Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

Traffic eavesdropping based scheme to deliver time-sensitive data in sensor networks

Due to the broadcast nature of wireless channels, neighbouring sensor nodes may overhear packets transmissions from each other even if they are not the intended recipients of these transmissions. This redundant packet reception leads to unnecessary expenditure of battery energy of the recipients. Particularly in highly dense sensor networks, overhearing or eavesdropping overheads can constitute a significant fraction of the total energy consumption. Since overhearing of wireless traffic is unavoidable and sometimes essential, a new distributed energy efficient scheme is proposed in this paper. This new scheme exploits the inevitable overhearing effect as an effective approach in order to collect the required information to perform energy efficient delivery for data aggregation. Based on this approach, the proposed scheme achieves moderate energy consumption and high packet delivery rate notwithstanding the occurrence of high link failure rates. The performance of the proposed scheme is experimentally investigated a testbed of TelosB motes in addition to ns-2 simulations to validate the performed experiments on large-scale network

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-smooth Data Field

Compressive Sensing (CS) has been applied successfully in a wide variety of applications in recent years, including photography, shortwave infrared cameras, optical system research, facial recognition, MRI, etc. In wireless sensor networks (WSNs), significant research work has been pursued to investigate the use of CS to reduce the amount of data communicated, particularly in data aggregation applications and thereby improving energy efficiency. However, most of the previous work in WSN has used CS under the assumption that data field is smooth with negligible white Gaussian noise. In these schemes signal sparsity is estimated globally based on the entire data field, which is then used to determine the CS parameters. In more realistic scenarios, where data field may have regional fluctuations or it is piecewise smooth, existing CS based data aggregation schemes yield poor compression efficiency. In order to take full advantage of CS in WSNs, we propose an Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) scheme. The proposed schemes dynamically chooses sparsity values based on signal variations in local regions. We prove that A-HDACS enables more sensor nodes to employ CS compared to the schemes that do not adapt to the changing field. The simulation results also demonstrate the improvement in energy efficiency as well as accurate signal recovery

Rate-distortion Balanced Data Compression for Wireless Sensor Networks

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world datasets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN