Internet of Things (IoT) in 5G Mobile Technologies

The Internet is now widely used by a billion people for a plethora of services: information retrieval, video streaming, file sharing, online shopping, banking, social networking, etc.

This chapter discusses equipment positioning, which has a large range of potential applications from per-user advertisement, through elderly-care until cop security. We propose a general system based on passive measurements that, in contrast to currently available solutions using one specific technology (e.g., Wi-Fi), runs in multi-technology environment. This means that it is possible to position radio equipment using any of the radio technologies: Wi-Fi, Bluethooth, RFID and other technologies based on IEEE 802.15.4 operating in the 2.4 GHz band. Thanks to that, our platform will significantly increase the number of monitored users. Service of abovementioned technologies will be implemented by means of a common hardware platform, using time multiplex in the radio space. Such a solution eliminates interference between antennas from different technologies and provides higher positioning accuracy at the same time. A second important feature is the openness and programmability of the platform, which distinguishes our solution from similar solutions on the market and is one of its competitive advantages.

Mobile communications industry is going through an era of very rapid advancement as multiple major innovations are about to take place. Fifth generation (5G) of mobile communication systems is developed to become an all-encompassing solution to fundamentally every broadband wireless communication need of the next decade. Since both the communication and electronic technologies are matured enough, machine-to-machine communication is also about to take off, placing a completely new set of demands on the wireless networks. As the spectrum is already limited in the conventional sub 6 GHz bands, in order to generate efficient applications for the Internet of Things (IoT) within the 5G systems, utilization of new frequency bands are needed. Comprising, both licensed and unlicensed, ample bandwidth, millimetre wave (mm-wave) band is the primary candidate for adoption. In line with these, in this chapter mm-wave band is analyzed for use in 5G IoT implementations. Subsequent to introduction, a brief description of mm-wave band channel characteristics is provided. Then, enabling physical layer techniques of modulation, error control coding and multiple input multiple output are reviewed from the 5G mm-wave point of view. Following conclusions, the chapter ends with open research issues and future research directions.

This chapter aims at identifying the main design and operation constraints, that smart environments are expected to experience within a 5G wireless/mobile network and how these constraints can be addressed using cognitive radio networks. This chapter first provides a general description of 5G wireless/mobile networks and stresses their role in the future wireless communications with emphasis given on smart environments. Then, the smart environments are presented based on their architecture characteristic and the applications associated with their operation. In addition, an overview of various current standards related to IoT applications is presented followed by the concept of cognitive radio networks and the available experimental platforms stressing the benefits of employing this technology in the future 5G wireless/mobile networks. Finally, the research challenges associated with integrating 5G wireless/mobile networks and IoT are outlined.

Large-Scale Highly-Dense Networks have been deployed in different application domains of Internet-of-Things for accurate event-detection and monitoring. Due to the high density and large scale, the nodes in these networks must perform some essential communication roles, namely sensing, relaying, data-fusion, and data-control (aggregation and replication). Since the energy consumption and the communication reliability is one of the major challenges in Large-Scale Highly-Dense Networks, the communication roles should be coordinated in order to efficiently use the energy resources and to meet a satisfactory level of communication reliability. In this chapter, we propose an on-demand and fully distributed framework for role coordination that is designed to detect events with different levels of criticality, adapting the data-aggregation and data-replication according to the urgency level of the detected event. Besides the criticality level, the proposed role coordination also takes into account the network information such as energy resources, memory, and link quality. This chapter also presents the related works and shows a qualitative comparison between the proposed framework and the most comprehensive related role coordination frameworks.

With the fast growth of heterogeneous low-cost and high-end mobile devices, there is a need for green designs for ubiquitous development of Internet of things (IoT) due to both health and environment concerns. Unlike other energy harvesting techniques, radio frequency (RF) energy harvesting offers controlled and predictable energy replenishment, which can aid meeting the quality of service requirements of machine-to-machine (M2M) communications. This chapter evaluates the major challenges on the feasibility of RF-powered sustainable M2M communications in 5G mobile technologies and state-of-the-art research toward their practical implementation. Strategies for improving the RF energy transfer efficiency to realize the perpetual operation of IoT are also discussed.

