Typical Internet Of Things Research Articles

An Investigation on Handling IoT Big Data with RBSEE Architecture

Internet of Things (IoT) devices have resulted in an unprecedented surge in data generation, presenting both opportunities and challenges in data management and analytics. This paper investigates the feasibility and effectiveness of utilizing the RBSEE (Resource-Based Storage, Edge Computing, and Elasticity) architecture for handling IoT Big Data. Firstly, the paper elucidates the fundamental concepts of IoT and Big Data, highlighting the unique characteristics and challenges associated with managing large volumes of heterogeneous data streams generated by IoT devices. Subsequently, it introduces the RBSEE architecture, which integrates resource-based storage, edge computing, and elasticity to address the scalability, latency, and resource constraints inherent in IoT environments. The architecture's ability to distribute data storage and processing tasks across edge devices and cloud infrastructure while dynamically adapting to fluctuating workloads makes it particularly suitable for handling IoT Big Data. In any typical IoT ecosystem, real-time and instantaneous handling of request and dataset is pivotal and a fundamental property. This aims to propose energy efficient distributed system architecture for persuasive, privacy-preserved and real time handling of data generated from IoT devices.

An improved clock synchronization model for typical IoT applications

In today’s scenario, the Internet of Things (IoT) has transformed people’s lives by enabling data exchange among pervasive devices in various applications. However, clock synchronization is essential to ensure seamless transmission and synchronization among IoT entities involved in processing and communication. This paper proposes a clock synchronization algorithm based on linear-quadratic regression (LQR) to address synchronization-errors in IoT applications. The algorithm uses a linear model of skew and offsets to estimate clock parameters, and performance is evaluated in terms of R-square error (RSE) and Root Mean Square Error (RMSE). Our proposed algorithm outperformed traditional algorithms with an R-Square Error of 0.71% and RMSE of 0.379%. This paper also evaluated the stability of the proposed model using the correlation coefficient, which indicated a high correlation among the variables at 86%. The results below demonstrate the proposed algorithm’s effectiveness and goodness of fit in addressing clock synchronization errors for IoT applications.

Benchmarking of Secure Group Communication schemes with focus on IoT

As Internet of Things (IoT) devices become ubiquitous, they face increasing cybersecurity threats. Unlike standard 1-to-1 communication, the unique challenge posed by n-to-n communication in IoT is that messages must not be encrypted for a single recipient but for a group of recipients. For this reason, using Secure Group Communication (SGC) schemes is necessary to encrypt n-to-n communication efficiently for large group sizes. To this end, the literature presents various SGC schemes with varying features, performance profiles, and architectures, making the selection process challenging. A selection from this multitude of SGC schemes should best be made based on a benchmark that provides an overview of the performance of the schemes. Such a benchmark would make it much easier for developers to select an SGC scheme, but such a benchmark still needs to be created. This paper aims to close this gap by presenting a benchmark for SGC schemes that focus on IoT. Since the design of a benchmark first requires the definition of the underlying business problems, we defined suitable problems for using SGC schemes in the IoT sector as the first step. We identified a common problem for the centralized and decentralized/hybrid SGC schemes, whereas the distributed/contributory SGC schemes required defining an independent business problem. Based on these business problems, we first designed a specification-based benchmark, which we then extended to a hybrid benchmark through corresponding implementations. Finally, we deployed our hybrid benchmark in a typical IoT environment and measured and compared the performance of different SGC schemes. Our findings reveal notable impacts on calculation times and storage requirements without a trusted Central Instance (CI) in distributed/contributory SGC schemes.

Energy-efficient routing protocol for reliable low-latency Internet of Things in oil and gas pipeline monitoring.

