Correlation In Wireless Sensor Networks Research Articles

Stability Analysis of Epidemic Modeling Based on Spatial Correlation for Wireless Sensor Networks

In wireless sensor networks (WSNs), the main task of each sensor node is to sense the physical activity and then report it to a remote central monitoring sink node. For this, sensor nodes are attached with many sensors having ability to measure the environmental information. To investigate the virus propagation in WSNs, existing epidemiological models are global without consideration of behavior of WSN. Different from existing epidemic models, we propose Correlation-based susceptible-infectious-recovered epidemic model that takes into account of spatial correlation characteristics of a WSN. Firstly, we show that how strongly correlated nodes and less correlated nodes are formed in the a WSN based on sensing range. Using epidemic theory, the differential equations are derived and the stability analysis has been investigated to determine the threshold about spatial correlation based reproduction number for WSNs. Simulation results and comparative studies are presented to varify the numerical results using various parameters such as basic reproduction number, spatial correlation, node density, correlated nodes. Modeling demonstrates the effective virus propagation dynamics with time that can be used to design the prevention mechanisms to control the infections with time. Comparative studies show the significant performance improvement based on spatial correlation of nodes in a WSN.

Entropy Correlation and Its Impacts on Data Aggregation in a Wireless Sensor Network.

A correlation characteristic has significant potential advantages for the development of efficient communication protocols in wireless sensor networks (WSNs). To exploit the correlation in WSNs, the correlation model is required. However, most of the present correlation models are linear and distance-dependent. This paper proposes a general distance-independent entropy correlation model based on the relation between joint entropy and the number of members in a group. This relation is estimated using entropy of individual members and entropy correlation coefficients of member pairs. The proposed model is then applied to evaluate two data aggregation schemes in WSNs including data compression and representative schemes. In the data compression scheme, some main routing strategies are compared and evaluated to find the most appropriate strategy. In the representative scheme, with the desired distortion requirement, a method to calculate the number of representative nodes and the selection of these nodes are proposed. The practical validations showed the effectiveness of the proposed correlation model and data reduction schemes.

A resilient data aggregation method based on spatio-temporal correlation for wireless sensor networks

In wireless sensor networks, the existing data aggregation algorithms usually cannot evaluate the extent of data damage in presence of additive attacks. To resolve such problem, a resilient data aggregation method based on spatio-temporal correlation for wireless sensor networks is presented in this paper. On the basis of the distributed data convergence model, the algorithm combines the centroid distance and similarity to measure the attack degree of each cluster node’s perceived data, and the weighted calculation can improve the convergence precision of data recovery. In addition, this method can obtain the estimated value of data sample of all clusters according to the temporal correlation characteristic of the nodes’ perceived data at different time. Using the chi-square fitting, the extent of the data being tampered in each cluster can be measured effectively. Theoretical analysis and simulation results show our method can improve the restoration convergence precision as the attack increment is small. Also, it can enhance the robustness from noise interference.

Efficient Measurement Method for Spatiotemporal Compressive Data Gathering in Wireless Sensor Networks

By means of compressive sensing (CS) technique, this paper considers the collection of sensor data with spatiotemporal correlations in wireless sensor networks (WSNs). In energy-constrained WSNs, one-dimensional CS methods need a lot of data transmissions since they are less applicable in fully exploiting the spatiotemporal correlations, while the Kronecker CS (KCS) methods suffer performance degradations when the signal dimension increases. In this paper, an appropriate sensing matrix as well as an efficient sensing method is proposed to further reduce the data transmissions without the loss of the recovery performance. Different matrices for the temporal signal of each sensor node are separately designed. The corresponding energy-efficient data gathering method is presented, which only transmitting a subset of sensor readings to recover data of the entire WSN. Theoretical analysis indicates that the sensing structure could have the relatively small mutual coherence according to the selection of matrix. Compared with the existing spatiotemporal CS (CS-ST) method, the simulation results show that the proposed efficient measurement method could reduce data transmissions by about 25% with the similar recovery performance. In addition, compared with the conventional KCS method, for 95% successful recovery, the proposed sensing structure could improve the recovery performance by about 20%.

A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless Sensor Networks.

Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings’ spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node’s residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets’ sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.

SPATIAL CORRELATION BASED CLUSTERING ALGORITHM FOR RANDOM AND UNIFORM TOPOLOGY IN WSNS

In Wireless Sensor Networks (WSN) sensor nodes with similar readings can be grouped such that, it is enough to report a single reading from the entire group. A representative node is selected from each cluster to do the reporting job. This helps to increase the battery life of sensor nodes. However, efficiently identifying sensor groups and their representative nodes is a challenging task. In this paper, a distributed algorithm is proposed to determine a set of representative nodes which exploits the tradeoff between data quality and energy consumption. In this paper, we group the sensor nodes based on their inherent spatial and data correlation in WSN. The proposed clustering algorithm is applied for uniform and random topology of sensor network. The results based on different metrics such as average number of clusters formed, energy consumption and average variation in cluster size are compared for both topologies.

Achieving Efficient Flooding by Utilizing Link Correlation in Wireless Sensor Networks

Although existing flooding protocols can provide efficient and reliable communication in wireless sensor networks on some level, further performance improvement has been hampered by the assumption of link independence, which requires costly acknowledgments (ACKs) from every receiver. In this paper, we present collective flooding (CF), which exploits the link correlation to achieve flooding reliability using the concept of collective ACKs. CF requires only 1-hop information at each node, making the design highly distributed and scalable with low complexity. We evaluate CF extensively in real-world settings, using three different types of testbeds: a single-hop network with 20 MICAz nodes, a multihop network with 37 nodes, and a linear outdoor network with 48 nodes along a 326-m-long bridge. System evaluation and extensive simulation show that CF achieves the same reliability as state-of-the-art solutions while reducing the total number of packet transmission and the dissemination delay by 30%-50% and 35%-50%, respectively.

A Rate-Distortion Based Aggregation Method Using Spatial Correlation for Wireless Sensor Networks

Today we are witnessing an amazing growth of wireless sensor networks due to many factors including but limited to reducing cost of semiconductor components, rapid deployment of wireless networks, and attention to low-power aspect that makes these networks suitable for energy sensitive applications to a large extent. The power consumption requirement has raised the demand for the new concepts such as data aggregation. Data correlation plays an important role in an efficient aggregation process. This paper introduces a new correlation-based aggregation algorithm called RDAC (Rate Distortion in Aggregation considering Correlation) that works based on centralized source coding. In our method, by collecting correlated data at an aggregation point while using the Rate-Distortion (RD) theory, we can reduce the load of data transmitted to the base station by considering the maximum tolerable distortion by the user. To the best of our knowledge, nobody has yet used the RD theory for the data aggregation in wireless sensor networks. In this paper, a mathematical model followed by implementations demonstrates the efficiency of the proposed method under different conditions. By using the unique features of the RD theory, the correlation matrix and observing the behavior of the proposed method in different network topologies, we can find the mathematical upper and lower bounds for the amount of aggregated data in a randomly distributed sensor network. The bounds not only determine the upper and lower limits of the data compressibility, it also makes possible the estimation of the required bit count of the network without having to invoke the aggregation algorithm. This method therefore, allows us to have a good estimation of the amount of energy consumed by the network.

Nearest neighbor imputation using spatial–temporal correlations in wireless sensor networks

Missing data is common in Wireless Sensor Networks (WSNs), especially with multi-hop communications. There are many reasons for this phenomenon, such as unstable wireless communications, synchronization issues, and unreliable sensors. Unfortunately, missing data creates a number of problems for WSNs. First, since most sensor nodes in the network are battery-powered, it is too expensive to have the nodes re-transmit missing data across the network. Data re-transmission may also cause time delays when detecting abnormal changes in an environment. Furthermore, localized reasoning techniques on sensor nodes (such as machine learning algorithms to classify states of the environment) are generally not robust enough to handle missing data. Since sensor data collected by a WSN is generally correlated in time and space, we illustrate how replacing missing sensor values with spatially and temporally correlated sensor values can significantly improve the network’s performance. However, our studies show that it is important to determine which nodes are spatially and temporally correlated with each other. Simple techniques based on Euclidean distance are not sufficient for complex environmental deployments. Thus, we have developed a novel Nearest Neighbor (NN) imputation method that estimates missing data in WSNs by learning spatial and temporal correlations between sensor nodes. To improve the search time, we utilize a kd-tree data structure, which is a non-parametric, data-driven binary search tree. Instead of using traditional mean and variance of each dimension for kd-tree construction, and Euclidean distance for kd-tree search, we use weighted variances and weighted Euclidean distances based on measured percentages of missing data. We have evaluated this approach through experiments on sensor data from a volcano dataset collected by a network of Crossbow motes, as well as experiments using sensor data from a highway traffic monitoring application. Our experimental results show that our proposed K-NN imputation method has a competitive accuracy with state-of-the-art Expectation–Maximization (EM) techniques, while using much simpler computational techniques, thus making it suitable for use in resource-constrained WSNs.

Non-linear coding and decoding strategies exploiting spatial correlation in wireless sensor networks

The authors consider the acquisition of measurements from a source, representing a physical phenomenon, by means of sensors deployed at different distances, and measuring random variables (r.v.'s) that are correlated with the source output. The acquired values are transmitted over a wireless channel to a sink, where an estimation of the source has to be constructed, according to a given distortion criterion. In the presence of Gaussian random variables (r.v.'s) and a Gaussian vector channel, the authors are seeking optimum real-time joint source-channel encoder–decoder pairs that achieve a distortion sufficiently close to the theoretically optimal one, under a global resource constraint, by activating only a subset of the sensors. The problem is posed in a team decision theoretic framework, and the optimal strategies are approximated by means of neural networks. The analysis investigates the generalisation capabilities of the proposed approach, by showing insights into the structure of the problem. The surprising outcome is that a quasi-static application of the approach reveals to be sufficient to maintain quasi-optimal performance under a dynamic environment (e.g. with respect to nodes' positions).

An Adaptive MAC Protocol Based on Spatial and Temporal Correlation in Wireless Sensor Networks

The correlation between neighbor nodes is a key factor in wireless sensor networks. It has a crucial impact on network load. But few researches refer to it. This paper discusses the correlation effect on network load firstly, and then presents DOSC and DOTC as parameters to quantify the spatial and time correlation of the neighbor nodes respectively. Based on these, the paper proposes STCAS-MAC. STCAS-MAC is evolved from AS-MAC by improving its adaptive scheduling and considering the validity period of information. Simulation results show that compared with the AS-MAC, the proposed protocol improves greatly in several performances. Especially when the network load is heavy or variable, the STCAS-MAC protocol can improve the delivery ratio effectively or adaptation obviously.

An Event Driven Clustering Algorithm Based on Spatial Correlation in Wireless Sensor Networks

分簇算法是传感器网络中减少能量消耗的一种关键技术，它能够增强网络的扩展性和延长网络的生存时间。针对传感器节点数据的空间相关性，该文提出了一种新的基于空间相关性的事件驱动传感器网络分簇算法。算法根据用户要求的误差门限及结合节点数据的空间相关性马尔可夫模型，将事件感知区域划分成虚拟极坐标等价层。每个等价层选取层内当前剩余能量最大的节点作为簇头，网络通过移动代理收集簇头感知信息，该方法减少了传输数据量，有效节省了网络能量。

Adaptive Sensor Management Based on Spatial Correlation in Wireless Sensor Networks

This paper addresses the problem of sensor selection in wireless sensor networks (WSN) subject to a distortion constraint. To do so, first, a cost function is derived based on the spatial correlation obtained using the best estimation of the event source. Then, a new adaptive algorithm is proposed in which the number of active sensors is adaptively determined and the best topology of the active set is selected based on the add-one-sensor-node-at-a-time method. Simulations results show that the active sensors selected using the proposed cost function have less event distortion. Also, it is shown that the proposed sensor selection algorithm is near optimum and it has better performance than other algorithms with regard to the computational burden and distortion.

Spatial correlation-based collaborative medium access control in wireless sensor networks

Wireless Sensor Networks (WSN) are mainly characterized by dense deployment of sensor nodes which collectively transmit information about sensed events to the sink. Due to the spatial correlation between sensor nodes subject to observed events, it may not be necessary for every sensor node to transmit its data. This paper shows how the spatial correlation can be exploited on the Medium Access Control (MAC) layer. To the best of our knowledge, this is the first effort which exploits spatial correlation in WSN on the MAC layer. A theoretical framework is developed for transmission regulation of sensor nodes under a distortion constraint. It is shown that a sensor node can act as a representative node for several other sensor nodes observing the correlated data. Based on the theoretical framework, a distributed, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is then designed which has two components: Event MAC (E-MAC) and Network MAC (N-MAC). E-MAC filters out the correlation in sensor records while N-MAC prioritizes the transmission of route-thru packets. Simulation results show that CC-MAC achieves high performance in terms energy, packet drop rate, and latency.

Cluster-based Routing Algorithms Using Spatial Data Correlation for Wireless Sensor Networks

In densely deployed wireless sensor networks, spatial data correlations are introduced by the observations of multiple spatially proximal sensor nodes on a same phenomenon or event. These correlations bring significant potential advantages for the development of efficient strategies for reducing energy consumption. In this paper, spatial data correlations are exploited to design clusterbased routing algorithms of high data aggregation efficiency. We define the problem of selecting the set of cluster heads as the weighted connected dominating set problem. Then we develop a set of centralized approximation algorithms to select the cluster heads. Simulation results demonstrate the effectiveness and efficiency of the designed algorithms.

