6,651 research outputs found
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Risk information formalisation with graphs
The logistics is an essential economic activity that is intended to manage the physical and data flows (informative, customs and financial), in order to provide the resources corresponding to more or less determined needs in compliance with the specified economic and legal conditions (subject to the quality- of-service targets and the security and safety conditions are satisfactory). The links between formalized information, risk management in production logistics and adaptation to technological and market changes, are essential to industrial companies. In this paper, we have followed a structured approach, keeping within a formal risk management framework, for continually improving production logistics practices and procedures by experience feedback processes. The information derived from the risk assessment in production logistics is formalized by the conceptual graphs, permitting to ease the logical expressions and enhance the semantic quality of visual representation produced. The proposal is illustrated more clearly by a concrete case study of the production logistics adopted for aircraft manufacturing in an European Aeronautic Company
Human Aspect on Chain of Custody (CoC) System Performance
The tropical forests cover 24% of tropical land area. They are the most productive terrestrial ecosystems
on earth with high priorities for biodiversity conservation. These forests store a substantial amount of carbon in
biomass and soil, and they also regulate the transfer of carbon into the atmosphere as carbon dioxide (CO2).
Indonesia is having the third tropical forest area in the world after Brazil and Congo. Over 50 years forest has been
felled both legally as well as illegally. High rate of forest degradation resulted from unsustainable forest
management, rampant illegal logging, forest area encroachment, conversion and natural disaster. All urges rapid
improvement of management system of Indonesia’s forest resources (Holmes, 2002). Forest certification is one tool
that can support the achievement of sustainable forest management goal. Under current operation of join
certification protocol between the Forest Stewardship Council (FSC) and the Indonesian Ecolabelling Institute (LEI)
in Indonesia, forest management units must be able to show the required performance indicated in LEI criteria and
indicator as well as FSC principles and criteria to attain certification of their products. The gap between current
practices and performance required by forest certifications schemes is still enormous. The performance of forest
certification system from LEI is determined very much by the human that is involved in the process of planning and
operation. The name of certification system is chain of custody (CoC) certification. CoC operation involves
activities such as tracing raw material from the forest to the factory, through shipping and manufacturing, to the
final end product. In all of the above processes, the roles of human are critical, although the specific roles played
from one process to another are different. In this paper we present an identification of human aspect and other
factors that predominantly affect CoC system performance
Systems Engineering: Availability and Reliability
Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling
Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches
In today's era of rapid smart equipment development and the Industrial Revolution, the application scenarios for Internet of Things (IoT) technology are expanding widely. The combination of IoT and industrial manufacturing systems gives rise to the Industrial IoT (IIoT). However, due to resource limitations such as computational units and battery capacity in IIoT devices (IIEs), it is crucial to execute computationally intensive tasks efficiently. The dynamic and continuous generation of tasks poses a significant challenge to managing the limited resources in the IIoT environment. This paper proposes a collaborative approach for optimal offloading and resource allocation of highly sensitive industrial IoT tasks. Firstly, the computation-intensive IIoT tasks are transformed into a directed acyclic graph. Then, task offloading is treated as an optimization problem, taking into account the models of processor resources and energy consumption for the offloading scheme. Lastly, a dynamic resource allocation approach is introduced to allocate computing resources to the edge-cloud server for the execution of computation-intensive tasks. The proposed joint offloading and scheduling (JOS) algorithm creates its DAG and prepare a offloading queue. This queue is designed using collaborative q-learning based reinforcement learning and allocate optimal resources to the JOS for execution of tasks present in offloading queue. For this machine learning approach is used to predict and allocate resources. The paper compares conventional and machine learning-based resource allocation methods. The machine learning approach performs better in terms of response time, delay, and energy consumption. The proposed algorithm shows that energy usage increases with task size, and response time increases with the number of users. Among the algorithms compared, JOS has the lowest waiting time, followed by DQN, while Q-learning performs the worst. Based on these findings, the paper recommends adopting the machine learning approach, specifically the JOS algorithm, for joint offloading and resource allocation
Industrial Internet of Things based Collaborative Sensing Intelligence: Framework and Research Challenges
The development of an efficient and cost-effective solution to solve a complex problem (e.g., dynamic detection of toxic gases) is an important research issue in the industrial applications of Internet of Things (IoT). An industrial intelligent ecosystem enables the collection of massive data from the various devices (e.g., sensor-embedded wireless devices) dynamically collaborating with humans. Effectively collaborative analytics based on the collected massive data from humans and devices is quite essential to improve the efficiency of industrial production/service. In this study, we propose a Collaborative Sensing Intelligence (CSI) framework, combining collaborative intelligence and industrial sensing intelligence. The proposed CSI facilitates the cooperativity of analytics with integrating massive spatio-temporal data from different sources and time points. To deploy the CSI for achieving intelligent and efficient industrial production/service, the key challenges and open issues are discussed as well
The Power of Patents: Leveraging Text Mining and Social Network Analysis to Forecast IoT Trends
Technology has become an indispensable competitive tool as science and
technology have progressed throughout history. Organizations can compete on an
equal footing by implementing technology appropriately. Technology trends or
technology lifecycles begin during the initiation phase. Finally, it reaches
saturation after entering the maturity phase. As technology reaches saturation,
it will be removed or replaced by another. This makes investing in technologies
during this phase unjustifiable. Technology forecasting is a critical tool for
research and development to determine the future direction of technology. Based
on registered patents, this study examined the trends of IOT technologies. A
total of 3697 patents related to the Internet of Things from the last six years
of patenting have been gathered using lens.org for this purpose. The main
people and companies were identified through the creation of the IOT patent
registration cooperation network, and the main groups active in patent
registration were identified by the community detection technique. The patents
were then divided into six technology categories: Safety and Security,
Information Services, Public Safety and Environment Monitoring, Collaborative
Aware Systems, Smart Homes/Buildings, and Smart Grid. And their technical
maturity was identified and examined using the Sigma Plot program. Based on the
findings, information services technologies are in the saturation stage, while
both smart homes/buildings, and smart grid technologies are in the saturation
stage. Three technologies, Safety and Security, Public Safety and Environment
Monitoring, and Collaborative Aware Systems are in the maturity stage
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