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.
The FIT IoT-LAB @cite_19 is a testbed equipped with thousands of wireless nodes located in six different sites across France. It allows users to evaluate and test their novel ideas ranging from low level protocols to advanced Analytic and services in a very large scale wireless IoT environment. Major services offered by IoT-LAB include: 1) Remote access to sensors and gateways: the testbed provides users with APIs to flash any firmware, design, build, and compile applications; 2) Access to the serial ports of reserved IoT devices; 3) Internet access for nodes with end-to-end IP connection using IPv6 and 6LoWPAN; 4) Power consumption monitoring per device; 5) and robots to test and improve real-time decision making in IoT context.
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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 commoditization of sensors and communication networks is enabling vast quantities of data to be generated by and collected from cyber-physical systems. This Internet-of-Things" (IoT) makes possible new business opportunities, from usage-based insurance to proactive equipment maintenance. While many technology vendors now offer Big Data" solutions, a challenge for potential customers is understanding quantitatively how these solutions will work for IoT use cases. This paper describes a benchmark toolkit called IoTAbench for IoT Big Data scenarios. This toolset facilitates repeatable testing that can be easily extended to multiple IoT use cases, including a user's specific needs, interests or dataset. We demonstrate the benchmark via a smart metering use case involving an eight-node cluster running the HP Vertica analytics platform. The use case involves generating, loading, repairing and analyzing synthetic meter readings. The intent of IoTAbench is to provide the means to perform apples-to-apples" comparisons between different sensor data and analytics platforms. We illustrate the capabilities of IoTAbench via a large experimental study, where we store 22.8 trillion smart meter readings totaling 727 TB of data in our eight-node cluster. With the development of new technologies, these last years have witnessed the emergence of a new paradigm: the Internet of Things (IoT) and of the physical world. We are now able to communicate and interact with our surrounding environment through the use of multiple tiny sensors, RFID technologies or small wireless robots. This allows a set of new applications and usages to be envisioned ranging from logistic and traceability purposes to emergency and rescue operations going through the monitoring of volcanos or forest fires. However, all this comes with several technical and scientific issues like how to ensure the reliability of wireless communications in disturbed environments, how to manage efficiently the low resources (energy, memory, etc.) or how to set a safe and sustainable (both hardware and software) platform maintenance. All these issues are addressed by researchers all around the world but solutions designed for IoT need to face real experimentations to be validated. To ease such experimentations for IoT, several experimental testbeds have been deployed offering diverse and heterogeneous services and tools. In this article, we study the different requirements and features such facilities should offer. We survey the different experimental facilities currently available for the community, describe their characteristics. In particular, we detail the different hardware used for sensor networks and robot platforms and the scope of services the different facilities offer with a specific focus on testbeds which enable experimentations with mobility. We expect this survey assist a potential user to easily choose the one to use regarding his own needs. Finally, we identify existing gaps and difficulties and investigate new directions for such facilities. This paper describes the deployment and experimentation architecture of the Internet of Things experimentation facility being deployed at Santander city. The facility is implemented within the SmartSantander project, one of the projects of the Future Internet Research and Experimentation initiative of the European Commission and represents a unique in the world city-scale experimental research facility. Additionally, this facility supports typical applications and services of a smart city. Tangible results are expected to influence the definition and specification of Future Internet architecture design from viewpoints of Internet of Things and Internet of Services. The facility comprises a large number of Internet of Things devices deployed in several urban scenarios which will be federated into a single testbed. In this paper the deployment being carried out at the main location, namely Santander city, is described. Besides presenting the current deployment, in this article the main insights in terms of the architectural design of a large-scale IoT testbed are presented as well. Furthermore, solutions adopted for implementation of the different components addressing the required testbed functionalities are also sketched out. The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions. The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage, physical, environmental and human systems in real-time. The inherent closedloop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) hosted on Cloud data-centers are becoming the vital engine for real-time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we develop RIoTBench, a Realtime IoT Benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as reusable micro-benchmarks. Further, we propose four IoT application benchmarks composed from these tasks, and that leverage various dataflow semantics of DSPS. The applications are based on common IoT patterns for data pre-processing, statistical summarization and predictive analytics. These are coupled with four stream workloads sourced from real IoT observations on smart cities and fitness, with peak streams rates that range from 500 to 10000 messages sec and diverse frequency distributions. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public Cloud, and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, and by IoT practitioners to evaluate DSPS platforms. Testbeds are indispensable for debugging and evaluating wireless embedded systems. While existing testbeds provide ample opportunities for realistic, large-scale experiments, they are limited in their ability to closely observe and control the distributed operation of resource-constrained nodes - access to the nodes is restricted to the serial port. This paper presents FlockLab, a testbed that overcomes this limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuation of GPIO pins to trigger actions on a node, and high-resolution power profiling. FlockLab's accurate timing information in the low microsecond range enables logical events to be correlated with power samples, thus providing a previously unattained level of visibility into the distributed behavior of wireless embedded systems. In this paper, we describe FlockLab's design, benchmark its performance, and demonstrate its capabilities through several real-world test cases. Narrow-Band Internet of Things (NB-IoT) is 3GPP's cellular technology designed for narrow-band and Low-Power Wide Area Network (LPWN). NB-IoT provides deep indoor coverage for thousands of low-data-rate and low-powered devices. Coverage enhancement for devices in weak coverage condition is enabled by having signal repetitions over extended period of time in order to boost the signal quality. Performance gain from repetitions of signals is limited by channel estimation quality. In this paper we analyze impact of channel coherence time on the uplink coverage. In addition to analytical performance bound and simulation results, we also present coverage performance results from practical measurements using a NB-IoT prototype. It is reported how the NB-IoT coverage improvement is limited by the channel estimation quality and coherence time of the channel. A wireless sensor network consists of many energy-autonomous microsensors distributed throughout an area of interest. Each node monitors its local environment, locally processing and storing the collected data so that other nodes can use it. To optimize power consumption, the Swiss Center for Electronics and Microtechnology has developed WiseNET, an ultralow-power platform for the implementation of wireless sensor networks that achieves low-power operation through a careful codesign approach. The WiseNET platform uses a codesign approach that combines a dedicated duty-cycled radio with WiseMAC, a low-power media access control protocol, and a complex system-on-chip sensor node to exploit the intimate relationship between MAC-layer performance and radio transceiver parameters. The WiseNET solution consumes about 100 times less power than comparable solutions. We present the philosophy, design, and initial evaluation of the Trio testbed, a new outdoor sensor network deployment that consists of 557 solar-powered motes, seven gateway nodes, and a root server. The testbed covers an area of approximately 50,000 square meters and was in continuous operation during the last four months of 2005. This new testbed in one of the largest solar-powered outdoor sensor networks ever constructed and it offers a unique platform on which both systems and application software can be tested safely at scale. The testbed is based on Trio, a new mote platform that provides sustainable operation, enables efficient in situ interaction, and supports fail-safe programming. The motivation behind this testbed was to evaluate robust multi-target tracking algorithms at scale. However, using the testbed has stressed the system software, networking protocols, and management tools in ways that have exposed subtle but serious weaknesses that were never discovered using indoor testbeds or smaller deployments. We have been iteratively improving our support software, with the eventual aim of creating a stable hardware-software platform for sustainable, scalable, and flexible testbed deployments. Narrowband Internet of Things (NB-IoT) is a new narrow-band radio technology introduced in the Third Generation Partnership Project release 13 to the 5th generation evolution for providing low-power wide-area IoT. In NB-IoT systems, repeating transmission data or control signals has been considered as a promising approach for enhancing coverage. Considering the new feature of repetition, link adaptation for NB-IoT systems needs to be performed in 2-D, i.e., the modulation and coding scheme (MCS) and the repetition number. Therefore, existing link adaptation schemes without consideration of the repetition number are no longer applicable. In this paper, a novel uplink link adaptation scheme with the repetition number determination is proposed, which is composed of the inner loop link adaptation and the outer loop link adaptation, to guarantee transmission reliability and improve throughput of NB-IoT systems. In particular, the inner loop link adaptation is designed to cope with block error ratio variation by periodically adjusting the repetition number. The outer loop link adaptation coordinates the MCS level selection and the repetition number determination. Besides, key technologies of uplink scheduling, such as power control and transmission gap, are analyzed, and a simple single-tone scheduling scheme is proposed. Link-level simulations are performed to validate the performance of the proposed uplink link adaptation scheme. The results show that our proposed uplink link adaptation scheme for NB-IoT systems outperforms the repetition-dominated method and the straightforward method, particularly for good channel conditions and larger packet sizes. Specifically, it can save more than 14 of the active time and resource consumption compared with the repetition-dominated method and save more than 46 of the active time and resource consumption compared with the straightforward method. Narrowband Internet of Things (NB-IoT) is a new radio access technology, recently standardized in 3GPP to enable support for IoT devices. NB-IoT offers a range of flexible deployment options and provides improved coverage and support for a massive number of devices within a cell. In this paper, we provide a detailed evaluation of the coverage performance of NBIoT and show that it achieves a coverage enhancement of up to 20 dB when compared with existing LTE technology. A significant number of promising wireless sensor applications for the Internet of Things (IoT) are becoming a reality. Most of these applications require a variety of heterogeneous and geographical sensor data to be delivered to users such as mobile devices, vehicles, and cloud systems for big data analysis to extract maximum value from the physical world. Data caching of popular data items in a network can significantly improve efficiency and reliability of content delivery by contributing the reduction of data access delay, bandwidth usage, and packet losses in resource-limited low-rate wireless sensor networks. In this paper, we propose an efficient cache placement protocol (BCDA) that minimizes data access cost and guarantees cache consistency in a lightweight manner. The BCDA decides where to store a copy of data content in a network using a distributed approach according to clients' access patterns, memory capacity, data size, and so on. To evaluate the feasibility and effectiveness, we implemented the BCDA in ns-2 and compared its performance with existing caching strategies. The simulation results highlight that the BCDA significantly reduces data access cost with minimizing cache management cost. Smart spaces, such as smart homes and smart offices, are common Internet of Things (IoT) scenarios for building automation with networked sensors. In this paper, we suggest a different notion of smart spaces, where the radio environment is programmable to achieve desirable link quality within the space. We envision deploying low-cost devices embedded in the walls of a building to passively reflect or actively transmit radio signals. This is a significant departure from typical approaches to optimizing endpoint radios and individual links to improve performance. In contrast to previous work combating or leveraging per-link multipath fading, we actively reconfigure the multipath propagation. We sketch design and implementation directions for such a programmable radio environment, highlighting the computational and operational challenges our architecture faces. Preliminary experiments demonstrate the efficacy of using passive elements to change the wireless channel, shifting frequency "nulls" by nine Wi-Fi subcarriers, changing the 2 x 2 MIMO channel condition number by 1.5 dB, and attenuating or enhancing signal strength by up to 26 dB. Internet of Things (IoT) domains generate large volumes of high-velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better performance, flexibility, and monetary costs for CEP. Here, we formulate an optimization problem for energy-aware placement of CEP queries, composed as an analytics dataflow, across a collection of edge and Cloud resources, with the goal of minimizing the end-to-end latency for the dataflow. We propose a Genetic Algorithm (GA) meta-heuristic to solve this problem and compare it against a brute-force optimal algorithm (BF). We perform detailed real-world benchmarks on the compute, network, and energy capacity of edge and Cloud resources. These results are used to define a realistic and comprehensive simulation study that validates the BF and GA solutions for 45 diverse CEP dataflows, LAN and WAN setup, and different edge resource availability. We compare the GA and BF solutions against random and Cloud-only baselines for different configurations for a total of 1,764 simulation runs. Our study shows that GA is within 97 of the optimal BF solution that takes hours, maps dataflows with 4--50 queries in 1--26s, and only fails to offer a feasible solution ≤20 of the time. Simulation has been the dominant research method- ology in wireless and sensor networking. When mobility is added, real-world