Efficient Packet Scheduling Technique for Data Merging in Wireless Sensor Networks

Wireless Sensor Networks(WSNs) has become a popular research topic due to its resource constraints. Energy consumption and transmission delay is crucial requirement to be handled to enhance the popularity of WSNs. In order to overcome these issues, we have proposed an Efficient Packet Scheduling Technique for Data Merging in WSNs. Packet scheduling is done by using three levels of priority queue and to reduce the transmission delay. Real-time data packets are placed in high priority queue and Non real-time data packets based on local or remote data are placed on other queues. In this paper, we have used Time Division Multiple Access(TDMA) scheme to efficiently determine the priority of the packet at each level and transmit the data packets from lower level to higher level through intermediate nodes. To reduce the number of transmission, efficient data merge technique is used to merge the data packet in intermediate nodes which has same destination node. Data merge utilize the maximum packet size by appending the merged packets with received packets till the maximum packet size or maximum waiting time is reached. Real-time data packets are directly forwarded to the next node without applying data merge. The performance is evaluated under various metrics like packet delivery ratio, packet drop, energy consumption and delay based on changing the number of nodes and transmission rate. Our results show significant reduction in various performance metrics.

Single Mobile Sink Based Energy Efficiency and Fast Data Gathering Protocol for Wireless Sensor Networks

Recently, the exponential rise in communication system demands has motivated global academia-industry to develop efficient communication technologies to fulfill energy efficiency and Quality of Service (QoS) demands. Wireless Sensor Network (WSN) being one of the most efficient technologies possesses immense potential to serve major communication purposes including civil, defense and industrial purposes etc. The inclusion of sensor-mobility with WSN has broadened application horizon. The effectiveness of WSNs can be characterized by its ability to perform efficient data gathering and transmission to the base station for decision process. Clustering based routing scheme has been one of the dominating techniques for WSN systems;however key issues like, cluster formation, selection of the number of clusters and cluster heads, and data transmission decision from sensors to the mobile sink have always been an open research area. In this paper, a robust and energy efficient single mobile sink based WSN data gathering protocol is proposed. Unlike existing approaches, an enhanced centralized clustering model is developed on the basis of expectation-maximization (EEM) concept. Further, it is strengthened by using an optimal cluster count estimation technique that ensures that the number of clusters in the network region doesn’t introduce unwanted energy exhaustion. Meanwhile, the relative distance between sensor node and cluster head as well as mobile sink is used to make transmission (path) decision. Results exhibit that the proposed EEM based clustering with optimal cluster selection and optimal dynamic transmission decision enables higher throughput, fast data gathering, minima delay and energy consumption, and higher

AEESPAN: Automata Based Energy Efficient Spanning Tree for Data Aggregation in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are developed densely. They have limit processing ca-pability and low power resources. Thus, energy is one of most important constraints in these networks. In some applications of sensor networks, sensor nodes sense data from the environment periodically and trans-mit these data to sink node. In order to decrease energy consumption and so, increase network’s lifetime, volume of transmitted data should be decreased. A solution, which is suggested, is aggregation. In aggrega-tion mechanisms, the nodes aggregate received data and send aggregated result instead of raw data to sink, so, the volume of the transmitted data is decreased. Aggregation algorithms should construct aggregation tree and transmit data to sink based on this tree. In this paper, we propose an automaton based algorithm to con-struct aggregation tree by using energy and distance parameters. Automaton is a decision-making machine that is able-to-learn. Since network’s topology is dynamic, algorithm should construct aggregation tree peri-odically. In order to aware nodes of topology and so, select optimal path, routing packets must be flooded in entire network that led to high energy consumption. By using automaton machine which is in interaction with environment, we solve this problem based on automat learning. By using this strategy, aggregation tree is reconstructed locally, that result in decreasing energy consumption. Simulation results show that the pro-posed algorithm has better performance in terms of energy efficiency which increase the network lifetime and support better coverage.

Energy-Efficient Data Collection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are usually self-organized wireless ad hoc networks comprising of a large number of resource constrained sensor nodes. One of the most important tasks of these sensor nodes is systematic collection of data and transmits gathered data to a distant base station (BS). Hence network life- time becomes an important parameter for efficient design of data gathering schemes for sensor networks. In this paper, we benefit both cluster and tree structures for data gathering. In our proposed energy-efficient mechanism, the most appropriates hops for data forwarding will be selected and the lifetime of the whole network will be maximized. The simulation results show that by using the proposed approach, the lifetime and the throughput of the network will be increased.

K-Nearest Neighbor Based Missing Data Estimation Algorithm in Wireless Sensor Networks

In wireless sensor networks, the missing of sensor data is inevitable due to the inherent characteristic of wireless sensor networks, and it causes many difficulties in various applications. To solve the problem, the missing data should be estimated as accurately as possible. In this paper, a k-nearest neighbor based missing data estimation algorithm is proposed based on the temporal and spatial correlation of sensor data. It adopts the linear regression model to describe the spatial correlation of sensor data among different sensor nodes, and utilizes the data information of multiple neighbor nodes to estimate the missing data jointly rather than independently, so that a stable and reliable estimation performance can be achieved. Experimental results on two real-world datasets show that the proposed algorithm can estimate the missing data accurately.

Anomaly Detection Based on Data-Mining for Routing Attacks in Wireless Sensor Networks

With the increasing deployment of wireless sensordevices and networks,security becomes a criticalchallenge for sensor networks.In this paper,a schemeusing data mining is proposed for routing anomalydetection in wireless sensor networks.The schemeuses the Apriori algorithm to extract traffic patternsfrom both routing table and network traffic packetsand subsequently the K-means cluster algorithmadaptively generates a detection model.Through thecombination of these two algorithms,routing attackscan be detected effectively and automatically.Themain advantage of the proposed approach is that it isable to detect new attacks that have not previouslybeen seen.Moreover,the proposed detection schemeis based on no priori knowledge and then can beapplied to a wide range of different sensor networksfor a variety of routing attacks.

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

Improved Data Discrimination in Wireless Sensor Networks

In Wireless Sensors Networks, the computational power and storage capacity is limited. Wireless Sensor Networks are operated in low power batteries, mostly not rechargeable. The amount of data processed is incremental in nature, due to deployment of various applications in Wireless Sensor Networks, thereby leading to high power consumption in the network. For effectively processing the data and reducing the power consumption the discrimination of noisy, redundant and outlier data has to be performed. In this paper we focus on data discrimination done at node and cluster level employing Data Mining Techniques. We propose an algorithm to collect data values both at node and cluster level and finding the principal component using PCA techniques and removing outliers resulting in error free data. Finally a comparison is made with the Statistical and Bucket-width outlier detection algorithm where the efficiency is improved to an extent.

Connectivity-Based Data Gathering with Path-Constrained Mobile Sink in Wireless Sensor Networks

The design of an effective and robust data gathering algorithm is crucial to the overall performance of wireless sensor networks (WSN). However, using traditional routing algorithms for data gathering is energy-inefficient for sensor nodes with limited power resources and multi-hop communication protocols. Data gathering with mobile sinks provided an effective solution to this problem. The major drawback of this approach is the time and path constraints of the mobile sink, which limit the mobile sink to collect data from all sensor nodes and, then, data routing is still required for these unreachable parts by the mobile sink. This paper presents a new data gathering algorithm called Connectivity-Based Data Collection (CBDC). The CBDC algorithm utilizes the connectivity between sensor nodes so as to determine the trajectory of the mobile sink whilst satisfying its path constraint and minimizing the number of multi-hop communications. The presented results show that CBDC, in comparison with the LEACH-C algorithm, prolongs the network life time at different connectivity levels of sensor networks, varying number of sensor nodes and at different path constraints of the mobile sink.

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation.Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network.These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner.We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption.Measured data is modeled as a Gaussian random variable with a changeable variance.A gird model is used to approximate the problem.We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search.Our experiments demonstrate that the algorithm is correct in a certain tolerance,and it is also efficient and scalable.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

Inter-Data Commonality Detection for Spectrum Monitoring in Wireless Sensor Networks

Cooperative spectrum monitoring with multiple sensors has been deemed as an efficient mechanism for improving the monitoring accuracy and enlarging the monitoring area in wireless sensor networks.However,there exists redundancy among the spectrum data collected by a sensor node within a data collection period,which may reduce the data uploading efficiency.In this paper,we investigate the inter-data commonality detection which describes how much two data have in common.We define common segment set and divide it into six categories firstly,then a method to measure a common segment set is conducted by extracting commonality between two files.Moreover,the existing algorithms fail in finding a good common segment set,so Common Data Measurement(CDM)algorithm that can identify a good common segment set based on inter-data commonality detection is proposed.Theoretical analysis proves that CDM algorithm achieves a good measurement for the commonality between two strings.In addition,we conduct an synthetic dataset which are produced randomly.Numerical results shows that CDM algorithm can get better performance in measuring commonality between two binary files compared with Greedy-String-Tiling(GST)algorithm and simple greedy algorithm.

Investigating Approaches of Data Integrity Preservation for Secure Data Aggregation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) typically use in-network processing to reduce the communication overhead. Due to the fusion of data items sourced at different nodes into a single one during in-network processing, the sanctity of the aggregated data needs to be ensured. Especially, the data integrity of the aggregated result is critical as any malicious update to it can jeopardize not one, but many sensor readings. In this paper, we analyse three different approaches to providing integrity support for SDA in WSNs. The first one is traditional MAC, in which each leaf node and intermediate node share a key with parent (symmetric key). The second is aggregate MAC (AMAC), in which a base station shares a unique key with all the other sensor nodes. The third is homomorphic MAC (Homo MAC) that is purely symmetric key-based approach. These approaches exhibit diverse trade-off in resource consumption and security assumptions. Adding together to that, we also propose a probabilistic and improved variant of homomorphic MAC that improves the security strength for secure data aggregation in WSNs. We carry out simulations in TinyOS environment to experimentally evaluate the impact of each of these on the resource consumption in WSNs.

A Power Graded Data Gathering Mechanism for Wireless Sensor Networks

The data gathering manner of wireless sensor networks, in which data is forwarded towards the sink node, would cause the nodes near the sink node to transmit more data than those far from it. Most data gathering mechanisms nowdo not do well in balancing the energy consumption among nodes with different distances to the sink, thus they can hardly avoid the problem that nodes near the sink consume energy more quickly, which may cause the network rupture from the sink node. This paper presents a data gathering mechanism called PODA, which grades the output power of nodes according to their distances from the sink node. PODA balances energy consumption by setting the nodes near the sink with lower output power and the nodes far from the sink with higher output power. Simulation results show that the PODA mechanism can achieve even energy consumption in the entire network, improve energy efficiency and prolong the network lifetime.

Reactive data collection protocol using mobile sink in wireless sensor network

We suggest event collection protocol in a specific region where sensors are deployed to detect and collect events.In the traditional multi-hop routing in wireless sensor networks reporting events to a sink node or base-station will cause imbalanced energy consumption of static sensors.To solve this problem,we use mobile sink.In this paper,we study the design of efficiency routing protocol for supporting efficient data collecting in mobile sink wireless sensor networks(mWSNs).We suggest the following two main ideas.First,we use reactive protocol to cut off unnecessary delay.Mobile sink makes a path to access to sensor node.Second,we model mobile sink movement depending on data frequency,so we can reduce moving distance efficiently.We simulate this protocol and compare it with the traditional method.Simulation results show this protocol reduces distance significantly and is suitable for mWSNs with heavy traffic.

A Lightweight Data Aggregation Protocol in Wireless Sensor Networks for the Protection of Ancient Sites

In the application of large-scale ancient site protection, it is necessary to continuously monitor the ambient light, temperature, humidity and so on. However, it is impractical to frequently replace the nodes’ battery in the protected areas. So, the key methods to prolong the network lifetime are to aggregate the collected data and reduce the number of transferring messages. In this paper a Lightweight Data Aggregation Protocol (LDAP) based on the characteristics of the environmental changes in ancient sites is proposed. It has been implemented in the Lab with a dozen of MICAz motes and deployed in the real ancient sites. The result shows that LDAP is effective in reducing the number of transferring packets and satisfies the real application requirements.

Symmetric-Key Based Homomorphic Primitives for End-to-End Secure Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, secure data aggregation protocols target the two major objectives, namely, security and en route aggregation. Although en route aggregation of reverse multi-cast traffic improves energy efficiency, it becomes a hindrance to end-to-end security. Concealed data aggregation protocols aim to preserve the end-to-end privacy of sensor readings while performing en route aggregation. However, the use of inherently malleable privacy homomorphism makes these protocols vulnerable to active attackers. In this paper, we propose an integrity and privacy preserving end-to-end secure data aggregation protocol. We use symmetric key-based homomorphic primitives to provide end-to-end privacy and end-to-end integrity of reverse multicast traffic. As sensor network has a non-replenishable energy supply, the use of symmetric key based homomorphic primitives improves the energy efficiency and increase the sensor network’s lifetime. We comparatively evaluate the performance of the proposed protocol to show its efficacy and efficiency in resource-constrained environments.

Robust UAV Aided Data Collection for Energy-Efficient Wireless Sensor Network with Imperfect CSI

Due to its air superiority and high mobility, unmanned aerial vehicle (UAV) can obtain better line-of-sight (LoS) link transmission channel. Therefore, UAV assisted data collection for wireless sensor networks (WSNs) has become an important research direction. This paper intends to minimize the loss of WSNs for the robust data acquisition and communication assisted by UAV under the imperfect channel state information (CSI). On the premise of ensuring the completion of the communication task, we jointly optimize the wake-up schedule of SNs and the flight trajectory of the UAV, by considering the flight speed of the UAV and the sparse access of all sensor nodes (SNs) in WSN. Because the formulated optimization problem is a mixed integer nonconvex problem, we decompose the original problem into the efficient suboptimal solutions to overcome the difficulty of the optimization. Finally, the number of access node corresponding to the optimized operation time and access efficiency is induced for the entire WSN system efficiency improving. The simulation shows the performance gains of our proposed scheme and the influences of the system parameters are analyzed.

Information Driven Data Gathering for Energy Efficient Wireless Sensor Network

Large scale dense Wireless Sensor Networks (WSNs) have been progressively employed for different classes of applications for the resolve of precise monitoring. As a result of high density of nodes, both spatially and temporally correlated information can be detected by several nodes. Hence, energy can be saved which is a major aspect of these networks. Moreover, by using these advantages of correlations, communication and data exchange can be reduced. In this paper, a novel algorithm that selects the data based on their contextual importance is proposed. The data, which are contextually important, are only transmitted to the upper layer and the remains are ignored. In this way, the proposed method achieves significant data reduction and in turn improves the energy conservation of data gathering.

A double weighted LS-SVM model for data estimation in wireless sensor networks

In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines （DWLS-SVM） model for the missing data estimation in wireless sensor networks is proposed in this paper. The algo- rithm first applies the weighted LS-SVM （WLS-SVM） to estimate the missing data on temporal do- main and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWLS-SVM considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outli- er robust and can estimate the missing values accurately.