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The rapid expansion of the Internet of Things (IoT) has led to increased concerns regarding the security of IoT systems. Attacks targeting IoT devices, such as code injection and code reuse, have become more sophisticated, compromising device control and functionality. Existing security schemes, designed primarily for the ×86 architecture, are not well-suited for resource-constrained IoT devices. While numerous studies aim to strengthen IoT device security, practical implementation faces challenges due to time-to-market requirements and manufacturing costs. To address these issues, this paper proposes an Augmented Security Module (ASM) that provides essential security services for IoT devices within the same network, requiring minimal device modifications. The ASM includes Hardware Security Modules (HSMs) like Trusted Platform Module (TPM) and Secure Element (SE) to ensure data integrity and execute requested security services. By adding ASM Agents, which perform minimal operations to request security services, IoT devices can easily access the provided security services. The proposed ASM enables flexible adaptation to evolving security requirements at a low cost, meeting practical manufacturing and time-to-market demands. To validate the effectiveness of the proposed ASM, four representative scenarios are presented and analyzed, demonstrating its ability to enhance IoT device security and prevent attacks. The proposed ASM contributes to the widespread adoption of secure IoT systems by ensuring the security of IoT devices within the network.

The proliferation of IoT comes with many challenges, among which security is one of the most serious issues. Since most IoT devices are designed based on bare-metal devices with low performance, low power, and small form factor, an attacker can easily perform firmware extraction and analysis. In addition, code reuse attacks, such as Return Oriented Programming (ROP) attacks, are possible based on the analyzed firmware information. Since most code reuse attacks are performed through a combination of gadgets that include return instructions, preventing an attacker from analyzing return instructions can be a fundamental solution for these attacks. To prevent code reuse attacks on bare-metal based IoT devices, this paper proposes Return Instruction Obfuscation (RIO). The proposed scheme encrypts all return instructions in the firmware and instruments the modules necessary to decrypt and execute the encrypted return instructions using a Low Level Virtual Machine (LLVM). Since all return instructions in the firmware are encrypted, the proposed scheme can prevent attackers from performing firmware analysis and gadget collection. The proposed scheme was implemented and evaluated on Nuvoton’s NuMaker-PFM-M2351 development board with an ARM Cortex-M23 based SoC.

Blockchain is a distributed database technology that operates in a P2P network and is used in various domains. Depending on its structure, blockchain can be classified into types such as public and private. A consensus algorithm is essential in blockchain, and various consensus algorithms have been applied. In particular, a non-competitive consensus algorithm called PBFT is mainly used in private blockchains. However, there are limitations to scalability. This paper proposes an enhanced PBFT with dynamic hierarchy management and location-based clustering to overcome these problems. The proposed method clusters nodes based on location information and adjusts the dynamic hierarchy to optimize consensus latency. As a result of the experiment, the proposed PBFT showed significant performance improvement compared to the existing typical PBFT and Dynamic Layer Management PBFT (DLM-PBFT). The proposed PBFT method showed a processing performance improvement rate of approximately 107% to 128% compared to PBFT, and 11% to 99% compared to DLM-PBFT.

A cognitive radio (CR) is a promising technology to solve the emerging spectrum crisis, especially for applications where thousands of wireless sensor nodes are deployed. Since continuous spectrum sensing will greatly reduce the lifetime of a network composed of energy-restricted CR nodes, an accurate method for predicting spectrum occupancy is necessary to improve energy efficiency. This paper proposes a hidden Markov model (HMM)-based cooperative spectrum sensing (CSS) that predicts the status of a network environment. The traditional prediction algorithms for cooperative spectrum sensing assume that all CR nodes have the same network environment. However, the channel availability of various CR nodes can be quite different, and thus the traditional algorithms will lead to low prediction accuracy in a complex radio environment. The proposed methods learn the historical spectrum sensing results and help the network to make an energy-efficient spectrum sensing decision. More specifically, the hidden state of HMM is set to different areas, where primary users (PUs) perform different activities. A Baum-Welch (BW) algorithm is employed to estimate the parameters of the HMM based on the past spectrum sensing results, and then the parameters are fed to a forward algorithm for the predicting of PUs' activity. Based on the prediction, secondary users (SUs) are classified into either ”interfered by PU” or ”not interfered by PU. ” The nodes selected as ”interfered by PU” will not perform spectrum sensing to reduce unnecessary energy consumption. The performance of the proposed method is evaluated using the simulations under different traffic conditions. The simulation results show that, compared with the conventional HMM-based methods, the effectiveness of the proposed algorithm in energy efficiency and spectrum utilization improved by about 13% and 15%, respectively.

In a wireless sensor network, sensor nodes are deployed in an ad hoc fashion and they deliver data packets using multihop transmission. However, transmission failures occur frequently in the multihop transmission over wireless media. Thus, a loss recovery mechanism is required to provide end-to-end reliability. In addition, because the sensor nodes are very small devices and have insufficient resources, energy-efficient data transmission is crucial for prolonging the lifetime of a wireless sensor network. This paper proposes a transmission power control mechanism for reliable data transmission, which satisfies communication reliability through recovery of lost packets. The proposed method calculates packet reception rate (PRR) of each hop to maintain end-to-end packet delivery rate (PDR), which is determined based on the desired communication reliability. Then, the transmission power is adjusted based on the PRR to reduce energy consumption. The proposed method was evaluated through extensive simulations, and the results show that it leads to more energy-efficient data transmission compared to existing methods.

The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment.