Content area
Purpose
Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT).
Design/methodology/approachThe paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study.
FindingsThis case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively.
Originality/valueThe literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.
1. Introduction
Knowledge is increasingly being recognised as the most important resource by organisations; in fact, it is a key differentiating factor in today’s business arena. Knowledge is defined as the dynamic human process of justifying one’s personal belief towards the truth (Nonaka and Takeuchi, 1995). It can also be defined as “know-why”, “know-how” and “know-who”, or as an intangible economic resource from which future resources will be derived (Rennie, 1999). Knowledge is built from data which are first processed into information (i.e. relevant associations and patterns). The literature defines information as “organized data” (Saint-Onge, 2002) and as “data endowed with relevance and purpose” (Drucker, 2001). Information becomes knowledge when it enters the system and when it is validated (either collectively or individually) as a relevant and useful piece of knowledge to implement within the system (Carrillo et al., 2000).
According to Brelade and Harman (2002), knowledge management (KM) is “obtaining and using resources to create an environment in which individuals have access to information and in which individuals obtain, share and use this information to raise the level of their knowledge”. In addition to this, individuals are encouraged and enabled to obtain new information to be used by the organisation. One of the main aims of KM is the process of refining data that come from unreliable sources (Raisinghani and Schkade, 1999). Today, KM is capable of handling real-time updates because of its use of the Internet of Things (IoT). KM today has the opportunity and the capability to synthesise data from diverse sources and to arrive at new knowledge.
Because of the IoT, businesses today are forced to rethink their goals and to reconsider how value is created. A business model describes the rationale of how an organisation creates, delivers and captures value. A service that is made up of a combination of physical and digital elements opens up new channels and opportunities for monetisation or value exchange. Because the IoT also takes advantage of new, cloud-based opportunities, companies need to fundamentally rethink their orthodoxies about value creation and value capture. Simply reusing well-known frameworks and streamlining established business models will not be enough for leveraging the IoT to create and capture value.
Value creation, which involves performing activities that increase the value of a company’s offering and encourages customer willingness to pay, is the heart of all business models. In companies that develop traditional products, creating value means identifying enduring customer needs and manufacturing well-engineered solutions (Hui, 2014). With the IoT, products can be tracked anytime, making it possible to respond to customer behaviour. It is also now possible for products to connect with other products, leading to new analytics and new services for more effective forecasting, process optimisation and customer service experiences. An example of this is Philips Hue light bulbs, whose development highlights the new possibilities of IoT-based value creation (Hui, 2014).
Sensors and embedded technology now enable the transmission of real-time data from wireless networks which will lead to the co-creation of new real-time knowledge among customers and vendors. Companies which use the IoT can gather data about how their products behave and interact, and can then use it to understand and predict future behaviours. Using data from sensors, companies can optimise performance and can drive profitable outcomes for themselves through better and better user experiences. Companies can use the data collected from sensors to change the way that they design, upgrade and maintain devices in the field. The result is not merely greater efficiency, but entirely new functionality and levels of service. Real-time monitoring and analysis of physical assets allow companies to understand and act on a variety of real-time insights. The real-time data can be accessible by all the stakeholders, thereby facilitating knowledge sharing. Organisations using KM have the ability to interpret the real-time data and convert them into real-time knowledge for competitive advantage.
This paper discusses how KM can benefit by embedding IoT. The paper begins with a brief review of KM and the IoT. The next section discusses how IoT can help KM to capture data that can be used in an organisation. This is followed by an example of the conversion of data into knowledge using a case study. The paper concludes with suggestions for further research.
2. Knowledge management
According to Bellinger et al. (2004), data are raw. Information is data that have been given meaning by way of a relational connection. This “meaning” can be useful, but it does not have to be. It provides answers to “who”, “what”, “where” and “when” questions. On the other hand, knowledge is the application of information. Knowledge is the appropriate collection of information and its intent is to be useful. Knowledge answers the “how” questions. Zins (2007) argues that knowledge is more subjective and intangible, when compared to information or data. It is what individuals take from information and data, and what they incorporate into their beliefs, values, procedures, actions, etc. It is heavily internally oriented, and it is understood completely only by the person possessing it. Sowell (1996) believes that knowledge includes both data and the relationships among data elements or their sets. He argues that this organisation of data based on relationships is what enables one to draw generalisations from the organised data and to formulate questions.
KM is the process of identifying, capturing, organising and disseminating the intellectual assets that are critical to the organisation’s long-term performance (Debowski, 2006). It is referred to as the process for creating, codifying and disseminating knowledge for a wide range of knowledge-intensive tasks (Harris et al., 1998). These tasks can be for decision support, computer assisted learning, research (e.g. hypothesis testing) or research support. Knowledge management systems (KMSs) are tools to effect the management of knowledge and are manifested in a variety of implementations (Davenport et al., 1998), including document repositories, expertise databases, discussion lists and context-specific retrieval systems incorporating collaborative filtering technologies. KM is supported by a range of technologies, broadly grouped into four areas of activity: business process management, content management, web content management and knowledge application management.
Knowledge is the key to future economic growth and national prosperity. Young (2014) argues that effective KM should dramatically reduce costs, increase the speed of response as a direct result of better knowledge access and application, provide the potential to expand and grow, increase value and/or profitability, improve products and services, accelerate knowledge creation, reduce cost, improve quality and derive greater productivity.
Knowledge has become the most important asset of an organisation. Although there are many benefits to KM, traditional KMSs have limitations. A new generation of KMSs should offer the following capabilities in the era of IoT. This new generation should:
create personalised experiences and track different multiple sources;
move and reposition data from different sources;
optimise data for different uses;
provide real-time on-demand data for decision-making;
collect instructional data and machine data; and
enable the KMS to be seamless and interoperable, and to have realistic connectivity that reduces costs, improves quality and derives greater productivity.
3. Overview of IoT
The term “IoT” was initially proposed to refer to uniquely identifiable interoperable connected objects with radio-frequency identification (RFID) technology (Ashton, 2009). Later on, researchers related IoT with more technologies, such as sensors, actuators, GPS devices and mobile devices. Today, a commonly accepted definition for IoT is:
a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual ‘Things’ have identities, physical attributes and virtual personalities and use intelligent interfaces, and are seamlessly integrated into the information network (van Kranenburg, 2008).
Specifically, the integration of sensors/actuators, RFID tags and communication technologies serves as the foundation of IoT and explains how a variety of physical objects and devices can be associated to the internet and can allow these objects and devices to cooperate and to communicate with one another to reach common goals (van Kranenburg et al., 2011; He and Xu, 2014).
There is a growing interest in using IoT technologies in various industries. A number of industrial IoT projects have been conducted in areas such as agriculture, the food processing industry, environmental monitoring, security surveillance and others. IDC predicts that IoT will grow to be used by approximately 212 billion devices by 2020, including 30 billion connected devices. According to Gartner, there will be nearly 26 billion wireless devices connected to the internet by 2020. IoT is being viewed as the next big disruptor that will change the way businesses are transacted, as customer needs are identified and serviced. According to PricewaterhouseCoopers (PwC), IoT will reach a $50bn market by 2020.
IoT is rapidly gaining momentum, bringing millions of devices and objects into the connected world and enabling whole new ways of managing assets and operations. Sensors, actuators and other means of connecting things in the physical world to networks are proliferating at astounding rates. IoT has the potential to transform manufacturing, healthcare and supply chains by monitoring and optimising activities and assets at a very granular level. The reasons for the rapid adoption of IoT are driven by a rapid decline in the cost of sensors and actuators (devices that act in the physical world); an increasing ability to connect to these sensors, often wirelessly; and the ability to analyse the huge amount of data generated.
IoT allows for greater connectivity among individuals and organisations, and thus presents opportunities to “leverage knowledge in new and innovative ways” (Trees, 2015). It enables a myriad of applications ranging from the micro to the macro, and from the trivial to the critical. According to Diffey (2014), the benefits of IoT can be categorised into the following five key segments:
With its strong operational efficiency, IoT can refine, integrate, optimise and automate business processes across an organisation.
IoT promotes innovation and new product development, enabling businesses to move to a serviced-based proposition, generating recurring revenues and servicing new markets that initiate new revenue streams.
The IoT promotes customer relationship management. It increases a company’s number of customer touchpoints, allowing businesses to strengthen their relationships with the end-user.
The IoT promotes safety and security. It helps businesses keep their staff and their customers safe by monitoring things such as fire alarm functionality and escape route access.
The IoT enables asset relationship management. It creates more touchpoints with the customer; it does the same with company assets.
The authors concur with Porter and Heppelmann (2015) that the IoT provides many advantages for companies. First, products can monitor and report on their own conditions and environments, helping to generate previously unavailable insights into their performance and use. Second, complex product operations can be controlled by the users, through numerous remote-access options. This gives users the unprecedented ability to customise the function, performance and interface of products and to operate them in hazardous or hard-to-reach environments. Third, the combination of monitoring data and remote-control capability creates new opportunities for optimisation. Fourth, the combination of monitoring data, remote control and optimisation algorithms allows for autonomy. Products can learn, adapt to the environment and to user preferences, service themselves and operate on their own.
4. IoT can help KM to capture data to be used in organisations
The world of connected devices today presents the opportunity for organisations to create new product experiences of tremendous value. These products are extensively networked, are more closely integrated into people’s daily lives and are capable of providing more engaging experiences than ever before. This is achieved through the IoT. The IoT, in principle, is designed for functioning without a human interface. The IoT brings with it the promise to dissolve the gap between the physical and digital worlds and the potential to integrate the elements of computing with just about any everyday activity, location or object (Atzori et al., 2010). This age of the IoT and of cloud computing (Goldner and Birch, 2012) facilitates the identifications of the real-time requirements for KMSs and serves as just-in-time and just-what-is-needed data to improve the systems’ efficiency.
The IoT enables things/objects to interact with each other and to create applications (services for reaching common goals). It enables real, digital and virtual worlds to converge to create smart environments that make energy, transport, cities and many other things more intelligent. The goal of the IoT is to enable things to be connected anytime, anyplace, with anything and anyone using a path/network and any service. Objects in the IoT are recognised and they obtain intelligence by making or enabling context-related decisions. These objects can communicate information about themselves, and can also access information that has been aggregated by other things (Kortuem et al., 2010). They can also be components of complex services.
The increasing use of internet-enabled devices with sensors provides more opportunities both to improve the way services are delivered and to harness those data to gain faster insights into whether interventions are working. Manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance to avoid failures in new products (Xu et al., 2014; He and Xu, 2015). The data also enable companies to design better products in the future.
Realising the full potential of the IoT requires solving serious technical and business problems: the identification of things, the organisation, the integration and management of big data and the effective use of knowledge-based decision systems (Dutton, 2014; He et al., 2014). Advances in wireless networking technology and the greater standardisation of communication protocols make it possible to collect data from diverse sensors almost anywhere at any time (Xu et al., 2014). KM today has the opportunity and the capability to synthesise data from diverse sources and arrive at new knowledge. The benefits of IoT for KM can be summarised as follows:
IoT enables a user to collect data from diverse products, company assets or the operating environment. It allows for the generation of better information and analysis, which can significantly enhance decision-making.
When products are embedded with sensors, companies can track the movements of these products and can monitor interactions with them. Businesses can take advantage of customers’ behavioural data to make appropriate decisions.
Data from large number of sensors, deployed in infrastructure (such as roads and buildings), can give decision makers a heightened awareness of real-time events, particularly when the sensors are used with advanced display or visualisation technologies (Bi et al., 2016).
The IoT can support longer-range and more complex human planning and decision-making.
The IoT can raise productivity, because the IoT can help systems adjust automatically to complex situations, which can make a number of human interventions unnecessary.
The IoT enables rapid, real-time sensing of unpredictable conditions and instantaneous responses guided by automated systems.
5. Conversion of big data into knowledge using a case study
The IoT and big data are closely connected because billions of internet-connected “things” will generate massive amounts of data available for analysis (McLellan, 2015). Currently, over 50 per cent of the IoT activity is centred in manufacturing, transportation, smart city and consumer applications (IDC, 2015). In this paper, the authors use a case study from the automotive domain to describe how to convert IoT-related big data into useful insights and knowledge. Researchers (Qin et al., 2013; Leng and Zhao, 2011; Lumpkins, 2013; Zhang et al., 2012; He et al., 2014; Arif et al., 2012; Yan et al., 2011) point out that IoT has important implications for transportation:
It allows real-time tracking of the location of automobiles using cloud-based intelligent monitoring control system.
It provides inter-equipment connection using devices attached to vehicles. For example, data transmission equipment A on a vehicle communicates with data transmission equipment B on another vehicle. This communication may allow drivers to take appropriate precautions to avoid delays or accidents.
Fuel management: Sensor data provide drivers with better visibility into fuel consumption and efficiency, potentially saving millions in fuel costs.
Improved passenger comfort and convenience: Travellers can be alerted about delays via their mobile devices.
Predictive maintenance: Vehicles can transmit defect data directly to engineers. Predictive maintenance can identify components in need of repair/replacement, eliminating the need to take equipment out of service for routine inspections and preventive maintenance.
The IoT embedded in KM can help improve KMSs. In this case study, the authors show how the IoT can be used to improve parking service in transportation. Important data can be captured from various IoT connections and devices:
Sensors: Sensors installed in vehicles offer the ability to track maintenance needs, driver safety, fuel usage and other related metrics in real time.
Roads: The IoT equips roadways with embedded road sensors to capture data related to temperature, humidity and traffic volumes. The data gathered by these sensors are transmitted over the wireless network for further processing and analysis. The data collected also provide real-time information about the condition of the road.
Parking: Sensors can be embedded on the pavement to collect data and then to make the data available to drivers and parking facility operators. Drivers can make use of a smart app to locate available parking spaces and to determine the cost. This can simplify the problem of finding an appropriate parking space.
Vehicles: Vehicles can be connected with IoT-enabled devices. A special device can be installed in the vehicle that can allow drivers to monitor and control their vehicles remotely. This device is GPS-enabled so that drivers can see maps of where and how far they have driven. This device also enables drivers to monitor the security of their vehicles, such as the locking and unlocking of the vehicle’s doors.
As vehicles are increasingly equipped with sensors, actuators and communication devices, it becomes easier for vehicles to communicate with other vehicles on the road. There is a growing interest in building vehicular networks with IoT technologies. IoT technologies make it possible to track each vehicle’s existing location, monitor its movement and predict its future location. He et al. (2014) have proposed the integration of various devices such as sensors, actuators, controllers, GPS devices, mobile phones and other internet-accessing equipment on vehicles to build vehicular data clouds in the automotive domain. Various cloud-based services, such as intelligent parking cloud service and real-time traffic monitoring services, can be further established on vehicular data clouds to offer a variety of benefits such as keeping drivers informed about potential traffic safety risks and increasing their awareness of road conditions and other traffic-related events. Building vehicular data clouds offers a promising opportunity to improve vehicle-to-vehicle communication and road safety, and has the potential to further address the increasing transportation issues such as heavy traffic, congestion and vehicle safety.
Some preliminary work using IoT technologies to improve transportation systems has been conducted in recent years. For example, an intelligent informatics system (the iDrive system) developed by BMW uses various sensors and tags to monitor the environment, such as tracking the vehicle’s location and the road conditions and providing driving directions (Qin et al., 2013). Leng and Zhao (2011) proposed an intelligent internet-of-vehicles system (known as IIOVMS) to collect traffic information from the external environment on an ongoing basis and to monitor and manage road traffic in real time. Lumpkins (2013) notes that intelligent transportation systems can use IoT devices in the vehicle to connect to the cloud and that numerous sensors on the road can be virtualised to leverage the processing capabilities of the cloud. Zhang et al. (2012) designed an intelligent monitoring system to track the location of refrigerator trucks using IoT technologies. Liao (2014) studied the freight performance of heavy commercial vehicles using 12 months of truck GPS data. Suhr and Jung (2014) proposed a vacant parking slot detection and tracking system that uses sensors to help drivers conveniently select one of the available parking slots and to support the parking control system by continuously updating the designated target positions. To monitor traffic, a sensor network was designed to detect all vehicles entering and leaving an area, as well as the zones to which they are relating (Bottero et al., 2013).
As more and more vehicles join vehicular networks, most, if not all, applications on vehicular networks will need to communicate with big data, which can share important road safety messages under a stringent time limit, finance information to be used for online payment (e.g. E-Z pass toll station and smart parking) and medical information which is needed for emergency rescue, among other uses. Novel IoT-based vehicular applications can be developed and deployed, and can bring a number of business benefits such as predicting better routes, reporting traffic incidents, increasing road safety, reducing road congestion, managing traffic and recommending car maintenance or repair (Yan et al., 2012; AbdelSalam and Olariu, 2011). It is widely acknowledged that big data are typically characterised by the four Vs: volume, velocity, variety and veracity (Zikopoulos and Eaton, 2011; Chen et al., 2012). Volume is the amount of data. Variety notes that data can be represented by many different formats. Velocity is the speed of the data that are generated and processed. Veracity refers to the quality or trustworthiness of the data. The four Vs will grow at a tremendous speed as more and more IoT devices are being used in vehicular networks to collect data. The four Vs of IoT-generated data in vehicular networks make it necessary to build an analytics solution that can convert the generated big data into useful business insight and knowledge.
With the advent of IoT technology, optimisation and sharing of all the aspects of transportation, including roads, vehicles, traffic and transit, are possible (Del Giudice et al., 2015; Luo et al., 2016). According to Miller (2015), several cities in California have used advanced sensor technology to solve parking and traffic problems. Miller (2015) describes the way in which Palo Alto used hundreds of sensors around the city to show which parking spots were vacant in the downtown area in real time. Google uses GPS information from Android phones to compare posted speed limits with the actual speed of the drivers. Next, Google uses the results in Google Maps to show the severity of traffic delays. According to Morgan Stanley, the use of this IoT scenario could save approximately 30,000 lives and avoid 2.12 million injuries each year (Evans, 2015).
Data do not equal information, and information does not equal knowledge. However, by analysing raw data, one can extract information, and the more information one gathers and validates, the more knowledge is ultimately created. The real value of collecting data comes through data processing and aggregation on a large scale where new knowledge can be extracted. Because IoT collects data from myriad sensors, these data need to be classified, mined, organised and used to make automated decisions. To do this, one needs KM skills.
It is the authors’ belief that KM plays an important role in the design of effective IoT systems. This section discusses the way in which KM plays a crucial role in the design of an effective IoT system.
First, strategy, which is central to IoT design, is not always addressed in the context of data access and analysis. Without a strategy, one does not know which data to focus on, how to allocate analytic resources or what it is being accomplished in a data-to-knowledge initiative. It is important to ask the following strategic questions: What are the core business processes? What key decisions in those processes, and elsewhere, need support from analytic insights? What information really matters to the business? How will knowing something help the business perform better? What are the information and knowledge leverage points of the company’s performance? The answers to these types of questions help to formulate the strategy for turning data into knowledge. Second, in addition to knowledge about strategy, one also needs an unusual combination of skills and knowledge to transform data into actionable decisions. Not even the most sophisticated data mining software can obviate the need for a high degree of human skill and experience in the successful analysis and use of business data. A deep understanding of how the data are produced and transformed often comes only from experience. Much of this knowledge is tacit. Designing, producing and presenting analytic outputs is also dependent on extensive contextual knowledge of the particular industry involved and of the business issues that the decision makers are concerned with.
KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. This can be critical in an automotive arena, because the road conditions can change very quickly and require fast responses and decision. An intelligent parking service supported by IoT devices of vehicles is described below.
Finding an available parking space can be challenging in many cities and can lead to issues such as congestion, road accidents and psychological frustration. To make it easier to find available parking spaces, an IoT-based intelligent parking cloud service that collects and analyses geographic location information, parking availability information, parking space reservation and order information, traffic information and vehicle information though sensor detection and through the clouds is needed (Arif et al., 2012; Yan et al., 2011). In this process, the use of vehicle-to-infrastructure (V2I) and infrastructure-to-infrastructure (I2I) communications are vitally important. Using a modular approach, software architecture for implementing the intelligent parking cloud service has been proposed by He et al. (2014) (Figure 1).
Each vehicle is fitted with a transceiver with a short transmission range and a processor with simple computing capacity. The transceiver can be a common device, such as zigbee, a Bluetooth device or an infrared device. Both the processor and the wireless transceiver are enlisted into an Enhanced Data Rate (EDR). A parking lot with a wi-fi network, infrared devices and parking belts can be used to detect mis-parked cars. When a car enters the parking lot and heads to its reserved parking slot, the entrance booth will validate the reservation. If the parking spot is validated, a direction-related guidance will be uploaded to the car to help the driver find the reserved spot. The infrared device, lights and parking belt work together to detect and prevent mis-parking. An infrared device can be used to validate whether the car is parked long-term instead of using the slot for a temporary purpose. If the parking lot is correctly parked with cars, the infrared device’s light will show a green colour; otherwise, the light shows a red colour. Sensors connect to a computer centre to report the real-time status of every parking slot on an ongoing basis. Because a city has a number of parking lots, these sensors generate massive amounts of data related to the status of every parking slot, each day. When there is a large event going on in the city, how can the city’s parking management office help drivers to quickly find the available parking lots without wasting time driving around looking for a place to park? It is the authors’ belief that a knowledge-based decision system can help the city parking management office turn the incoming big data collected from IoT devices into useful knowledge more quickly and effectively to better achieve the goal of optimising parking space usage, thereby improving the efficiency of the parking operations and helping traffic flow more freely. The integration of a massive amount of real-time heterogeneous data from different sources, including traffic information, bus timetables, waiting times at events, event calendars, environmental sensors for pollution or weather warnings, GIS databases, parking availability information, parking space reservation and order information, traffic information and vehicle information is a big challenge that cannot be solved without effective KM. Kolozali et al. (2014) propose a knowledge-based approach for real-time IoT data stream annotation and processing to enhance the smart management of IoT data from various sources. A knowledge-based decision system is further recommended to integrate and analyse these data to generate better information and analysis including real-time parking lot statistics/reports, which can enhance decision-making significantly and can make the parking administrators’ jobs easier. The knowledge-based decision system can also conduct historical or longitudinal data analysis to generate new knowledge. This can help parking administrators to develop a long-term car parking solution that can guide drivers to find parking spaces in a more efficient and convenient way when similar events happen in the future.
