Conference Paper

A unified architecture for integrating energy harvesting IoT devices with the Mobile Edge Cloud

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  • CNRS|University of Paris Saclay|Centralesupelec
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... One key feature of the IoT Manager is to be able to accommodate a variety of sensor devices as long as they communicate under HTTP or HTTPS-based API protocols. Balasubramanian et al. [31] proposed a MEC-based architecture for energy harvesting IoT devices integration, known as the 2EA (Energy-Aware-Edge-Aware) architecture that mainly tackle two problems: (1) the offloading of data traffic from IoT devices; and (2) resources assignment at the Mobile Edge Computing system. The proposal comes with a data-driven model for MEC computation and network resources allocation. ...
... DIAT [15] Yes No Yes Condense [14] No No Yes Atlas CEB [16] Yes No Yes Predescu's model [33] Yes No No Al-Ali's model [34] No No No 2EA [31] Yes No No FIFu [12] No No No MsM [32] No Yes Yes IoT-CANE [11] No No No IoT Manager [18] No Yes Yes ...
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The advancement of the Internet of Things (IoT) as a solution in diverse application domains has nurtured the expansion in the number of devices and data volume. Multiple platforms and protocols have been introduced and resulted in high device ubiquity and heterogeneity. However, currently available IoT architectures face challenges to accommodate the diversity in IoT devices or services operating under different operating systems and protocols. In this paper, we propose a new IoT architecture that utilizes the component-based design approach to create and define the loosely-coupled, standalone but interoperable service components for IoT systems. Furthermore, a data-driven feedback function is included as a key feature of the proposed architecture to enable a greater degree of system automation and to reduce the dependency on mankind for data analysis and decision-making. The proposed architecture aims to tackle device interoperability, system reusability and the lack of data-driven functionality issues. Using a real-world use case on a proof-of-concept prototype, we examined the viability and usability of the proposed architecture.
... Researchers have done a lot of research work on the power consumption control of the Internet of Things system. Venkatraman Balasubramanian and others defined a unified resource allocation architecture for edge computing devices with limited resources, maximizing the survival time of edge devices [5] Mobile devices in mine IoT systems are often battery-powered, and it is also necessary to avoid increasing their power consumption burden. The network parameters of the equipment are optimized from the level of the mine Internet of Things infrastructure to avoid the additional power loss of terminal equipment. ...
... The meaning of Precision is to predict how many of them are really positive. Recall represents the ratio of the number of correctly identified targets to the number of all targets in the test set, and its definition is shown in (5). According to the classification accuracy of each category shown in Table 4, the performance of the classifier is generally satisfactory. ...
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In order to ensure production safety and improve production efficiency, mining enterprises are constantly accelerating the construction of mine Internet of Things systems. In the context of a substantial increase in the number of devices with network communication capabilities in the mine, the mine network communication facilities are under tremendous pressure. We propose a device business classifier based on convolutional neural networks to improve the service quality of mine network communication infrastructure. The classifier uses wavelet transform to extract the data flow and construct behavior characteristics to classify device business categories. According to the classification results, the system flexibly adjusts the parameters of network services provided to the terminal equipment. In this way, the network resources of the system can be allocated reasonably. We evaluate the performance of the classifier model through the test data set. The performance evaluation results show that the comprehensive recognition rate of the classifier model reaches 97.2%. We optimize and adjust the classifier model according to the hardware environment in which the classifier is actually deployed.
... Second, due to the reduced communication distance, MEC can facilitate ultra-reliable and low-latency services, which is very critical for IoT applications. Third, MEC can help IoT devices (limited and unstable power supply, e.g., energy harvesting sensor nodes) mitigate computational and communication overhead by offloading their heavy workloads to MEC servers [11]. In doing so, the lifetime of IoT devices or even the entire IoT network could be enhanced. ...
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Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.
... The content providers and application developers can use the edge computing systems by offering the users services closer to them. Edge Computing allows for improving the performance of computer systems by lowering latency, reducing the cost of resources and increasing responsiveness, scalability, reliability, security or privacy [19,20]. ...
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... A reward based model is also proposed to encourage user participation, which further improved the performance gain. An architecture combining energy harvesting IoT sensors and MEC servers was developed in [33]. The study optimally allocated network edge resources and achieved energy savings, which eventually improved the lifetime of the IoT sensors. ...
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... • A MEC server can be deployed to support a cluster of mobile/sensor nodes in EH-enabled wireless networks. At the node level, MEC can help each EH device reduce processing time and reserve more time for EH by offloading its heavy workloads to fog servers [107]. At the network-level, MEC can allow deploying a centralized EH strategy to tune the functionality of all devices for better EH and performance [108]. ...
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... In pursuit of this, we propose a framework that exploits the connected vehicle paradigm to provide low latency cloud services [11,12]. As investigated before in [13], the Edge Computing paradigm provides a suitable solution for integrating computation with the last-mile networks. However, it is of high significance to notice the fact that, these in-network deployments are costly and require infrastructural changes to an extent where legacy networks might prohibit such a change. ...
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With 5G network services around the corner, vehicular cloud networks providing computation capabilities have taken precedence over traditional costly cloud solutions. However, with vehicular cloud computing, a variety of new challenges have grown. In this paper we propose an intra-vehicle resource sharing model to provide a range of cloud services such as on-demand entertainment and speech recognition for driver assistance. The proposed solution forms nearby low-latency Vehicular Service Clouds (VSC) on-the-fly as per the needs of vehicular users. Vehicles in parking lots or moving on the road collaborate and share their computation and storage resources to complete different vehicular service requests. We develop an incentive-based model that uses edge-based Road Side Units (RSU) to compose heterogeneous node resources and produce a usable resource that satisfies users’ requests with minimal delays. Through proof of concept simulations, we compare our solution against traditional cloud solutions to showcase the effectiveness of adopting our proposed framework.
... 3) Security Protocols: The limited power of IoT devices requires energy-aware IoT ecosystems. To this end, Balasubramanian at al. [126] designed an Energy-Aware-Edge-Aware (2EA) architecture in which an IoT sensor can rely on energy harvesting. The framework maintains the energy profile with power metrics of each sensor in the network. ...
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The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics including intrusion detection systems, threat modeling and emerging technologies. In contrast, in this work, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this work also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements' data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this work, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.
... Balasubramanian et al., [9] proposed A Unified Architecture for Integrating Energy Harvesting IoT devices. This system uses Mobile Edge Cloud. ...
... It overcomes the drawbacks of the traditional grid systems by improving the accuracy in data collection and operation and efficiency in data management and customer satisfaction handling [37]. The conventional AMI is limited with the generation of power consumption reports but AMI for SG has multiple features like billing with multi-rates, control on the meter from a remote location, tracking of power thieving, power consumption rate monitoring, and management [45]. We have studied, the well-known existing variants of AMI for proposing a new model that can reach the requirement of SG [38]. ...
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... For example, the work in [5] leverages DRL to decide which edge device will offload the task so as to minimize their energy consumption and task latency. The authors in [6] propose an architecture and a threshold policy to achieve energy-aware edge task offloading from EH sensors. Similarly in [7], the authors rely on online Lyapunov based task offloading algorithm to investigate the trade-off between energy consumption and execution delay. ...
Preprint
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... • An MEC server can be deployed to support a cluster of mobile/sensor nodes in EH enabled wireless networks. At the node level, MEC can help each EH device reduce processing time and reserve more time to harvest energy by offloading its heavy workloads to fog servers [127], [134]. At the network level, MEC can allow to deploy a centralized EH strategy to tune the functionality of all devices to better exploit the harvestable energy source and improve the network performance [109]. ...
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... The finite capacity of IoT sensors includes environmentally sensitive IoT ecosystems. To minimize energy consumption, Balasubramanian et al. [206] developed an Energy-Aware-Edge-Aware (2EA) architecture in which a system can use energy harvesting. The framework manages the system's energy profile with power calculations for each sensor. ...
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... It addresses the limitations of the traditional power grid by enhancing data collecting and operating reliability, as well as data management and service satisfaction management effectiveness [37]. The creation of power usage data is restricted by traditional AMI, whereas AMI for SG offers several capabilities, including multi-rate invoicing, meter management from the distant area, power theft tracing, power usage rate tracking, and monitoring [8]. We investigated well-current AMI variations to propose a novel design that meets the SG criterion [9]. ...
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... The limited energy resources of IoT devices bring up the challenge of implementing an energy-aware security protocol. In the article published by Balasubramanian et al. [107] designed an Energy-Aware-Edge-Aware (2EA), an architecture, where each node depends on the energy harvesting. In the proposed framework, it creates an energy matrix of every node. ...
