Swarms of drones are more and more used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for such a sake. Because of the absence of central management in swarms of drones, IDSs constitute a good second-line protective measure. Investigating the execution of IDS (resource-hungry) algorithms on drone (resource-constrained) devices is crucial when it comes to optimizing energy, response time, memory footprint and algorithm precision. In addition, embedded platforms used in drones often incorporate heterogeneous computing platforms on which IDSs could be executed. In this paper, we present a methodology and results about characterizing the execution of different IDS models on various platform (CPUs, GPUs). In effect, as swarm of drones operate in different mission contexts (e.g. criticity level) and states (e.g. energy budget, memory footprint), it is important to explore which IDS model to run on which platforms for a given mission in a given context. For this sake, we evaluated several metrics on different platforms: energy and resource consumption, accuracy for malicious traffic detection and response time. The models tested (RF, CNN, DNN) have shown different performance according to the measured metrics and the chosen platform and proved to be relevant in different mission states.

While a growing number of contributions rely on the concept of coopetition, they adopt very different, and sometimes contradictory, perspectives. Our article aims to lay a foundation for future research on coopetition by defining what can and cannot be categorized as coopetition. Building on a Lakatosian approach, we identify three assumptions that compose the “hard core” of coopetition as a research program. We argue that coopetition requires (1) simultaneous competition and cooperation; (2) an intense competition between partnering firms in critical markets, and (3) an intense cooperation between competing firms in critical activities or markets. In addition to the hard core, the Lakatosian approach enables us to highlight eight key debates that compose the “protective belt” of coopetition and that are represented as many research avenues. As coopetition becomes a trending research topic, defining its nature to lay its foundation is now more important than ever. This research thus contributes to a clear definition of what coopetition is and what it is not.

Swarms of drones are gaining more and more autonomy and efficiency during their missions. However, security threats can disrupt their missions' progression. To overcome this problem, Network Intrusion Detection Systems ((N)IDS) are promising solutions to detect malicious behavior on network traffic. However, modern NIDS rely on resource-hungry machine learning techniques, that can be difficult to deploy on a swarm of drones. The goal of the DISPEED project is to leverage the heterogeneity (execution platforms, memory) of the drones composing a swarm to deploy NIDS. It is decomposed in two phases: (1) a characterization phase that consists in characterizing various IDS implementations on diverse embedded platforms, and (2) an IDS implementation mapping phase that seeks to develop selection strategies to choose the most relevant NIDS depending on the context. On the one hand, the characterization phase allowed us to identify 36 relevant IDS implementations on three different embedded platforms: a Raspberry Pi 4B, a Jetson Xavier, and a Pynq-Z2. On the other hand, the IDS implementation mapping phase allowed us to design both standalone and distributed strategies to choose the best NIDSs to deploy depending on the context. The results of the project have led to three publications in international conferences, and one publication in a journal.

Abstract While the open innovation literature has acknowledged competitors as a source of innovative knowledge, competitors have been relatively neglected relative to other sources such as universities, suppliers, customers, and employees. Research in open innovation increasingly includes this counter-intuitive partner and acknowledges that the drivers and management of the open innovation practice with competitors are different from those with a non-competitive partner. In parallel and independently from the open innovation literature, research on coopetition and coopetitive innovation has grown and explored when, why, and how a competitor is a relevant partner for innovation. These frameworks develop by the coopetition literature brought into the open innovation research to generate new insights and a whole research agenda. The main insight is: coopetitive open innovation, defined as open innovation with competitors embracing a “coopetitive mindset” and specific managerial principles (i.e., cooperation and competition, should be simultaneously pursued and the competition dimension should not be reduced).

In this paper, we address the challenge of a real-time solution for the critical detection step in a LTE-advanced (LTE-A) cognitive radio (CR) network implemented using limited computing resources. The detection step is required to identify a free radio channel that can be used to transmit data. We first present two new detectors and their naive implementations on a CR platform running over LTE-A, an orthogonal frequency division multiple access (OFDMA) network. The first detection method is based on the subband’s energy and the second uses both correlation on the cyclic prefix (CP)’s part and the energy of the useful orthogonal frequency division multiplexing (OFDM) signal. We then optimize them to satisfy the low-latency detection constraint on a low-cost embedded board using a ZYNQ XC7Z020 system on a chip (SoC). We use Xilinx Vitis high-level synthesis (HLS) computer-aided design (CAD) tool to design our solutions. Finally we achieve the implementation of a solution that requires less than one OFDM symbol period (70 s) for a detection system that complies with the worst scenario timing constraint of the LTE-A standard.

