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  • 1.
    Chen, Zhe
    et al.
    Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China..
    Guo, Shize
    Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China..
    Wang, Jian
    Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China..
    Li, Yubai
    Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Sichuan, Peoples R China..
    Lu, Zhonghai
    KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Electronic and embedded systems.
    Toward FPGA Security in IoT: A New Detection Technique for Hardware Trojans2019In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 4, p. 7061-7068Article in journal (Refereed)
    Abstract [en]

    Nowadays, field programmable gate array (FPGA) has been widely used in Internet of Things (IoT) since it can provide flexible and scalable solutions to various IoT requirements. Meanwhile, hardware Trojan (HT), which may lead to undesired chip function or leak sensitive information, has become a great challenge for FPGA security. Therefore, distinguishing the Trojan-infected FPGAs is quite crucial for reinforcing the security of IoT. To achieve this goal, we propose a clock-tree-concerned technique to detect the HTs on FPGA. First, we present an experimental framework which helps us to collect the electromagnetic (EM) radiation emitted by FPGA clock tree. Then, we propose a Trojan identifying approach which extracts the mathematical feature of obtained EM traces, i.e., 2-D principal component analysis (2DPCA) in this paper, and automatically isolates the Trojan-infected FPGAs from the Trojan-free ones by using a BP neural network. Finally, we perform extensive experiments to evaluate the effectiveness of our method. The results reveal that our approach is valid in detecting HTs on FPGA. Specifically, for the trust-hub benchmarks, we can find out the FPGA with always on Trojans (100% detection rate) while identifying the triggered Trojans with high probability (by up to 92%). In addition, we give a thorough discussion on how the experimental setup, such as probe step size, scanning area, and chip ambient temperature, affects the Trojan detection rate.

  • 2. Dai, B.
    et al.
    Ma, Zheng
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Luo, Y.
    Liu, X.
    Zhuang, Z.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Enhancing Physical Layer Security in Internet of Things via Feedback: A General Framework2020In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 7, no 1, p. 99-115, article id 8856252Article in journal (Refereed)
    Abstract [en]

    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.

  • 3.
    Gisdakis, Stylianos
    et al.
    KTH, School of Electrical Engineering (EES), Communication Networks. Networked Systems Security Group.
    Giannetsos, Thanassis
    KTH, School of Electrical Engineering (EES), Communication Networks. Networked Systems Security Group.
    Papadimitratos, Panagiotis
    KTH, School of Electrical Engineering (EES), Communication Networks. Networked Systems Security Group.
    Security, Privacy, and Incentive Provision for Mobile Crowd Sensing Systems2016In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 3, no 5, p. 839-853, article id 7463023Article in journal (Refereed)
    Abstract [en]

    Recent advances in sensing, computing, and networking have paved the way for the emerging paradigm of mobile crowd sensing (MCS). The openness of such systems and the richness of data MCS users are expected to contribute to them raise significant concerns for their security, privacy-preservation and resilience. Prior works addressed different aspects of the problem. But in order to reap the benefits of this new sensing paradigm, we need a holistic solution. That is, a secure and accountable MCS system that preserves user privacy, and enables the provision of incentives to the participants. At the same time, we are after an MCS architecture that is resilient to abusive users and guarantees privacy protection even against multiple misbehaving and intelligent MCS entities (servers). In this paper, we meet these challenges and propose a comprehensive security and privacy-preserving architecture. With a full blown implementation, on real mobile devices, and experimental evaluation we demonstrate our system's efficiency, practicality, and scalability. Last but not least, we formally assess the achieved security and privacy properties. Overall, our system offers strong security and privacy-preservation guarantees, thus, facilitating the deployment of trustworthy MCS applications.

  • 4.
    Hossain, Mohammad Istiak
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Kumar, Niroop
    KTH.
    Markendahl, Jan
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Techno-Economic Framework for IoT Service Platform:: A Cost-Structure Aspects of IoT Service ProvisioningIn: IEEE Internet of Things Journal, ISSN 2327-4662Article in journal (Refereed)
    Abstract [en]

    A plethora of Internet of Things (IoT) platforms are available in the market today. Most of the IoT platforms are used mainly for service prototyping. Cost-efficient service scalability on any platform is still an unresolved concern that, so far, has been addressed qualitatively. A quantitative method for IoT platform economics is missing in the literature. In this paper, we propose a generic framework to address this gap. Our proposed framework covers the dimensioning of the platform's software and hardware to envisage the design, deployment, and operation cost of platform services. Then, we use the framework to perform a quantitative study of platform rollout in three platform business contexts. Our analysis shows the applicability of different deployment and platform integration choices. Our results suggest that storage and energy are the main cost drivers for platforms' hardware scalability, where the main cost driver is the intensity of the sensors' message transmission rate. Additionally, our use-case based study suggests that platform as a service (PaaS) is only beneficial for actors who have limited scale or niche market need.