The oil and gas industries (OGI) are the primary global energy source, with pipelines as vital components for OGI transportation. However, pipeline leaks pose significant risks, including fires, injuries, environmental harm, and property damage. Therefore, maintaining an effective pipeline maintenance system is critical for ensuring a safe and sustainable energy supply. The Internet of Things (IoT) has emerged as a cutting-edge technology for efficient OGI pipeline leak detection. However, deploying IoT in OGI monitoring faces significant challenges due to hazardous environments and limited communication infrastructure. Energy efficiency and fault tolerance, typical IoT concerns, gain heightened importance in the OGI context. In OGI monitoring, IoT devices are linearly deployed with no alternative communication mechanism available along OGI pipelines. Thus, the absence of both communication routes can disrupt crucial data transmission. Therefore, ensuring energy-efficient and fault-tolerant communication for OGI data is paramount. Critical data needs to reach the control center on time for faster actions to avoid loss. Low latency communication for critical data is another challenge of the OGI monitoring environment. Moreover, IoT devices gather a plethora of OGI parameter data including redundant values that hold no relevance for transmission to the control center. Thus, optimizing data transmission is essential to conserve energy in OGI monitoring. This article presents the Priority-Based, Energy-Efficient, and Optimal Data Routing Protocol (PO-IMRP) to tackle these challenges. The energy model and congestion control mechanism optimize data packets for an energy-efficient and congestion-free network. In PO-IMRP, nodes are aware of their energy status and communicate node's depletion status timely for network robustness. Priority-based routing selects low-latency routes for critical data to avoid OGI losses. Comparative analysis against linear LEACH highlights PO-IMRP's superior performance in terms of total packet transmission by completing fewer rounds with more packet's transmissions, attributed to the packet optimization technique implemented at each hop, which helps mitigate network congestion. MATLAB simulations affirm the effectiveness of the protocol in terms of energy efficiency, fault-tolerance, and low latency communication.

Wearables for Health Tourism: Perspectives and Model Suggestion

INTRODUCTION: Internet of Things (IoT) has been taking wide place in our daily lives. Among different solution ways in terms of IoT, wearables take a remarkable role because of their compact structures and the mobility. By using wearables, it is very easy to sense a person’s movements and gather characteristic data, which may be processed for desired outcomes if intelligent inferencing. As associated with this, wearables can be effectively used for health tourism operations. As wearables already proved their capabilities for healthcare-oriented applications, the perspective may be directed to health tourism purposes. In this way, positive contributions may be done in the context of not only patients’ well-being but also other actors such as health staff and tourism agencies.OBJECTIVES: Objective of this paper is to evaluate the potential of wearables in health tourism applications, provide a model suggestion, and evaluate it in the view of different actors enrolling in health tourism ecosystems. Within this objective, research targets were directed to the usage ways of wearables in health tourism, ensuring model structures as meeting with the digital transformation advantages, and gather some findings thanks to feedback by patients, health staff, and agencies.METHODS: The research firstly included some views on what is health tourism, how the IoT, mobile solutions as well as wearables may be included in the ecosystem. Following to that, the research ensured a model suggestion considering wearables and their connections to health tourism actors. Finally, the potentials of wearables and the model suggestion was evaluated by gathering feedback from potential / active health tourists, health staff, and agency staff. RESULTS: The research revealed that the recent advancements in wearables and the role of digital transformation affects health tourism. In this context, there is a great potential to track and manage states of all actors in a health tourism eco system. Thanks to data processing and digital systems, it is effective to rise fast and practical software applications for health tourism. In detail, this may be structured in a model where typical IoT and wearable interactions can be connected to sensors, databases, and the related users. According to the surveys done with potential / active health tourists, health staff, and agency staff, such a model has great effect to advance the health tourism.CONCLUSION: The research study shows positive perspectives for both present and future potentials of wearable and health tourism relation. It is remarkable that rapid advancements in IoT can trigger health tourism and the future of health tourism may be established over advanced applications including data and user-oriented relations.

A scalable blockchain-enabled federated learning architecture for edge computing.

Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services.

Analyzing the impact of quantum computing on IoT security using computational based data analytics techniques

<abstract> <p>The Internet of Things (IoT) market is experiencing exponential growth, with projections increasing from 15 billion dollars to an estimated 75 billion dollars by 2025. Quantum computing has emerged as a key enabler for managing the rapid expansion of IoT technology, serving as the foundation for quantum computing support. However, the adoption of quantum computing also introduces numerous privacy and security challenges. We delve into the critical realm of quantum-level security within a typical quantum IoT. To achieve this objective, we identified and precisely analyzed security attributes at various levels integral to quantum computing. A hierarchical tree of quantum computing security attributes was envisioned, providing a structured approach for systematic and efficient security considerations. To assess the impact of security on the quantum-IoT landscape, we employed a unified computational model based on Multi-Criteria Decision-Making (MCDM), incorporating the Analytical Hierarchy Process (AHP) and the Technique for Ordering Preferences by Similarity to Ideal Solutions (TOPSIS) within a fuzzy environment. Fuzzy sets were used to provide practical solutions that can accommodate the nuances of diverse and ambiguous opinions, ultimately yielding precise alternatives and factors. The projected undertaking was poised to empower practitioners in the quantum-IoT realm by aiding in the identification, selection, and prioritization of optimal security factors through the lens of quantum computing.</p> </abstract>