In summary, the IoT offers several advantages for effective KM applications for vehicle parking. First, the IoT in KM enables the comprehensive monitoring of a product’s condition, operation and external environment through sensors and external data sources (Luo et al., 2016). Using data from the IoT, a product can alert users or others to changes in circumstances or performance. Monitoring also allows companies and customers to track a product’s operating characteristics and history to better understand how the product is actually used. It helps to reveal warranty compliance issues as well as new sales opportunities. In addition, IoT devices in KM can be controlled through remote commands or algorithms that are built into the device or that reside in the product cloud. This allows the product to respond to specified changes in its condition or environment. It also enables users to control and to personalise their interaction with the product in many new ways. For example, users can be alerted by their smartphones that parking lots are full or where they can find an available free slot. Third, real-time monitoring of the data regarding product condition and product control capability in KM enables organisations to optimise service by performing preventative maintenance when failure is imminent and by accomplishing repairs remotely, thereby reducing product downtime and the need to dispatch repair personnel. Finally, using the IoT in KM to monitor, control and optimise products allows real-time KM applications to attain autonomy. The IoT in KM will also be able to learn about the environment, to self-diagnose the users’ own service needs and to adapt to users’ preferences. Autonomy not only can reduce the need for operators, but can improve safety in dangerous environments and can facilitate operations in remote locations.
6. Conclusion
KMSs today enable users to be geared more and more for the updating of real-time knowledge. Sensors and embedded technology enable the transmission of real-time data through wireless networks. This will lead to co-creation of new real-time knowledge with customers and vendors on a regular basis.
However, realising the full potential of the IoT requires solving serious technical and business problems:
the identification of things;
the organisation, integration and management of big data; and
the effective use of knowledge-based decision systems.
This case study shows the potential to use the IoT in a KM system to help make better decisions about vehicle control.
By integrating various devices (such as sensors, actuators, controllers, GPS devices, mobile phones and other internet access equipment) and by using networking technologies (wireless sensor networks, cellular networks, satellite networks and others) as well as cloud computing, the IoT and middleware embedded in the KM, one is able to collect and exchange data among drivers, vehicles and roadside infrastructure (such as cameras and street lights). This provides real-time, economic, secure and on-demand services to customers through the IoT KM system. The use of real-time information from various sources through collaboration offers the means to make better decisions based on the diverse opinions and varied experiences of the different stakeholders involved.
The reuse of knowledge in repositories allows decision-making to be based on actual experience, large sample sizes and practical lessons learned, as well as real-time information obtained from sensors or from other devices.
Combining real-time events with historic patterns allows for the emergence of predictive and prescriptive analytics. Such evolutionary analytics allow KM applications to solve issues and prescribe solutions in real time. The use of the IoT combined with KM allows for the delivering of operational effectiveness in this vehicle application. Operational effectiveness is the primary business driver in an organisation. The use of the IoT in KM will facilitate the identification of the real-time requirements of users.
But the design of effective KM-IoT systems is not trivial. Central to the design elements for the IoT are awareness and control. User’s mental models of what’s going to happen must be supported. If a certain object is moved and this triggers a digital activity, the user has to have the ability to inspect to find out what exactly is going on in that interaction. Also, the user has to have an understanding of where the data go, what the data are being used for and how to shut off the system.
The IoT in KM also presents previously unforeseen risks to the organisation and to its knowledge assets. Workers need to work with security and legal professionals to create secure ways of sharing knowledge across the organisation and across geographical boundaries.
Design of KM-IoT systems should be considered from an ecosystem of devices; these services will be accessed and experienced through many different screens, interfaces, objects and environments. Both the physical and digital components of a KMS need to be considered as one unit. Other issues include design for collaboration, the various contexts in which the systems will be used and the enabling of better and faster decision-making. The authors’ future work involves the design of KM-embedded IoT systems based on users’ mental models to enable the design of transparent and usable systems for users.
Figure 1
Software architecture for intelligent parking cloud service
[Figure omitted. See PDF]
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