| 1. |
- Aboelwafa, Mariam M. N., et al.
(författare)
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A Machine-Learning-Based Technique for False Data Injection Attacks Detection in Industrial IoT
- 2020
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Ingår i: IEEE Internet of Things Journal. - Piscataway : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:9, s. 8462-8471
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Tidskriftsartikel (refereegranskat)abstract
- The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data. © 2014 IEEE.
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| 2. |
- Chen, Jialu, et al.
(författare)
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Lightweight Privacy-preserving Training and Evaluation for Discretized Neural Networks
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:4, s. 2663-2678
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning, particularly the neural network, is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this paper, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed. Firstly, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain including multivariate polynomial, nonlinear activation function, gradient function and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized neural networks is proposed, which can also be extended to convolutional neural network. Finally, we give the formal security proofs for dataset privacy, model training privacy and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in discretized neural network to demonstrate the high efficiency and accuracy of our proposed LPTE.
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| 3. |
- Dai, B., et al.
(författare)
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Enhancing Physical Layer Security in Internet of Things via Feedback : A General Framework
- 2020
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:1, s. 99-115
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Tidskriftsartikel (refereegranskat)abstract
- In this article, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established. To be specific, first, we study the compound wiretap channel (WTC) with feedback, which can be viewed as an ideal model for enhancing the PLS in the downlink transmission of IoT systems via feedback. A novel feedback strategy is proposed and a corresponding lower bound on the secrecy capacity is constructed for this ideal model. Next, we generalize the ideal model (i.e., the compound WTC with feedback) by considering channel states and feedback delay, and this generalized model is called the finite state compound WTC with delayed feedback. The lower bounds on the secrecy capacities of this generalized model with or without delayed channel output feedback are provided, and they are constructed according to variations of the previously proposed feedback scheme for the ideal model. Finally, from a Gaussian fading example, we show that the delayed channel output feedback enhances the achievable secrecy rate of the finite state compound WTC with only delayed state feedback, which implies that feedback helps to enhance the PLS in the downlink transmission of the IoT systems.
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| 4. |
- Guo, Shize, et al.
(författare)
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Securing IoT Space via Hardware Trojan Detection
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 7:11, s. 11115-11122
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Tidskriftsartikel (refereegranskat)abstract
- Hardware Trojan (HT) is a malicious modification in the chip circuitry, which may lead to undesired chip function changing or sensitive information leaking once activated. As recently studied, HT has become one of the main threats for Internet-of-Things (IoT) security, and therefore, protecting IoT against the HT attack attracts growing attention from IoT researchers. In this article, we propose an HT detection technique which makes use of chip temporal thermal information and self-organizing map (SOM) neural network to automatically isolate the Trojan-infected chips with the Trojan-free ones, and meanwhile, confirm the Trojan location at the infected chips. The experimental results reveal that our method is effective. Specifically, for the Trust-hub benchmarks, it can detect HTs which increase only 0.02% power consumption of the original design and localize the Trojan positions precisely without any error. In addition, we demonstrate the advantages of our method over two existing HT detection methods, namely, the thermal and power map (TPM) and ring oscillator net (RON), and make a thorough discussion on how the thermal image resolution, chip technology, and clustering algorithm affect the Trojan detection results.
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| 5. |
- Minovski, Dimitar, 1990-, et al.
(författare)
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Modeling Quality of IoT Experience in Autonomous Vehicles
- 2020
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Ingår i: IEEE Internet of Things Journal. - Canada : IEEE Computer Society Digital Library. - 2327-4662. ; 7:5, s. 3833-3849
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Tidskriftsartikel (refereegranskat)abstract
- Today's research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.
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| 6. |
- Moradi, Farnaz, et al.
(författare)
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Modeling DRX for D2D Communication
- 2020
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 8:4, s. 2574-2584
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Tidskriftsartikel (refereegranskat)abstract
- Discontinuous Reception (DRX) has been included in 4G-LTE as the main power saving mechanism for User Equipment (UE). However, the existing 3-state DRX model is not sufficient for new use cases introduced by 4G and 5G. For example, the device discovery process in Device to Device (D2D) communication has a significant impact on delay and power consumption, but the existing conventional 3-state DRX model is unable to capture this impact. In this paper, we propose a novel 5-state DRX model for D2D communications using a semi-Markov process, with separate arrival processes for data and discovery messages. The proposed model includes separate states for the discovery process, and is general and easy to extend for possible future changes. Our model allows us to investigate the behavior of the system under various DRX and discovery parameters. Numerical results show significant influence of the discovery phase on the performance of the DRX mechanism and provide new insights into how it affects power saving and delay.
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| 7. |
- Paniagua, Cristina, 1993-, et al.
(författare)
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Efficient Device-to-Device Service Invocation Using Arrowhead Orchestration
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:1, s. 429-439
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Things (IoT) enables interaction from real-world physical objects using sensors to the virtual world of computers and the Internet. The use of service-oriented architecture (SOA) is one step in the creation of basic and complex interactions between several sensors and actuators. However, the use of SOA-enabled technologies alone does not meet all requirements of how sensor and actuator systems could be integrated to create distributed monitoring and control applications. The centralized, traditional method of communication in wireless sensor networks via a gateway presents drawbacks that have to be addressed; device-to-cloud communication adds higher latency and higher power consumption and is less robust than the device-to-device (D2D) communication approach. Moreover, all these characteristics reduce the scalability of the network, thus limiting the use of IoT in the industry. In this article, the proposed method utilizes the arrowhead framework orchestration system to generate service composition within a (wireless) network formed by IoT devices. The aim is to achieve efficient D2D service invocation to reduce the drawbacks of today's widely used device-to-cloud approach. The method in this article performs efficient service composition for industrial IoT, including mapping SOA service composition in very small resource-constrained devices using the arrowhead orchestration. The results presented in this article at the service level can increase performance and robustness in fog computing on resource-constrained devices.
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| 8. |
- Rondón, Raúl, et al.
(författare)
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Understanding the Performance of Bluetooth Mesh : Reliability, Delay and Scalability Analysis
- 2020
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; 7:3, s. 2089-2101
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Tidskriftsartikel (refereegranskat)abstract
- This article evaluates the quality-of-service performance and scalability of the recently released Bluetooth Mesh protocol and provides general guidelines on its use and configuration. Through extensive simulations, we analyzed the impact of the configuration of all the different protocol's parameters on the end-to-end reliability, delay, and scalability. In particular, we focused on the structure of the packet broadcast process, which takes place in time intervals known as \textit{Advertising Events} and \textit{Scanning Events}. Results indicate a high degree of interdependence among all the different timing parameters involved in both the scanning and the advertising processes and show that the correct operation of the protocol greatly depends on the compatibility between their configurations. We also demonstrated that introducing randomization in these timing parameters, as well as varying the duration of the \textit{Advertising Events}, reduces the drawbacks of the flooding propagation mechanism implemented by the protocol. Using data collected from a real office environment, we also studied the behavior of the protocol in the presence of WLAN interference. It was shown that Bluetooth Mesh is vulnerable to external interference, even when implementing the standardized limitation of using only 3 out of the 40 Bluetooth Low Energy frequency channels. We observed that the achievable average delay is relatively low, of around 250~ms for over 10 hops under the worst simulated network conditions. However, results proved that scalability is especially challenging for Bluetooth Mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments.
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| 9. |
- Sisinni, Emiliano, et al.
(författare)
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Emergency Communication in IoT Scenarios by Means of a Transparent LoRaWAN Enhancement
- 2020
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers Inc.. - 2327-4662. ; 7:10, s. 10684-10694
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Tidskriftsartikel (refereegranskat)abstract
- This work deals with the management of sporadic and rare events linked with emergency situations in wireless Internet-of-Things (IoT) scenarios. The goal is to increase the performance of the emergency communication, when low-power wide area networks (LPWANs) are used as IoT backbone. In the proposed approach, a device usually operates as a normal node but, in case of emergency, can use the novel LoRa-REP access method. In this work, the LoRa-REP, based on message replication, is discussed focusing on its capability of reducing average transaction time and increasing success probability. Two operational paradigms have been considered and tested: public LoRaWAN infrastructure with cloud-based backend, and private LoRaWAN networks with local backend (edge/fog computing). Typical examples of public networks are smart cities, whereas local networks are often used in industry or building automation. Additionally, two real use cases (for public and local scenarios) are provided to show the effectiveness of the proposed approach. The experimental results (with a prototype device implementing LoRa-REP and named eNode) show that the success probability of the emergency communication can be increased up to 99.5%, and the average transaction time can be reduced up to 15% with respect to LoRaWAN without retries or up to 50% with respect to LoRaWAN with retries. © 2014 IEEE.