Cell-Free IoT With Non-Orthogonal Random Pilots: Key Research and Future Directions

With the proliferation of Internet of things (IoT) applications, the massive machine-type connections (MTCs) bring great challenges to traditional cellular networks, wherein cellboundary devices with severe path-loss and inter-cell interference normally suffer from poor performance. By contrast, cell-free massive MIMO is a promising technique to support massive MTCs, since its user-centric architecture can shorten the distance between devices and access points, and boost the network coverage performance significantly. In this article, we first introduce the benefits and the main challenges of cell-free IoT with non-orthogonal random pilots. Then, we present the key research topics and several promising solutions for two typical IoT application scenarios: grant-based cell-free IoT and grantfree cell-free IoT. Some future research directions of cell-free IoT are also highlighted.

Review on Applicability and Utilization of Blockchain Technology in Ubiquitous Computing

Abstract: In typical Internet of Things (IoT) networks, data is sent from sensors to fog devices and then onto a central cloud server. One single point of failure, a slowdown in the flow of data, identification, security, connection, privacy concerns caused by a third party managing cloud servers, and the difficulty of frequently updating the firmware on millions of smart devices from both a maintenance and a security point of view are just some of the problems that can occur. The evolution of ubiquitous computing and blockchain technology has inspired researchers worldwide in recent years. Key features of blockchain technology, such as the fact that it can't be changed and a decentralised and distributed approach to data security, have made it a popular choice for developing diverse applications. With the practically significant applicability of blockchain concepts (specifically consensus methods), modern-day applications in ubiquitous computing and other related areas have significantly benefited. In addition, we have taken advantage of the widely available blockchain platforms and looked into potential new study fields. As a result, this review paper elaborates the novel alternative privacy preservation options while simultaneously focusing on the universal domain as a starting point for blockchain technology applications. We also discuss obstacles, research gaps, and solutions. This review can assist early researchers who are beginning to investigate the applicability of blockchain technology in ubiquitous computing. It is also possible to use it as a reference in order to speed up the process of finding the appropriate markers for ongoing research subjects that are of interest.

ALSNAP: Attention-based long and short-period network security situation prediction

The Internet of Things (IoT) and other emerging communication and network technologies enable improved efficiency, convenience, and connectivity in various domains. However, these advancements also pose significant security risks due to the increasing complexity of attacks and the lack of effective security strategies. Traditional security schemes, such as signature-based intrusion detection systems, have limited predictive power and require a considerable number of cybersecurity staff. Therefore, proactive defense techniques based on network security situation prediction are essential. Network security situation prediction aims to forecast the future state of network security based on historical data and current observations. However, current prediction methods can only capture either long-term or short-term data features, leading to unsatisfactory results. In contrast, this paper proposes a novel attention-based long and short-period network security situation prediction (ALSNAP) scheme that combines improved bidirectional long short-term memory, vector autoregression, and multi-scale convolution with branching attention. It applies an attention mechanism to optimize the prediction model and enhance its accuracy. We evaluated ALSNAP on four public network datasets, including three typical IoT datasets, and compared it with other state-of-the-art methods. Our results demonstrate that ALSNAP achieves higher prediction accuracy than other methods. We also analyzed the potential applications of our scheme to real networks and its integration into intelligent network security situation awareness systems to deliver timely and effective countermeasures.

Electrochemical Methods in the Cloud: FreiStat, an IoT-Enabled Embedded Potentiostat.

Embedded potentiostats enable electrochemical measurements in the Internet-of-Things (IoT) or other decentralized applications, such as remote environmental monitoring, electrochemical energy systems, and biomedical point-of-care applications. We report on Freiburg's Potentiostat (FreiStat) based on the AD5941 potentiostat circuit from Analog Devices, together with custom firmware, as the key to precise and advanced electrochemical methods. We demonstrated its analytical performance by various cyclic voltammetry measurements, advanced techniques such as differential pulse voltammetry, and a lactate biosensor measurement with currents in the nA range and a resolution of 54 pA. The FreiStat yielded analytical results comparable to benchtop devices and outperformed current commercial embedded potentiostats at significantly lower cost, smaller size, and lower power consumption. Decentralized corrosion analysis by a Tafel plot using the IBM Cloud showed its applicability in a typical IoT scenario. The developed open-source software framework facilitates the integration of electrochemical instrumentation into applications utilizing machine learning and other artificial intelligence. Together with the affordable and highly capable embedded potentiostat, our approach can leverage analytical chemistry toward increasingly important, more widespread and decentralized applications.

