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... FIT IoT-LAB is a member of the OneLab consortium. It has six different locations spread across France [28]. In total, it is made up of more than 2700 low-power wireless IoT sensor nodes and over one hundred mobile robots equipped with low-rate wireless personal area network (LR WPAN) connectivity, i.e., IEEE 802. ...
The era of the Internet-of-things (IoT) comes with tremendous burdens on pre-existing network infrastructures and protocols due to spectrum scarcity and reliability concerns. Cognitive radio (CR) technology is proposed for IoT applications to alleviate the spectrum scarcity paradigm. In CR-IoT-based networks, the IoT devices/nodes share the spectrum with primary users (PUs). However, in order not to interfere with PUs communications and to conform with the elevating throughput requirements, efficient multi-radio/multi-channel assignment algorithms are required. Additionally, in order to ensure reliable transmission, algorithms need to be resilient to jamming attacks, which have detrimental impacts on network performance. In this paper, parallel-channel security-aware medium access control (PCS-MAC) is proposed as a probabilistic-based jamming resilient multi-channel assignment algorithm proposed for medical networks. PCS-MAC considers primary user activity, channel conditions, jamming attack levels, and data-rate requirements to provide spectrally efficient data transmission between CR-IoT nodes subject to delay constraints under jamming attacks to assure the delivery of time-critical patient data. The performance of PCS-MAC is practically validated using the open large-scale future Internet-of-things (FIT) IoT-LAB testbed. Practical results show that our proposed algorithm significantly enhances network performance, yielding throughput rates that supersedes the state-of-the-art algorithms presented in literature.
... • The proposed algorithm is extensively investigated via simulations to study its performance under various conditions including jamming severity, number of users, rate demands, PU activity, and number of transceivers. • The simulation results are experimentally validated using real-life experiments which are orchestrated to mimic practical deployment settings on a large-scale testbed, the Future Internet-of-Things (FIT) IoT-LAB [13]. Note that our proposed protocol has several applications in medical networks. ...
Cognitive radio (CR) technology is proposed to provide huge spectrum opportunities to enable a large-scale deployment of Internet-of-Things (IoT) sensing-based applications. An important challenge in this domain is how to design efficient channel assignment algorithms for delay-sensitive CRIoT-based sensor networks while being resilient to jamming attacks (the most common threat to IoT network reliability). Such channel assignment algorithms must enable parallel multi-channel transmissions in order to satisfy the imposed quality-of-service and delay requirements of the CRIoT devices while being spectrum efficient. This article introduces a multi-channel batch-based security-aware medium access control (MAC) design proposed for time-critical CRIoT deployments, referred to as BMRJA-MAC. The proposed design aims at improving the overall network performance by serving the largest possible number of CRIoT nodes by utilizing their multi-channel transmission capabilities while being reactive jamming-aware. The performance of the proposed MAC design is validated via simulations and experimentally using the Future Internet-of-Things (FIT) IoT-LAB testbed. Compared with reference protocols, the results show that BMRJA-MAC significantly improves network performance and spectrum efficiency.
... The Future Internet Testing-Internet of Things Laboratory (hereafter referred to as FIT IoT LAB) is an infrastructure that is made available for researchers that wish to experiment over an IoT infrastructure with wireless sensor nodes and various heterogeneous devices [41][42][43]. Figure 4a. It is essentially a scientific testbed that provides full control over wireless devices and access to libraries that help researchers to extract various information about the sensor nodes deployed, such as power consumption, end-to-end delays, and congestion. ...
One of the most significant challenges in Internet of Things (IoT) environments is the protection of privacy. Failing to guarantee the privacy of sensitive data collected and shared over IoT infrastructures is a critical barrier that delays the wide penetration of IoT technologies in several user-centric application domains. Location information is the most common dynamic information monitored and lies among the most sensitive ones from a privacy perspective. This article introduces a novel mechanism that aims to protect the privacy of location information across Data Centric Sensor Networks (DCSNs) that monitor the location of mobile objects in IoT systems. The respective data dissemination protocols proposed enhance the security of DCSNs rendering them less vulnerable to intruders interested in obtaining the location information monitored. In this respect, a dynamic clustering algorithm is that clusters the DCSN nodes not only based on the network topology, but also considering the current location of the objects monitored. The proposed techniques do not focus on the prevention of attacks, but on enhancing the privacy of sensitive location information once IoT nodes have been compromised. They have been extensively assessed via series of experiments conducted over the IoT infrastructure of FIT IoT-LAB and the respective evaluation results indicate that the dynamic clustering algorithm proposed significantly outperforms existing solutions focusing on enhancing the privacy of location information in IoT.
