology for the purpose of maximum data storage. KeywordsWireless sensor networksLow energy adaptive clustering hierarchy (LEACH)Data aggregationCluster headBlockchainAnd Energy consumption

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.

This paper puts forward an optimization in routing processing for wireless sensor networks. The sensor node in such network is a battery-operated sensing device. These nodes are placed in space with a consideration to a base station that centralizes and consolidates the data transmitted or forwarded by sensor nodes. When optimizing the management of a Wireless Sensor Network, we have to take care of the consumption of energy that takes place while sensor nodes are running the routing process to send or relay a message to neighbors or to a Base Station. In this paper, we put forward a study of sensors' behavior by modeling and implementing a controller with Coloured Petri Network in order to ensure the reduction of energy consumption in some sensor networks in which each node can be activated or suspended as need be. The controller must deal with activation and suspension to apply a reconfiguring policy to nodes depending on an energy supply level in every sensor in order to obtain the lowest level possible of energy consumption and maintain the same service quality consisting in total coverage of the region to survey and the connection of active nodes to the functional network. The evaluation of our presented model is made by CPN-Tools.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.