Resilient Localization and Coverage in the Internet of Things

The proliferation of the Internet of Things (IoT) has revolutionized traditional services, giving rise to emerging smart infrastructures by connecting the physical and digital worlds. Sensory data is essential in IoT-based systems for providing context-aware and location-based services. Hence, the accurate localization of IoT devices is paramount. Anchor misplacement can significantly affect location information and coverage services in IoT. We study the effect of anchor misplacement in typical IoT settings where sensors are randomly deployed, can be mobile, and may belong to multiple providers. We identify sensing coverage holes formed by anchor misplacement and analyze their presence and impact. To mitigate the impact of anchor misplacement on network reliability, we propose a framework to identify the affected sensor nodes and then identify and remove misplaced anchor nodes. The validity of our approach is verified, and its effectiveness is demonstrated by several experiments with different network topologies and parameters. Our results are promising and can be utilized in multiple coverage applications, such as smart agriculture systems and habitat monitoring, regardless of the sensors or deployment types. It also sheds light on best practices and methods for a reliable design of IoT-based systems.

EScope: Effective Event Validation for IoT Systems Based on State Correlation

Typical Internet of Things (IoT) systems are event-driven platforms, in which smart sensing devices sense or subscribe to events (device state changes), and react according to the preconfigured trigger-action logic, as known as, automation rules. “Events” are essential elements to perform automatic control in an IoT system. However, events are not always trustworthy. Sensing fake event notifications injected by attackers (called event spoofing attack) can trigger sensitive actions through automation rules without involving authorized users. Existing solutions verify events via “event fingerprints” extracted by surrounding sensors. However, if a system has homogeneous sensors that have strong correlations among them, traditional threshold-based methods may cause information redundancy and noise amplification, consequently, decreasing the checking accuracy. Aiming at this, in this paper, we propose “EScope”, an effective event validation approach to check the authenticity of system events based on device state correlation. EScope selects informative and representative sensors using an Neural-Network-based (NN-based) sensor selection component and extracts a verification sensor set for event validation. We evaluate our approach using an existing dataset provided by Peeves. The experiment results demonstrate that EScope achieves an average 67% sensor amount reduction on 22 events compared with the existing work, and increases the event spoofing detection accuracy.

Bringing intelligence to Edge/Fog in Internet of Things‐based healthcare applications: Machine learning/deep learning‐based use cases

SummaryInternet of things and smart medical applications are deeply changing the way healthcare is delivered worldwide. A typical Internet of Things (IoT)‐based eHealth system includes medical sensors for data collection, access network to transmit data, and Cloud servers for data processing and storage. Machine learning/deep learning (ML/DL) have proven to be a powerful tool for data classification, disease prognosis, and diagnosis and even medication prescription. ML/DL models need a large amount of data and significant computational and storage capacities especially for the training phase. Deploying ML/DL algorithms in the Cloud can be effectively done due to the processing and storage power of Cloud data centers. However, it raises many issues related to the availability, latency, energy consumption, bandwidth, security, and privacy. Recently, there is a growing interest to run as much processing as possible nearer the data sources, in the Edge, to compensate the Cloud‐based solutions limitations. In this paper, we propose to investigate the benefits of using IoT, ML/DL, and Edge Computing to enhance healthcare applications. Then, we are going to review the main approaches and trends for executing ML/DL in the Edge, to give their benefits and limitations, and draw finally conclusions about existing research issues.

Collaborative device-level botnet detection for internet of things

Cyber attacks on the Internet of Things (IoT) have seen a significant increase in recent years. This is primarily due to the widespread adoption and prevalence of IoT within domestic and critical national infrastructures, as well as inherent security vulnerabilities within IoT endpoints. Therein, botnets have emerged as a major threat to IoT-based infrastructures targeting firmware vulnerabilities such as weak or default passwords to assemble an army of compromised devices which can serve as a lethal cyber-weapon against target systems, networks, and services. In this paper, we present our efforts to mitigate this challenge through the development of an intrusion detection system that resides within an IoT device to provide enhanced visibility thereby achieving security hardening of such devices. The device-level intrusion detection presented here is part of our research framework BTC_SIGBDS (Blockchain-powered, Trustworthy, Collaborative, Signature-based Botnet Detection System). We identify the research challenge through a systematic critical review of existing literature and present detailed design of the device-level component of the BTC_SIGBDS framework. We use a signature-based detection scheme with trusted signature updates to strengthen protection against emerging attacks. We have evaluated the suitability and enhanced the capability through the generation of custom signatures of two of the most famous signature-based IDS with ISOT, IoT23, and BoTIoT datasets to assess the effectiveness with respect to detection of anomalous traffic within a typical resource-constrained IoT network in terms of number of alerts, detection rates, detection time as well as in terms of peak CPU and memory usage.

