A C-Based Framework for Low-Cost Real-Time Embedded Systems

Deeply embedded systems powered by microcontrollers are becoming popular with the emergence of Internet-of-Things (IoT) technology. However, these devices primarily run C/C \({+}{+}\) code and are susceptible to memory bugs, which can potentially lead to both control data attacks and non-control data attacks. Existing defense mechanisms (such as control-flow integrity (CFI), dataflow integrity (DFI) and write integrity testing (WIT), etc.) consume a massive amount of resources, making them less practical in real products. To make it lightweight, we design a bitmap-based allowlist mechanism to unify the storage of the runtime data for protecting both control data and non-control data. The memory requirements are constant and small, regardless of the number of deployed defense mechanisms. We store the allowlist in the TrustZone to ensure its integrity and confidentiality. Meanwhile, we perform an offline analysis to detect potential collisions and make corresponding adjustments when it happens. We have implemented our idea on an ARM Cortex-M-based development board. Our evaluation results show a substantial reduction in memory consumption when deploying the proposed CFI and DFI mechanisms, without compromising runtime performance. Specifically, our prototype enforces CFI and DFI at a cost of just 2.09% performance overhead and 32.56% memory overhead on average.

The exponential growth in the number of Internet of Things (IoT) devices, the sensitive nature of data they produce, and the simple nature of these devices makes IoT systems vulnerable to a wide range cyber-threats. Physical attacks are one of the major concerns for IoT device security. Security solutions for the IoT have to be accurate and quick since many real time applications depend on the data generated by these devices. In this article, we undertake the IoT authentication problem by proposing a fast protocol RapidAuth, which also restricts physical attacks. The proposed protocol uses Physical Unclonable Functions to achieve the security goals and requires the exchange of only two messages between the server and an IoT device. The analysis of RapidAuth proves its’ robustness against various types of attacks as well as its’ efficiency in terms of computation, communication, memory overheads and energy consumption.

Internet of Medical Things (IoMT) has revolutionized the modern healthcare by integrating medical devices and real-time patient monitoring into it. However, the limited resource environment in IoMT devices makes the deployment of strong security mechanisms a great challenge. In this paper, lightweight cryptography-based security mechanisms particularly for node authentication and access control in IoMT systems are analyzed. The particular security needs of IoMT systems are considered, and reviewed the new developments in lightweight cryptography for defending against specific cyber-attacks including impersonation, replay and unauthorized access. A comparison of current authentication protocols and access control technologies which evaluates computational cost, scalability, and attack tolerance is presented. The recent trends and future research directions on improving the resilience of IoMT networks with minimal overhead is also discussed. With the proliferation of IoMT usage, it is more important than ever for patient data to remain confidential and for the medical systems to function correctly. This paper focuses on the security versus device trade-offs, and presents cryptography tools that achieve a good trade-off between the strength obtained and the power, memory, and processing limitations. By examining the present situation and open challenges, this research work is an attempt to pave the way for designing next-generation lightweight cryptography for secure and efficient IoMT systems. This research is helpful for numerous scholars and medical experts to analyse the advantages and limitations of traditional authentication and access control models in IoMT and to design innovative solutions.

This article focuses on prospective applications of the Internet of Things. The Internet of Things (IoT) refers to the interconnected network of physical entities “things” equipped with sensors, software, and various other technologies to facilitate data exchange with other devices and systems over the Internet. As the IoT continues to develop as the next stage in the development of the Internet, it is essential to identify the many possible application areas of IoT and the research problems linked to these applications. The Internet of Things (IoT) is anticipated to permeate almost all facets of everyday life, including smart cities, healthcare, smart agriculture, logistics, retail, smart living, and smart surroundings. Despite significant advancements in IoT enabling technology in recent years, various issues still need attention. The IoT notion, stemming from diverse technologies, is likely to provide several research hurdles. The extensive nature of IoT, impacting almost all aspects of our life, makes it a crucial subject for study across numerous related disciplines, including information technologies and computer science. Consequently, IoT is facilitating new avenues for study exploration. This study delineates the current advancements in IoT technology and examines prospective applications and research obstacles.