Past research showed how multinational enterprises (MNEs) create specific organizational structures to manage tensions in collaborations with competitors (i.e., coopetition). In this study, we explore how MNEs design their organizational structure to approach tensions specifically in the formation phase of coopetition. Formation is the first and arguably most difficult step in coopetition when tensions are particularly high. Based on an in-depth case study in the agrochemical industry, we find that MNEs create dedicated Coopetition Formation Teams (CFTs), moving within and between their firms to tackle a mix of four paradoxical tension types: performing tensions (conflicting goals), belonging tensions (incompatible values and beliefs), organizing tensions (dysfunctional processes), and learning tensions (conflicts between prior and new knowledge). Separated from the rest of their organization and equipped with unique capabilities to manage conflicts, CFTs combine the separation and integration principles to dynamically address these tensions. However, when paradoxical tensions persist and start to exacerbate, CFTs rely on conciliation by top management as a critical third principle to resolve conflicts. This study is the first to analyze the formation of coopetition between MNEs, proposing an integrated framework that connects the organizational design, the four paradoxical tension types, and the three principles to manage them.

Existing research on coopetition acknowledges the dilemma companies face regarding sharing and protecting knowledge when developing coopetitive product innovation. To manage this dilemma, coopetitors can rely on two different organizational designs: the coopetitive project team (CPT) or the separated project team (SPT). The CPT fosters high knowledge sharing but creates uncertainty about knowledge protection. In contrast, the SPT limits the knowledge sharing but offers higher knowledge protection. While the CPT design has been studied in-depth, the SPT design remains an underexplored black box. We know little about how knowledge is shared and protected within the SPT. To fill this gap, we conducted an ethnographic case study of a joint innovation project using SPT in the Chinese electric car industry. Our research shows how the SPT design manages the necessary knowledge sharing while protecting the knowledge. The project managers facilitate knowledge sharing by acting as the interface coordinator with the coopetitor, and the knowledge protection is guaranteed by a strict separation between the two coopetitors' operators. In SPT, the nature and the management of knowledge flows are not rigid but evolve according to the project stage.

Automatic identification system (AIS) is a maritime communication system that uses a transceiver to automatically transmit navigational data. These data help navigation and allow to monitor the maritime traffic. However, this system can be hacked and malicious users can easily transmit false data to mislead the coastguards or vessels navigating around. While previous research has proposed methods to detect these falsifications, none of them suggest strategies that detect AIS identity spoofing combining the tracking of the ship position and AIS transceiver’s carrier frequency offset (CFO). The CFO, caused by the carrier frequencies mismatch between emitter and receiver and Doppler effect, is used as a radiometric signature to identify materially every transceiver independently of its transmitted identity. It can drift over time and this is why it is tracked thanks to a Kalman filter (KF). In addition, position is also considered to reduce the miss probability of spoofing detection. The KF is noise adaptive to be robust against various CFO drifts and noise levels of the environment. The strategy is tested on real AIS data and the results demonstrate its efficacy: false alarm and miss probabilities were respectively 1% and 1.7%. These results show the ability of the test to correctly detect identity spoofing and the interest of CFO as a radiometric signature. This signature, used for the first time in an AIS application, could be used with other signatures in a future work to improve identity spoofing detection. This is why we made open source in GitHub our algorithm and the real AIS data used.

A growing literature explores the phenomenon of simultaneous cooperating and competing firms. Mostly in strategic management, this literature primarily focused upon large firms. The dynamics of coopetition in small firms, including new and micro-firms, however, remains under-explored raising questions about how they both engage with and manage this complex paradoxical relationship. Shifting the focus in coopetition research to small firms is vital as despite many noted benefits, evidence suggests that liabilities of newness and smallness affect engagement in cooperative relationships. Due to the specificities of small firms, it remains unlikely that the knowledge derived from their larger counterparts will adequately reflect the drivers and mechanisms of small firm coopetition. How does coopetition unfold in the small firm sector? How do challenges and specificities of small firms influence principles and mechanisms identified by the literature on large firm coopetition? To analyse such issues, in this annual review article, we evaluate the emerging body of research on small firm coopetition. Connecting coopetition strategy with small firms and entrepreneurship, we provide a comprehensive review of this emerging literature drawing from which, we then formulate future research needs and directions.

Drone swarms are increasingly being used to perform critical missions, such as inspection of ports and industrial installations. Each drone can embed heterogeneous execution platforms to successfully perform various computing tasks. As security threats may disrupt the progression of the drone mission, network intrusion detection systems (IDSs) are used. They analyze network traffic to detect malicious behaviors, but generally rely on resource-hungry machine learning models. To adapt to the dynamic nature of the mission, it is necessary to embed several IDS implementations leveraging heterogeneous computing resources of the drone and presenting a trade-off between security, throughput, and energy consumption. To address this issue, we propose, in this paper, an end-to-end flow composed of an offline phase to choose the IDS implementations to embed on the drone platform and an online phase to select the best implementation online considering the mission conditions at a given time. We devised a MILP formulation for the offline phase that proved to provide a 89.41% better Inverted Generational Distance (IGD) than a random choice. For the online phase, we investigated several solutions and designed a novel optimized strategy that proved to be around 16.76 times faster than TOPSIS while having comparable QoS metrics.

Automatic identification system is a navigation aid system that allows vessels to exchange automatically positions, identity and other information. It improves the safety of the maritime traffic and helps to monitor it. However, this system can be manipulated to send falsified information and to mask illicit activities. While previous research has proposed strategies to detect these threats, none of them suggest a solution that considers the time-division multiple access (TDMA) communication protocol. In this work, the compliance of the sent messages with this protocol, specified by the system's standard, is checked for every ship to detect message falsifications. Furthermore, because the ships velocity affects TDMA protocol, a strategy based a Kalman filter is applied to track every ship and to assess the consistency of their velocity data sent. The proposed strategy was validated on real data and showed very promising results. Being computationally cheap, the method can be run in real time. Source codes of the method are open-source to foster research activities from both industry and academia in this field.

The aim of this research is to study the impact of coopetition on the market performance of product innovation. Previous research suggests, on the one hand, that coopetition is a powerful strategy for innovation but, on the other hand, that coopetition creates opportunism risk. Therefore, the impact of coopetition on innovation depends on external and internal conditions. This impact also differs according to the radicalness of the focal innovation. Past studies have identified many different factors that influence the impact of coopetition on innovation. However, they have not taken into account the different types of coopetition. To fill this gap, here, 1) we introduce a key distinction between the two main types of coopetition, i.e., vertical vs horizontal coopetition and 2) we distinguish between the impacts of these two types of coopetition on the market performance of two types of innovation, i.e., incremental vs radical innovation. We build a set of four hypotheses and test them on a sample of 763 new products in the video game industry. The results show that 1) horizontal coopetition has a positive impact on the market performance of radical and incremental innovation, 2) horizontal coopetition has a greater impact on the market performance of radical innovation 3) vertical coopetition has no impact on the market performance of innovation, and 4) the null impact of vertical coopetition is true for both radical and incremental innovation.

We propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets. It consists of performing traffic analysis of communication link between the drone and its controller. Conversely to classic drone traffic analysis working at data link layer (or at upper layers), it performs traffic analysis directly from the corresponding I/Q signal, i.e., at the physical layer. The approach shows interesting properties such as scale invariance, frequency invariance, and noise robustness. Furthermore, the classification method allows us to distinguish WiFi drones from other WiFi devices due to underlying requirement of drone communications such as good reactivity in control. Finally, we propose different experiments to highlight theses properties and performances. The physical-layer protocol statistical fingerprint exploiting communication specificities could also be used in addition of RF fingerprinting method to perform authentication of devices at the physical-layer.

The design of cyber-physical systems remains challenging because of their highly heterogeneous nature that makes modeling, design and analysis hard. Despite extensive work in model-based approaches, few unified simulation tools are available today for such systems. This paper proposes a simulation strategy that benefits from the characteristics of recent FPGA platforms and advances in the high-level synthesis tools. Our proposal consists in using these tools to build a cyber-physical system simulator running at high-speed on a FPGA; in this view, high-level synthesis is used not only in the traditional prototyping phase of the embedded systems, but also to synthesize its physical environment, which is jointly simulated on the FPGA. Our paper proposes a case study illustrating this approach: the simulation of the automatic identification system required in maritime communications. The simulation executed on the latest FPGA generation is accelerated by a factor ×654 compared to software alternatives demonstrating that FPGAs exhibit appealing characteristics for such simulations.

In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components’ impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung–Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context.

The Doppler effect in radio systems has been widely explored by the radio communication community. However, these studies have been limited to simple motion such as linear translation. This paper presents a model for the Doppler modulation effect, i.e., the effect of complex movement on the received signal, using a geometrical approach. Particularly, we focused on studying micro-Doppler in radio communications produced by vibrations. Exploiting this phenomenon would allow the performance of passive micro-Doppler effect sensing based on communication. In this paper, we also propose signal processing techniques to detect the presence of the micro-Doppler effect and to estimate its parameters. Then, we present some experiments which highlight the micro-Doppler effect in a radio communication context. Finally, the end of the paper discusses some potential applications that exploit this phenomenon.

Spectrum Sensing is an important part of Cognitive Radio (CR) process. It can be used to determine if a Primary User (PU) (i.e. a licensed user) is emitting or not in the communication channel. This paper presents and compares three types of FFT-based detection algorithms for LTE-Advanced (LTE-A) cellular network at Orthogonal Frequency Division Multiple Access (OFDMA) level. These detectors sense the usage of the minimum time-frequency called Resource Block (RB). They are also low latency detectors and they only need one particular Orthogonal Frequency Division Multiplexing (OFDM) symbol to detect the usage of one RB. The three new detectors are based respectively on energy, correlation, and one what will be called eogration which combines energy and correlation. We analyze them with the Fisher's ratio and simulations of hypothesis test. The computing complexity of these detectors is also theoretically analyzed to provide guidance for future implementations.