When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

Data interplay: A model to optimize data usage in the Internet of Things

AbstractThe Internet of Things (IoT) has been playing an important role in the technology scenario due to its high potential and impact on different society segments. Estimates suggest a trend for an increase in the number of IoT devices connected to the Internet for the next few years. Hence, the volume of data produced by IoT devices will follow this growth perspective, and there will be a demand for systems that can process, store, and promote access to large amounts of data. The data collected from sensors in typical IoT systems is stored and processed in cloud servers. However, some IoT solutions use edge devices to perform specific actions, such as processing, storage, and access, using only local infrastructure for low latency requirements. Fog computing has been used to improve IoT solutions and to transfer some of the complexity from the cloud to the edge of the network, that is, closer to devices, applications and users, working as a kind of “local and private cloud.” The cooperation of devices and applications between edge and cloud creates a need for an interplay to enable data flow among the layers of IoT systems deployed on edge and in the cloud. Thus, IoT data systems should support the data life cycle through its collection, analysis, and use. Performance efficiency is a quality factor of systems and software engineering, which measures “performance relative to the number of resources used under stated conditions.” In particular, in IoT systems that involve a large volume of data, the performance efficiency of data interplay is a relevant requirement. This work proposes a data interplay model of IoT to define and deploy the IoT data life cycle in the collection, analytics, and data use stages. This data interplay proposal aims to improve performance efficiency in IoT data life cycle operations: collection, analytics, and use among devices and applications in edge and cloud infrastructures.

Intrusion Detection for Electric Vehicle Charging Systems (EVCS)

The market for Electric Vehicles (EVs) has expanded tremendously as seen in the recent Conference of the Parties 27 (COP27) held at Sharm El Sheikh, Egypt in November 2022. This needs the creation of an ecosystem that is user-friendly and secure. Internet-connected Electric Vehicle Charging Stations (EVCSs) provide a rich user experience and add-on services. Eventually, the EVCSs are connected to a management system, which is the Electric Vehicle Charging Station Management System (EVCSMS). Attacking the EVCS ecosystem remotely via cyberattacks is rising at the same rate as physical attacks and vandalism happening on the physical EVCSs. The cyberattack is more severe than the physical attack as it may affect thousands of EVCSs at the same time. Intrusion Detection is vital in defending against diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to determine if each traffic stream is legitimate or malicious, that is, binary classification. Furthermore, the IDS can identify the type of malicious traffic, which is called multiclass classification. In this paper, we address IoT security issues in EVCS by using different machine learning techniques and using the native IoT dataset to discover fraudulent traffic in EVCSs, which has not been performed in any previous research. We also compare different machine learning classifier algorithms for detecting Distributed Denial of Service (DDoS) attacks in the EVCS network environment. A typical Internet of Things (IoT) dataset obtained from actual IoT traffic is used in the paper. We compare classification algorithms that are placed in line with the traffic and contain DDoS attacks targeting the EVCS network. The results obtained from this research improve the stability of the EVCS system and significantly reduce the number of cyberattacks that could disrupt the daily life activities associated with the EVCS ecosystem.

An Ethereum Private Network for Data Management in Blockchain of Things Ecosystem

The advent of blockchain technology in the development and design of smart internet of things (IoT) systems offers the opportunity to secure and transfer data flow, preserve its integrity, and provide transparent mechanisms for its management. Blockchain has actually attracted applications in vital fields because it provides many advantages over centralized database such as traceability, confidentiality, availability and trust. A private network offers the most secure and peer-restricted environment for big data flow, specifically in IoT ecosystems. An integrated blockchain-IoT ecosystem in which three Raspberry Pi 4 nodes communicate and interact in a closed loop to control smart applications via an Ethereum platform in a secure and an efficiently emulated environment is piloted. The proposed blockchain of things (BCoT) ecosystem adds a new layer to the physical, network and application layers of a typical IoT architecture. The concept of a fully decentralized private Ethereum BCoT network may find applications in several fields that call for the removal of single-point of failure and ensures data integrity and transparency.

Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.