... • FIT IoT-LAB [70][71][72]: is the first open access scientific testbed for testing and managing a real large scale open WSN; it has been developed to evaluate the scalable WSN protocols and applications. This testbed consists of 2728 sensor nodes and 117 mobile robots over six sites across France, as shown in Figure 31. ...
... IoT-LAB infrastructure offers reliable access to all nodes, real-time monitoring, and controlling the experiments, security, and data integrity. IoT-LAB hardware • FIT IoT-LAB [70][71][72]: is the first open access scientific testbed for testing and managing a real large scale open WSN; it has been developed to evaluate the scalable WSN protocols and applications. This testbed consists of 2728 sensor nodes and 117 mobile robots over six sites across France, as shown in Figure 31. ...
The rabid growth of today’s technological world has led us to connecting every electronic device worldwide together, which guides us towards the Internet of Things (IoT). Gathering the produced information based on a very tiny sensing devices under the umbrella of Wireless Sensor Networks (WSNs). The nature of these networks suffers from missing sharing among them in both hardware and software, which causes redundancy and more budget to be used. Thus, the appearance of Shared Sensor Networks (SSNs) provides a real modern revolution in it. Where it targets making a real change in its nature from domain specific networks to concurrent running domain networks. That happens by merging it with the technology of virtualization that enables the sharing feature over different levels of its hardware and software to provide the optimal utilization of the deployed infrastructure with a reduced cost. This article is concerned with surveying the idea of SSNs, the difference between it and the traditional WSNs, the requirements for its construction, challenges facing it, and the opportunities that are provided by it, then describing our proposed architectures. As a result of using virtualization technology as a basic block in building SSNs, using different types of virtualization will produce different types of SSNs that will give different usages to it. This article proposes a novel approach of taxonomy for SSNs that is based on the used virtualization techniques, and it describes the needs and usages of each one. It presents a wide array of previously proposed solutions comparing them to each other and a brief description of the issues addressed by each category of that taxonomy. Additionally, the shared sensor architecture and shared network architecture were depicted. Finally, some of its applications in some daily life fields are listed.
... The collected data can be processed and analyzed in real time using high-speed network. 13 IoT Lab is particularly designed for the educational and industrial purposes and is deployed at six various sites in France [17]. It provides the platform to deploy a huge number of tiny wireless sensor nodes. ...
... In [1,2], the authors have introduced IoT course based Sense as visual programming language and SenseBoard as hardware platform. In [3,4], the course IoT was offered as elective at eight semester Computer Science undergraduate students. ...
... PSVR delivers all publications as long as no error in the underlying routing structure occurs. Figure 11 shows the delivery ratio for multiple tests on a real sensor network deployment at the Fit-IoT Lab in France (Fambon et al. 2014). For each network, 20 tests were conducted, each lasting 2 hours. ...
This article presents a middleware that provides a communication and data dissemination infrastructure suitable for the operation environment of the Internet of Things (IoT). The middleware realizes the channel-based publish/subscribe paradigm that has been identified as a valid means to asynchronously disseminate data in IoT applications. The novelty lies in the routing algorithm PSVR that greatly reduces the path lengths to deliver publications and its suitability for scenarios with a high subfluctuation rate. The middleware is self-stabilizing and eventually provides safety and liveness properties such as the guaranteed delivery of all published messages to all subscribers and the correct handling of subscriptions and unsubscriptions, while no error occurs. The evaluation of the middleware, based on simulations and a real deployment, shows that it has a low memory footprint and scales well with the number of nodes.
... IoT-LAB [120] 1144 One of the first open LLN testbeds, MySQL back-end server, a PHP web server, Javabased data logger and a Job Daemon for assigning tasks to the motes, Wall-powered with in-situ power measurement device in addition to temperature, humidity and light sensors. NetEye [127] 130 TelosB motes 15 laptops An open LLN experimental testbed equipped with light sensors and a mixed USB and Ethernet backchannel. ...
... ORBIT [128] 400 nodes with more than 1,500 radio devices A radio grid network testbed that consist of a remotely accessible indoor testbed, in addition to an outdoor trial network with mobile nodes. Tutornet [129] 13 platforms to interconnect under a common framework in order to share their resources and provide more powerful evaluations, similar to WISEBED [133] and FIT-IoT [120] testbeds. x ...
Envisioned communication densities in Internet of Things (IoT) applications are increasing continuously. Because these wireless devices are often battery powered, we need specific energy efficient (low-power) solutions. Moreover, these smart objects use low-cost hardware with possibly weak links, leading to a lossy network. Once deployed, these Low-power Lossy Networks (LLNs) are intended to collect the expected measurements, handle transient faults and topology changes, etc. Consequently, validation and verification during the protocol development are a matter of prime importance. A large range of theoretical or practical tools are available for performance evaluation. A theoretical analysis may demonstrate that the performance guarantees are respected, while simulations or experiments aim on estimating the behaviour of a set of protocols within real-world scenarios. In this article, we review the various parameters that should be taken into account during such a performance evaluation. Our primary purpose is to provide a tutorial that specifies guidelines for conducting performance evaluation campaigns of network protocols in LLNs. We detail the general approach adopted in order to evaluate the performance of layer 2 and 3 protocols in LLNs. Furthermore, we also specify the methodology that should be adopted during the performance evaluation, while reviewing the numerous models and tools that are available to the research community.
... This chapter gives an overview of WSN testbeds with general architecture and some basic requirements. Furthermore it encompasses two well-known testbeds namely Indriya [7], IoT-LAB [8] and the detailed description of the TWIN Testbed. Indriya and IoT- LAB have been surveyed because of their popularity among researchers and both of these testbeds provide more information on their infrastructure. ...
... IoT-LAB[8] provides an environment for experimentations in WSNs on a very large scale. It provides different sensor nodes to users and takes into consideration diversified aspects of conducting scientific experiments. ...
... The testbed currently consists of 2728 wireless sensor nodes [13] at different sites. The distribution of sensor nodes is described in Table 2.1 surveyed via [13], [8]. The nodes WSN430(800) represents the WSN430 800 MHz operating frequency and WSN430(2.4) ...
Wireless Sensor Networks (WSNs) are proving to be an influential field of research in recent times with progress traversing from small scale deployment such as Home Automation and Wearable Technology to large scale deployment in Industrial Automation and Environmental Monitoring. Research areas range widely in terms of creation of robust protocols, data acquisition and dissemination methods, security and data integrity. Although sophisticated simulation tools provide valuable information about the behaviour of a newly developed idea; verification in real time can only be achieved using testbeds. TWIN is a novel testbed for WSNs proposed by the Sustainable Communication Networks department at the University of Bremen. Unlike other testbeds, TWIN does not use a wired medium as a backchannel. It uses the IEEE 802.11 Wireless Local Area Network (WLAN) technology in ad-hoc mode instead of expensive active USB cables. A wireless backchannel makes the testbed more flexible in terms of physical infrastructure. Each hardware device in the testbed known as a TWIN Node consists of a Raspberry Pi 2 with a WLAN USB adapter, an arbitrary sensor node connected via USB cable and a USB-Switch for electrical isolation between the Raspberry Pi and the sensor node. With the USB-Switch, the power supply to the sensor node can be switched off which becomes an essential feature for collecting real world battery values of the sensor nodes for applications. This feature is not available in other testbeds, since all sensor nodes in other such testbeds are permanently connected to the backchannel cables. Since the backchannel may use the IEEE 802.11 technology which utilizes the ubiquitous 2.4 GHz frequency band, providing an efficient way to accumulate and dispense data between TWIN nodes is particularly a challenging task. Distribution of Firmware for the sensor nodes and the Raspberry Pi through the testbed with minimum data loss and without disrupting communication of the IEEE 802.15.4 sensor nodes is of the essence. Obstacles such as distributing large quantities of data to every node in the network with reliability and quick dissemination through the network form the core of this Master Thesis. As the number of firmware files increases with increasing density of TWIN-nodes, uploading this firmware on each node individually becomes a tedious and time consuming job along with higher bandwidth requirements. Above mentioned obstacles can be tackled using a Forward Error Correction (FEC) technique like Luby-Transform codes [1] in combination with the Trickle Algorithm [2]. This thesis proposes the Sprinkler Protocol, which uses the combination of Luby-Transform codes with the code propagation characteristics of the Trickle algorithm. Sprinkler is evaluated over the backchannel of TWIN with various firmware sizes. The results of Sprinkler protocol gives an inference that it works well for different firmware sizes and keeps the backchannel less occupied. Sprinkler thus, is an effective method of data distribution for TWIN testbed.
... PSVR delivers all publications while no error in the underlying routing structure occurs. Figure 11shows the delivery ratio in percent for multiple tests on a real sensor network deployment at the Fit-IoT Lab in France [5]. For each number of Time in h n = 10 n = 20 n = 50 Fig. ...
This paper presents the novel routing algorithm PSVR for pub/sub systems in ad-hoc networks. Its focus is on scenarios where communications links are unstable and nodes frequently change subscriptions. PSVR presents a compromise of size and maintenance effort for routing tables due to sub- and unsubscriptions and the length of routing paths. Designed in a self-stabilizing manner it scales well with network size. The evaluation reveals that PSVR only needs slightly more messages than a close to optimal routing structure for publication delivery, and creates shorter routing paths than an existing self-stabilizing algorithm. A real world deployment shows the usability of the approach
