INTERNET OF THINGS

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The central idea of the internet of things is to connect everyday objects to the internet and thus enable comprehensive and autonomous communication.  Devices should be seamlessly integrated into everyday life and help to increase quality of life.  Applications range from traffic and health to agriculture.  

The package includes the following topics, discussed briefly here.  If you would like to obtain more details, you can purchase the full package.

1.1 IoT-Secutriy

Security is one of the most important topics pertaining to the internet of things, because it collects and processes a comprehensive amount of personal data.  Other parties tampering with the networked devices poses a considerable problem, since they have a wide range of functions.  Security concerns are probably the biggest obstacle to widespread adoption of the internet of things (Zeadally et al.  2016; Tomovic et al. 2015).

  • SDN (Software-defined Networking): Software-defined networking is a new approach to network architectures.  Network control and data transmission are separated to increase performance, controllability, manageability and flexibility.  The new possibilities allow the addition of security mechanisms at various points in the network (Benzekki et al.  2016).
  • eHealth security:  A detailed look at eHealth can be found in Section 7.11.2. Security and privacy are of particular importance in this area.  Health data is intimate data and tampering with the data would have serious consequences, such as incorrect treatment or even the death of the patient. Customers are willing to prefer safety to comfort.  Consequently, stricter security systems are needed than in the rest of the internet of things (Zeadally et al.  2016).
  • Protection against malware: Dynamic algorithms can detect data traffic anomalies, thus protecting networks and devices from malicious software and unauthorized access (Summerville et al.  2015).
  • Trustworthiness:  To ensure security and privacy on the internet of things, remote attestation is used.  A remote verifier checks the integrity of the devices and the software.   Scalability for fast verification of a large number of devices is a topic that is being debated. (Ambrosin, et al., 2016).  Trusted devices can mutually share data, processing power, or network capacity to improve the user experience (Ambrosin et al.  2016; Kim et al. 2015a).
  • CoAP (Constrained Application Protocol):  In the internet of things, devices with low energy, computational and network resources are often part of the network.  Conventional network protocols are unsuitable for these so-called “constrained devices”.  The Constrained Application Protocol enables secure data transmission combined with comparatively low computing power requirements for encryption.  CoAP is also used as an application protocol in Lightweight M2M (LWM2M), a protocol for machine communication (Bhattacharyya et al.  2015; Lessa dos Santos et al.  2015).
  • 1.2 IoT:  Energy & communication
  • In the context of sustainability, not only is the energy efficiency of the devices in the internet of things a challenge, but also their energy supply.  The sensors should be as small as possible and are sometimes located in inaccessible places, which is why neither external power sources nor batteries or accumulators can be a suitable solution.  Researchers are looking for new ways of optimizing data transfer within the context of the internet of things (Perez-Penichet 2016).
  • Communication via backscatter:  Backscatter communication enables the transmission of data from sensors without a power source.  Existing radio signals from televisions or WiFi are used to generate energy. The sensors absorb or reflect a signal from a transmitter.  This represents the two states with which the response signal is transmitted.  Switching between the two states requires so little energy that enough energy can be derived from the absorption of radio wave energy for this purpose (Perez-Penichet 2016).
  • Cognitive Internet of Things (CIoT):  CIoT refers to the use of cognitive radio with regard to the internet of things.  Due to the large number of devices, the frequency spectrum of wireless networks is nearly fully occupied.  Cognitive radio automatically identifies available frequencies in the wireless spectrum, thus increasing the load capacity of the network (Li et al.  2016c).
  • Home energy management:  Home energy management involves analysis of energy consumption in buildings.  Data regarding lighting, temperature regulation and the like enable the optimization of consumption as well as the optimal use of local energy resources such as private solar cells and wind turbine farms.  This constitutes the consumer side of the smart grid (Lan and Tan 2015, Y. et al. 2015).
  • 6LoWPAN:  6LoWPAN (IPv6 over Low Power Wireless Personal Area Network) is a communication protocol for wireless networks with low power consumption.  It is often used in the internet of things (Yushev et al.  2015).
  • Body sensor networks:  Body sensor networks (BSN) collect physiological data from humans or animals.  The sensors are either mounted directly on the body or are placed on clothing.  For example, blood sugar, blood pressure, heart rate, sleep phases and movement can be measured by BSN.  The most important requirement for the sensors is extremely low energy consumption and high security standards (Floos and Al-Mogren 2015).

1.2 IoT: Energy & Communication

In the context of sustainability, not only is the energy efficiency of the devices in the internet of things a challenge, but also their energy supply.  The sensors should be as small as possible and are sometimes located in inaccessible places, which is why neither external power sources nor batteries or accumulators can be a suitable solution.  Researchers are looking for new ways of optimizing data transfer within the context of the internet of things (Perez-Penichet 2016).

  • Communication via backscatter:  Backscatter communication enables the transmission of data from sensors without a power source.  Existing radio signals from televisions or WiFi are used to generate energy. The sensors absorb or reflect a signal from a transmitter.  This represents the two states with which the response signal is transmitted.  Switching between the two states requires so little energy that enough energy can be derived from the absorption of radio wave energy for this purpose (Perez-Penichet 2016).
  • Cognitive Internet of Things (CIoT):  CIoT refers to the use of cognitive radio with regard to the internet of things.  Due to the large number of devices, the frequency spectrum of wireless networks is nearly fully occupied.  Cognitive radio automatically identifies available frequencies in the wireless spectrum, thus increasing the load capacity of the network (Li et al.  2016c).
  • Home energy management:  Home energy management involves analysis of energy consumption in buildings.  Data regarding lighting, temperature regulation and the like enable the optimization of consumption as well as the optimal use of local energy resources such as private solar cells and wind turbine farms.  This constitutes the consumer side of the smart grid (Lan and Tan 2015, Y. et al. 2015).
  • 6LoWPAN:  6LoWPAN (IPv6 over Low Power Wireless Personal Area Network) is a communication protocol for wireless networks with low power consumption.  It is often used in the internet of things (Yushev et al.  2015).
  • Body sensor networks:  Body sensor networks (BSN) collect physiological data from humans or animals.  The sensors are either mounted directly on the body or are placed on clothing.  For example, blood sugar, blood pressure, heart rate, sleep phases and movement can be measured by BSN.  The most important requirement for the sensors is extremely low energy consumption and high security standards (Floos and Al-Mogren 2015).
  • Visible Light Communication:  Due to increased wireless communication, the frequency spectrum below ten gigahertz that has been used up until now is no longer sufficient.  Visible Light Communication (VLC) transmits data using the frequency range of visible light.  The transmitter is a light emitting diode, the receiver, a photodiode.  The signal is generated by modulation of intensity.  This type of communication could be used in the smart home, to network the devices in the house.  Its advantages are that the existing lighting infrastructure can be used and that the bandwidth is very high.  With DarkVLC, the light pulses used are so short that they are imperceptible to the human eye (Tian et al.  2016; Haas et al. 2016).
  • Fiber-Wireless Communications:  Like VLC, fiber-wireless communications provide a solution for the overused frequency range below ten gigahertz.  Higher frequencies have the problem that the transmission losses are very high; they are therefore only suitable for short distances. For fiber-wireless communication, wireless transmitters are installed close to consumers and connected to the internet via fiber optic cables.  As a result, the use of frequencies for wireless communication is limited to local use (Liu et al.  2016; Van et al. 2016).
  • Time-slotted channel hopping:  Time-slotted channel hopping is part of the IEEE 802.15.4e standard for network communication and enables the use of highly reliable and energy-efficient wireless sensor networks (Chang et al. 2015).

1.3 Cloud Computing

Cloud computing is the outsourcing of computing power and data storage over the internet to large data centers. This is particularly relevant in the internet of things, because many of the connected devices have only rudimentary computing resources and very large amounts of data are collected (Pietri and Sakellariou 2016).

  • Complex event processing (CEP):  Complex event processing involves the interpretation of the primitive flow of data from the sensors of the internet of things and captures event patterns to respond in real time to real, complex situations. This enables the realization of a distributed artificial intelligence (Mayer et al. 2015; Mehdiyev et al. 2015).
  • Environment: The large number of sensors of the internet of things provide vast amounts of data that can be used to analyze climate change and make very accurate weather forecasts (Muller et al. 2015).
  • Edge and Fog Computing:  Fog computing is an extension of cloud computing. Cloud computing involves storage and processing of data in computing centers because the individual devices do not have the required resources. However, the disadvantages of this system are long latencies and very high data streams in the data centers. Fog computing adds a layer between the end device and the data center. The data is processed on the local network and only aggregated and sent to the cloud when necessary. This reduces the volume of data to the cloud and improves response time. The intermediate layer can consist of both local small data centers and locally available computing resources such as smartphones (Qaisar and Riaz 2016; Lee et al. 2015). Edge computing and fog computing both have the same purpose. However, data processing takes place directly within the device. Consequently, sensors that are suitable for edge computing not only deliver measurement data, but also processed data.   Whether cloud, fog or edge computing is used depends on each individual case. Cloud computing does not require local resources and enables complex calculations, but it can be too slow. Fog computing enables faster response times but requires local resources. For complex calculations, one still has to use the cloud. Edge computing also has fast response times, but resources are located directly within or on the device. As a result, sensors are larger and require an energy supply, which is a problem in some cases. In addition, fog and edge computing enhance security and privacy (Salman et al. 2015).

1.4 Smart Cities

Smart cites are cities that use information and communication technology to intelligently respond to the needs of residents. The use of information and communication technology aids sustainability and safety, as well as the management of traffic and energy (Harmon et al.  2015).

  • Smart parking:  Smart parking involves detection of available parking spaces by sensors. The available spaces are suggested to the user. This eliminates the search for suitable parking.  The parking fee can be paid automatically via RFID (Bagula et al.  2015).
  • Waste management:  If public waste containers are equipped with sensors, one can measure when waste collection is needed and a dynamic route can be calculated. Certain areas such as schools or hospitals can be given a higher priority.  The number of vehicles required is reduced and cleanliness increased (Anagnostopoulos et al.  2015b; Anagnostopoulos et al. 2015a).
  • Tourism and noise measurement:  There are very different articles in this cluster.  Topics range from comprehensive noise measurements in cities to personalized tourist itineraries (Segura Garcia et al.  2016; Yin and Wang 2015).
  • Smart grid:  Small power generation installations such as wind turbines and solar cells are referred to as distributed energy resources. The large power plants of the energy suppliers differ from these small power sources (Rana and Li 2015).  The smart grid collects and analyzes data from the power grid.  This enables predictions to be made for producers and consumers, and in turn allows for corresponding adjustments in and management of the network.  All this results in higher energy efficiency and grid stability, optimal integration of distributed energy resources, and dynamic pricing.  Consumers can also benefit from the smart grid (Ghasempour and Moon 2016).

1.5 Smart Home

Smart home refers to the networking and automation of residential buildings. As a result, housing quality and safety for instance can be enhanced and energy consumption reduced (Jayatilaka et al. 2016).
Ambient assisted living:  Due to demographic change, there are more and more old people who want to live in their own apartment in spite of certain limitations. Ambient assisted living incorporates technological support for residents, for example with cooking and cleaning and the control of lighting and temperature (Jayatilaka et al. 2016).

1.6 Machine-to-Machine Communication

The networking of industrial machines is referred to as “machine-to-machine” or “M2M” communication.  This allows machines to be remotely maintained and the collection of telemetry data. All of this enhances efficiency.  Machine-to-machine communication differs from the networking of everyday devices in that there are sometimes different requirements (Berrhouma et al.  2016).

1.7 Environmental Applications Of IoT

The more data we have about our environment, the more precisely we can understand and respond to it.  This allows resources to be used optimally and enhances protection of the environment as well as our health.

  • Air pollution:  In urban areas, air pollution is a growing public health problem.  Air quality is measured very accurately, but at only a few locations.  A sensor network enables significant improvement of spatial resolution and enhances the protection of the population (Breitegger and Bergmann 2016).
  • Smart healthcare:  Smart healthcare uses new digital solutions to support physicians, caregivers and patients. Personal data collection improves the quality of care, reduces costs and improves treatment outcomes.  Patients can avoid having to go to a clinic to have their diagnosis made because a lot of data can also be collected from home (Sun et al.  2016; Gómez et al. 2016).
  • Smart greenhouses:  Environmental conditions in greenhouses are easy to customize.  They are therefore optimally suited for precision farming (Xu et al. 2016c).
  • Intelligent water management:  Water scarcity is affecting more and more parts of the world.  Sensors in the earth can gauge the situation with regard to water resources in order to optimize their use.  Landslides can also be predicted and prevented by such sensors (Cheng and Liu 2016).

1.8 IoT And Industry

In order to differentiate the internet of things with regard to industry from the everyday application of IoT, the term industrial Internet of Things (iIoT) or Industry 4.0 was introduced (Wan et al.  2016).

  • Food tracking:  The internet of things and RFID enable the tracking of products in real time from production through storage and transportation to sale.  This increases food safety and prevents fraud (Han et al.  2015).
  • Programmable logic controller (PLC):  The industrial internet of things enables the close and seamless interaction of all functional units of an industrial process, from the lowest level (sensors, for instance) to higher levels such as management, logistics and maintenance.  This helps to increase efficiency and flexibility, and reduce maintenance costs and unforeseen downtime.  Automation is often supported by PLCs, easy-to-program control units (Sousa et al.  2015).
  • Precision agriculture:  Precision agriculture optimizes production efficiency, improves quality, minimizes environmental impact and reduces the consumption of resources such as energy and water.  Sensors measure plant growth, soil quality, irrigation and temperature.  The conditions for optimal plant growth are determined and automatically adjusted individually for each plant (Ferrandez-Pastor et al.  2016).
  • New IoT business models:  The internet of things has an enormous effect on a wide variety of industries.  Competitors eagerly hurry to enter the market with new business models and competition flourishes.  As a result, flexibility and innovation are essential to ensure business success (Lee and Lee 2015).

1.9 RFID

RFID (radio-frequency identification) is a key technology for the internet of things.  The chips are suitable for identification and localization, but still manage to remain very small and inexpensive (Mohamedatni et al.  2015).

  • Position determination in the smart home:  RFID could become even more important as it has the added application in the smart home of determining the position of persons and devices.  In particular, low costs make the technology interesting (Tesch et al. 2015).
  • Patch antennas:  Patch antennas are extremely compact and lightweight antennas used primarily for RFID (Thirumalai and Kashwan 2015).

Nanodevices is an umbrella term for very small devices in the nanometer range.  Often, the structures performing the simplest functions consist of individual molecules or atoms (Masek et al.  2015).

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