Showing papers by "Nathalie Mitton published in 2021"

Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey

Abstract: With the ever advancing expansion of the Internet of Things (IoT) into our everyday lives, the number of attack possibilities increases. Furthermore, with the incorporation of the IoT into Critical Infrastructure (CI) hardware and applications, the protection of not only the systems but the citizens themselves has become paramount. To do so, specialists must be able to gain a foothold in the ongoing cyber attack war-zone. By organising the various attacks against their systems, these specialists can not only gain a quick overview of what they might expect but also gain knowledge into the specifications of the attacks based on the categorisation method used. This paper presents a glimpse into the area of IoT Critical Infrastructure security as well as an overview and analysis of attack categorisation methodologies in the context of wireless IoT-based Critical Infrastructure applications. We believe this can be a guide to aid further researchers in their choice of adapted categorisation approaches. Indeed, adapting appropriated categorisation leads to a quicker attack detection, identification, and recovery. It is, thus, paramount to have a clear vision of the threat landscapes of a specific system.

K-predictions based data reduction approach in WSN for smart agriculture

Abstract: Nowadays, climate change is one of the numerous factors affecting the agricultural sector. Optimising the usage of natural resources is one of the challenges this sector faces. For this reason, it could be necessary to locally monitor weather data and soil conditions to make faster and better decisions locally adapted to the crop. Wireless sensor networks (WSNs) can serve as a monitoring system for these types of parameters. However, in WSNs, sensor nodes suffer from limited energy resources. The process of sending a large amount of data from the nodes to the sink results in high energy consumption at the sensor node and significant use of network bandwidth, which reduces the lifetime of the overall network and increases the number of costly interference. Data reduction is one of the solutions for this kind of challenges. In this paper, data correlation is investigated and combined with a data prediction technique in order to avoid sending data that could be retrieved mathematically in the objective to reduce the energy consumed by sensor nodes and the bandwidth occupation. This data reduction technique relies on the observation of the variation of every monitored parameter as well as the degree of correlation between different parameters. This approach is validated through simulations on MATLAB using real meteorological data-sets from Weather-Underground sensor network. The results show the validity of our approach which reduces the amount of data by a percentage up to 88% while maintaining the accuracy of the information having a standard deviation of 2 $$^{\circ }$$ for the temperature and 7% for the humidity.

A Vehicle-to-Infrastructure Data Offloading Scheme for Vehicular Networks with QoS Provisioning

Abstract: In vehicular networks, vehicles carry various types of data that need to be offloaded to the RoadSide Units (RSUs) through Vehicle-to-Infrastructure (V2I) communications when vehicles come into their coverage. Since, RSUs are not widely deployed, vehicles have intermittent connectivity with RSUs. The data that vehicles carry to offload could be urgent data (such as accident data of nearby incident or emergency health data) that needs to be offloaded to the RSUs as soon as possible. Therefore, the consideration of Quality of Service (QoS) provisioning is imperative for data offloading in vehicular networks. In this paper, we propose V2I-Q, a V2I data offloading scheme with QoS provisioning by using three QoS functions: traffic classification, overload control and admission control. Traffic classification organizes the data into three priorities: high, medium and low. Overload control avoids overloading the RSUs to enable it to receive high priority data as soon as possible. Admission control allows RSU s to stop servicing existing vehicles offloading low priority data in order to receive high priority data from other vehicles. The performance evaluation shows that V2I-Q is able to offload more high priority data by incurring lower maximum offloading delay as compared to the traditional V2I data offloading schemes.

