Huge Number Of Devices Research Articles

Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology: Future Perspectives.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

Sensing System for Remote Areas in Antarctica

AbstractEvery year, the number of Internet of Things devices is growing exponentially. The current Internet of Things technology to support the connectivity of such a huge number of devices is limited by the coverage of the base stations deployed. In case of remote areas without coverage of any operator, the use of a satellite connection is such a high‐cost option. The only alternative option for very remote sensor is high frequency (HF) communications with ionospheric reflection. The HF band (3–30 MHz) with Near Vertical Incidence Skywave allows a large coverage area (up to 250 km) without the need of line of sight. The HF radio links usually need higher power transmissions with larger antennas supported by a mast. In this paper, we explore a new transmission scheme for low‐power transmissions, which is equivalent to use small and low gain HF antennas. We study the performance of several digital modulations using different bandwidths and transmission power. The field tests have been done around the Spanish Antarctic Base at Livingston Island to ensure the availability of the system even in polar areas where the behavior of the ionosphere is quite different from lower latitudes. However, the proposed physical layer fits well with any other remote location that requires low power data communication.

WoT Store: Managing resources and applications on the web of things

The chaotic growth of the Internet of Things (IoT) determined a fragmented landscape with a huge number of devices, technologies and platforms available on the market, and the consequential issues of interoperability on many system deployments. The Web of Things (WoT) architecture recently proposed by the W3C consortium constitutes a novel solution to enable interoperability across IoT platforms and application domains. At the same time, in order to see an effective improvement, a wide adoption of the W3C WoT solutions from the academic and industrial communities is required; this translates into the need of well-defined and complete support tools easing the deployment of W3C WoT applications. In this paper, we meet such requirement by proposing the WoT Store, a novel platform for managing and easing the deployment of Things and applications on the W3C WoT. The WoT Store allows the dynamic discovery of the resources available in the environment, i.e. the Things, and to interact with each of them through a dashboard, by visualizing their properties, executing commands or observing the notifications produced. In addition, similar to popular app stores, the WoT Store allows the search and execution of third-party WoT applications that interact with the available Things again in a seamless way. We validate the operations of our framework with two evaluation studies. First, through a small-case testbed, we demonstrate the Thing discovery and the possibility to run WoT applications that orchestrate the operations of multiple, heterogeneous Wireless Sensor Networks (WSNs). Second, through a mixed real/simulated large-scale crowdsensing scenario, we demonstrate the scalability of the platform, and the possibility to aggregate and visualize the data-streams produced by the WoT applications with minimal efforts for the users.

MLITS:Multi-Level tasks scheduling model for IoT Service Provisioning

In Internet of Things (IoT) environment there are a huge number of devices that need to communicate and send data continuously between these devices also between them and the cloud data center. This data is increasing exponentially, exposing the IoT environment to collapse. Therefore, so we need an environment that supports the stability of the IoT and at the same time increases the speed of exchange huge data between IoT devices. Fog computing and mist computing support real time data collection and analysis locally at IoT devices. In addition, fog computing and mist computing overcome on various challenges like network bandwidth, and reliability. Resource management and allocation for IoT tasks in three levels mist-fog-cloud architecture suffer from a lack of approaches and frameworks that handle this situation in an efficient manner. In order to address this shortage, Multi-Level IoT Tasks Scheduling (MLITS) model is introduced in this paper. MLITS is an orchestration system for managing and allocating IoT tasks over the mist-fog-cloud architecture. The proposed model performs IoT tasks based on their deadline and the urgency of their execution. In addition, it performs various types of IoT tasks without rapidly consuming available sources. Moreover, the proposed model maintains the resource usage balanced. Finally, The MLITS is simulated and evaluated on truthful fog resources and various workload circumstances. Also, the proposed scheduling model is compared with three scheduling model, namely; Min-Min, Credit-Based-Scheduling (CBS) and Earliest-Feasible-Deadline-First (EFDF). Through extensive simulations, we show that our proposed model enhances the performance metrics, namely; turnaround time, waiting time and throughput.

MCCM: An Approach for Connectivity and Coverage Maximization

The internet of Things (IoT) has attracted significant attention in many applications in both academic and industrial areas. In IoT, each object can have the capabilities of sensing, identifying, networking and processing to communicate with ubiquitous objects and services. Often this paradigm (IoT) using Wireless Sensor Networks must cover large area of interest (AoI) with huge number of devices. As these devices might be battery powered and randomly deployed, their long-term availability and connectivity for area coverage is very important, in particular in harsh environments. Moreover, a poor distribution of devices may lead to coverage holes and degradation to the quality of service. In this paper, we propose an approach for self-organization and coverage maximization. We present a distributed algorithm for “Maintaining Connectivity and Coverage Maximization” called M C C M . The algorithm operates on different movable devices in homogeneous and heterogeneous distribution. It does not require high computational complexity. The main goal is to keep the movement of devices as minimal as possible to save energy. Another goal is to reduce the overlapping areas covered by different devices to increase the coverage while maintaining connectivity. Simulation results show that the proposed algorithm can achieve higher coverage and lower nodes’ movement over existing algorithms in the state of the art.

Information security of Internet things

Abstract The development of methods of information protection against cyber threats is a priority and the most labor-intensive direction in the development of the IoT sector. The relevance of the topic is growing due to the growing user interest in the Internet of things. Basic for studying in this work was the traditional IoT architecture format, which consists of three slices. This perception, network and application levels. Each cut characterizes its key problems in the field of information security. The most difficult part is the network layer. The arising difficulties are provoked by the features of the structure (multivariance of things, different methods of networks) and a high numerical index of objects. The Internet of things accumulates information data from a huge number of devices that have different formats and various characteristics. As a result, there are failures of DoS, which arise due to a heavy load on the network, as well as disruptions in the operation of programs.

Machine learning based cell association for mMTC 5G communication networks

With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases.A5Gnetwork is a cloud native, massively connected internet of things (IoT) platform with a huge number of devices hosted on the network now known as massive machine type communication (mMTC). As ultra-low latency is pivotal in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, opposed to older cell association schemes which were based only on the reference signal received power (RSRP). This paper proposes an unsupervised machine learning algorithm, namely hidden Markov model (HMM) learning on the network's telemetry data, which is used to learn network parameters and select the best eNodeB for cell association. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.

Deep-Reinforcement-Learning-Based QoS-Aware Secure Routing for SDN-IoT

Recently, with the proliferation of communication devices, Internet of Things (IoT) has become an emerging technology which facilitates massive devices to be enabled with connectivity by heterogeneous networks. However, it is usually a technical challenge for traditional networks to handle such a huge number of devices in an efficient manner. Recently, the software-defined network (SDN) technique with its agility and elasticity has been incorporated into IoT to meet the potential scale and flexibility requirements and form a novel IoT architecture also known as SDN-IoT. As the size of SDN-IoT increases, efficient routing protocols with low latency and high security are required, while the default routing protocols of SDN are still vulnerable to dynamic change of flow control rules especially when the network is under attack. To address the above issues, a deep-reinforcement-learning-based quality-of-service (QoS)-aware secure routing protocol (DQSP) is proposed in this article. While guaranteeing the QoS, our method can extract knowledge from history traffic demands by interacting with the underlying network environment, and dynamically optimize the routing policy. Extensive simulation experiments have been conducted with respect to several network performance metrics, demonstrating that our DQSP has good convergence and high effectiveness. Moreover, DQSP outperforms the traditional OSPF routing protocol, at least 10% relative performance gains in most cases.

Evolution of random access process: From Legacy networks to 5G and beyond

AbstractIn this paper, we discuss the random access procedures in legacy networks, long‐term evolution, LTE‐Advanced, and 5G networks. Random access is the first and incumbent step for connection establishment between base station/eNodeB/gNodeB and user equipment. Keeping in view its importance, there has been a regular thrive to improve the procedures of random access in communication networks. With the advent of new concepts such as machine‐type communication and Internet of Things, random access becomes extremely critical because huge number of devices try to connect to the network simultaneously. We present the evolution of random access process from legacy networks such as global system for mobile or wideband code division multiple access to 5G networks and beyond and their analysis. Simulations are performed in MATLAB to show various performance metrics such as access delay, total service time, and collision probability to demonstrate the pros and cons of one technique over another.

Research on Internet of Things security architecture based on fog computing

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.

Relay-Aided Multiple Access Scheme in Two-Point Joint Transmission

As the era of Internet of Things is approaching, huge number of devices need to be included in the network, and the long standby time of devices should also be satisfied. To solve these problems, in this paper, a relay-aided multiple access (RAMA) transmission scheme based on network coding is considered in joint transmission with two base stations (BSs). In the scheme, the near user acts as a two-way relay node between the BS and the far user based on machine-to-machine communications for both downlink and uplink transmissions. At first for two users, non-orthogonal multiple access (NOMA) is employed to compare with the proposed RAMA scheme in terms of the achievable sum rates. From the analysis, we can show that for NOMA and RAMA, one outperforms the other under different conditions. Then, we consider the case of multiple user pairs, for which an adaptive RAMA-NOMA transmission scenario is developed to make full use of their respective advantages. In the scenario, an optimal power allocation scheme based on the branch and bound (BB) algorithm and a low-complexity user pairing scheme based on a greedy algorithm are proposed. Numerical results show that our proposed adaptive RAMA-NOMA transmission scenario can achieve a higher sum rate compared with RAMA or NOMA only scheme, and under the optimal power allocation based on BB, the performance of our proposed user pairing scheme is close to that of the optimal pairing method using an exhaustive search, and significantly outperforms the random selection method.

Improved LTE like initial uplink synchronization via reduced problem dimension

Initial uplink synchronization (IUS) is a random access process in LTE that enables the eNodeB to detect, and uplink synchronize new user equipment. In future networks with huge number of devices, the number of simultaneous IUS users will increase significantly. In addition, it is desirable to serve users moving at high speed. We exploit the structure of the physical random access channel (PRACH) in LTE to reduce the dimension of the underlying data model. This reduction gives a very compact representation of channel impulse response (CIR). We utilize this representation to develop an efficient algorithm which can work in presence of large multiple access interference (MAI) and high carrier frequency offsets (CFO). When compared with the state of the art methods, the proposed method is capable of detecting a significantly higher number of IUS users and can allow high values of CFO. In addition, it produces very reliable estimates of both CIR and CFO of the detected users.

A semantic-based discovery service for the Internet of Things

With the Internet of Things (IoT), applications should interact with a huge number of devices and retrieve context data produced by those objects, which have to be discovered and selected a priori. Due to the number, heterogeneity, and dynamicity of resources, discovery services are required to consider many selection criteria, e.g., device capabilities, location, context data type, contextual situations, and quality. In this paper, we describe QoDisco, a semantic-based discovery service that addresses this requirement in IoT. QoDisco is composed of a set of repositories storing resource descriptions according to an ontology-based information model and it provides multi-attribute and range querying capabilities. We have evaluated different approaches to reduce the inherent cost of semantic search, namely parallel interactions with multiple repositories and publish-subscribe interactions. This paper also reports the results of some performance experiments on QoDisco with respect to these approaches to handle resource discovery requests in IoT.

An Advanced Algorithm for Higher Network Navigation in Social Internet of Things Using Small-World Networks

The Internet of Things (IoT) is a recent evolutionary technology that has been the primary focus of researchers for the last two decades. In the IoT, an enormous number of objects are connected together using diverse communications protocols. As a result of this massive object connectivity, a search for the exact service from an object is difficult, and hence the issue of scalability arises. In order to resolve this issue, the idea of integrating the social networking concept into the IoT, generally referred as the Social Internet of Things (SIoT) was introduced. The SIoT is gaining popularity and attracting the attention of the research community due to its flexible and spacious nature. In the SIoT, objects have the ability to find a desired service in a distributed manner by using their neighbors. Although the SIoT technique has been proven to be efficient, heterogeneous devices are growing so exponentially that problems can exist in the search for the right object or service from a huge number of devices. In order to better analyze the performance of services in an SIoT domain, there is a need to impose a certain set of rules on these objects. Our novel contribution in this study is to address the link selection problem in the SIoT by proposing an algorithm that follows the key properties of navigability in small-world networks, such as clustering coefficients, path lengths, and giant components. Our algorithm empowers object navigability in the SIoT by restricting the number of connections for objects, eliminating old links or having fewer connections. We performed an extensive series of experiments by using real network data sets from social networking sites like Brightkite and Facebook. The expected results demonstrate that our algorithm is efficient, especially in terms of reducing path length and increasing the average clustering coefficient. Finally, it reflects overall results in terms of achieving easier network navigation. Our algorithm can easily be applied to a single node or even an entire network.

Fully printed 3D cube-shaped multiband fractal rectenna for ambient RF energy harvesting

The Internet of Things (IoT) is an emerging paradigm that requires billions of wirelessly connected devices in a complex radio-frequency (RF) environment. With such a huge number of devices in this scenario, recharging or replacing batteries becomes impractical. Therefore, powering IoT devices by harvesting energy from ambient sources, such as that available in the RF spectrum, is an attractive solution. The antenna for the RF energy harvester must work on multiple bands to capture as much power as possible. This paper presents a fully printed 3D cube triple band Cantor fractal rectenna based on the system-on-package (SoP) concept; the antenna is realized on the package that houses a rectifier circuit and a multiband matching network. The combination of additive manufacturing and SoP ensures a lower cost and the efficient use of available space. The rectenna harvests RF power from GSM900, GSM1800, and 3 G 2.1 GHz frequency bands. Field tests of the RF energy harvester conducted in a real ambient environment confirm that up to 200 mV output voltage can be harvested. Also, 550 mV is harvested when the rectenna is near a smartphone during a live phone call. The promising results and lower cost make this design a suitable candidate to power IoT devices.

An energy-efficient model for fog computing in the Internet of Things (IoT)

A huge number of devices like sensors in addition to computers are interconnected in the IoT (Internet of Things). In the cloud computing model, sensor data is transmitted to servers in networks and processed on the servers in a cloud. Here, networks are congested and servers are overloaded due to heavy traffic from sensors. In order to reduce the delay time and network traffic and increase the performance of the system, data and processes are distributed to not only servers in a cloud but also fog nodes in fog computing models. While the traffic of servers in a cloud can be reduced, the total electric energy consumed by fog nodes increases to process sensor data. In this paper, we newly propose a tree-based fog computing (TBFC) model to distribute processes and data to servers and fog nodes so that the total electric energy consumption of nodes can be reduced in the IoT. In the evaluation, we show the total electric energy consumption of nodes in the TBFC model is smaller than the cloud computing model.

Home security system using internet of things

IoT refers to the infrastructure of connected physical devices which is growing at a rapid rate as huge number of devices and objects are getting associated to the Internet. Home security is a very useful application of IoT and we are using it to create an inexpensive security system for homes as well as industrial use. The system will inform the owner about any unauthorized entry or whenever the door is opened by sending a notification to the user. After the user gets the notification, he can take the necessary actions. The security system will use a microcontroller known as Arduino Uno to interface between the components, a magnetic Reed sensor to monitor the status, a buzzer for sounding the alarm, and a WiFi module, ESP8266 to connect and communicate using the Internet. The main advantages of such a system includes the ease of setting up, lower costs and low maintenance.

Mathematical Representation of Quality of Service (QoS) Parameters for Internet of Things (IoT)

Now we are in the era of ubiquitous computing. Internet of things (IoT) is getting matured in various parts of the world. In coming few years' billions and trillions of things will be connected to the internet. To deal with these huge number of devices in a network we need to consider Quality of Service (QoS)parameters so that system operations can be performed in a smoother way. Mathematical modelling of these QoS parameters gives an idea about which factors are needs to consider while designing any IoT-enabled system at the same time it will give the performance analysis of the system before implementation. In this paper comprehensive literature survey is done to discuss various issues related to QoS and gap analysis is also done for IoT Enabled systems. This paper proposes general steps to build a mathematical model for a system. It also proposes the mathematical model for QoS parameters like reliability, communication complexities, latency and aggregation of data for IoT. To support proposed mathematical model proof of concept also given.

An Efficient Proposed MAC Protocol for M2M Networks

Machine to machine (M2M) communications refer to the technology that enables huge number of devices to be wired or wireless communication with each other’s. Neither the contention based medium access control (MAC) protocols nor the reservation based MAC protocols can provide a flexible, scalable solution for M2M networks. That is because of the huge number of communicating devices. In this paper, an efficient proposed MAC protocol for M2M networks is suggested. It is divided into three periods, namely the notification, the contention and the transmission periods. During the contention period, the contention is occurred with equal probabilities between the devices which have traffic to be sent. The non-persistent carrier sense multiple access protocol is selected for the contention strategy. In the notification period, the devices get information about the contention state. Furthermore, the transmission time slots location for the succeeded ones are announced. Finally, in the transmission period, the packets from each successful contended device are sent. This is done using time division multiple access (TDMA) strategy in the reserved time slot. Moreover, more than one device can transmit in one TDMA slot as long as the signal to interference plus noise ratio condition is not violated. In addition, the analytical analysis of the proposed MAC protocol is produced. Furthermore, extensive simulation programs are executed using MATLAB to measure the system performance. The system throughput, average delay time, and packet collision ratio are our performance metrics. Further, our MAC protocol outperforms other traditional ones.

Efficient quality of service‐aware packet chunking scheme for machine‐to‐machine cloud services

SummaryWith the recent advances in machine‐to‐machine communications, huge numbers of devices have become connected and massive amounts of traffic are exchanged. Machine‐to‐machine applications typically generate small packets, which can profoundly affect the network performance. Namely, even if the packet arrival rate at the router is lower than the link bandwidth, bits per second, it can exceed the router forwarding capacity, which indicates the maximum number of forwarded packets per second. This will cause the decrease in the network throughput. Therefore, eliminating the packets per second limitation by chunking small packets will enable machine‐to‐machine cloud services to spread further. This paper proposes new packet‐chunking schemes aimed at meeting both application requirements and improving achievable router throughput. In our schemes, multiple buffers, each of which accommodates packets classified based on their delay requirement, are installed in parallel. Herein, we report on analysis of the theoretically performance of these schemes, which enabled us to derive some important features. We also propose a scheme whereby a single chunking buffer and parallel multiple buffers were arranged in tandem. Through our simulation and numerical results, we determined that these schemes provide excellent performance in reducing the number of outgoing packets from the router while meeting various delay requirements.