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The Technology of the Internet of Things. Enchanted Objects. Who is Making the Internet of Things? Summary. Chapter 2: Design Principles for . founded MCQN Ltd., an Internet of Things product agency and (along with capturing some of what we Des Building Arduino Projects for the Internet of Things. Take your idea from concept to production with this unique guide. Whether it's called physical computing, ubiquitous computing, or the Internet of Things, it's a.

Designing The Internet Of Things Pdf

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IoT design, whether you're involved in environmental monitoring, building USA, 12 Editorial Reviews. Review. According to friends of mine who work in the disciplines above, this Designing the Internet of Things 1st Edition, Kindle Edition. by. PDF | On Dec 6, , Ali A Abed and others published Internet of Things (IoT): Architecture and Design.

In order to program and control the flow of information in the Internet of things, a predicted architectural direction is being called BPM Everywhere which is a blending of traditional process management with process mining and special capabilities to automate the control of large numbers of coordinated devices. With billions of devices [] being added to the Internet space, IPv6 will play a major role in handling the network layer scalability.

Fog computing is a viable alternative to prevent such large burst of data flow through Internet. Limited processing power is a key attribute of IoT devices as their purpose is to supply data about physical objects while remaining autonomous. Heavy processing requirements use more battery power harming IoT's ability to operate. Scalability is easy because IoT devices simply supply data through the internet to a server with sufficient processing power.

At the overall stage full open loop it will likely be seen as a chaotic environment since systems always have finality. As a practical approach, not all elements in the Internet of things run in a global, public space. Subsystems are often implemented to mitigate the risks of privacy, control and reliability. For example, domestic robotics domotics running inside a smart home might only share data within and be available via a local network.

Human beings in surveyed urban environments are each surrounded by to trackable objects. This number is expected to grow to million devices by Note that some things in the Internet of things will be sensors, and sensor location is usually important. However, the challenges that remain include the constraints of variable spatial scales, the need to handle massive amounts of data, and an indexing for fast search and neighbor operations.

In the Internet of things, if things are able to take actions on their own initiative, this human-centric mediation role is eliminated. Thus, the time-space context that we as humans take for granted must be given a central role in this information ecosystem. Just as standards play a key role in the Internet and the Web, geospatial standards will play a key role in the Internet of things.

Others are turning to the concept of predictive interaction of devices, "where collected data is used to predict and trigger actions on the specific devices" while making them work together. Crucial to the field is the network used to communicate between devices of an IoT installation, a role that several wireless or wired technologies may fulfill: [] [] [] Addressability[ edit ] The original idea of the Auto-ID Center is based on RFID-tags and distinct identification through the Electronic Product Code.

The objects themselves do not converse, but they may now be referred to by other agents, such as powerful centralized servers acting for their human owners. Due to the limited address space of IPv4 which allows for 4. To a large extent, the future of the Internet of things will not be possible without the support of IPv6; and consequently, the global adoption of IPv6 in the coming years will be critical for the successful development of the IoT in the future.

Light-Fidelity Li-Fi — Wireless communication technology similar to the Wi-Fi standard, but using visible light communication for increased bandwidth. Data Infrastructure for Medical Research. Digital Built Britain, February , 1— Cyber security standards and issues in V2X communications for Internet of Vehicles. Ownership of personal data in the Internet of Things. Neighborhood view consistency in wireless sensor networks. A cost-effective SCTP extension for hybrid vehicular networks.

Journal of Communications and Information Networks, 2 2 , 18— Van, Seymour, W. PayBreak: Defense against cryptographic ransomware. Crowdsourcing mobile coverage. Telecommunications Policy, 40 6 , — IET Conference Proceedings, 9 8 pp. Response of electric vehicle drivers to dynamic pricing of parking and charging services: Risky choice in early reservations.

Trust in IoT-enabled mobility services: Predictive analytics and the impact of prediction errors on the quality of service in bike sharing. The impact of free-floating carsharing on car ownership: Early-stage findings from London.

Transport Policy. Blockchain-based dynamic key management for heterogeneous intelligent transportation systems. Informatica, 28 1 , — In CHI Workshop. Lindley, J. The Design Journal, 20 sup1 , S—S Implications for Adoption. How to build time-lock encryption. Designs, Codes and Cryptography, 86 11 , — Transforming health care: body sensor networks, wearables, and the Internet of Things.

Proof of Kernel Work: a democratic low-energy consensus for distributed access-control protocols.

Royal Society Open Science, 5 8. Public priorities and consumer preferences for selected attributes of automated vehicles. Journal of Modern Transportation, 26 1 , 72— A multi-modelling based approach to assessing the security of smart buildings. Story Blocks: Reimagining narrative through the blockchain. Convergence, 23 1 , 79— Consumer Perceived Vulnerability, privacy calculus and information disclosure: an empirical investigation in retailer loyalty program.

In Naples Forum on Service. Sorrento, Italy. Mikusz, M. Next generation physical analytics for digital signage. Smart IoT and soft AI. Satisfiability modulo theories for process systems engineering.

Ethics of the health-related internet of things: a narrative review. Ethics and Information Technology, 19 3 , — The ethics of algorithms: Mapping the debate. Sikos Ed. Cham: Springer International Publishing. International Journal of Research in Marketing, 34 1 , 3— IT Professional, 19 5 , 20— The reality of assessing security risks in Internet of Things systems. IET Conference Proceedings. Transformers: Robust spatial joins on non-uniform data distributions. We have tried to cover all subareas and recent technologies in our taxonomy.

There have been many survey papers on the Internet of Things in the past. Table 1 shows how our survey is different from other highly cited surveys in the literature.

Table 1: Comparison with other surveys on the basis of topics covered. Let us first consider our novel contributions. Our paper looks at each and every layer in the IoT stack, and as a result the presentation is also far more balanced. A novel addition in our survey is that we have discussed different IoT architectures.

This has not been discussed in prior surveys on the Internet of Things. The architecture section also considers newer paradigms such as fog computing, which have also hitherto not been considered. Moreover, our survey nicely categorizes technologies based on the architectural layer that they belong to. We have also thoroughly categorized the network layer and tried to consolidate almost all the technologies that are used in IoT systems.

Such kind of a thorough categorization and presentation of technologies is novel to the best of our knowledge. Along with these novel contributions our survey is far more comprehensive, detailed, and exhaustive as compared to other surveys in the area. Most of the other surveys look at only one or two types of sensors, whereas we describe 9 types of sensors with many examples. Other surveys are also fairly restricted when they discuss communication technologies and applications.

We have discussed many types of middleware technologies as well. Prior works have not given middleware technologies this level of attention. We cover 10 communication technologies in detail and consider a large variety of applications encompassing smart homes, health care, logistics, transport, agriculture, environment, smart cities, and green energy.

No other survey in this area profiles so many technologies, applications, and use cases. Sensors and Actuators All IoT applications need to have one or more sensors to collect data from the environment. Sensors are essential components of smart objects. One of the most important aspects of the Internet of Things is context awareness, which is not possible without sensor technology. IoT sensors are mostly small in size, have low cost, and consume less power.

They are constrained by factors such as battery capacity and ease of deployment. Schmidt and Van Laerhoven [ 25 ] provide an overview of various types of sensors used for building smart applications. Mobile Phone Based Sensors First of all, let us look at the mobile phone, which is ubiquitous and has many types of sensors embedded in it. In specific, the smartphone is a very handy and user friendly device that has a host of built in communication and data processing features.

With the increasing popularity of smartphones among people, researchers are showing interest in building smart IoT solutions using smartphones because of the embedded sensors [ 16 , 26 ]. Some additional sensors can also be used depending upon the requirements. Applications can be built on the smartphone that uses sensor data to produce meaningful results.

Some of the sensors inside a modern smartphone are as follows.

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It typically measures changes in velocity of the smartphone in three dimensions. There are many types of accelerometers [ 27 ]. In a mechanical accelerometer, we have a seismic mass in a housing, which is tied to the housing with a spring. The mass takes time to move and is left behind as the housing moves, so the force in the spring can be correlated with the acceleration.

In a capacitive accelerometer, capacitive plates are used with the same setup. With a change in velocity, the mass pushes the capacitive plates together, thus changing the capacitance. The rate of change of capacitance is then converted into acceleration. In a piezoelectric accelerometer, piezoelectric crystals are used, which when squeezed generate an electric voltage.

The changes in voltage can be translated into acceleration. The data patterns captured by the accelerometer can be used to detect physical activities of the user such as running, walking, and bicycling.

Orientation is measured using capacitive changes when a seismic mass moves in a particular direction. To make sense of the audio data, technologies such as voice recognition and acoustic features can be exploited. This can be used as a digital compass and in applications to detect the presence of metals. The location is detected using the principle of trilateration [ 28 ]. The distance is measured from three or more satellites or mobile phone towers in the case of A-GPS and coordinates are computed.

It can be used for setting the brightness of the screen and other applications in which some action is to be taken depending on the intensity of ambient light.

For example, we can control the lights in a room. These rays bounce back when they strike some object. Based on the difference in time, we can calculate the distance. In this way, the distance to different objects from the phone can be measured. For example, we can use it to determine when the phone is close to the face while talking. It can also be used in applications in which we have to trigger some event when an object approaches the phone. We have studied many smart applications that use sensor data collected from smartphones.

For example, activity detection [ 29 ] is achieved by applying machine learning algorithms to the data collected by smartphone sensors. It detects activities such as running, going up and down stairs, walking, driving, and cycling. The application is trained with patterns of data using data sets recorded by sensors when these activities are being performed. Wang et al. They use it to assess the overall mental health and performance of a college student.

To track the location and activities in which the student is involved, activity recognition accelerometer and GPS data are used. To keep a check on how much the student sleeps, the accelerometer and light sensors are used.

For social life and conversations, audio data from a microphone is used. The application also conducts quick questionnaires with the students to know about their mood. All this data can be used to assess the stress levels, social life, behavior, and exercise patterns of a student. Another application by McClernon and Choudhury [ 31 ] detects when the user is going to smoke using context information such as the presence of other smokers, location, and associated activities.

To summarize smartphone sensors are being used to study different kinds of human behavior see [ 32 ] and to improve the quality of human life. Medical Sensors The Internet of Things can be really beneficial for health care applications. We can use sensors, which can measure and monitor various medical parameters in the human body [ 33 ]. Subsequently, they can provide real time feedback to the doctor, relatives, or the patient.

Designing for Internet of things

There are many wearable sensing devices available in the market. They are equipped with medical sensors that are capable of measuring different parameters such as the heart rate, pulse, blood pressure, body temperature, respiration rate, and blood glucose levels [ 35 ]. These wearables include smart watches, wristbands, monitoring patches, and smart textiles.

Moreover, smart watches and fitness trackers are becoming fairly popular in the market as companies such as Apple, Samsung, and Sony are coming up with very innovative features.

For example, a smart watch includes features such as connectivity with a smartphone, sensors such as an accelerometer, and a heart rate monitor see Figure 4. Another novel IoT device, which has a lot of promise are monitoring patches that are pasted on the skin. Monitoring patches are like tattoos.

They are stretchable and disposable and are very cheap. These patches are supposed to be worn by the patient for a few days to monitor a vital health parameter continuously [ 15 ]. All the electronic components are embedded in these rubbery structures. They can even transmit the sensed data wirelessly. Just like a tattoo, these patches can be applied on the skin as shown in Figure 5.

One of the most common applications of such patches is to monitor blood pressure. Figure 5: Embedded skin patches source: MC10 Electronics. A very important consideration here is the context [ 34 ]. The data collected by the medical sensors must be combined with contextual information such as physical activity. For example, the heart rate depends on the context.

It increases when we exercise. In that case, we cannot infer abnormal heart rate. Therefore, we need to combine data from different sensors for making the correct inference. Neural Sensors Today, it is possible to understand neural signals in the brain, infer the state of the brain, and train it for better attention and focus. This is known as neurofeedback [ 36 ] see Figure 6.

The technology used for reading brain signals is called EEG Electroencephalography or a brain computer interface. The neurons inside the brain communicate electronically and create an electric field, which can be measured from outside in terms of frequencies. Brain waves can be categorized into alpha, beta, gamma, theta, and delta waves depending upon the frequency. Based on the type of wave, it can be inferred whether the brain is calm or wandering in thoughts.

This type of neurofeedback can be obtained in real time and can be used to train the brain to focus, pay better attention towards things, manage stress, and have better mental well-being. Environmental and Chemical Sensors Environmental sensors are used to sense parameters in the physical environment such as temperature, humidity, pressure, water pollution, and air pollution.

Parameters such as the temperature and pressure can be measured with a thermometer and barometer. Air quality can be measured with sensors, which sense the presence of gases and other particulate matter in the air refer to Sekhar et al.

Chemical sensors are used to detect chemical and biochemical substances. These sensors consist of a recognition element and a transducer. The electronic nose e-nose and electronic tongue e-tongue are technologies that can be used to sense chemicals on the basis of odor and taste, respectively [ 38 ]. The e-nose and e-tongue consist of an array of chemical sensors coupled with advance pattern recognition software. The sensors inside the e-nose and e-tongue produce complex data, which is then analyzed through pattern recognition to identify the stimulus.

These sensors can be used in monitoring the pollution level in smart cities [ 39 ], keeping a check on food quality in smart kitchens, testing food, and agricultural products in supply chain applications. The tag transmits the data stored in it via radio waves. It is similar to bar code technology. But unlike a traditional bar code, it does not require line of sight communication between the tag and the reader and can identify itself from a distance even without a human operator.

The range of RFID varies with the frequency. It can go up to hundreds of meters. RFID tags are of two types: active and passive. Active tags have a power source and passive tags do not have any power source.

Passive tags draw power from the electromagnetic waves emitted by the reader and are thus cheap and have a long lifetime [ 40 , 41 ]. There are two types of RFID technologies: near and far [ 40 ].

A near RFID reader uses a coil through which we pass alternating current and generate a magnetic field. The tag has a smaller coil, which generates a potential due to the ambient changes in the magnetic field. This voltage is then coupled with a capacitor to accumulate a charge, which then powers up the tag chip. The tag can then produce a small magnetic field that encodes the signal to be transmitted, and this can be picked up by the reader.

The tag also has a dipole antenna on which an alternating potential difference appears and it is powered up.

It can then use this power to transmit messages. RFID technology is being used in various applications such as supply chain management, access control, identity authentication, and object tracking. The RFID tag is attached to the object to be tracked and the reader detects and records its presence when the object passes by it.

In this manner, object movement can be tracked and RFID can serve as a search engine for smart things. For access control, an RFID tag is attached to the authorized object.

For example, small chips are glued to the front of vehicles. When the car reaches a barricade on which there is a reader, it reads the tag data and decides whether it is an authorized car. If yes, it opens automatically. The low level data collected from the RFID tags can be transformed into higher level insights in IoT applications [ 42 ]. There are many user level tools available, in which all the data collected by particular RFID readers and data associated with the RFID tags can be managed.

The high level data can be used to draw inferences and take further action. Actuators Let us look at some examples of actuators that are used in the Internet of Things. An actuator is a device, which can effect a change in the environment by converting electrical energy into some form of useful energy.

Some examples are heating or cooling elements, speakers, lights, displays, and motors. The actuators, which induce motion, can be classified into three categories, namely, electrical, hydraulic, and pneumatic actuators depending on their operation.

Hydraulic actuators facilitate mechanical motion using fluid or hydraulic power. Pneumatic actuators use the pressure of compressed air and electrical ones use electrical energy.

As an example, we can consider a smart home system, which consists of many sensors and actuators. Preprocessing As smart things collect huge amount of sensor data, compute and storage resources are required to analyze, store, and process this data.

The most common compute and storage resources are cloud based because the cloud offers massive data handling, scalability, and flexibility. But this will not be sufficient to meet the requirements of many IoT applications because of the following reasons [ 43 ].

Their changing location makes it difficult to communicate with the cloud data center because of changing network conditions across different locations. Latency sensitive applications, which need real time responses, may not be feasible with this model. Also, the communication may be lossy due to wireless links, which can lead to unreliable data. They thus cannot afford to communicate all the time. To solve the problem of mobility, researchers have proposed mobile cloud computing MCC [ 44 ].