Image Similarity Based Data Reduction Technique in Wireless Video Sensor Networks for Smart Agriculture

Abstract: Nowadays, to improve animal well being in livestock farming or beekeeping application, a wireless video sensor network (WVSN) can be deployed to early detect injury or Asiatic hornets attacks. WVSN represents a low-cost monitoring solution compared to other technologies such as the closed circuit television technology (CCTV). WVSNs are composed of low-power resource-constrained video sensor nodes (motes). These nodes capture frames from videos at a given frequency (frame rate) and wirelessly send them to the sink. The big amount of data transferred from the nodes to the sink consumes a lot of energy on the sensor node, which represents a major challenge for energy-limited nodes. In this paper, we introduce two complementary mechanisms to reduce the overall number of frames sent to the sink. First, the Transmission Data Reduction algorithm (TDR) run on the sensor node leverages the similarity degree of consecutive images. Second, the Inter-Nodes Similarity algorithm (INS) exploits the spatio-temporal correlation between neighbouring nodes in order reduce the number of captured frames. The results show a \(95\%\) data reduction, surpassing other techniques in the literature by \(30\%\) at least.

Automated and Reproducible Application Traces Generation for IoT Applications

Abstract: In this paper, we investigate and present how to generate application traces of IoT (Internet of Things) Applications in an automated, repeatable and reproducible manner. By using the FIT IoT-Lab large scale testbed and relying on state-of-the-art software engineering techniques, we are able to produce, collect and share artifacts and datasets in an automated way. This makes it easy to track the impact of software updates or changes in the radio environment both on a small scale, e.g. during a single day, and on a large scale, e.g. during several weeks. By providing both the source code for the trace generation as well as the resulting datasets, we hope to reduce the learning curve to develop such applications and encourage re-usability as well as pave the way for the replication of our results. While we focus in this work on IoT networks, we believe such an approach could be of used in many other networking domains.

An Open Experimental Platform for Ranging, Proximity and Contact Event Tracking using Ultra-Wide-Band and Bluetooth Low-Energy

Abstract: The need for cheaper and more precise localisation techniques has recently amplified. The initial approach has been to roll out high-level software running on smartphones and leveraging Bluetooth proximity sensing. However this approach lacks both precision in terms of ranging, and flexibility in terms of experimental framework to fully explore alternative schemes for contact event tracing. In this context, we thus provide open-access nodes in an open-access experimental platform for ranging and proximity tracking, letting researchers tinker freely with the full software stack on a swarm of multi-radio, low-power devices based on cheap microcontrollers. We provide a tutorial on how to use the platform and open source code building blocks to to program the devices, bare-metal. We then report on initial measurements we have performed using the platform. Perspectives with our platform include applicability studies and comparative evaluation for a large variety of localisation schemes combining the use of Ultra-Wide Band and Bluetooth Low-Energy for better precision and smaller energy budgets – and the use of complementary mechanism guaranteeing privacy protection, able to run directly on-board cheap IoT microcontrollers.

DIVINE: Data offloading in vehicular networks with QoS provisioning

Abstract: In vehicular networks, vehicles may carry various types of data that need to be offloaded to the RoadSide Units (RSUs) through Vehicle-to-Infrastructure (V2I) communications when vehicles come into their coverage. RSUs are not widely deployed everywhere, which causes intermittent connectivity between vehicles and RSUs. In this paper, we propose DIVINE, a Data offloading In VehIcular NEtworks scheme with QoS provisioning, which enables a vehicle to offload its data to RSU directly through V2I communications or using other neighboring vehicles through Vehicle-to-Vehicle (V2V) communications. DIVINE considers the connectivity time of an offloading vehicle with the RSU, with other vehicles heading either on the same or opposite direction, offloading capacity, expected time to reach RSU and contact duration with neighboring vehicles. Additionally, the Quality of Service (QoS) is an important consideration for data offloading in vehicular networks due to the coexistence of urgent data to offload (e.g., accident or emergency data). Therefore, for QoS provisioning, DIVINE uses three QoS functions: traffic classification, overload control and admission control. DIVINE is presented with algorithms and procedures, as well as with illustrative examples. The performance evaluation in network simulator OMNeT++ with Veins and SUMO frameworks shows that DIVINE outperforms other schemes in terms of average offloading delay, maximum offloading delay and running time for a varying number of vehicles, maximum speed values, number of RSUs and RSUs’ capacity. It also best behaves in terms of amount of offloaded important data.

Lightweight network interface selection for reliable communications in multi-technologies wireless sensor networks

Abstract: Wireless sensor networks (WSN) are composed of hardware constrained and battery-powered devices that communicate wirelessly. WSN find more and more applications, but their deployment is limited among others by the range and the throughput of the communication technology used. Several technologies are available nowadays, with various performances, cost and coverage. One solution to overcome the deployment limitations and in some cases extend the coverage would be to dynamically select the technology based on the data requirements, environment, geographic location, etc. Thus we need multi-technologies WSN devices and efficient algorithms to select the best available technology in an autonomous and local way. This issue is known as Network Interface Selection (NIS). Multi-Attribute Decision Making (MADM) methods are an efficient tool to tackle NIS. Among MADM methods is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). However, TOPSIS suffers from a rank reversal issue, which may alter the ranking quality. Furthermore, TOPSIS method is computationally heavy, which might increase the energy consumption of the constrained devices and the latency of the network. In this paper, we introduce a lightweight TOPSIS-based method tailored for NIS in WSN, allowing more reliable communications. Experimental results obtained on real hardware, i.e., Pycom FiPy modules, show an improvement in computation time of 38% while maintaining a selection similar to TOPSIS in 82% of runs.

Joint Technology and Route Selection in Multi-RAT Wireless Sensor Networks with RODENT

Abstract: Wireless Sensor Networks (WSN) are limited by the characteristics of the Radio Access Technologies (RAT) their are based on We call a wireless multi-hop network composed of nodes able to use several RAT a Multiple Technologies Network (MTN) Nodes must manage the RAT and route selection, in a local and distributed way, with an suitable communication protocol stack Nodes may share multiple common RAT with multiple neighbors Thus the devices' heterogeneity of technologies has to be taken into account by each of the stack's layer In this article, we introduce our custom Routing Over Different Existing Network Technologies protocol (RODENT), designed for MTN It is capable of dynamically (re)selecting the best RAT and route based on data requirements evolving over time RODENT is based on a multi-criteria route selection via a custom lightweight TOPSIS method from our previous work [1] For an evaluation of performance, we implemented a functional prototype of RODENT on Pycom FiPy devices Results show that RODENT enables multiple data requirements support and energy savings, while increasing effective coverage

Routing Over Multiple Technologies with RODENT

Abstract: Wireless Sensor Networks (WSN) are limited by the characteristics of the Radio Access Technologies (RAT) they are based on. What we refer to as Multiple Technologies Network (MTN) is a network composed of nodes able to use several RAT. The management of the RAT and routes must be handled by the nodes themselves, in a local way, with a suited communication protocols stack. Each stack's layer has to take the technologies' heterogeneity of the devices into account. In this demonstration paper, we show the practical implementation of our custom routing protocol Routing Over Different Existing Network Technologies (RODENT), designed for MTN. It enables dynamic (re)selection of the best route and RAT based on the data type and requirements that may evolve over time. To assess its performance, we have implemented a functional prototype on real WSN hardware, Pycom FiPy devices.

Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN.

Abstract: Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the greedy-face-greedy (GFG) routing extended with auctions are proposed in this paper. In this paper, we assume that all robots are mobile, and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots’ remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing, and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to a seven-times-longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.

Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN

Abstract: Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in wireless sensor and robot networks based on the Greedy-Face-Greedy (GFG) routing extended with auctions are proposed in this paper. In this paper we assume that all robots are mobile and after the event is found (reported by sensors), the goal is to allocate the task to the most suitable robot to act upon the event, using either distance or the robots' remaining energy as metrics. The proposed algorithms consist of two phases. The first phase of algorithms is based on face routing and we introduced the parameter called search radius (SR) at the end of this first phase. Routing is considered successful if the found robot is inside SR. After that, the second phase, based on auctions, is initiated by the robot found in SR trying to find a more suitable one. In the simulations, network lifetime and communication costs are measured and used for comparison. We compare our algorithms with two similar algorithms from the literature (k-SAAP and BFS) used for the task assignment. RFTA2 and RFTA2GE feature up to 7 times longer network lifetime with significant communication overhead reduction compared to k-SAAP and BFS. Among our algorithms, RFTA2GE features the best robot energy utilization.