China
Africa
Explore chapters and articles related to this topic
The IoT Vision from an Opportunistic Networking Perspective
Published in Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka, The Internet of Things, 2017
Zoran Bojkovic, Bojan Bakmaz, Miodrag Bakmaz
First of all, two applications of IoT in SCC are identified: mobile crowdsensing and cyber-physical cloud computing. Mobile crowdsensing serves as a relay for data collection from large number of mobile sensing devices (Ganti et al., 2011). Based on the type of shape being measured or mapped, mobile crowdsensing applications are categorized into three groups: environmental, infrastructural, and social. Compared with the classical wireless sensor networks, mobile crowdsensing advantages include significantly more storage, computation, and networking resources equipped with multimodality sensing capabilities. Also, mobile devices avoid the cost and time of deploying large-scale sensor networks. Next, IoT represents a networking infrastructure for cyber-physical systems (CPSs). These are smart networked systems with embedded sensors and actuators designed to sense and interact with physical world including the human users. Moreover, CPSs support real-time, guaranteed performance in safety-critical applications. A new computing paradigm that can modify and rapidly build these systems is cloud computing. Cyber-physical cloud computing is characterized by efficient use of resources, modular composition, rapid development, and scalability. Cyber-physical cloud computing is of great significance for numerous SCC applications such as smart grid, smart health care, and smart disaster management.
Sustainable Crowdsensing Data Dissemination Using Public Vehicles
Published in Khan Pathan Al-Sakib, Crowd-Assisted Networking and Computing, 2018
Rashmi Munjal, William Liu, Xue Jun Li, Jairo Gutierrez, Marija Furdek
Mobile crowdsensing is a relatively new discipline, in which the users of modern smartphones use the rich sensing capabilities of their devices to collect and share information while on the move, as well as to form microcrowds around a certain crowdsensing activity.
PrivBCS: a privacy-preserving and efficient crowdsourcing system with fine-grained worker selection based on blockchain
Published in Connection Science, 2023
Juan Chen, Wei Liang, Lijun Xiao, Ce Yang, Ronglin Zhang, Zhenwen Gui, Aneta Poniszewska-Marańda
Nowadays, almost all citizens own smartphones and tablet PCs, etc. These devices have several built-in sensors, such as microphones, cameras, pedometers, which allow people to shift from the traditional data collection mode to the crowdsensing mode. Crowdsensing refers to a new model of data acquisition that combines the idea of crowdsourcing and the sensing capability of mobile devices, which is a typical service model of crowdsourcing technology for sensing tasks. In this way, service providers can get the sensory data collected by mobile devices uploaded by users across the country and process it to provide services to users. Crowdsensing focuses on using sensing devices to obtain information from the physical world and can be seen as a data collection channel. But crowdsourcing focuses on using human intelligence to get online solutions and can be seen as a form of organisation for data acquisition. We can simply understand that crowdsourcing is a group of people working together to accomplish the same thing. Crowdsourcing divides a complex task into several simple subtasks, then assigns these subtasks to workers on the crowdsourcing platform to complete, and finally aggregates the results submitted by the workers to get the final distributed solution. The concept of crowdsourcing first appears in an article written by Jeff Howe (Jeff, 2006) in 2006. Over time, the Internet and the sharing economy have flourished, and crowdsourcing has been widely used as a new business model in a variety of industries. Unlike traditional outsourcing, crowdsourcing provides a very flexible idea of distributed problem solving, especially for solving certain problems that are complex for computers but simple for human communities, which brings great economic benefits and research value to society. In crowdsourcing platforms, a requester can openly collect solutions by posting crowdsourcing tasks, and a worker can apply for suitable tasks for rewards based on his or her area of expertise and interests, which can be non-spatial tasks (e.g. image annotation, writing translation) or spatio-temporal tasks (e.g. environmental monitoring, delivery services). Some of the more popular crowdsourcing platforms are Amazon Mechanical Turk, CrowdFlower (2023), TopCoder (2023), TaskRabbit (2023), Upwork (2023), etc. These crowdsourcing platforms are usually centralised, i.e. crowdsourcing tasks are required to be posted/assigned/submitted through a third-party central organisation between requesters and workers. This may lead to a series of problems, such as privacy disclosure (sensitive data is stored centrally), denial of service attacks (the central system is vulnerable to attacks by malicious people), single point of attack (central system becomes suddenly inaccessible due to facilities), unfair evaluation (due to opaque black box operations), free riding/false reporting (in the same stream as the system administrator). Therefore, it is necessary to introduce the idea of decentralisation into the crowdsourcing platform.
A semantic similarity analysis of Internet of Things
Published in Enterprise Information Systems, 2018
Chun Kit Ng, Chun Ho Wu, Kai Leung Yung, Wai Hung Ip, Tommy Cheung
Realization of IoT requires integration and innovations of a series of important enabling elements including automatic identification, sensing systems, embedded computing, cloud computing, context-aware computing, networking and Internet-based services. To be basic building blocks of IoT, smart objects are proposed and they are the enhancement of everyday objects integrating with modern technologies such as RFID, WSN, embedded technology and M2M communication. The aim of the design of smart objects is beyond the integration of hardware and software, rather than that, Human-Computer Interaction (HCI) of smart objects is considered as the ultimate aim for IoT realization (Kortuem et al. 2010 (#4)). Smart objects are defined to have some fundamental features including unique IDs, sensors, communication capability and decision making capability (López et al. 2012 (#41)). As a result of development of information technologies, some new concepts are considered for the design of smart objects. Firstly, low-power Wi-Fi has been considered as a competitive candidate to traditional energy efficient communication protocol such as ZigBee for network communications of smart objects. The low-power Wi-Fi shows promised performance on energy efficiency, anti-interference, communication range and ease of network integration due to the built-in IP network compatibility (Tozlu et al. 2012 (#40)). Secondly, sensors-rich mobile smart devices such as smart phones and smart tablets can be utilized as smart objects to perform mobile crowdsensing which the sensing and computing capability of individuals can be shared for sensor data collection, exchange, process and analysis (Ganti, Ye, and Lei 2011 (#8)). Thirdly, the sensor data generated from IoT are huge and increase in a high speed. To efficiently process and interpret these data, context-aware computing is adopted to tackle this challenge in IoT since it established links between sensor data and pre-defined context information, thereby making data interpretation and M2M communication easier (Perera et al. 2014a (#19)). Besides the development of smart object, the development of IoT experimental facilities is also growing. The main reason is that, a IoT solution usually involve many hardware and software integrations and interactions, thorough evaluations of the solution under a real-world condition is essential before the solution is launched. SmartFactoryKL is a research testbed towards to the perspective of factory-of-things, which is an IoT enabled future factory model. Through this testbed, researchers from both academia and industry can cooperate to develop and test existing and new technologies for the future factory in five dimensions including technical, architectural, planning, safety and security, and human dimension (Zuehlke 2010 (#29)). For the current and future IoT testbed development, Gluhak et al. (2011) (#28) raised out that while the development of IoT testbeds is towards promising solutions, developers should consider seven identified requirements including heterogeneity, federation, user involvement, repeatability, mobility, scale and concurrency (Gluhak et al. 2011 (#28)).