Traceability system for capturing, processing and providing consumer-relevant information about wood products: System solution and its economic feasibility

Current research and practice reports indicate the existence of purchase barriers concerning eco-friendly products, e.g. wood products. These can be ascribed to consumers' mistrust regarding the non-observable environmental impact of wood products. To counter the mistrust, wood products are commonly endowed with eco-labels, which may be perceived mostly as a marketing tool, therefore not fulfilling their intended purpose. Current studies have shown that providing consumers with wood product information based on traceability systems increases product trust and purchase intentions, with those information items most valued by consumers being identified as well. Based on this, the paper proposes a traceability information system for the capturing, processing, and provision of product information using examples of wood furniture. Furthermore, a cost-benefit model for the proposed solution is developed. The calculations indicate the possibility of implementing traceability at the item level based on a four-layer system architecture enabling the capture and delivery of all information valued by consumers at acceptable costs. The proposed system helps to overcome purchase barriers of eco-friendly products, increasing consumers' product trust and purchase intentions

WSN and RFID integration to support intelligent monitoring in smart buildings using hybrid intelligent decision support systems

The real time monitoring of environment context aware activities is becoming a standard in the service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt reaction to potential hazards identified at an early stage to engage appropriate control actions. This requires capturing real time data to process locally at the device level or communicate to backend systems for real time decision making. This research examines the wireless sensor network and radio frequency identification technology integration in smart homes to support advanced safety systems deployed upstream to safety and emergency response. These systems are based on the use of hybrid intelligent decision support systems configured in a multi-distributed architecture enabled by the wireless communication of detection and tracking data to support intelligent real-time monitoring in smart buildings. This paper introduces first the concept of wireless sensor network and radio frequency identification technology integration showing the various options for the task distribution between radio frequency identification and hybrid intelligent decision support systems. This integration is then illustrated in a multi-distributed system architecture to identify motion and control access in a smart building using a room capacity model for occupancy and evacuation, access rights and a navigation map automatically generated by the system. The solution shown in the case study is based on a virtual layout of the smart building which is implemented using the capabilities of the building information model and hybrid intelligent decision support system.The Saudi High Education Ministry and Brunel University (UK

Capturing Data Uncertainty in High-Volume Stream Processing

We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuses on data that is naturally modeled as continuous random variables. For such data, our system employs an approach grounded in probability and statistical theory to capture data uncertainty and integrates this approach into high-volume stream processing. The first component of our system captures uncertainty of raw data streams from sensing devices. Since such raw streams can be highly noisy and may not carry sufficient information for query processing, our system employs probabilistic models of the data generation process and stream-speed inference to transform raw data into a desired format with an uncertainty metric. The second component captures uncertainty as data propagates through query operators. To efficiently quantify result uncertainty of a query operator, we explore a variety of techniques based on probability and statistical theory to compute the result distribution at stream speed. We are currently working with a group of scientists to evaluate our system using traces collected from the domains of (and eventually in the real systems for) hazardous weather monitoring and object tracking and monitoring.Comment: CIDR 200

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Smart automatic petrol pump system based on internet of things

IoT is that a rapid expanding program presently for blend all equipment things like (sensors, gadgets, hardware and so on.) assemble and embed those with programming creating our own gadgets use The petroleum pump is these days running physically. it's an activity that fundamentally a drawnout time and requires more workforce. Additionally, put fuel stations in away zones is extermely costly. So achievement an automatic fuel filling system using web technology to solve these problems. There are dense proposed systems which goal to improve the fueling operation so as to form it less difficulty and more dependabl and more-safe, guarinte that the purchaser gets the same quantity of fuel in interchange for what he/she pays, so assist to end fraud at different fuel stations. these systems take human-software interaction by the web-enabeled procedure, thus keep off all errors made by people. The fundamental objective of this review paper is to survey of recent projects in design protype of smart petro pump based on RFID as payment tool and control on it remotely with high security level and concluded with future potential direction in design of smart petrol pump system

A framework for distributed managing uncertain data in RFID traceability networks

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.Jiangang Ma, Quan Z. Sheng, Damith Ranasinghe, Jen Min Chuah and Yanbo W