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Book
This book reviews IoT-centric vulnerabilities from a multidimensional perspective by elaborating on IoT attack vectors, their impacts on well-known security objectives, attacks which exploit such vulnerabilities, coupled with their corresponding remediation methodologies. This book further highlights the severity of the IoT problem at large, through disclosing incidents of Internet-scale IoT exploitations, while putting forward a preliminary prototype and associated results to aid in the IoT mitigation objective. Moreover, this book summarizes and discloses findings, inferences, and open challenges to inspire future research addressing theoretical and empirical aspects related to the imperative topic of IoT security. At least 20 billion devices will be connected to the Internet in the next few years. Many of these devices transmit critical and sensitive system and personal data in real-time. Collectively known as “the Internet of Things” (IoT), this market represents a $267 billion per year industry. As valuable as this market is, security spending on the sector barely breaks 1%. Indeed, while IoT vendors continue to push more IoT devices to market, the security of these devices has often fallen in priority, making them easier to exploit. This drastically threatens the privacy of the consumers and the safety of mission-critical systems. This book is intended for cybersecurity researchers and advanced-level students in computer science. Developers and operators working in this field, who are eager to comprehend the vulnerabilities of the Internet of Things (IoT) paradigm and understand the severity of accompanied security issues will also be interested in this book.
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The Internet of Things-based systems and software allow computations anywhere at any time by interconnecting individuals, networks, services, computers and artefacts that allow autonomous systems to form digitized communities. As the blueprint for software-intensive applications, and software architecture that precise the complexity of a network's planning, development , and changing phases to effectively and efficiently build complex IoT-driven applications. In any case, there exists no comprehensive analysis in the state of the research on the adoption of MSA for IoT systems. This study effort is needed to explore architectural concepts and practices for designing and developing IoT software to excel state-of-the-art for IoTs along with suggestions and recommendations for IoT software to the adoption of MSA to fulfil the identified gaps. A systematic analysis was coordinated, covering up the literature on existing IoT solutions by studying 140 qualitatively selected articles performed between 2005 and Jan 2020. One hundred forty articles were comprised in this SLR. The findings of this study demonstrated different research topics including software architectural styles, patterns, and models to build IoT software. This research presents cloud-based computing environments, autonomous, software-defined networking, and responsive applications, and IoT-driven agent-based systems, (1) thirteen MSA architectural and design patterns for IoTs and classification of patterns, (2) classification of software architectures for IoTs into nine main categories and their sub-categories, (3) twenty-three most investigated IoT challenges, and (4) mapping of IoT challenges with software architectural solutions. The study revealed the innovative work on IoT software architecture and trends that help in the creation and dynamic adaptation of IoT software for reusability, automation and human decision-making. The outputs of this SLR are useful in revealing many recommendations to the software industry, software engineering community, and computer sciences community with over the past 15 years of research into the adoption of MSA. This study reflects a distilled awareness of architectural practices and principles to assist researchers and practitioners in promoting information sharing software architectural roles and responsibilities for the Internet of Things software.
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IoT has contributed heavily in the growth of Internet with its versatile applications. The IoT devices act as a bridge between the digital world and the real world. Therefore, the previous embankment of securities does not keep all these attacks at bay in recent years. Still, it is undeniable that IoT devices have become an integral part of our daily life. From emergency notification systems to health monitoring devices, IoT plays a vital role. As the versatility of the IoT devices is expanding, so the security challenges. The security issues impacting the IoT devices have become an enormous concern for the organizations spread across the world. The root cause of modern security threats in IoT devices is the lack of refined cybersecurity implementation towards real-time communications, data sharing, remote access, etc. For every smart business or home solutions, it is essential to provide suitable cybersecurity solutions in IoT devices to maintain their supremacy in the future digital world. The IoT devices most often become vulnerable towards modern security threats because of their elementary level security protocol.
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Although a plethora of security mechanisms currently exist aiming at enhancing IoT security, many research and operational problems remain unsolved, raising various concerns and thus undermining the confidence in the IoT paradigm. To put forward a new perspective related to IoT security, in this chapter, the taxonomy of IoT vulnerabilities in the context of various dimensions is given and potential future directions are discussed.
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