A new management method for wireless sensor networks

International audienceThe Wireless Sensor Networks (WSN) with their constant evolution, need new management methods to be monitored efficiently by taking into account the context and their constraints such as energy consumption, reliability and remote monitoring. WSN has diverse application domains: smart home, smart care, environmental data collection etc. In order to manage a large scale WSN, several Wireless sensor network Management Tools (WMTs) are developed. Some of them use SNMP protocol like because it is impossible to implement the full compliance classical SNMP standard on each wireless sensor node. Therefore, it is important to develop a new WMT with a restricted SNMP standard dedicated to WSN applications. In this paper, we present a new WMT named LiveNCM: LiveNode Non invasive Context-aware, and modular Management tool. LiveNCM is divided into two main parts: one is centralized on the fixed network structure and another one, distributed on each node. Each part introduces the concept of non-invasive context-aware to reduce data exchanges and diagnoses the wireless sensor node state with few messages. Moreover, nodes are based on a configurable modular architecture enables to adapt to an application and to a local node constraints. LiveNCM is implemented on the LiveNode platform to validate the energy consumption and on a UNIX system to validate the adopted SNMP sub-agent. Ultimately, a decreasing data exchange and an improvement in the energy consumption in the entire WSN were observed. An implementation of the proposed management method is presented

Smart context-aware QoS-based admission control for biomedical wireless sensor networks

Wireless sensor networks are being used as the enabling technology that helps to support the development of new applications and services targeting the domain of healthcare, in particular, regarding data collection for continuous health monitoring of patients or to help physicians in their diagnosis and further treatment assessment. Therefore, due to the critical nature of both medical data and medical applications, such networks have to satisfy demanding quality of service requirements. Despite the efforts made in the last few years to develop quality of service mechanisms targeting wireless sensor networks and its wide range of applications, the network deployment scenario can severely restrict the network's ability to provide the required performance. Furthermore, the impact of such environments on the network performance is hard to predict and manage due to its random nature. In this way, network planning and management, in complex environments like general or step-down hospital units, is a problem still looking for a solution. In such context, this paper presents a smart context-aware quality of service based admission control method to help engineers, network administrators, and healthcare professionals managing and supervising the admission of new patients to biomedical wireless sensor networks. The proposed method was tested in a small sized hospital. In view of the results achieved during the experiments, the proposed admission control method demonstrated its ability, not only to control the admission of new patients to the biomedical wireless sensor network, but also to find the best location to admit the new patients within the network. By placing the new sensor nodes on the most favourable locations, this method is able to select the network topology in view of mitigating the quality of service provided by the network.Work supported by the Portuguese Foundation for Science and Technology, FCT, PhD Grant SFRH/BD/61278/2009. Miranda was supported by Portuguese funds through the CIDMA - Center for Research and Development in Mathematics and Applications, and the Portuguese Foundation for Science and Technology.info:eu-repo/semantics/publishedVersio

Cognitive Wireless Sensor Networks: Intelligent Channel Assignment

One of the major problems in Wireless Sensor Networks (WSNs) is the traffic congestion caused by increasing number of devices sharing the limited spectrum of the ISM (Industrial, Scientific, and Medical radio) band. As a result, a new concept of Cognitive Wireless Sensor Networks (CWSNs) has been proposed in order to achieve reliable and efficient communication via spectrum awareness and smart adaption. Based on such concept, this paper proposes the intelligent channel assignment technique for channel management in CWSNs. The proposed method is based on the learning and prediction technique so called Policy Gradient together with our proposed virtual channel environment classification. Simulation model is used for the system performance evaluation. The simulation results show that our proposed intelligent channel assignment provides substantially higher performance in terms of system throughput and average packet end-to-end delay than the traditional IEEE 802.15.4 based system. It also outperforms the systems integrated with Episodic Reinforcement and GPOMDP learning technique

On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants

The deteriorating water environment demands new approaches and technologies to achieve sustainable and smart management of urban water systems. Wireless sensor networks represent a promising technology for water quality monitoring and management. The use of wireless sensor networks facilitates the improvement of current centralized systems and traditional manual methods, leading to decentralized smart water quality monitoring systems adaptable to the dynamic and heterogeneous water distribution infrastructure of cities. However, there is a need for a low-cost wireless sensor node solution on the market that enables a cost-effective deployment of this new generation of systems. This paper presents the integration to a wireless sensor network and a preliminary validation in a wastewater treatment plant scenario of a low-cost water quality monitoring device in the close-to-market stage. This device consists of a nitrate and nitrite analyzer based on a novel ion chromatography detection method. The analytical device is integrated using an Internet of Things software platform and tested under real conditions. By doing so, a decentralized smart water quality monitoring system that is conceived and developed for water quality monitoring and management is accomplished. In the presented scenario, such a system allows online near-real-time communication with several devices deployed in multiple water treatment plants and provides preventive and data analytics mechanisms to support decision making. The results obtained comparing laboratory and device measured data demonstrate the reliability of the system and the analytical method implemented in the device.Ingeniería, Industria y Construcció

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

RRSEB: a reliable routing scheme for energy-balancing using a self-adaptive method in wireless sensor networks

Over recent years, enormous amounts of research in wireless sensor networks (WSNs) have been conducted, due to its multifarious applications such as in environmental monitoring, object tracking, disaster management, manufacturing, monitoring and control. In some of WSN applications dependent the energy-efficient and link reliability are demanded. Hence, this paper presents a routing protocol that considers these two criteria. We propose a new mechanism called Reliable Routing Scheme for Energy-Balanced (RRSEB) to reduce the packets dropped during the data communications. It is based on Swarm Intelligence (SI) using the Ant Colony Optimization (ACO) method. The RRSEB is a self-adaptive method to ensure the high routing reliability in WSNs, if the failures occur due to the movement of the sensor nodes or sensor node’s energy depletion. This is done by introducing a new method to create alternative paths together with the data routing obtained during the path discovery stage. The goal of this operation is to update and offer new routing information in order to construct the multiple paths resulting in an increased reliability of the sensor network. From the simulation, we have seen that the proposed method shows better results in terms of packet delivery ratio and energy efficiency