Alireza Masoum

A Distributed Compressive Sensing Technique for Data Gathering in Wireless Sensor Networks

Compressive sensing is a new technique utilized for energy efficient data gathering in wireless sensor networks. It is characterized by its simple encoding and complex decoding. The strength of compressive sensing is its ability to reconstruct sparse or compressible signals from small number of measurements without requiring any a priori knowledge about the signal structure. Considering the fact that wireless sensor nodes are often deployed densely, the correlation among them can be utilized for further compression. By utilizing this spatial correlation, we propose a joint sparsity-based compressive sensing technique in this paper. Our approach employs Bayesian inference to build probabilistic model of the signals and thereafter applies belief propagation algorithm as a decoding method to recover the common sparse signal. The simulation results show significant gain in terms of signal reconstruction accuracy and energy consumption of our approach compared with existing approaches.

An energy-efficient adaptive sampling scheme for wireless sensor networks

Wireless sensor networks are new monitoring platforms. To cope with their resource constraints, in terms of energy and bandwidth, spatial and temporal correlation in sensor data can be exploited to find an optimal sampling strategy to reduce number of sampling nodes and/or sampling frequencies while maintaining high data quality. Majority of existing adaptive sampling approaches change their sampling frequency upon detection of (significant) changes in measurements. There are, however, applications that can tolerate (significant) changes in measurements as long as measurements fall within a specific range. Using existing adaptive sampling approaches for these applications is not energy-efficient. Targeting this type of applications, in this paper, we propose an energy-efficient adaptive sampling technique ensuring a certain level of data quality. We compare our proposed technique with two existing adaptive sampling approaches in a simulation environment and show its superiority in terms of energy efficiency and data quality.

A Decentralized Quality Aware Adaptive Sampling Strategy in Wireless Sensor Networks

Since WSNs suffer from sever resource constraints, in terms of energy, memory and processing, temporal, spatial and spatio-temporal correlation among sensor data can be exploited by adaptive sampling approaches to find out an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this paper, a quality aware decentralized adaptive sampling strategy is proposed which benefit from the data correlation for predicting future samples. In this algorithm, sensor nodes adjust their sampling rates, based on environmental conditions and user defined data range. Simulation results show that proposed approach provides 90 percentage event detection accuracy level while consumes lesser energy rather than existing adaptive sampling approach.

A New Multi Level Clustering Model to Increase Lifetime in Wireless Sensor Networks

Most of clustered models in wireless sensor networks use a double-layered structure. In these structures a node is considered as cluster-head and has the responsibility of gathering information of environment. Static attribute is the main disadvantage of these clustering method because as traffic rises in environment as time passes, cluster-head nodes energy and its close nodes, is consumed rapidly and so these nodes that has important role in data gathering, are break down. This issue is considered as age decrease in network life time and finally, network death. In this paper, a new three-layered dynamic model is introduced that its main goal is to increase network life time. The dynamic of this model is because the cluster-head node and the nodes nearby, that build layer 1 and layer 2 respectively, according to location analysis and traffic rate are replaceable and can change network connectivity. This paper discusses when and how network connectivity should change. The proof of the proposed issues is done using simulation methods.

Survivability Analysis of Wireless Sensor Network with Transient Faults

To the best knowledge of us, survivability for WSN has never been studied considering failure affects on network. We perceive the network survivability as a composite measure consisting of both network failure duration and failure impact on the network. In this paper we will study network survivability in unstably state: in this state, network is affected by the failures that occur temporarily and instantly also may occur several times. In other words, the main characteristics of these failures are their frequency and being temporary. In this paper we will propose a survivability model for network in unstable state that is based on network availability. This availability model, presents frequent availability of a route. During this paper, first we acquire an availability model for network in unstable state that shows frequent availability of a node. We also use Markov model for nodes to show their transmission state according to availability model. Then we use a computational method for proving our model.

Analysis of the impact of data correlation on adaptive sampling in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are often densely deployed to monitor a physical phenomenon, whose nature often exhibits temporal correlation in sequential readings. Such a dense deployment results in high correlation of sensing data in the space domain. Since WSNs suffer from sever resource constraints, temporal, spatial and spatio-temporal correlation among sensor data can be exploited to find an optimal sampling strategy, which reduces the number of sampling nodes and/or sampling rates while maintaining high data quality. In this study, we investigate the impact of the data correlation on sampling strategies, by taking both data quality and energy consumption into account.

Compressive sensing based data collection in wireless sensor networks

Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.