Phenomena In Sensor Networks Research Articles

Missing Data Problem in the Monitoring System: A Review

Missing data is a common phenomenon in sensor networks, especially in the large-scale monitoring system. It can be affected by various kinds of reasons. Moreover, incomplete data or information may affect the subsequent data processing and reasoning, resulting in a wrong decision. Hence, missing data recovery has always been a hot topic in the literature. However, although researchers have developed many different methods to recover missing data, this problem is still far away from being solved. To better track research progress and identify potential challenges, in this paper, we give a detailed review in the context of large-scale monitoring system. Mainly, we first introduce the basic concept of missing data, including the definition, causes, types, and performance evaluation. Then, a series of traditional and classical missing data recovery methods are analyzed and compared where their characteristics and scope of application are given. Furthermore, we present two current mainstream approaches from methodology to the existing literature, which are data recovery based on data mining algorithms and low rank algorithms, respectively. Finally, we conclude this paper with several promising directions for future research.

Sequential Anomaly Detection in Wireless Sensor Networks and Effects of Long-Range Dependent Data

Anomaly detection is important for the correct functioning of wireless sensor networks. Recent studies have shown that node mobility along with spatial correlation of the monitored phenomenon in sensor networks can lead to observation data that have long range dependency, which could significantly increase the difficulty of anomaly detection. In this article, we develop an anomaly detection scheme based on multiscale analysis of the long-range dependent traffic to address this challenge. In this proposed detection scheme, the discrete wavelet transform is used to approximately de-correlate the traffic data and capture data characteristics in different timescales. The remaining dependencies are then captured by a multilevel hidden Markov model in the wavelet domain. To estimate the model parameters, we develop an online discounting expectation maximization (EM) algorithm, which also tracks variations of the estimated models over time. Network anomalies are detected as abrupt changes in the tracked model variation scores. Statistical properties of our detection scheme are evaluated numerically using long-range dependent time series. We also evaluate our detection scheme in malicious scenarios simulated using the NS-2 network simulator.

Deterministic Recurrent Communication and Synchronization in Restricted Sensor Network

Distributed Computing and Networking Monitoring physical phenomena in Sensor Networks requires guaranteeing permanent communication between nodes. Moreover, in an effective implementation of such infrastructure, the delay between any two consecutive communications should be minimized. The problem is challenging because, in a restricted Sensor Network, the communication is carried out through a single and shared radio channel without collision detection. Dealing with collisions is crucial to ensure effective communication between nodes. Additionally, minimizing them yields energy consumption minimization, given that sensing and computational costs in terms of energy are negligible with respect to radio communication. In this work, we present a deterministic recurrent-communication protocol for Sensor Networks. After an initial negotiation phase of the access pattern to the channel, each node running this protocol reaches a steady state, which is asymptotically optimal in terms of energy and time efficiency. As a by-product, a protocol for the synchronization of a Sensor Network is also proposed. Furthermore, the protocols are resilient to an arbitrary node power-up schedule and a general node failure model.

Towards Spatial Window Queries Over Continuous Phenomena in Sensor Networks

Recent research on sensor networks has focused on the efficient processing of declarative SQL queries over sensor nodes. Users are often interested in querying an underlying continuous phenomenon such as a toxic plume, whereas only discrete readings of sensor nodes are available. Therefore, additional information estimation methods are necessary to process the sensor readings to generate the required query results. Most estimation methods are computationally intensive, even when computed in a traditional centralized setting. Furthermore, energy and communication constraints of sensor networks challenge the efficient application of established estimation methods in sensor networks. In this paper, we present an approach using Gaussian kernel estimation to process spatial window queries over continuous phenomena in sensor networks. The key contribution of our approach is the use of a small number of Hermite coefficients to approximate the Gaussian kernel function for subclustered sensor nodes. As a result, our algorithm reduces the size of messages transmitted in the network by logarithmic order, thus saving resources while still providing high-quality query results.