Collaborative wireless sensor networks in industrial and business processes

Wireless Sensor Networks (WSNs) create the technological basis for building pervasive, large-scale distributed systems, which can sense their environment in great detail, communicate the relevant information via the wireless medium, reason collectively upon the observed situation and react according to the application-specific goals. Embedding sensing, processing and communication in one tiny device (the sensor node or simply mote), which can subsequently collaborate with peers and build a self-organizing, self-healing network, stimulates a long list of applications from various domains, ranging from environmental monitoring to industrial processes, and even further to cognitive robotic systems or space exploration. At first glance the complexity of such applications is overwhelming, given the serious resource limitations of sensor nodes, in terms of computational power, storage space, radio performance and battery power. However, WSNs have a unique feature that balances the inherent resource limitations: the ability of in-network collaboration at scale. Through collaboration WSNs can organize efficiently, prolong system lifetime, handle dynamics, detect and correct errors, all with the final goal of eventually executing reliably the user application. Following this line, researchers devised an impressive number of collaborative WSN algorithms and protocols in recent years. Significant progress has also been made on the market side, so that nowadays we can claim that WSNs are no longer just lab prototypes. Standardization initiatives (such as IEEE 802.15.4) are being put into practice and the general industry trend strongly suggests that the epoch of pioneering research in building and experimenting with “motes” is approaching an end. It is now the logical time for system integration and for creating bridges to connected fields. This thesis focuses on WSN integration in industrial and business processes, and, more specifically, on exploring collaborative techniques to make WSNs more reliable, intelligent, effective and easy-to-use in industry-related scenarios

Recent advances in industrial wireless sensor networks towards efficient management in IoT

With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service

Engine performance characteristics and evaluation of variation in the length of intake plenum

In the engine with multipoint fuel injection system using electronically controlled fuel injectors has an intake manifold in which only the air flows and, the fuel is injected into the intake valve. Since the intake manifolds transport mainly air, the supercharging effects of the variable length intake plenum will be different from carbureted engine. Engine tests have been carried out with the aim of constituting a base study to design a new variable length intake manifold plenum. The objective in this research is to study the engine performance characteristics and to evaluate the effects of the variation in the length of intake plenum. The engine test bed used for experimental work consists of a control panel, a hydraulic dynamometer and measurement instruments to measure the parameters of engine performance characteristics. The control panel is being used to perform administrative and management operating system. Besides that, the hydraulic dynamometer was used to measure the power of an engine by using a cell filled with liquid to increase its load. Thus, measurement instrument is provided in this test to measure the as brake torque, brake power, thermal efficiency and specific fuel consumption. The results showed that the variation in the plenum length causes an improvement on the engine performance characteristics especially on the fuel consumption at high load and low engine speeds which are put forward the system using for urban roads. From this experiment, it will show the behavior of engine performance

An efficient genetic algorithm for large-scale planning of robust industrial wireless networks

An industrial indoor environment is harsh for wireless communications compared to an office environment, because the prevalent metal easily causes shadowing effects and affects the availability of an industrial wireless local area network (IWLAN). On the one hand, it is costly, time-consuming, and ineffective to perform trial-and-error manual deployment of wireless nodes. On the other hand, the existing wireless planning tools only focus on office environments such that it is hard to plan IWLANs due to the larger problem size and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh industrial indoor environments. To fill this gap, this paper proposes an overdimensioning model and a genetic algorithm based over-dimensioning (GAOD) algorithm for deploying large-scale robust IWLANs. As a progress beyond the state-of-the-art wireless planning, two full coverage layers are created. The second coverage layer serves as redundancy in case of shadowing. Meanwhile, the deployment cost is reduced by minimizing the number of access points (APs); the hard constraint of minimal inter-AP spatial paration avoids multiple APs covering the same area to be simultaneously shadowed by the same obstacle. The computation time and occupied memory are dedicatedly considered in the design of GAOD for large-scale optimization. A greedy heuristic based over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as benchmarks. In two vehicle manufacturers with a small and large indoor environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD outputted up to 25% less APs than a random OD algorithm. Furthermore, the effectiveness of this model and GAOD was experimentally validated with a real deployment system

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure