Computing the geometric mean over multiple-access channels: Error analysis and comparisons

Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Naïve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

For the purpose of saving the resources of wireless sensor networks, it is necessary to fuse the existing data in wireless sensor networks. When using the current method to fuse data in wireless sensor networks, the fusing time is too long to remove the noise signal in wireless sensor networks effectively. In the process of fusing, data packet loss is easy to occur. There are some problems such as low fusion efficiency, poor denoising performance and high packet loss rate. A multi-path data hierarchical fusion method based on routing algorithm is proposed in this paper. The redundant node decision method based on mesh partitioning is combined with the grid algorithm and EECRS protocol to establish a routing protocol based on redundant node elimination, and the redundant data existing in the redundant nodes of the wireless sensor network is removed. Based on the statistical data of the data, the probability density function is deduced and analyzed, and the noise filtering matrix is constructed to optimize the measurement matrix and remove the noise existing in the wireless sensor network. The multi-path scheduling technology is used to synthesize the pheromone table and the data forwarding flow table, and the ant is used to actively monitor the link QOS real-time information in the path finding process to update the pheromone table and the flow table, and calculate the link idle rate according to the update result. According to the calculation result, the data transmission path is selected, and the multi-path data layer fusion is completed. The experimental results show that the proposed method has high fusion efficiency, good denoising performance and low packet loss rate.

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

With the limited resources, it is difficult for wireless sensor network to achieve a long time, continuous, high-speed acquisition of multimedia information and a real-time, reliable transmission of high-volume sampling data. This article proposed the novel data acquisition strategy based on compressed sensing theory, which can perfectly achieve the long time, real-time, reliable transmission of high-volume multimedia data in wireless sensor network. Reasonable experiments were designed to verify the effectiveness of the algorithms, and the experiment results show that: the proposed multimedia signal acquisition strategy is reasonable, practicable, and more suitable for the wireless multimedia sensor networks.

In this paper, the reliability of data transmission in social networks is thoroughly studied and analyzed using wireless sensor network topology technology. This paper, based on the introduction of sensor network reliability analysis-related technology, combined with the characteristics, and needs of the sensor network itself, focuses on the study of the reliability analysis of the sensor network under the state of perturbation scheme. Based on the idea of making full use of data changes to respond to the sensor state, this paper takes the actual monitoring data of the wireless sensor network as the research object, selects the temporal correlation and spatial correlation of the measured environmental data as the reliability index by extracting the features of the wireless sensor network data, and proposes the Evidential reasoning rule- (ER-) based wireless sensor network data reliability assessment model based on Evidential reasoning rule (ER) is proposed. The data are mined, analyzed, and quantified from the perspective of content popularity, and the interest indicators of nodes on data under content popularity are analyzed to derive stable interest quantification values. Combined with the network properties, i.e., node autoassembly community, we analyze the data dissemination characteristics of social networks in wireless sensor network topology environment and derive the upper and lower bounds of data transmission capacity under node interest-driven and its variation on network performance. Social relationships among nodes affected by social attributes are considered; in turn, the data forwarding behavior of nodes is modeled using data transmission probability and data reception probability; finally, the data forwarding process is analyzed and a closed expression for the average end-to-end transmission capacity is derived in turn.

In this paper propose an several concepts for optimal algorithm of aggregation of data in wireless sensor network. Given analysis of problems for information aggregation in wireless sensor network. Proposed optimise of structure the node and given basic concepts for reducing energy consumption. Also proposed network partitioning into piconet with next clustering. In final proposed are two modifications for ant algorithms to optimize data collection in wireless sensor network.

Wireless sensor networks (WSNs) are vulnerable, that is, they can be physically or remotely exposed to destructive effects. In other words, security is critical in many wireless sensor network applications. Sensor readings are used to locate events such as fires, floods, etc. Because wireless sensor networks are vulnerable, there is a need to protect the network by detecting the introduction of false data. We have developed an algorithm to identify and fix malicious data in the network. The developed optimized swarm intelligence algorithm is applied to different datasets to analyze its performance. A simulator is used to test the algorithm. The analysis and simulation results show the detection and correction of malicious data in wireless sensor networks. In this paper we test simulation of data injection attack in WSN and reach the accuracy of 95% using Matlab.

A Technique of Filtering Dirty Data Based on Temporal- Spatial Correlation in Wireless Sensor Network

Information delivered through sensor networks is used in industries to increase quality of life (QoL). Lossless data in wireless sensor networks (WSNs) is a communications challenge that stands in the way of accurate data delivery. Although end-to-end data retransmission has evolved as a reliable mode of data transportation for the Internet, it is not applicable to WSNs due to the lack of reliable wireless links and resource constraints in sensor nodes. In this paper, we propose an efficient and reliable overhearing-based data transfer protocol for WSNs by introducing selective “direct acknowledgement (ACKdir)” or “implicit acknowledgement (ACKimp)” in cross-layer design. This protocol assesses the path (or link) quality and delegates the ACK message if it has good communication on paths. In addition, the protocol uses implicit ACK in order to be energy efficient, reducing traffic. Simulation results show that energy efficiency is improved by 30% compared with other approaches.

Energy consumption is the key design criterion for routing data in wireless sensor Network. Recently the use of multiple sinks instead of single is considered as a research topic for energy saving. The objective is to maximize the network lifetime by reducing distance from source node to sink during data transmission. This paper proposes a network partitioning algorithm based on selection of farthest node from k-Nearest Neighbour Graph. After partitioning, a restricted flooding-based routing protocol is used for data transmission. It is shown that the network lifetime significantly increases in a partitioned network in comparison with a non-partitioned network.

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensor nodes to monitor physical or environmental conditions. These distributed autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. The distributed sensor nodes communicate wirelessly to a central gateway, which provides a connection to the wired world where you can collect, process, analyze, and present your measurement data. Due to the broadcast nature of the transmission media they use, sensor networks are vulnerable to various security attacks, such as eavesdropping, jamming and node capture attacks. In this paper, we provide a solution for node capture attack. Node capture attacks result from the combination of active, passive and physical attacks by an intelligent adversary. In order to initialize or set up a node capture attack, the adversary will collect information about the WSN by eavesdropping on message exchanges, either local to a single adversarial device or throughout the network with the aid of a number of adversarial devices deployed throughout the network. Hence in order to securely aggregate data in a wireless sensor network, we must not only provide protection against eavesdroppers, but we should also prevent intermediate sensors from having access to the data.

In this paper, we study two problems of providing reliable data transmission and developing aggregation techniques for correlated data in wireless sensor networks. A system with forward error correction (FEC) can provide an objective reliability while using less transmission power than a system without FEC. Because of the additional parity bits and encoding/decoding energy consumptions, we study the effect of FEC on energy efficiency. We propose to use LDPC codes for FEC. We show that wireless sensor networks using LDPC codes are almost 45% more energy efficient than those that use BCH codes, which were shown to be 15% more energy efficient than the best performing convolutional codes. Then we study the problem of providing aggregation for two and three correlated nodes in wireless sensor networks. We propose to use LDPC codes in wireless sensor networks for source and channel coding to obtain a two-fold energy savings. For two correlated nodes, we study both distributed source coding and joint source-channel coding. While distributed source coding using LDPC codes was studied before, joint source-channel coding using LDPC codes is introduced for the first time. The difference between our work in distributed source coding using LDPC codes and the previous work lies in the LDPC code design procedure. The simulation results show that our proposed design criteria improves the performance of the source coding. The convergence of the non-uniform LDPC code of our design technique is almost 60% closer to the Slepian-Wolf limit. For three correlated nodes, we study distributed source coding using LDPC codes. We simplified the problem of design procedure to randomly punctured LDPC codes. This is a new approach for designing LDPC codes and the simulation results for code of length 1000 shows that the convergence of the LDPC code is achieved at compression rate 0.3174 which is only 0.08 away from the Slepian-Wolf limit.

A general wireless sensor network (WSN) is a system composed of a large number of sensor nodes. In the WSN system, all sensor nodes are responsible for sensing the interested events and transmitting the related information back to the sink node. The sink node acts like the brain of the whole WSN system for processing the collected data and giving control commands. The position of the sink node directly affects the data transmission efficiency and the distance from the terminal sensor node to the sink node. Thus, choosing the correct place to place the sink node in the whole network is a delicate work. In this paper, we propose a sink node placement method by proposing a newly designed object function and utilizing Cat Swarm Optimization (CSO) to find out the near best location for placing the sink node. The simulation results indicate that our proposed method presents good performance compared with the conventional Particle Swarm Optimization (PSO) algorithm in reducing the overall energy consumption and prolongs the lifetime of network in an efficient way.

In practical applications, because of some reasons, such as node failure or link failure, data loss is a normal occurrence in wireless sensor networks. For many applications on which data has a great impact, it is important to recover the lost data. Because of the sensory data has spatial correlation and temporal correlation, it is difficult to recover the lost data. Consider this, in this paper, we propose a novel algorithm based on the Markov Random Field to reduce the data error ratio during the data recovery process. In our proposed algorithm, we use the curved face recovery to utilize the spatial correlation in the sensory data, then we explore the temporal correlation between data in wireless sensor networks so that it can improve the accuracy of data recovered. A large amount of experiments show that, compared to other relevant works, the data recovery algorithm we proposed can reduce the error rate of the fitting data, especially data lost seriously in the wireless sensor network.

Recently, association rules for sensors have received a great deal of attention due to their importance in capturing the temporal relations between sensor nodes in wireless sensor networks (WSNs). Because of this capability, these rules can be used to improve the Quality of Service (QoS) of wireless sensor networks by participating in the resource management process. To mine sensor association rules, behavioral data that describes the sensors' activities over time must be extracted and accumulated at the central node (the Sink) for further analysis. Given the limited resources of sensor nodes, a well designed data gathering algorithm is required for gathering the behavioral data efficiently. In this paper, an in-network data reduction technique is proposed to reduce the amount of data that needs to be routed to the Sink by exploiting the redundancy between sensors' activities. The in-network reduction technique will be implemented on top of a data gathering tree that we refer to as the Minimum Nodes Data Gathering Tree (MNDGT), which consists of the nodes that will participate in formulating the sensor rules and those that are necessary for maintaining the minimum distance to the Sink. To report on the performance of the reduction mechanism, a comparison analysis with other two gathering schemas is introduced. Indeed, the results show that the in-network reduction technique is able to reduce the number of messages by a factor ranging from 10% to 70% compared to the other techniques.