In this paper, we investigate the static task allocation problem for heterogeneous distributed computing systems with the goal of minimizing total execution and communication time (i.e. task completion time). An objective function is formulated to evaluate the optimality of mapping modules of a task to a set of heterogeneous processors connected with some communication links of varying transmission rates. We present three algorithms: one to obtain optimal task allocation and other two to obtain suboptimal allocation. The accuracy and efficiency of the proposed algorithms are demonstrated through a simulation study and results are reported.

A lightweight machine learning-based authentication framework for smart IoT devices

Sensor networks deployed in hostile areas are subject to node replication attacks, in which an adversary compromises a few sensors, extracts the security keys, and clones them in a large number of replicas, which are introduced into the network to perform insider attacks. Memory overhead, energy efficiency and detection probability are the main technical concerns for any replication detection protocol. The previous distributed solutions either require network-wide spontaneous change of pseudo-random numbers or incur significant memory and energy overhead to the sensors, especially in the central area of the deployment. In this paper, we propose four replication detection protocols that have high detection probability, low memory requirement, and balanced energy consumption. The new protocols use Bloom filters to compress the information stored at the sensors, and use two new techniques, called cell forwarding and cross forwarding, to improve detection probability, further reduce memory consumption, and in the mean time distribute the memory and energy overhead evenly across the whole network. Simulations show that the protocols can achieve nearly 100% detection probability with average memory reduction up to 91%.

The Internet of Things (IoT) is a diverse technology that primarily utilizes TCP/IP protocols in IoT environments. However, it is often argued that the existing IP protocol stack is insufficient to support data sharing and storage capabilities for mobile IoT applications. Named Data Networking (NDN) has gained significant attention over time as a solution to the limitations of host-centric services in traditional IP networks. The key advantages of NDN include name-based routing and network caching, along with mobility support in wireless networks. The integration of NDN and IoT can address issues related to frequent node mobility, name-based routing, and storage constraints on resource-limited devices. IoT devices are frequently in mobility, creating routing challenges when transitioning from one domain to another. Interest packets remain unsatisfied until the producer reconnects to a new Point of Attachment (PoA) and updates its location. Despite these reconnections, challenges such as interest and data packet losses, increased latency, and suboptimal routing paths persist during communication. Consequently, managing producer mobility remains difficult during the frequent movement of nodes across different IoT domains. This paper proposes a novel Zone-Based Producer Mobility Management (ZBP-MM) protocol for NDN-based IoT environments. The proposed technique is evaluated and compared against the OPMSS scheme using the ndnSIM simulator. Results demonstrated that the proposed scheme achieves better hop count and cache hit ratio, with minimized interest satisfaction delay and handover delay, compared to the OPMSS technique during producer mobility scenarios in intra- and inter-zones.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target Denial of Service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new committee members auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir's secret-sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB's resistance against double-spend attacks, an improved Nakamoto's attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

RectilinearSteiner minimal tree (RSMT) construction is a fundamental issue in designing very large scale integrated Integration (VLSI). FLUTE (Fast Look-Up table) based approach presented a fast and accurate RSMT construction for both smaller and higher degree nets. The model reduces the time complexity for RSMT construction for smaller nets, however for larger nets there exists memory overhead. Since flute basedmodel did not consider the memory requirement in constructing RSMT, the proposed work presents a memory optimized RSMT (MORSMT) construction in order to address the memory overhead for larger nets. Experiments are conducted to evaluate the performance of proposed approach over existing model for varied benchmarks in terms of computation time, memory overhead and wire length. The experimentalresults showthat the proposed model is scalable and efficient.

