Topology partitioning in wireless sensor networks using multiple sinks

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.

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.

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.

Due to the resource constraint, in wireless sensor network, the node processing ability, wireless bandwidth and battery capacity and other resources are scarcer. For improving the energy efficient and extend the lifetime of the network, this paper proposes a novel algorithm with the distributed and energy-efficient for collecting and aggregating data of wireless sensor network. In the proposed protocol, nodes can autonomously compete for the cluster head based on its own residual energy and the signal strength of its neighbouring nodes. To reduce the energy overhead of cluster head nodes, with a multi-hop way among cluster heads, the collected data from cluster heads is sent to a designated cluster head so as to further send these data to a base station. For improving the performance of the proposed protocol, a new cluster coverage method is proposed to fit the proposed protocol so that when the node density increases, network lifetime can be increased linearly as the number of nodes is increased. Simulations experiments show that network lifetime adopting the proposed protocol is sharply increased. And, the proposed protocol makes all the nodes die (network lifetime is defined as the death of last one node) in the last 40 rounds so that networks adopting the proposed protocol have higher reliability than networks adopting compared protocols.

Data aggregation is a promising approach for reducing energy consumption and relaying of redundant data in wireless sensor networks for monitoring applications. However data aggregation implies, on the one hand, an increase in network processing at certain nodes, referred to as aggregators, possibly leading to a higher delivery delay to the sink. On the other hand, the advantages of using aggregation are numerous, especially under the perspective of energy consumption reduction and network lifetime increase. In this paper we provide a closed form expression for the end-to-end delay and network lifetime distributions in a wireless sensor network employing aggregation. These derivations can be used to provide some insights on the expected performance in terms of the metrics above mentioned, when data aggregation is used. For example, if a certain constraint on the maximum tolerable delivery delay is given, ¿*, this can be useful to characterize what is the probability to satisfy this constraint.

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.

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.

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.

The paper deals with the problem of data transmission and function computation of the sensed data in wireless sensor networks, in which multiple sensor nodes transmit their data to one sink node over a wireless multiple-access channel. We focus on the problem of computing the geometric mean at the sink node by merging the data transmission and function computation into one step via an explicit utilization of channel collisions caused by simultaneous transmissions of sensor nodes. The paper provides the analysis of the estimation error and compares the scheme with traditional time division multiple-access based schemes to indicate potential for significant performance gains.

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.

In this paper we extend our previously designed trust model in wireless sensor networks to include both; communication trust and data trust. Trust management in wireless sensor networks is predominantly based on routing messages; whether the communication has happened or not (successful and unsuccessful transactions). The uniqueness of sensing data in wireless sensor networks introduces new challenges in calculating trust between nodes (data trust). If the overall trust is based on just the communication trust, it might mislead the network, that is; untrustworthy nodes in terms of sensed data can be classified as trusted nodes due to their communication capabilities. Hence we need to develop new trust models to address the issue of the actual sensed data. Here we are comparing the two trust models and proving that one model by itself is not enough to decide on the trustworthiness of a node, so new techniques are required to combine both data trust and communication trust.

Tree-based routing structures are widely used to gather data in wireless sensor networks. Along with tree structures, in-network aggregation is adopted to reduce transmissions, to save energy and to prolong the network lifetime. Most existing works that focus on the lifetime optimization for data aggregation do not take the link quality into consideration. In this paper, we study the problem of Maximizing Reliability of Lifetime Constrained data aggregation trees (MRLC) in WSNs. Considering the NP-completeness of the MRLC problem, we propose an algorithm, namely Iterative Relaxation Algorithm (IRA), to iteratively relax the optimization program and to find the aggregation tree subject to the lifetime bound with a sub-optimal cost. To adapt to the distributed nature of the WSNs in practice, we further propose a Prufer code based distributed updating protocol. Through extensive simulations, we demonstrate that IRA outperforms the best known related work in term of reliability.