Supporting the traffic emanating from the internet of things (IoT) is a major challenge for 5G systems. A significant portion of this traffic will be generated indoors. Therefore, in this chapter, femtocell networks designed for supporting IoT traffic are studied. A deployment scenario of femtocell networks with centralized control is investigated. It consists of an integrated wired/wireless system, where the femtocell access points (FAPs) are controlled by a single entity. This permits performing joint radio resource management in a centralized and controlled way in order to enhance the quality of service performance for all users in the network. It also allows an energy efficient operation of the network by switching off redundant femtocells whenever possible. Two algorithms are proposed and analyzed. The first one is a utility maximizing radio resource management algorithm, whereas the second one is a FAP switch off algorithm, implemented at the central controller. The joint wired/wireless resource management approach is compared to the distributed resource management case, where each femtocell acts as an independent wireless network unaware of the channel and interference conditions with the other cells. The proposed algorithm was shown to lead to significant gains. Furthermore, considerable energy savings were obtained with the green algorithm.

The Internet of Things (IoT) has been a new trend in the IT business and the assembling group for quite a while. Yet, in this way, the battle with IoT is that it is attempting to locate an extraordinary advertising message about how it will specifically enhance human lives. It has been stated that the ones who are tied in a social network can give significantly give more exact responses to complicated issues than an individual alone. This rule has been seriously considered in different websites. Lately, with the help of IoT frameworks, it was made possible to connect billions of objects in a very short term. The Social Internet of Things (SIoT) is characterized as an IoT where things are fit for building social associations with different items, independently regarding people. In this chapter we propose to discuss on the origin, development and current status of SIoT and propose some scope for future studies.

The internet of things (IoT) has been a prevalent research topic in recent years in both academia and industry. The main idea of this framework is the integration of physical objects into a global information network. The vision of the IoT is to integrate and connect anything at any time and any place. For this reason, it is being applied in various areas such as power system monitoring, environment monitoring, network control system, smart health care, military, smart cities management and industry revolution. To achieve the goals, the fifth generation (5G) technology will be the potential infrastructure that will assist the visions of the IoT. Starting with the visions and requirements of the IoT with 5G networks, this chapter proposes a distributed approach for microgrid state estimation. After modelling the microgrid, it is linearized around the operating point, so that the proposed distributed state estimation using the IoT with 5G networks can be applied. Moreover, we propose a wireless sensor network based communication network to sense, transmit and estimate the microgrid states. At the end, the simulation results show that the proposed method is able to estimate the system state properly using the IoT with 5G networks.

While it is difficult to find a consistent definition of Internet of Things (IoT) in research literature, the intersection of most research says that IoT is about a wide array of devices at network edge, that people and devices should be able to interact seamlessly, and that devices and people should be able to interact with each other across the global network. This chapter points that local interaction of people and devices is covered by the topic of mobile clouds and therefore can benefit from the concept of GroupConnect and the related virtualization of local wireless resource. The wide array of devices is covered in this chapter by a new cloud platform referred to as Local Hardware Awareness Platform (LHAP)—where the main feature of the platform is in the name. LHAP allows for devices to advertise and make their physical function useful to nearby applications. Finally, this chapter discusses the concept of cloudification in which LHAP is implemented as a cloud platform that can host traditional Virtual Machines (VMs) or container-based apps. End-to-end connectivity is discussed as part of the cloudification process where it may not be necessary to assign IP addresses to all devices at network edge, instead relying on delegated networking via smartphones and WiFi Access Points.

This chapter presents a web-based cross-platform architecture based on HTML5 APIs and other state-on-the-art web technologies. In fact, our architecture is a crowd sensing application which exploits the ubiquitous capabilities of modern mobile devices, along with their built-in sensing capabilities, in order to motivate the users to collect, share and use different kind of sensor data. The platform consists of two application specific components: the first, the client part, runs in the user device to collect sensor data and transmit them; the second, the server part, runs in the cloud and is responsible for analyzing and visualizing the data from all devices in a human friendly format, e.g. a map. The application is multi-sensor as it can collect data from almost all sensors of mobile devices. Besides the use of the platform as a participatory and opportunistic sensing architecture, our endmost aim is to be used with other Internet of Things equipment for the introduction to the third generation of Web characterized as ubiquitous web.

Object and service identification is recognized as one of the main challenges on the way to developing global Internet of Things (IoT). In this chapter we present the current State of the Art and research trends in the area of identification and access methods for IoT objects. We describe existing IoT identification technologies, which already have practical applications, such as IPv6 addressing, EPC, ucode and HIP. We also provide an overview of solutions investigated by research projects, where two main research trends can be distinguished. The first one are advanced methods for objects discovery based on semantic web, and the second one aims to improve efficiency of IoT systems by introducing additional identifier layer. In summary, we foresee that future 5G IoT will be based on IPv6 in general, due to immensity of devices and services existed in the current Internet, with islands of non-IP solutions dedicated for specific purposes.

Applications in the IoT domain are in need of information coming from many different sources. To mitigate the processing overhead for increased volumes of raw data (seen at the application layer) on vast pervasive networks (a significant pillar of 5G networks), we introduce a customizable middleware platform. Such platform allows the treatment of incoming data flows through complex, yet fully specifiable/controllable data processing workflows. The derivation of new information through this high level processing task is termed data fusion, hence, the presented architecture is named “Fusion Box”. The middleware platform is customizable through a Domain Specific Language that allows the domain (and not the IT) expert to easily specify the needed processing and automatically transform such specification to executable workflows. The coupling between the Domain Specific Language and the Fusion Box is based on the concept of contextors, a versatile processing unit that can be instantiated and managed in many different ways as needs dictate.

The advent of IoT (the Internet of Things) has led to the necessity of fast and secure communication between devices, ranging from small sensors to top-of-the-line smartphones or laptops. One proposal for IoT communication is through 5G, which is estimated to be rolled out by 2020. However, the infrastructure for 5G communication might not always be present, or it should be avoided because of congestion. Moreover, employing it in smaller IoT networks can prove too expensive in some cases, while some small devices such as sensors might not even have 5G capabilities (or having them would greatly increase their price). For these reasons, opportunistic communication is an alternative for IoTs where mobile broadband connections cannot be used. Opportunistic networks are formed of mobile devices (such as smartphones and tablets belonging to social users) that communicate using close-range protocols such as Bluetooth or WiFi Direct. These networks are based on the store-carry-and-forward paradigm, where contacts between nodes are used opportunistically to transport data from a source to a destination, even though the two nodes might never be in direct communication range. Data dissemination assumes that nodes do not send directed messages (i.e., from a source to a pre-set destination), instead using channels to perform communication. Nodes are able to subscribe to channels, which are represented by interests (e.g., a node interested in “IT” will need to receive all messages marked with that tag). The main requirement of opportunistic networks is that the participating nodes should be altruistic, since communication is performed with the help of other nodes. However, this might not always be the case, since selfish nodes might decide that they do not want to help others. Such nodes should be detected and not allowed to participate in the dissemination process. This way, their messages will not be delivered, so they will be forced to become altruistic if they want a good networking experience. In this chapter, we propose a method for detecting and punishing selfish nodes in opportunistic networks dissemination, using gossiping mechanisms over the dynamic social network. Nodes learn about the behavior of other nodes and, when a contact occurs, share this information with an encountered device. We apply this method to an existing social and interest-based dissemination algorithm (ONSIDE) and show that it correctly detects and punishes selfish nodes, thus increasing the network’s behavior in terms of message delivery and congestion.

Middleware for IoT is the software technology that has been used as the basis for the development, management, and integration of both heterogeneous devices and applications in IoT environments. Despite the intended definition of a horizontal architecture approach (i.e., a common system approach to manage different application domains or verticals) for IoT middleware has been one of the main requirements by global IoT projects during the last years, the imminent arrival of 5G technology is revealing that current middleware approaches possibly will face some challenges due to new application requirements imposed by 5G (e.g., big data bandwidth and infinity, reliable, and efficient capability of networking, joining massive user experiences on mobile communications with multimedia sharing). In this way, this chapter not only presents concepts and architectural layers of IoT Middleware, but also helps in the identification of future challenges and further perspectives regarding the IoT Middleware ability to provide pervasive systems services able to cope with 5G-based application requirements in IoT environments. The intention of this chapter is to identify what will be the next step of IoT Middleware technology and also the R&D technological impact of this step toward the real maturity of 5G.

The emergence of the 5th generation wireless standard for telecommunications (5G) will enable the Internet of Things (IoT), a huge network of interconnected devices that can be utilized in almost every aspect of our daily lives, either that is in healthcare, transportation, environmental monitoring, and so on. As good as it sounds though, individuals with malicious intent will always be around to try and compromise what has been built for their own personal gain. Therefore, nothing can be accomplished unless the system and communication between devices is secured, and we are positive that the privacy and well being of users, and society in general, is ensured. This chapter will present the notion of smart spaces and smart grids along with their architecture and way of operation. It will also stress the necessity for securing the various processes and services of such technologies, in order to predict, identify, prevent and counter any potential attacks on the system as well as protecting and preserving the privacy of the users. Furthermore, the chapter will include the security requirements a system needs to fulfill, along with any security threats that might compromise the system and various measures that need to be taken to achieve a secure environment.

The emergence of 5G technology in the coming years will probably result in many new challenges in several computing areas. 5G is expected to be deployed around 2020 and has been considered an important building block for the consolidation of the Internet of Things (IoT). IoT is a contemporary computing paradigm that has been recognized for allowing the connection of the physical and virtual worlds. However, its growth in several application domains requires a well-defined infrastructure of systems that provides services for devices abstraction and data management, and also supports the development of applications. IoT middleware has been recognized as the system that can provide this necessary infrastructure of services and has become increasingly important for IoT over the last years. IoT middleware systems have security as one of their main challenges, and, with the arrival of the 5G, these systems will be target of new security threats. In this chapter we present the main threats and security requirements envisaged to be introduced by 5G in IoT middleware systems. In addition, we also analyze the current security approaches of IoT middleware systems, and present some challenges related to security aiming the 5G-based IoT middleware technologies.

The next generation of communication networks, known as 5G technologies, is envisioned to address several major technical challenges like increased data rates, efficient spectral use, higher capacity, etc. One of the core pillars of the 5G technologies is the Internet of Things (IoT) use-case. This employs hundreds or even thousands of smart objects serving numerous applications (e.g. environmental monitoring, smart homes, smart traffic management, etc.). Typical IoT applications become feasible through the use of large-scale Wireless Sensor Networks deployed using a number of miniature devices called as sensors or motes. In this chapter, we demonstrate how two popular signal processing techniques, namely Compressive Sensing and Matrix Completion can be used to make feasible energy efficiency, lightweight encryption, and packet loss mitigation. Furthermore, we present an IoT platform based on a Software Defined Radio that provides multiple channel support for both IEEE 802.11 and IEEE 802.15.4 standards.

Internet of Things (IoT) is one of the most important future evolutions of the Internet where computing as well as non-computing elements will be Internet-enabled. It is expected that over 50 billion such things will be Internet-enabled in the next half a decade. The technologies that enable such a large and complex future Internet will be a collection of communication approaches commonly referred to as enablers of IoT. A detailed survey of the potential enablers of IoT has been done in this chapter and an estimation of their current state of development along with their security and privacy issues presented. The key enablers of IoT considered are: (i) Electronic Product Code Global (EPCGlobal), (ii) Wireless Highway Addressable Remote Transducer (WirelessHART), (iii) ZigBee, (iv) Near Field Communication (NFC), (v) IPv6 over Low power Wireless Personal Area Network (6LoWPAN), and (vi) Developers’ Alliance for Standards Harmonization (Dash7). Finally, a detailed qualitative comparison of the technological merits and demerits of the enablers of IoT has been done.

This chapter presents data and traffic analyses in 5G networks. We setup experiments with Zigbee sensors and measure different traffic patterns by changing the environmental conditions and number of channels. Due to the differences in read, write operations, message fragmentations and backoff of the Carrier Sense Multiple Access/Collision Avoidance algorithm we demonstrated that the traffic flows are changing dynamically. This leads to different behaviour of the network domain and requires special attention to network design. Statistical analyses are performed using Easyfit tool. It allows to find best fitting probability density function of traffic flows, approximation toward selected distributions as Pareto and Gamma and random number generation with selected distribution. Our chapter concludes with future plan for distribution parameters mapping to different traffic patterns, network topologies, different protocols and experimental environment.