File Name: rfid and sensor networks architectures protocols security and integrations .zip
Pallavi Sethi, Smruti R. The Internet of Things IoT is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose.
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Pallavi Sethi, Smruti R. The Internet of Things IoT is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly.
As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications. Today the Internet has become ubiquitous, has touched almost every corner of the globe, and is affecting human life in unimaginable ways. However, the journey is far from over. We are now entering an era of even more pervasive connectivity where a very wide variety of appliances will be connected to the web.
This term has been defined by different authors in many different ways. Let us look at two of the most popular definitions. Vermesan et al. The digital world interacts with the physical world using a plethora of sensors and actuators. We use these capabilities to query the state of the object and to change its state if possible. In common parlance, the Internet of Things refers to a new kind of world where almost all the devices and appliances that we use are connected to a network.
We can use them collaboratively to achieve complex tasks that require a high degree of intelligence. For this intelligence and interconnection, IoT devices are equipped with embedded sensors, actuators, processors, and transceivers. IoT is not a single technology; rather it is an agglomeration of various technologies that work together in tandem. Sensors and actuators are devices, which help in interacting with the physical environment.
The data collected by the sensors has to be stored and processed intelligently in order to derive useful inferences from it. An actuator is a device that is used to effect a change in the environment such as the temperature controller of an air conditioner. The storage and processing of data can be done on the edge of the network itself or in a remote server.
If any preprocessing of data is possible, then it is typically done at either the sensor or some other proximate device. The processed data is then typically sent to a remote server. The storage and processing capabilities of an IoT object are also restricted by the resources available, which are often very constrained due to limitations of size, energy, power, and computational capability.
As a result the main research challenge is to ensure that we get the right kind of data at the desired level of accuracy.
Along with the challenges of data collection, and handling, there are challenges in communication as well. The communication between IoT devices is mainly wireless because they are generally installed at geographically dispersed locations. The wireless channels often have high rates of distortion and are unreliable.
In this scenario reliably communicating data without too many retransmissions is an important problem and thus communication technologies are integral to the study of IoT devices. Now, after processing the received data, some action needs to be taken on the basis of the derived inferences. The nature of actions can be diverse. We can directly modify the physical world through actuators. Or we may do something virtually.
For example, we can send some information to other smart things. The process of effecting a change in the physical world is often dependent on its state at that point of time. This is called context awareness. Each action is taken keeping in consideration the context because an application can behave differently in different contexts. For example, a person may not like messages from his office to interrupt him when he is on vacation. Sensors, actuators, compute servers, and the communication network form the core infrastructure of an IoT framework.
However, there are many software aspects that need to be considered. First, we need a middleware that can be used to connect and manage all of these heterogeneous components. We need a lot of standardization to connect many different devices. We shall discuss methods to exchange information and prevailing standards in Section 7. The Internet of Things finds various applications in health care, fitness, education, entertainment, social life, energy conservation, environment monitoring, home automation, and transport systems.
We shall focus on these application areas in Section 9. We shall find that, in all these application areas, IoT technologies have significantly been able to reduce human effort and improve the quality of life. There is no single consensus on architecture for IoT, which is agreed universally. Different architectures have been proposed by different researchers.
The most basic architecture is a three-layer architecture [ 3 — 5 ] as shown in Figure 1. It was introduced in the early stages of research in this area. It has three layers, namely, the perception, network, and application layers.
It senses some physical parameters or identifies other smart objects in the environment. Its features are also used for transmitting and processing sensor data. It defines various applications in which the Internet of Things can be deployed, for example, smart homes, smart cities, and smart health.
The three-layer architecture defines the main idea of the Internet of Things, but it is not sufficient for research on IoT because research often focuses on finer aspects of the Internet of Things. That is why, we have many more layered architectures proposed in the literature. One is the five-layer architecture, which additionally includes the processing and business layers [ 3 — 6 ]. The five layers are perception, transport, processing, application, and business layers see Figure 1.
The role of the perception and application layers is the same as the architecture with three layers. We outline the function of the remaining three layers. It stores, analyzes, and processes huge amounts of data that comes from the transport layer. It can manage and provide a diverse set of services to the lower layers.
It employs many technologies such as databases, cloud computing, and big data processing modules. The business layer is out of the scope of this paper. Hence, we do not discuss it further. Another architecture proposed by Ning and Wang [ 7 ] is inspired by the layers of processing in the human brain.
It is inspired by the intelligence and ability of human beings to think, feel, remember, make decisions, and react to the physical environment.
It is constituted of three parts. First is the human brain, which is analogous to the processing and data management unit or the data center.
Second is the spinal cord, which is analogous to the distributed network of data processing nodes and smart gateways. Third is the network of nerves, which corresponds to the networking components and sensors. Let us now discuss two kinds of systems architectures: cloud and fog computing see the reference architectures in [ 8 ]. Note that this classification is different from the classification in Section 2.
In particular, we have been slightly vague about the nature of data generated by IoT devices, and the nature of data processing. In some system architectures the data processing is done in a large centralized fashion by cloud computers. Such a cloud centric architecture keeps the cloud at the center, applications above it, and the network of smart things below it [ 9 ]. Cloud computing is given primacy because it provides great flexibility and scalability. It offers services such as the core infrastructure, platform, software, and storage.
Developers can provide their storage tools, software tools, data mining, and machine learning tools, and visualization tools through the cloud. Lately, there is a move towards another system architecture, namely, fog computing [ 10 — 12 ], where the sensors and network gateways do a part of the data processing and analytics. A fog architecture [ 13 ] presents a layered approach as shown in Figure 2 , which inserts monitoring, preprocessing, storage, and security layers between the physical and transport layers.
The monitoring layer monitors power, resources, responses, and services. The preprocessing layer performs filtering, processing, and analytics of sensor data. The temporary storage layer provides storage functionalities such as data replication, distribution, and storage. Monitoring and preprocessing are done on the edge of the network before sending data to the cloud.
The latter term predates the former and is construed to be more generic. Fog computing originally termed by Cisco refers to smart gateways and smart sensors, whereas edge computing is slightly more penetrative in nature. This paradigm envisions adding smart data preprocessing capabilities to physical devices such as motors, pumps, or lights. The aim is to do as much of preprocessing of data as possible in these devices, which are termed to be at the edge of the network.
In terms of the system architecture, the architectural diagram is not appreciably different from Figure 2. As a result, we do not describe edge computing separately. Finally, the distinction between protocol architectures and system architectures is not very crisp. Often the protocols and the system are codesigned. We shall use the generic 5-layer IoT protocol stack architectural diagram presented in Figure 2 for both the fog and cloud architectures.
Here, we consider social relationships between objects the same way as humans form social relationships see [ 14 ]. We can start with one device and navigate through all the devices that are connected to it. It is easy to discover new devices and services using such a social network of IoT devices. In a typical social IoT setting, we treat the devices and services as bots where they can set up relationships between them and modify them over time.
This will allow us to seamlessly let the devices cooperate among each other and achieve a complex task.
RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.
Context-aware systems have traditionally used distributed sensors to gather context information. The unique identity provided by radio frequency identification RFID tags could provide additional information to the sensor data. However, the task of matching identity and sensor information in the same context is not trivial. By placing wireless sensor nodes in the same RFID tagged objects, we can build distributed wireless networks that collaborate to produce uniquely identified context. In this paper, we introduce a set of network protocols that dictate the formation of context-specific wireless sensor networks WSNs.
Filling this need, RFID and Sensor Networks: Architectures, Protocols, Security, and Integrations is the authoritative reference on RFID and.
The secure integration of RFID technology into the personal network paradigm, as a context-aware technology which complements body sensor networks, would provide notable benefits to applications and potential services of the personal network PN. RFID security as an independent technology is reaching an adequate maturity level thanks to research in recent years; however, its integration into the PN model, interaction with other network resources, remote users and service providers requires a specific security analysis and an architecture prepared to support these resource-constrained pervasive technologies. This paper provides such PN architecture and analysis.
The escalating demand for ubiquitous computing along with the complementary and flexible natures of Radio Frequency Identification RFID and Wireless Sensor Networks WSNs have sparked an increase in the integration of these two dynamic technologies. Although a variety of applications can be observed under development and in practical use, there has been a need for a resource that brings together timely coverage of RFIS, Sensor Networks, and their integration. Covering a broad range of topics that includes everything from the basics to insights into future directions, this cutting-edge book reviews architectures, protocols, standards, security, and applications. With sections devoted to each individual element, the text starts by covering the tags, readers, and middleware associated with RFID.
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