Quadrotor unmanned aerial vehicles (UAVs) have emerged as ubiquitous and agile robots and data carriers within the framework of the future Internet of Things (IoT) and mobile wireless networks. Yet, the insufficient onboard battery necessitates the optimization of energy consumption for both the UAV and IoT devices while ensuring that communication requirements are met. This paper therefore proposes a more accurate and mathematically tractable model for characterizing a UAV’s energy consumption concerning desired trajectories. This nonlinear model takes into account the UAV’s dynamics, brushless direct current (BLDC) motor dynamics, and aerodynamics. To optimize the communication time between IoT devices and the UAV, IoT devices are clustered using a modified GAK-means algorithm, with dynamically optimized communication coverage radii. Subsequently, a fly–circle–communicate (FCC) trajectory design algorithm is introduced and derived to conserve energy and save mission time. Under the FCC approach, the UAV sequentially visits the cluster centers and performs circular flight and communication. Transitions between cluster centers are smoothed via 3D Dubins curves, which provide physically achievable trajectories. Comprehensive numerical studies indicate that the proposed trajectory planning method reduces overall communication time and preserves UAV battery energy compared to other benchmark schemes.

In this paper, a methodology based on a mix-mode interconnection architecture is proposed for constructing application specific networks on chip to minimize the total communication time. The proposed architecture uses a globally asynchronous communication network and a locally synchronous bus (or cross-bar or Multistage Interconnection Network MIN). A complete graph is constructed such that each vertex represents an IP core or a set of IP cores connecting by a locally synchronous bus (or cross-bar or MIN) and each edge has a weight (profit) representing a communication ratio (CR) for each pair of vertices. A maximal-profit spanning tree is constructed to represent the final network on chip. Comparisons on cost and total communication times with the SPIN architecture, mesh architecture, and binary tree architecture using Huffman-coding-like algorithm show that the new methodology has better results.

Dynamic Automatic Forecaster Selection via Artificial Neural Network Based Emulation to Enable Massive Access for the Internet of Things

The increasing deployment of internet of things(IoT) systems across critical domains has broadened the threat landscape, and being the catalyst for a variety of security concerns, including very stealthy slow denial of service (slow DoS) attacks. These exploit the hypertext transfer protocol’s (HTTP) application-layer protocol to either close down service requests or degrade responsiveness while closely mimicking legitimate traffic. Current available datasets fail to capture the more stealthy operational profiles of slow DoS attacks or account for the presence of genuine slow nodes (SN), which are devices experiencing high latency. These can significantly degrade detection accuracy since slow DoS attacks closely emulate SN. This paper addresses these problems by synthesising a realistic HTTP slow DoS dataset derived from a live IoT network, that incorporates both stealth-tuned slow DoS traffic and legitimate SN traffic, with the three main slow DoS variants of slow GET, slow Read, and slow POST being critically evaluated under these network conditions. A limited packet capture (LPC) strategy is adopted which focuses on just two metadata attributes, namely packet length (lp) and packet inter-arrival time (Δt). Using a resource lightweight decision tree classifier, the proposed model achieves over 96% accuracy while incurring minimal computational overheads. Experimental results in a live IoT network reveal the negative classification impact of including SN traffic, thereby underscoring the importance of modelling stealthy attacks and SN latency in any slow DoS detection framework. Finally, a MPerf (Modelling Performance) is presented which quantifies and balances detection accuracy against processing costs to facilitate scalable deployment of low-cost detection models in resource-constrained IoT networks. This represents a practical solution to improving IoT resilience against stealthy slow DoS attacks whilst pragmatically balancing the resource-constraints of IoT nodes. By analysing the impact of SN on detection performance, a robust reliable model has been developed which can both measure and fine tune the accuracy-efficiency nexus.

The Internet of Things (IoT) demands real-time, low-latency processing of data generated by thousands of heterogeneous, resource-constrained devices. In such dynamic environments, ensuring fault tolerance becomes critical, especially for safety-sensitive applications like disaster management or patient monitoring. However, existing centralized fault tolerance solutions face serious scalability challenges across large, hierarchically connected IoT topologies. In this paper, we present Meerkat, a network-aware fault-tolerance protocol explicitly designed for IoT environments. Meerkat achieves zero-downtime recovery via redundant operator execution on disjoint paths and efficient duplicate detection. It also includes dynamic load balancing that adapts operator placement to device volatility, ensuring fair resource use. Compared to state-of-the-art techniques, Meerkat sustains up to 70× higher throughput with only 28% network overhead. These results highlight Meerkat's ability to deliver efficient fault tolerance with minimal overhead at IoT scale.