IIoTEED: An Enhanced, Trusted Execution Environment for Industrial IoT Edge Devices

Pinto, Sandro; Gomes, Tiago; Pereira, Jorge; Cabral, Jorge; Tavares, Adriano

doi:10.1109/mic.2017.17

Cited by 107 publications

(53 citation statements)

References 13 publications

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“…The TrustZone bit is not self-contained into the processor, extending from the processor through the bus till the inner logic of hardware-Zynq-based FPSoCs are a good example. This technology has been largely used both by academia and industry as a key-enabler for enforcing trusted execution environments (TEE) and providing rigid isolation (security through separation) across mixed-criticality environments [60][61][62]. More recently, and pushed by the huge hype around the RISC-V ISA, reconfigurable platforms are enjoying widespread adoption for the realization of RISC-V cores.…”

Section: Rec-tandc #3 (Security)mentioning

confidence: 99%

“…This technology allows the isolation of applications that control sensitive peripherals or memory zones from the operating system and other hardware modules of the platform. Arm TrustZone-M [60][61][62] promotes the hardware as the initial root of trust and typically enables any resource of the system (e.g., processor, memory, peripherals) to be trusted. The Platform Security layer is also responsible for ensuring the integrity and authenticity of the software being executed within the IoT device.…”

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confidence: 99%

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The Future of Low-End Motes in the Internet of Things: A Prospective Paper

et al. 2020

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Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device’s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow’s IoT devices.

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Section: Rec-tandc #3 (Security)mentioning

confidence: 99%

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confidence: 99%

The Future of Low-End Motes in the Internet of Things: A Prospective Paper

et al. 2020

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“…Trusted execution environment (TEE) is proposed by GlobalPlatform. It is a trusted computing environment to provide security services to applications [25,26]. TEE architecture is shown in Figure 1.…”

Section: Tee and Arm Trustzonementioning

confidence: 99%

SoftME: A Software-Based Memory Protection Approach for TEE System to Resist Physical Attacks

Zhang

Zhao

et al. 2019

Security and Communication Networks

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The development of the Internet of Things has made embedded devices widely used. Embedded devices are often used to process sensitive data, making them the target of attackers. ARM TrustZone technology is used to protect embedded device data from compromised operating systems and applications. But as the value of the data stored in embedded devices increases, more and more effective physical attacks have emerged. However, TrustZone cannot resist physical attacks. We propose SoftME, an approach that utilizes the on-chip memory space to provide a trusted execution environment for sensitive applications. We protect the confidentiality and integrity of the data stored on the off-chip memory. In addition, we design task scheduling in the encryption process. We implement a prototype system of our approach on the development board supporting TrustZone and evaluate the overhead of our approach. The experimental results show that our approach improves the security of the system, and there is no significant increase in system overhead.

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“…Fog computing is one of promising technologies that provide computation and communication services to IoT applications [3,4]. The concept of fog computing is similar to the edge computing [5][6][7][8][9]. Both of them are devoted to provide computation and communication resources for IoT users in the proximate area of IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.Sensors 2020, 20, 122 2 of 20 aggregation site, etc. [5]. On the other side, the fog computing nodes are not necessarily deployed around these access points. To distinguish these two concepts, in this paper, nodes with sufficient communication and computing resources scattered among IoT devices are referred to as fog nodes. The nano-server clusters deployed at the access points are referred to as MEC nodes, which usually have more resources than fog nodes. The collaborative computing of the IoT-end nodes, the fog nodes, the MEC nodes and the data center are referred to as IoT-Fog-Edge computing in this context.The studies on collaboration of Fog, MEC, and Cloud computing usually focus on finding an optimal solution to allocate the IoT tasks to appropriate virtual machines which are hosted on fog, MEC or cloud nodes. In [10], the authors propose to allocate the workload among local MEC servers, neighborhood MEC servers or cloud servers to minimize the energy consumption of MEC nodes subject to delay constraints. Lyapunov drift-plus-penalty-based dynamic queue evaluation is used for the online allocation algorithm. In [11], an optimal algorithm is put forward to determine that the tasks should be allocated to clouds near the end devices or to the one far from the end devices for the energy efficient big data processing. The delay constraints to tasks in near clouds and far cloud are taken into account. In [12], an optimal algorithm for joint task allocation among mobile devices, the computing access point and the remote cloud is proposed, where computing access point can be treated as an MEC node. The studies introduced above are not correlated with the continuous data-fl...

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IIoTEED: An Enhanced, Trusted Execution Environment for Industrial IoT Edge Devices

Cited by 107 publications

References 13 publications

The Future of Low-End Motes in the Internet of Things: A Prospective Paper

The Future of Low-End Motes in the Internet of Things: A Prospective Paper

SoftME: A Software-Based Memory Protection Approach for TEE System to Resist Physical Attacks

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

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