experimentation is especially rare. However, it is becoming clear that simulation models do not sufficiently capture radio and sensor irregularity in a complex, real-world environment, especially indoors. Unfortunately, the high labor and equipment costs of truly mobile experimental infrastructure present high barriers to such experimentation. We describe our experience in creating a testbed to lower those barriers. We have extended the Emulab network testbed software to provide the first remotely-accessible mobile wireless and sensor testbed. Robots carry motes and single board computers through a fixed indoor field of sensor-equipped motes, all running the user's selected software. In real-time, interactively or driven by a script, remote users can position the robots, control all the computers and network interfaces, run arbitrary programs, and log data. Our mobile testbed provides simple path planning, a vision-based tracking system accurate to 1 cm, live maps, and webcams. Precise positioning and automation allow quick and painless evaluation of location and mobility effects on wireless protocols, location algorithms, and sensor-driven applications. The system is robust enough that it is deployed for public use. We present the design and implementation of our mobile testbed, evaluate key aspects of its performance, and describe a few experiments demonstrating its generality and power. The Internet of Things (IoT) vision is increasingly being realized to facilitate convenient and efficient human living. To conduct effective IoT research using the most appropriate tools and techniques, we discuss recent research trends in the IoT area along with current challenges faced by the IoT research community. Several existing and emerging IoT research areas such as lightweight energy-efficient protocol development, object cognition and intelligence, as well as the critical need for robust security and privacy mechanisms will continue to be significant fields of research for IoT. IoT research can be a challenging process spanning both virtual and physical domains through the use of simulators and testbeds to develop and validate the initial proof-of-concepts and subsequent prototypes. To support researchers in planning IoT research activities, we present a comparative analysis of existing simulation tools categorized based on the scope of coverage of the IoT architecture layers. We compare existing large-scale IoT testbeds that have been adopted by researchers for examining the physical IoT prototypes. Finally, we discuss several open challenges of current IoT simulators and testbeds that need to be addressed by the IoT research community to conduct large-scale, robust and effective IoT simulation, and prototype evaluations. The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols. Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will rapidly spread over large areas, provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform. The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes. It enables the attacker to turn all the city lights on or off, to permanently brick them, or to exploit them in a massive DDOS attack. To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lamps in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already). To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key (for each device type) that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product. This paper shares experiences and lessons learned from designing and developing the bespoke Internet of Things (IoT) sensing platforms of a sensor platform for healthcare in a residential environment (SPHERE). These IoT platforms, which include a wrist-worn wearable sensor, a room-level environmental sensor, and a dual-radio gateway, constitute integral parts of the SPHERE system that is currently getting deployed in up to 100 houses of volunteers for up to 12 months. This paper is focused on sharing several years’ worth of insight and experiences from making IoT sensing platforms for large-scale academic deployments, providing information that can accelerate the development and deployment of prototypes and enable timely research in the space of IoT. From wearables to smart appliances, the Internet of Things (IoT) is developing at a rapid pace. The challenge is to find the best fitting solution within a range of different technologies that all may be appropriate at the first sight to realize a specific embedded device. A single tool for measuring power consumption of various wireless technologies and low power modes helps to optimize the development process of modern IoT systems. In this paper, we present an accurate but still cost-effective measurement solution for tracking the highly dynamic power consumption of wireless embedded systems. We extended the conventional measurement of a single shunt resistor's voltage drop by using a dual shunt resistor stage with an automatic switch-over between two stages, which leads to a large dynamic measurement range from μA up to several hundreds mA. To demonstrate the usability of our simple-to-use power measurement system different use cases are presented. Using two independent current measurement channels allows to evaluate the timing relation of proprietary RF communication. Furthermore a forecast is given on the expected battery lifetime of a Wifi-based data acquisition system using measurement results of the presented tool.