Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.
Since traditional wireless sensor networks and IoT simulators do not focus on modeling of large scale deployments, @cite_4 proposed a simulation methodology for IoT systems with a large number of interconnected devices. It is designed to study low-level networking aspects. In summary, the main advantages of their approach are 1) simulation of IoT systems with geographically distributed devices; 2) simulation of are IoT devices with multiple network interfaces and protocols, as well as different mobility, network, and energy consumption models.
@cite_4
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The Internet of Things (IoT) refers to the interconnection of billions of IP-enabled devices, denoted as "smart objects", with limited capabilities, in terms of computational power and memory capacity, which typically operate in constrained environments, in an Internet-like structure. Large-scale systems and applications that rely on such a high number of devices, due to their complexity, need careful analysis and test, before being deployed to target environments. Traditional IoT simulators do not focus on the simulation of large scale deployments, as they are intended to evaluate and analyze low-level networking aspects, with groups of smart objects arranged in specific topologies. In this paper, we illustrate an efficient simulation methodology, which is particularly suitable to test IoT systems with a large number of interconnected devices in Urban environments from an application-layer perspective. The main advantages of such an approach are: i) the capability to simulate large-scale systems with thousands of geographically distributed devices; ii) the maximization of code reuse; and iii) the high generality of simulated nodes, which can be characterized by multiple network interfaces and protocols, as well as different mobility, network, and energy consumption models. The focus of the Internet has recently shifted from current computers and mobile devices to everyday objects, people, and places; consequently, the Internet of Things (IoT) promises to be not only a compelling vision but the actual driving force of the upcoming fourth Industrial Revolution. Novel cyber-physical, customized, and highly pervasive services are impacting our lives, involving several stakeholders and fostering an unseen globally interconnected ecosystem. However, IoT system development is a multifaceted process that’s complex, error-prone, and time-consuming. Although modeling and simulation are crucial aspects that could effectively support IoT system development, an integrated approach synergistically providing both of them is lacking. The authors propose a hybrid approach that uses agents for IoT modeling and OMNeT for simulation, providing mapping guidelines between the agent paradigm and the OMNeT simulator abstractions. The proposed approach has been applied in small-, medium-, and large-scale IoT scenarios, where relevant performance indexes of IoT entities communication have been measured and analyzed. In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs’ locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45 compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28 enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs. In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45 compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28 enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs. Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances. Machine to Machine (M2M) systems are actively spreading, with mobile networks rapidly evolving to provide connectivity beyond smartphones and tablets. With billions of embedded devices expected to join cellular networks over the next few years, novel applications are emerging and contributing to the Internet of Things (IoT) paradigm. The new generation of mobile networks, the Long Term Evolution (LTE), has been designed to provide enhanced capacity for a large number of mobile devices and is expected to be the main enabler of the emergence of the IoT. In this context, there is growing interest in the industry and standardization bodies on understanding the potential impact of the scalability of M2M systems on LTE networks. The highly heterogeneous traffic patterns of most M2M systems, very different from those of smartphones and other mobile devices, and the surge of M2M connected devices over the next few years, present a great challenge for the network. This paper presents the first insights and answers on the scalability of the IoT on LTE networks, determining to what extent mobile networks could be overwhelmed by the large amount of devices attempting to communicate. Based on a detailed analysis with a custom-built, standards-compliant, large-scale LTE simulation testbed, we determine the main potential congestion points and bottlenecks, and determine which types of M2M traffic present a larger challenge. To do so, the simulation testbed implements realistic statistical M2M traffic models derived from fully anonymized real LTE traces of six popular M2M systems from one of the main tier-1 operators in the United States. Large-scale deployments of IoT devices are subject to energy and performance issues. Fortunately, offloading is a promising technique to enhance those aspects. However, several problems still remain open regarding cloud deployment and provisioning. In this paper, we address the problem of provisioning offloading as a service in large-scale IoT deployments. We design and develop an AutoScaler, an essential component for our offloading architecture to handle offloading workload. In addition, we also develop an offloading simulator to generate dynamic offloading workload of multiple devices. With this toolkit, we study the effect of task acceleration in different cloud servers and analyze the capacity of several cloud servers to handle multiple concurrent requests. We conduct multiple experiments in a real testbed to evaluate the system and present our experiences and lessons learned. From the results, we find that the AutoScaler component introduces a very small overhead of ≈150 milliseconds in the total response time of a request, which is a fair price to pay to empower the offloading architectures with multi-tenancy ability and dynamic horizontal scaling for IoT scenarios. The ubiquitous deployment of mobile and sensor devices is creating a new environment, namely the Internet of Things(IoT), that enables a wide range of future Internet applications. In this work, we present Mobile Fog, a high level programming model for the future Internet applications that are geospatially distributed, large-scale, and latency-sensitive. We analyze use cases for the programming model with camera network and connected vehicle applications to show the efficacy of Mobile Fog. We also evaluate application performance through simulation. Wireless powered communication networks (WPCNs), where multiple energy-limited devices first harvest energy in the downlink and then transmit information in the uplink, have been envisioned as a promising solution for the future Internet-of-Things (IoT). Meanwhile, nonorthogonal multiple access (NOMA) has been proposed to improve the system spectral efficiency (SE) of the fifth-generation (5G) networks by allowing concurrent transmissions of multiple users in the same spectrum. As such, NOMA has been recently considered for the uplink of WPCNs based IoT networks with a massive number of devices. However, simultaneous transmissions in NOMA may also incur more transmit energy consumption as well as circuit energy consumption in practice which is critical for energy constrained IoT devices. As a result, compared to orthogonal multiple access schemes such as time-division multiple access (TDMA), whether the SE can be improved and or the total energy consumption can be reduced with NOMA in such a scenario still remains unknown. To answer this question, we first derive the optimal time allocations for maximizing the SE of a TDMA-based WPCN (T-WPCN) and a NOMA-based WPCN (N-WPCN), respectively. Subsequently, we analyze the total energy consumption as well as the maximum SE achieved by these two networks. Surprisingly, it is found that N-WPCN not only consumes more energy, but also is less spectral efficient than T-WPCN. Simulation results verify our theoretical findings and unveil the fundamental performance bottleneck, i.e., “worst user bottleneck problem”, in multiuser NOMA systems. The Internet of Things has drawn lots of research attention as the growing number of devices connected to the Internet. Long Term Evolution-Advanced (LTE-A) is a promising technology for wireless communication and it's also promising for IoT. The main challenge of incorporating IoT devices into LTE-A is a large number of IoT devices attempting to access the network in a short period which will greatly reduce the network performance. In order to improve the network utilization, we adopted a hierarchy architecture using a gateway for connecting the devices to the eNB and proposed a multiclass resource allocation algorithm for LTE based IoT communication. Simulation results show that the proposed algorithm can provide good performance both on data rate and latency for different QoS applications both in saturated and unsaturated environment. The 3GPP has introduced the LTE-M and NB-IoT User Equipment categories and made amendments to LTE release 13 to support the cellular Internet of Things. The contribution of this paper is to analyze the coverage probability, the number of supported devices, and the device battery life in networks equipped with either of the newly standardized technologies. The study is made for a site specific network deployment of a Danish operator, and the simulation is calibrated using drive test measurements. The results show that LTE-M can provide coverage for 99.9 of outdoor and indoor devices, if the latter is experiencing 10 dB additional loss. However, for deep indoor users NB-IoT is required and provides coverage for about 95 of the users. The cost is support for more than 10 times fewer devices and a 2-6 times higher device power consumption. Thus both LTE-M and NB- IoT provide extended support for the cellular Internet of Things, but with different trade- offs. Easily establishing pairing between Internet-of-Things (IoT) devices is important for fast deployment in many smart home scenarios. Traditional pairing methods, including passkey, QR code, and RFID, often require specific user interfaces, surface's shape material, or additional tags readers. The growing number of low-resource IoT devices without an interface may not meet these requirements, which makes their pairing a challenge. On the other hand, these devices often already have sensors embedded for sensing tasks, such as inertial sensors. These sensors can be used for limited user interaction with the devices, but are not suitable for pairing on their own. In this paper, we present UniverSense, an alternative pairing method between low-resource IoT devices with an inertial sensor and a more powerful networked device equipped with a camera. To establish pairing between them, the user moves the low-resource IoT device in front of the camera. Both the camera and the on-device sensors capture the physical motion of the low-resource device. UniverSense converts these signals into a common state-space to generate fingerprints for pairing. We conduct real-world experiments to evaluate UniverSense and it achieves an F1 score of 99.9 in experiments carried out by five participants. Internet of things (IoT) or Web of Things (WoT) is a wireless network between smart products or smart things connected to the internet. It is a new and fast developing market which not only connects objects and people but also billions of gadgets and smart devices. With the rapid growth of IoT, there is also a steady increase in security vulnerabilities of the linked objects. For example, a car manufacturer may want to link the systems within a car to smart home network networks to increase sales, but if all the various people involved do not embrace security the system will be exposed to security risks. As a result, there are several new published protocols of IoT, which focus on protecting critical data. However, these protocols face challenges and in this paper, numerous solutions are provided to overcome these problems. The widely used protocols such as, 802.15.4, 6LoWPAN, and RPL are the resenting of the IoT layers PHY MAC, Adoption and Network. While CoAP (Constrained Application Protocol) is the application layer protocol designed as replication of the HTTP to serve the small devices coming under class 1 and 2. Many implementations of CoAP has been accomplished which indicates it's crucial amd upcoming role in the future of IoT applications. This research article explored the security of CoAP over DTLS incurring many issues and proposed solutions as well as open challenges for future research. There has been increasing interest in adopting BlockChain (BC), that underpins the crypto-currency Bitcoin, in Internet of Things (IoT) for security and privacy. However, BCs are computationally expensive and involve high bandwidth overhead and delays, which are not suitable for most IoT devices. This paper proposes a lightweight BC-based architecture for IoT that virtually eliminates the overheads of classic BC, while maintaining most of its security and privacy benefits. IoT devices benefit from a private immutable ledger, that acts similar to BC but is managed centrally, to optimize energy consumption. High resource devices create an overlay network to implement a publicly accessible distributed BC that ensures end-to-end security and privacy. The proposed architecture uses distributed trust to reduce the block validation processing time. We explore our approach in a smart home setting as a representative case study for broader IoT applications. Qualitative evaluation of the architecture under common threat models highlights its effectiveness in providing security and privacy for IoT applications. Simulations demonstrate that our method decreases packet and processing overhead significantly compared to the BC implementation used in Bitcoin. The Internet of Things (IoT) will feature pervasive sensing and control capabilities via a massive deployment of machine-type communication (MTC) devices. The limited hardware, low-complexity, and severe energy constraints of MTC devices present unique communication and security challenges. As a result, robust physical-layer security methods that can supplement or even replace lightweight cryptographic protocols are appealing solutions. In this paper, we present an overview of low-complexity physical-layer security schemes that are suitable for the IoT. A local IoT deployment is modeled as a composition of multiple sensor and data subnetworks, with uplink communications from sensors to controllers, and downlink communications from controllers to actuators. The state of the art in physical-layer security for sensor networks is reviewed, followed by an overview of communication network security techniques. We then pinpoint the most energy-efficient and low-complexity security techniques that are best suited for IoT sensing applications. This is followed by a discussion of candidate low-complexity schemes for communication security, such as on–off switching and space-time block codes. The paper concludes by discussing open research issues and avenues for further work, especially the need for a theoretically well-founded and holistic approach for incorporating complexity constraints in physical-layer security designs. The imminent arrival of the Internet of Things (IoT), which consists of a vast number of devices with heterogeneous characteristics, means that future networks need a new architecture to accommodate the expected increase in data generation. Software defined networking (SDN) and network virtualization (NV) are two technologies that promise to cost-effectively provide the scale and versatility necessary for IoT services. In this paper, we survey the state of the art on the application of SDN and NV to IoT. To the best of our knowledge, we are the first to provide a comprehensive description of every possible IoT implementation aspect for the two technologies. We start by outlining the ways of combining SDN and NV. Subsequently, we present how the two technologies can be used in the mobile and cellular context, with emphasis on forthcoming 5G networks. Afterward, we move to the study of wireless sensor networks, arguably the current foremost example of an IoT network. Finally, we review some general SDN-NV-enabled IoT architectures, along with real-life deployments and use-cases. We conclude by giving directions for future research on this topic. This paper introduces the FIT IoT-LAB testbed, an open testbed composed of 2728 low-power wireless nodes and 117 mobile robots available for experimenting with large-scale wireless IoT technologies, ranging from low-level protocols to advanced Internet services. IoT-LAB is built to accelerate the development of tomorrow's IoT technology by offering an accurate open-access and open-source multi-user scientific tool. The IoT-LAB testbed is deployed in 6 sites across France. Each site features different node and hardware capabilities, but all sites are interconnected and available through the same web portal, common REST interfaces and consistent CLI tools. The result is a heterogeneous testing environment, which covers a large spectrum of IoT use cases and applications. IoT-LAB is a one-of-a-kind facility, allowing anyone to test their solution at scale, experiment and fine-tune new networking concept. The Internet-of-Things (IoT) envisions a world where billions of everyday objects and mobile devices communicate using a large number of interconnected wired and wireless networks. Maximizing the utilization of this paradigm requires fine-grained QoS support for differentiated application requirements, context-aware semantic information retrieval, and quick and easy deployment of resources, among many other objectives. These objectives can only be achieved if components of the IoT can be dynamically managed end-to-end across heterogeneous objects, transmission technologies, and networking architectures. Software-defined Networking (SDN) is a new paradigm that provides powerful tools for addressing some of these challenges. Using a software-based control plane, SDNs introduce significant flexibility for resource management and adaptation of network functions. In this article, we study some promising solutions for the IoT based on SDN architectures. Particularly, we analyze the application of SDN in managing resources of different types of networks such as Wireless Sensor Networks (WSN) and mobile networks, the utilization of SDN for information-centric networking, and how SDN can leverage Sensing-as-a-Service (SaaS) as a key cloud application in the IoT. Large-scale deployments of massive machine type communications involve several challenges on cellular networks. To address the challenges of massive machine-type communications, or more generally, the Internet of Things (IoT), the 3GPP has developed Narrowband IoT (NB-IoT) as part of Release 13. NB-IoT is designed to provide better indoor coverage, support of a massive number of low-throughput devices, with relaxed delay requirements, and lower energy consumption. NB-IoT reuses Long Term Evolution functionality with simplifications and optimizations. Particularly for small data transmissions, NB-IoT specifies two procedures to reduce the required signaling: one of them based on the control plane and the other on the user plane (UP). In this work, we provide an overview of these procedures as well as an evaluation of their performance. The results of the energy consumption show both optimizations achieve a battery lifetime extension of more than two years for a large range in the considered cases, and up to eight years for CP with good coverage. In terms of cell capacity relative to Service Request, CP achieves gains from 26 to 224 percent, and UP ranges from 36 to 165 percent. The comparison of CP and UP optimizations yields similar results, except for some specific configurations. New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and or multiple sinks.