In-band network telemetry in industrial wireless sensor networks

With the emergence of the Internet of Things (IoT) and Industry 4.0 concepts, industrial applications are going through a tremendous change that is imposing increasingly diverse and demanding network dynamics and requirements with a wider and more fine-grained scale. Therefore, there is a growing need for more flexible and reconfigurable industrial networking solutions complemented with powerful monitoring and management functionalities. In this sense, this paper presents a novel efficient network monitoring and telemetry solution for Industrial Wireless Sensor Networks mainly focusing on the 6TiSCH Network stack, a complete protocol stack for ultra-reliable ultra-low-power wireless mesh networks. The proposed monitoring solution creates a flexible and powerful in-band network telemetry design with minimized resource consumption and communication overhead while supporting a wide range of monitoring operations and strategies for dealing with various network scenarios and use cases. Besides, the technical capabilities and characteristics of the proposed solution are evaluated via a real-life implementation, practical and theoretical analysis. These experiments demonstrate that in-band telemetry can provide ultra-efficient network monitoring operations without any effect on the network behavior and performance, validating its suitability for Industrial Wireless Sensor Networks

Efficiency of integration between sensor networks and clouds

Numerous wireless sensor networks (WSN) applications include monitoring and controlling various conditions in the environment, industry, healthcare, medicine, military affairs, agriculture, etc. The life of sensor nodes largely depends on the power supply type, communication ability, energy storage capacity and energy management mechanisms. The collection and transmission of sensor data streams from sensor nodes lead to the depletion of their energy. At the same time, the storage and processing of this data require significant hardware resources. Integration between clouds and sensor networks is an ideal solution to the limited computing power of sensor networks, data storage and processing. One of the main challenges facing systems engineers is to choose the appropriate protocol for integrating sensor data into the cloud structure, taking into account specific system requirements. This paper presents an experimental study on the effectiveness of integration between sensor networks and the cloud, implemented through three protocols HTTP, MQTT and MQTT-SN. A model for studying the integration of sensor network - Cloud with the communication models for integration - request-response and publish- subscribe, implemented with HTTP, MQTT and MQTT-SN. The influence of the number of transmitted data packets from physical sensors to the cloud on the transmitted data delay to the cloud, the CPU and memory load was studied. After evaluating the results of sensor network and cloud integration experiments, the MQTT protocol is the most efficient in terms of data rate and power consumption

Autonomous Energy Management system achieving piezoelectric energy harvesting in Wireless Sensors

International audienceWireless Sensor Networks (WSNs) are extensively used in monitoring applications such as humidity and temperature sensing in smart buildings, industrial automation, and predicting crop health. Sensor nodes are deployed in remote places to sense the data information from the environment and to transmit the sensing data to the Base Station (BS). When a sensor is drained of energy, it can no longer achieve its role without a substituted source of energy. However, limited energy in a sensor's battery prevents the long-term process in such applications. In addition, replacing the sensors' batteries and redeploying the sensors is very expensive in terms of time and budget. To overcome the energy limitation without changing the size of sensors, researchers have proposed the use of energy harvesting to reload the rechargeable battery by power. Therefore, efficient power management is required to increase the benefits of having additional environmental energy. This paper presents a new self-management of energy based on Proportional Integral Derivative controller (PID) to tune the energy harvesting and Microprocessor Controller Unit (MCU) to control the sensor modes

Design and Development of Smart, Intelligent & LOT Enabled Remote Health Monitoring System

Drastic developments in the area of Wireless Sensor Networks (WSNs) have paved a path for ap- plications in so called Internet of Things (IoT). Some major applications include remote health monitoring, environment monitoring, smart grids etc. In this work we have identified the primary issues that IoT architectures are facing and developed a system architecture to address the issues identified. IoT architectures primarily face the power constraints due to their battery dependency in remote deployment such as remote health monitoring. Other issue is the hyper connectivity scenario, where the data generated by the sensor networks should have to be limited for efficient management of networks. The proposed architecture consists of an intelligent data transmission mechanism with smart sensing and IEEE 802.15.4 - PHY is considered for communication facility

Innovative Self-Organization Wireless Sensor Networks for Electrical Power Systems

Wireless Sensor Networks (WSNs) gather information for electrical power systems and help to manage demand Response and demand side strategies. Optimization of WSNs depends on their physical deployment, and it will bring to the fore a very focused number of parameters to be optimized. Selforganization of WSNs is an important issue to be considered, and it requires the nodes to form a network by collaboration with each other without using manual intervention. Moreover, the WSNs implemented in electrical power systems should be secured and energy efficient in order to provide highly reliable data for monitoring and control. In this paper, we will propose Self-Organization WSNs with Security and Energy-efficient Clustering algorithms (SOSEC) including cluster formation, data encryption keys establishment and management, and an energy efficient routing protocol. SOSEC can optimize the energy efficiency of the whole network and ensure a secured channel for data transmissions in WSNs for electrical power systems

Link Quality Based Power Efficient Routing Protocol (LQ-PERP)

Recent years have witnessed a growing interest in deploying infrastructure-less, self configurable, distributed networks such as Mobile AdHoc Networks (MANET) and Wireless Sensor Networks (WSN) for applications like emergency management and physical variables monitoring respectively. However, nodes in these networks are susceptible to high failure rate due to battery depletion, environmental changes and malicious destruction. Since each node operates with limited sources of power, energy efficiency is an important metric to be considered for designing communication schemes for MANET and WSN. Energy consumed by nodes in MANET or WSN can be reduced by optimizing the internode transmission power which is uniform even with dynamic routing protocols like AODV. However, the transmission power required for internode communication depends on the wireless link quality which inturn depends on various factors like received signal power, propagation path loss, fading, multi-user interference and topological changes. In this paper, link quality based power efficient routing protocol (LQ-PERP) is proposed which saves the battery power of nodes by optimizing the power during data transmission. The performance of the proposed algorithm is evaluated using QualNet network simulator by considering metrics like total energy consumed in nodes, throughput, packet delivery ratio, end-to-end delay and jitter

Wireless acoustic sensor networks and edge computing for rapid acoustic monitoring

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity monitoring. The scale of the data involved requires automated methods, however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive, hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ analysis. Recording and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination server. Benchmark tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions

Design and Test of a High-Performance Wireless Sensor Network for Irradiance Monitoring

Cloud-induced photovoltaic variability can affect grid stability and power quality, especially in electricity systems with high penetration levels. The availability of irradiance field forecasts in the scale of seconds and meters is fundamental for an adequate control of photovoltaic systems in order to minimize their impact on distribution networks. Irradiance sensor networks have proved to be efficient tools for supporting these forecasts, but the costs of monitoring systems with the required specifications are economically justified only for large plants and research purposes. This study deals with the design and test of a wireless irradiance sensor network as an adaptable operational solution for photovoltaic systems capable of meeting the measurement specifications necessary for capturing the clouds passage. The network was based on WiFi, comprised 16 pyranometers, and proved to be stable at sampling periods up to 25 ms, providing detailed spatial representations of the irradiance field and its evolution. As a result, the developed network was capable of achieving comparable specifications to research wired irradiance monitoring network with the advantages in costs and flexibility of the wireless technology, thus constituting a valuable tool for supporting nowcasting systems for photovoltaic management and control

Sensorcam: An Energy-Efficient Smart Wireless Camera for Environmental Monitoring

Reducing energy cost is crucial for energy-constrained smart wireless cameras. Existing platforms impose two main challenges: First, most commercial smart phones have a closed platform, which makes it impossible to manage low-level circuits. Since the sampling frequency is moderate in environmental monitoring context, any improper power management in idle period will incur significant energy leak. Secondly, low-end cameras tailored for wireless sensor networks usually have limited processing power or communication range, and thus are not capable of outdoor monitoring task under low data rate. To tackle these issues, we develop Sensorcam, a long-range, smart wireless camera running a Linux-base open system. Through better power management in idle period and the "intelligence" of the camera itself, we demonstrate an energy-efficient wireless monitoring system in a real deployment