RSQS: Resource-Saving Multi-query Scheduling 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.

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

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.

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.

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.

Wireless Sensor Networks is dispersed by a group composed of sensors and Base Stations. It can be used in various applications of remote monitoring system. Sensor nodes route the collected data to the Base Station via multi-hop wireless links. These sensors typically use batteries to operate, the battery cannot be replaced when the depletion of energy resources. Therefore, how to use energy-saving sensor networks to extend the overall working time efficiently is an important subject. Our research is based on A Power Control Mechanism based on Sleep Scheduling for Wireless Sensor Networks. We proposed a refined mechanism of routing protocol for wireless sensor networks with sleep scheduling. In wireless sensor network with sleep scheduling, active node adjusts its working time and each step in sleeping state earlier. In the multi-hop transmission mode, each node broadcasts the packet to confirm the receiving node. Therefore, we use routing information to reduce the number of broadcasting during transmission. When route node with low rest power cannot transfer, in order to remain completed route, we can execute interrupt handler procedure to reduce the package loss. Finally, to achieve power efficiency, we will simulate if the proposed algorithm can reduce energy consumption effectively in the data transmission procedure.

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.

Wireless Sensor Networks (WSNs) consist of a large number of small and low cost sensor nodes powered by small batteries and equipped with various sensing devices. Usually, for many applications, once a WSN is deployed, probably in an inhospitable terrain, it is expected to gather the required data for quite some time, say for years. Since each sensor node has limited energy, these nodes are usually put to sleep to conserve energy, and this helps to prolong the network lifetime. There are two major approaches to sleep scheduling of sensor nodes, viz. (i) random (ii) synchronized. Any sleep scheduling scheme has to ensure that data can always be routed from source to sink. In this paper, we propose a novel approach for sleep scheduling of sensor nodes using a tree and an energy aware routing protocol which is integrated with the proposed sleep scheduling scheme. The tree is rooted at the sink node. The internal nodes of the tree remain awake and the leaf nodes are made to sleep. This provides an assured path from any node to the sink node. The tree is periodically reconstructed considering the remaining energy of each node with a view to balance energy consumption of nodes, and remove any failed nodes from the tree. The proposed approach also considerably reduces average energy consumption rate of each node as we are able to put more number of nodes to sleep in comparison to other approaches. Additional fault-tolerance is provided by keeping two paths from each node towards the sink. Extensive simulation studies of the proposed routing protocol has been carried out using Castalia simulator, and its performance has been compared with that of a routing protocol, called GSP, which incorporates sleep scheduling using random approach. The simulation results show that the proposed approach has longer network lifetime in comparison to that provided by GSP, and the energy consumption of nodes is also balanced.

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.

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.

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.

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

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.

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.