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| 10. |
- Stamatakis, George, et al.
(författare)
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Optimal Policies for Status Update Generation in an IoT Device With Heterogeneous Traffic
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 7:6, s. 5315-5328
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Tidskriftsartikel (refereegranskat)abstract
- A large body of applications that involve monitoring, decision making, and forecasting require timely status updates for their efficient operation. Age of Information (AoI) is a newly proposed metric that effectively captures this requirement. Recent research on the subject has derived AoI optimal policies for the generation of status updates and AoI optimal packet queueing disciplines. Unlike previous research, we focus on low-end devices that typically support monitoring applications in the context of the Internet of Things. We acknowledge that these devices host a diverse set of applications some of which are AoI sensitive while others are not. Furthermore, due to their limited computational resources, they typically utilize a simple first-in-first-out (FIFO) queueing discipline. We consider the problem of optimally controlling the status update generation process for a system with a source-destination pair that communicates via a wireless link, whereby the source node is composed of a FIFO queue and serves two applications, one that is AoI sensitive and one that is not. We formulate this problem as a dynamic programming problem and utilize the framework of Markov decision processes to derive the optimal policy for the generation of status update packets. Due to the lack of comparable methods in the literature, we compare the derived optimal policies against baseline policies such as the zero-wait policy. Results indicate that the baseline policy fails to capture the complex system dynamics that determine the relationship between the frequency of status update generation and the resulting queueing delay and thus perform poorly. To the best of our knowledge, the derived optimal policy does not exhibit a simple structure; thus, we utilized the baseline policies, whose operation is intuitive, to gain insight into the inner workings of the optimal policy.
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| 11. |
- Sun, Gang, et al.
(författare)
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Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:7, s. 6116-6131
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Tidskriftsartikel (refereegranskat)abstract
- The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features. 1) Energy-aware routing (EAR): By considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost. 2) Adaptive delayed shutdown (ADS): The delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, EAR-ADS can achieve dual energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. Simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.
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| 12. |
- Sun, Gang, et al.
(författare)
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Low-latency and Resource-efficient Service Function Chaining Orchestration in Network Function Virtualization
- 2020
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 7:7, s. 5760-5772
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Tidskriftsartikel (refereegranskat)abstract
- Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this paper, we propose a service function chain deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.
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| 13. |
- Turchet, Luca, et al.
(författare)
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The Internet of Audio Things: state-of-the-art, vision, and challenges
- 2020
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 7:10
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Forskningsöversikt (refereegranskat)abstract
- The Internet of Audio Things (IoAuT) is an emerging research field positioned at the intersection of the Internet of Things, sound and music computing, artificial intelligence, and human-computer interaction. The IoAuT refers to the networks of computing devices embedded in physical objects (Audio Things) dedicated to the production, reception, analysis and understanding of audio in distributed environments. Audio Things, such as nodes of wireless acoustic sensor networks, are connected by an infrastructure that enables multidirectional communication, both locally and remotely. In this paper, we first review the state of the art of this field, then we present a vision for the IoAuT and its motivations. In the proposed vision, the IoAuT enables the connection of digital and physical domains by means of appropriate information and communication technologies, fostering novel applications and services based on auditory information. The ecosystems associated with the IoAuT include interoperable devices and services that connect humans and machines to support human-human and human-machines interactions. We discuss challenges and implications of this field, which lead to future research directions on the topics of privacy, security, design of Audio Things, and methods for the analysis and representation of audio-related information.
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| 14. |
- Wang, X., et al.
(författare)
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FANN-on-MCU : An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things
- 2020
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers Inc.. - 2327-4662. ; 7:5, s. 4403-4417
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Tidskriftsartikel (refereegranskat)abstract
- The growing number of low-power smart devices in the Internet of Things is coupled with the concept of 'edge computing' that is moving some of the intelligence, especially machine learning, toward the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic, and firmware implementations. This article presents a FANN-on-MCU, an open-source toolkit built upon the fast artificial neural network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-V-based parallel ultralow-power (PULP) platform. The toolkit takes multilayer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This article also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multisensor bracelet. The experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22× and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification. © 2014 IEEE.
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