Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

AI in Retail Industry: Reshaping Shopping Experience and Business Profitability

Abstract Artificial Intelligence (AI) is depicted as the actions of machines doing tasks that usually require human intelligence. Though this idea of Artificial Intelligence can be traced back to Greek mythology, it was only during modern history, the world witnessed the development of programme-driven computer machines. Now it has become a web of millions of codes and algorithms and is increasingly playing an integral role in every industry sector. E-commerce Industry had once disrupted the age-old traditional retail industry, especially in India where the retailers now largely depend on these platforms for their product’s sale. Today it's coming in the form of Artificial Intelligence which will be a great advantage for the retail industry. To be competitive today, retailers need to respond to their customers like never before, at the same time reducing the amount of waste and inefficiency in their operations. They can get there with data, but it takes intelligent analysis to make sense of the vast amount of data. This article analyses the emerging trends and practices of using Artificial Intelligence in retail industry in order to enhance customer experiences and result in value additions to retail businesses. Keywords: Artificial Intelligence, Retail Business, Digital Retailing, Smart Retailin

Omnichannel Value Chain: Mapping Digital Technologies for Channel Integration Activities

In order to provide a seamless customer experience, researchers and practitioners have proposed creation of an omnichannel retailing environment by integrating online and offline channels. Channel integration necessitates use of digital technologies and there are myriads of technological solutions available. However, retailers are struggling with selection and implementation of suitable technologies to add value through channel integration. Despite the strong practical need, this issue has not been effectively addressed in the academic literature. This paper presents an omnichannel value chain underpinned by Porter’s value chain model. We identify ten channel integration activities for value creation by carrying out a synthesis of current research on omnichannel retailing. Enabling digital technologies are then mapped to these activities using technology implementation examples and provide a guideline for retailers to select appropriate technologies for the identified value creation activities

Customer Knowledge Management In the Cloud Ecosytem

The evolution of the economy has gone through the agricultural era, followed by the industrial era focusing on the production of goods and the postindustrial era accentuated by information and services. The new economy of the 21stcentury is characterized by knowledge and relationships. Galbreath (2002) described the transition to a new economic order driven by knowledge and based on the value of relationships. Customer knowledge management (CKM) synthesizes the customer knowledge and relationships assets in the extended enterprise to create advantages for companies in a very competitive market. Technologies have changed alongside the economy. Big Data and Cloud Computing are the latest stages of the evolution of technologies in business. This paper builds upon the work done in the areas of Customer Relationship Management, Knowledge Management, Big Data and Cloud Computing. It examines the CKM process enabled by a dynamic actionable demand driven service platform leveraging Big Data, knowledge analytics, and cloud computing

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

The evolution of business analytics : based on case study research

While business analytics is becoming more significant and widely used by companies from increasing industries, for many the concept remains a complex illusion. The field of business analytics is considerably generic and fragmented, leaving managers confused and ultimately inhibited to make valuable decisions. This paper presents an evolutionary depiction of business analytics, using real-world case studies to illustrate a distinct overview that describes where the phenomenon was derived from, where it currently stands, and where it is heading towards. This paper provides eight case studies, representing three different eras: yesterday (1950s to 1990s), today (2000s to 2020s), and tomorrow (2030s to 2050s). Through cross-case analysis we have identified concluding patterns that lay as foundation for the discussion on future development within business analytics. We argue based on our findings that automatization of business processes will most likely continue to increase. AI is expanding in numerous areas, each specializing in a complex task, previously reserved by professionals. However, patterns show that new occupations linked to artificial intelligence will most probably be created. For the training of intelligent systems, data will most likely be requested more than ever. The increasing data will likely cause complications in current data infrastructures, causing the need for stronger networks and systems. The systems will need to process, store, and manage the great amount of various data types in real-time, while maintaining high security. Furthermore, data privacy concerns have become more significant in recent years, although, the case study research indicates that it has not limited corporations access to data. On the contrary, corporations, people, and devices will most likely become even more connected than ever before.nhhma