Online Distributed Fault Detection of Sensor Measurements

In wireless sensor networks (WSNs), a faulty sensor may produce incorrect data and transmit them to the other sensors. This would consume the limited energy and bandwidth of WSNs. Furthermore, the base station may make inappropriate decisions when it receives the incorrect data sent by the faulty sensors. To solve these problems, this paper develops an online distributed algorithm to detect such faults by exploring the weighted majority vote scheme. Considering the spatial correlations in WSNs, a faulty sensor can diagnose itself through utilizing the spatial and time information provided by its neighbor sensors. Simulation results show that even when as many as 30% of the sensors are faulty, over 95% of faults can be correctly detected with our algorithm. These results indicate that the proposed algorithm has excellent performance in detecting fault of sensor measurements in WSNs.

The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks

Sensed data in Wireless Sensor Networks (WSN) reflect the spatial and temporal correlations of physical attributes existing intrinsically in the environment. In this article, we present the Clustered AGgregation (CAG) algorithm that forms clusters of nodes sensing similar values within a given threshold (spatial correlation), and these clusters remain unchanged as long as the sensor values stay within a threshold over time (temporal correlation). With CAG, only one sensor reading per cluster is transmitted whereas with Tiny AGgregation (TAG) all the nodes in the network transmit the sensor readings. Thus, CAG provides energy efficient and approximate aggregation results with small and often negligible and bounded error. In this article we extend our initial work in CAG in five directions: First, we investigate the effectiveness of CAG that exploits the temporal as well as spatial correlations using both the measured and modeled data. Second, we design CAG for two modes of operation (interactive and streaming) to enable CAG to be used in different environments and for different purposes. Interactive mode provides mechanisms for one-shot queries, whereas the streaming mode provides those for continuous queries. Third, we propose a fixed range clustering method, which makes the performance of our system independent of the magnitude of the sensor readings and the network topology. Fourth, using mica2 motes, we perform a large-scale measurement of real environmental data (temperature and light, both indoor and outdoor) and the wireless radio reliability, which were used for both analytical modeling and simulation experiments. Fifth, we model the spatially correlated data using the properties of our real world measurements. Our experimental results show that when we compute the average of sensor readings in the network using the CAG interactive mode with the user-provided error threshold of, 20%, we can save 68.25% of transmissions over TAG with only 2.46% inaccuracy in the result. The streaming mode of CAG can save even more transmissions (up to 70.24% in our experiments) over TAG, when data shows high spatial and temporal correlations. We expect these results to hold in reality, because we used the mica2 radio profile and empirical datasets for our simulation study. CAG is the first system that leverages spatial and temporal correlations to improve energy efficiency of in-network aggregation. This study analytically and empirically validates CAG's effectiveness.

Spatio-temporal correlation: theory and applications for wireless sensor networks

Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. These spatial and temporal correlations along with the collaborative nature of the WSN bring significant potential advantages for the development of efficient communication protocols well-suited for the WSN paradigm. In this paper, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to model the spatial and temporal correlations in WSN. The objective of this framework is to enable the development of efficient communication protocols which exploit these advantageous intrinsic features of the WSN paradigm. Based on this framework, possible approaches are discussed to exploit spatial and temporal correlation for efficient medium access and reliable event transport in WSN, respectively.

Spatio-temporal correlation: theory and applications for wireless sensor networks

Wireless Sensor Networks (WSN) are characterized by the dense deployment of sensor nodes that continuously observe physical phenomenon. Due to high density in the network topology, sensor observations are highly correlated in the space domain. Furthermore, the nature of the physical phenomenon constitutes the temporal correlation between each consecutive observation of a sensor node. These spatial and temporal correlations along with the collaborative nature of the WSN bring significant potential advantages for the development of efficient communication protocols well-suited for the WSN paradigm. In this paper, several key elements are investigated to capture and exploit the correlation in the WSN for the realization of advanced efficient communication protocols. A theoretical framework is developed to model the spatial and temporal correlations in WSN. The objective of this framework is to enable the development of efficient communication protocols which exploit these advantageous intrinsic features of the WSN paradigm. Based on this framework, possible approaches are discussed to exploit spatial and temporal correlation for efficient medium access and reliable event transport in WSN, respectively.