But there are still problems associated with latency and power. MCC also suffers from mobility problems such as frequently changing network conditions due to which problems such as signal fading and service degradation arise.

As a solution to these problems, we can bring some compute and storage resources to the edge of the network instead of relying on the cloud for everything. This concept is known as fog computing [ 11 , 45 ] also see Section 2.

The fog can be viewed as a cloud, which is close to the ground. Data can be stored, processed, filtered, and analyzed on the edge of the network before sending it to the cloud through expensive communication media. The fog and cloud paradigms go together. Both of them are required for the optimal performance of IoT applications.

A smart gateway [ 13 ] can be employed between underlying networks and the cloud to realize fog computing as shown in Figure 7. Figure 7: Smart gateway for preprocessing. The features of fog computing [ 11 ] are as follows: 1 Low latency: less time is required to access computing and storage resources on fog nodes smart gateways. This is beneficial as context awareness is an important feature of IoT applications. Multiple fog nodes need to be deployed in distributed geographical areas in order to provide services to mobile devices in those areas.

The tasks performed by a smart gateway [ 46 ] are collecting sensor data, preprocessing and filtering collected data, providing compute, storage and networking services to IoT devices, communicating with the cloud and sending only necessary data, monitoring power consumption of IoT devices, monitoring activities and services of IoT devices, and ensuring security and privacy of data. Some applications of fog computing are as follows [ 10 , 11 ]: 1 Smart vehicular networks: smart traffic lights are deployed as smart gateways to locally detect pedestrians and vehicles through sensors, calculate their distance and speed, and finally infer traffic conditions.

This is used to warn oncoming vehicles. These sensors also interact with neighboring smart traffic lights to perform traffic management tasks. For example, if sensors detect an approaching ambulance, they can change the traffic lights to let the ambulance pass first and also inform other lights to do so. The data collected by these smart traffic lights are locally analyzed in real time to serve real time needs of traffic management.

Further, data from multiple gateways is combined and sent to the cloud for further global analysis of traffic in the city. This is done in order to switch automatically to alternative sources of energy such as solar and wind power. This balancing can be done at the edge of the network using smart meters or microgrids connected by smart gateways.

These gateways can analyze and process data. They can then project future energy demand, calculate the availability and price of power, and supply power from both conventional and alternative sources to consumers. Communication As the Internet of Things is growing very rapidly, there are a large number of heterogeneous smart devices connecting to the Internet. IoT devices are battery powered, with minimal compute and storage resources.

Because of their constrained nature, there are various communication challenges involved, which are as follows [ 19 ]: 1 Addressing and identification: since millions of smart things will be connected to the Internet, they will have to be identified through a unique address, on the basis of which they communicate with each other.

For this, we need a large addressing space, and a unique address for each smart object. Therefore, we need a solution that facilitates communication with low power consumption.

This stack is very complex and demands a large amount of power and memory from the connecting devices. The IoT devices can also connect locally through non-IP networks, which consume less power, and connect to the Internet via a smart gateway.

Therefore, their applications are limited to small personal area networks. Personal area networks PAN are being widely used in IoT applications such as wearables connected to smartphones. For increasing the range of such local networks, there was a need to modify the IP stack so as to facilitate low power communication using the IP stack. Near Field Communication NFC Near Field Communication [ 47 — 49 ] is a very short range wireless communication technology, through which mobile devices can interact with each other over a distance of few centimeters only.

All types of data can be transferred between two NFC enabled devices in seconds by bringing them close to each other. This technology is based on RFID. It uses variations in the magnetic field to communicate data between two NFC enabled devices.

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NFC operates over a frequency band of This balancing can be done at the edge of the network using smart meters or microgrids connected by smart gateways. We cover 10 communication technologies in detail and consider a large variety of applications encompassing smart homes, health care, logistics, transport, agriculture, environment, smart cities, and green energy. Better off: when should pervasive displays be powered down?

Zigbee supports star, tree, and mesh topologies. Finally, any IoT middleware needs to perform load balancing, manage devices based on their levels of battery power, and report problems in devices to the users.

There are many wearable sensing devices available in the market.