  • 5.
    Li, Nan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Rasmussen, Lars Kildehöj
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Spectrum Sharing With Network Coding for Multiple Cognitive Users2019In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 1, p. 230-238Article in journal (Refereed)
    Abstract [en]

    In this paper, an intelligently cooperative communication network with cognitive users is considered, where in a primary system and a secondary system, respectively, a message is communicated to their respective receiver over a packet-based wireless link. The secondary system assists in the transmission of the primary message employing network coding, on the condition of maintaining or improving the primary performance, and is granted limited access to the transmission resources as a reward. The users in both systems exploit their previously received information in encoding and decoding the binary combined packets. Considering the priority of legitimate users, a selective cooperation mechanism is investigated and the system performance based on an optimization problem is analyzed. Both the analytical and numerical results show that the condition for the secondary system accessing the licensed spectrum resource is when the relay link performs better than the direct link of the primary transmission. We also extend the system model into a network with multiple secondary users and propose two relay selection algorithms. Jointly considering the related link qualities, a best relay selection and a best relay group selection algorithm are discussed. Overall, it is found that the throughput performance can be improved with multiple secondary users, especially with more potential users cooperating in the best relay group selection algorithm.

  • 6.
    Ma, Zheng
    et al.
    Southwest Jiaotong Univ, Int Cooperat Res Ctr China, Commun & Sensor Networks Modern Transportat, Chengdu 610031, Sichuan, Peoples R China..
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Xiao, Yue
    Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Sichuan, Peoples R China..
    Pang, Zhibo
    ABB Corp Res, Automat Solut, S-72178 Västerås, Sweden..
    Poor, H. Vincent
    Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA..
    Vucetic, Branka
    Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia..
    High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies2019In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 5, p. 7946-7970Article in journal (Refereed)
    Abstract [en]

    As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and lowlatency (HRLL) wireless IoT networks. We discuss physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grant-free schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.

  • 7.
    Ma, Zheng
    et al.
    Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Sichuan, Peoples R China..
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Xiao, Yue
    Univ Elect Sci & Technol China, Chengdu, Sichuan, Peoples R China..
    Pang, Zhibo
    ABB Res, S-72226 Västerås, Sweden..
    Poor, H. Vincent
    Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA..
    Vucetic, Branka
    Univ Sydney, Sch Elect & Informat Engn, Camperdown, NSW 2006, Australia..
    Special Issue on Low-Latency High-Reliability Communications for the IoT2019In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 5, p. 7811-7815Article in journal (Other academic)
  • 8.
    Mahmood, Aamir
    et al.
    Mid Sweden Univ, Dept Informat Syst & Technol, S-85170 Sundsvall, Sweden..
    Hossain, Aftab M M
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS.
    Cavdar, Cicek
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Gidlund, Mikael
    Mid Sweden Univ, Dept Informat Syst & Technol, S-85170 Sundsvall, Sweden..
    Energy-Reliability Aware Link Optimization for Battery-Powered IoT Devices With Nonideal Power Amplifiers2019In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 6, no 3, p. 5058-5067Article in journal (Refereed)
    Abstract [en]

    In this paper, we study cross-layer optimization of low-power wireless links for reliability-aware applications while considering both the constraints and the nonideal characteristics of the hardware in Internet-of-Things (IoT) devices. Specifically, we define an energy consumption (EC) model that captures the energy cost-of transceiver circuitry, power amplifier (PA), packet error statistics, packet overhead, etc.-in delivering a useful data bit. We derive the EC models for an ideal and two realistic nonlinear PA models. To incorporate packet error statistics, we develop a simple, in the form of elementary functions, and accurate closed-form packet error rate approximation in Rayleigh block-fading. Using the EC models, we derive energy-optimal yet reliability and hardware compliant conditions for limiting unconstrained optimal signal-to-noise ratio (SNR), and payload size. Together with these conditions, we develop a semianalytic algorithm for resource-constrained IoT devices to jointly optimize parameters on physical (modulation size, SNR) and medium access control (payload size and the number of retransmissions) layers in relation to link distance. Our results show that despite reliability constraints, the common notion-higherorder M-ary modulations are energy optimal for short-range communication-prevails, and can provide up to 180% lifetime extension as compared to often used OQPSK modulation in IoT devices. However, the reliability constraints reduce both their range and the energy efficiency, while nonideal traditional PA reduces the range further by 50% and diminishes the energy gains unless a better PA is used.

1 - 8 of 8
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