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

Assessment of the Causes and Effects of Packet Loss in Wireless Sensor Networks

A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices using sensors to monitor physical or environmental conditions. A WSN system incorporates a gateway that provides wireless connectivity back to the wired world and distributed nodes.[i] Like any other wireless network, loss of packets is a common occurrence in WSNs. This may be caused by a variety of events and occurrences on the network which may in the long run affect the performance of the network. This paper therefore studied the connection between the causes of packet loss in wireless sensor networks and their net effect on the outcome and performance of the said WSN in the monitoring of physical and environmental conditions.Primarily the paper relied on secondary data and review of past literature and research and in the process was able to observe that weak signals and malicious attacks such as the black hole attack, selective forwarding attack and radio interferences are the major causes of packet loss whose effects include reduced network life and throughput; higher consumption of energy; denial of service attacks; reduced network efficiency; packet degradation and inconsistent packets. Keywords: Packet loss, Wireless sensor networks, malicious attacks, Received Signal strength [i] Anna Hac, (2003)Wireless Sensor Network Designs,John Wiley and Son

Development of a multi-hop wireless sensor system for the dynamic event monitoring of civil infrastructure and its extension for seismic response monitoring

The dynamic response of civil infrastructures under transient dynamic events is of particular interests for structural engineers, because these event-induced responses usually provide useful insights into the real dynamic behavior of civil infrastructures under extreme conditions. Monitoring these dynamic event induced vibrations are among the most frequently conducted measurements and experiments in the structural engineering field, and a cheaper, simpler and more flexible monitoring system is always under pursuit of civil engineers. One particular such request comes from the seismic response monitoring applications. Seismic response monitoring for general civil infrastructure is critical in high-risk earthquake areas like Japan. It contributes to earthquake safety by providing quantitative measurement that enables improved understanding and predictive modeling of the earthquake response of these engineered systems. However, due to the limitations of the current monitoring systems, such seismic response records of general civil infrastructure are usually not available. Therefore, this research describes a novel development of an autonomous dynamic event monitoring system using Wireless Smart Sensor Network(WSSN), which is further extended to support the purpose of long-term seismic response monitoring. This developed WSSN monitoring system is portable and low-cost, it has a potential to provide long-term seismic response monitoring for a wide range of civil infrastructure. This system can run on existing power sources readily available in common civil infrastructure and thus is able to perform long-term continuous sensing as demanded by the seismic response monitoring applications. A quick and stable event detection method is developed to trigger the recording of the complete seismic response and also eliminate possible false alerts caused by unexpected disturbance. Long-term network-wide time synchronization is guaranteed by a customized long-term Flooding Time Synchronization Protocol(FTSP) so that the all sensor nodes in the network can provide consistent time records of their captured seismic response. An efficient multi-hop service module is also incorporated into the system to disseminate commands and accommodate the need of collecting data in a reliable and prompt manner after major earthquakes, the integrated multi-hop data collection protocol provides a theoretically optimum data collection efficiency. Various experiments have been done to validate the developed programs. Suggestions are also given towards the final realization of successful long-term implementation of the developed monitoring system.報告番号: ; 学位授与年月日: 2012-09-27 ; 学位の種別: 修士 ; 学位の種類: 修士（工学） ; 学位記番号: ; 研究科・専攻: 工学系研究科社会基盤学専

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

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment