EILID: Execution Integrity for Low-end IoT Devices

This paper tries to answer a question: “Can we achieve spatial-selective transmission on IoT devices?” A positive answer would enable more secure data transmission among IoT devices. The challenge, however, is how to manipulate signal propagation without relying on beamforming antenna arrays which are usually unavailable on low-end IoT devices. We give an affirmative answer by introducing SpotSound, a novel acoustic communication system that exploits the diversity of multi-path indoors as a natural <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beamformer</i>. By judiciously controlling the way how the information is embedded into the signal, SpotSound can make the signal decodable only when the signal propagates along a certain multipath channel. Since the multipath channel decorrelates rapidly over the distance between receivers, SpotSound can ensure the signal is decodable only at the target position, achieving precise physical isolation. SpotSound is a purely software-based solution that can run on most IoT devices where speakers and microphones are widely used. We implement SpotSound on Raspberry Pi connected with COTS microphone and speaker. Experimental results show that SpotSound could precisely focus its signal on spots with customized sizes ranging from 0.04<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 0.5<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) which are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this paper, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relative computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes and the cloud. We conduct extensive evaluations and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state of art under dynamic workloads, while incurring acceptable overhead.

Trusted identification is critical to secure IoT devices. However, the limited memory and computation power of low-end IoT devices prevent the direct usage of conventional identification systems. RF fingerprinting is a promising technique to identify low-end IoT devices since it only requires the RF signals that most IoT devices can produce for communication. However, most existing RF fingerprinting systems are data-dependent and/or not robust to impacts from wireless channels. To address the above problems, we propose to exploit the mathematical expression of the physical-layer process, regarded as a function $\mathbf{\mathcal{F}(\cdot)}$, for device identification. $\mathbf{\mathcal{F}(\cdot)}$ is not directly derivable, so we further propose a model to learn it and employ this function model as the device fingerprint in our system, namely $\mathcal{F}$ID. Our proposed function model characterizes the unique physical-layer process of a device that is independent of the transmitted data, and hence, our system $\mathcal{F}$ID is data-independent and thus resilient against signal replay attacks. Modeling and further separating channel effects from the function model makes $\mathcal{F}$ID channel-robust. We evaluate $\mathcal{F}$ID on thousands of random signal packets from $33$ different devices in different environments and scenarios, and the overall identification accuracy is over $99\%$.

Recent advancement in IoT with next generation wireless systems produces a data security challenge due to bulky encryption standards which are not capable to provide efficient hardware performance on tiny and low-end IoT devices. Numerous lightweight ciphers are proposed to address data security in constrained IoT environment. It is essential to evaluate the hardware performance of these designed ciphers which are proved their robustness against various cryptanalysis technique. This paper implements and analyses the new block cipher ANU on tiny and low-end device with the aim of secured IoT node development. Additionally, we implement popular block cipher PRESENT which was designed with consideration of constrained IoT applications such as Radio Frequency Identification (RFID). Finally, we compare our achieved results of ANU with PRESENT for performance metrics including flash memory, code size, machine cycle and execution time. Both designs are evaluated and analysed on 8-bit <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> -controller (AVR ATmega32) simulation platform.

Networks for the Internet of Things typically use a gateway to provide connectivity between a low bit rate, low capability sensor network and the broader Internet. The gateway can be subject to very high traffic loads, many concurrent processes and needs to be highly reliable. Functional programming languages such as Erlang and Elixir have proven to be an effective programming paradigm for such scenarios, notably in large-scale telecommunications switches. In this paper, we report on our experience of developing a gateway between a LoRa network and an MQTT broker using the functional programming language Elixir and the more conventional language C++. To obtain an understanding of this approach to development, we first developed an initial prototype on a single-board computer using Elixir. We then developed the same system in C++ and ran experiments to compare the two systems’ performance. In order to understand the performance of such systems on low-end IoT devices, we then developed the same system on a low-cost ESP32 micro-controller in both C++ and Elixir. We were able to run the Elixir-based system on an ESP32 micro-controller but found that its performance was significantly poorer than the same system written in C++. We conclude that functional programming has great potential for the development of IoT systems, but work needs to be carried out to improve the supporting libraries and underlying virtual machines. We also note that learning to program in a functional programming language has quite a steep learning curve.

The advancement of the Internet of Things has enabled numerous applications ranging from smart wearable to connected vehicles. However, the limited energy and memory resources of low-end IoT devices significantly impede their further flourish. In this article, we consider a system that consists of an IoT device and an edge server. The edge server stores code blocks for the IoT device and loads required blocks to the IoT device for execution thereby alleviates the latter from the limited memory resource. Furthermore, the IoT device can harvest energy from the ambient energy sources to achieve a sustainable operation. To deal with the dynamic energy harvesting process and block request process, we propose a stochastic block prefetching framework (BPF) to optimize the user experienced delay. The BPF assists the IoT device to intelligently prefetch blocks from the edge server according to the historical user behaviors. The BPF consists of three modules, i.e., estimation module, prefetching module, and dual learning module. The estimation module measures the probability of block being requested in the future. The prefetching module requests blocks from the edge server according to the available energy and memory. The dual learning module helps to accelerate the convergence of the framework. The numerous simulation results are provided to verify the effectiveness of the proposed framework.

The elliptic curve family of schemes has the lowest computational latency, memory use, energy consumption, and bandwidth requirements, making it the most preferred public key method for adoption into network protocols. Being suitable for embedded devices and applicable for key exchange and authentication, ECC is assuming a prominent position in the field of IoT cryptography. The attractive properties of the relatively new curve Curve448 contribute to its inclusion in the TLS1.3 protocol and pique the interest of academics and engineers aiming at studying and optimizing the schemes. When addressing low-end IoT devices, however, the literature indicates little work on these curves. In this paper, we present an efficient design for both protocols based on Montgomery curve Curve448 and its birationally equivalent Edwards curve Ed448 used for key agreement and digital signature algorithm, specifically the X448 function and the Ed448 DSA, relying on efficient low-level arithmetic operations targeting the ARM-based Cortex-M4 platform. Our design performs point multiplication, the base of the Elliptic Curve Diffie-Hellman (ECDH), in 3,2KCCs, resulting in more than 48% improvement compared to the best previous work based on Curve448, and performs sign and verify, the main operations of the Edwards-curves Digital Signature Algorithm (EdDSA), in 6,038KCCs and 7,404KCCs, showing a speedup of around $$11\%$$ compared to the counterparts. We present novel modular multiplication and squaring architectures reaching $$\sim $$ 25% and $$\sim $$ 35% faster runtime than the previous best-reported results, respectively, based on Curve448 key exchange counterparts, and $$\sim $$ 13% and $$\sim $$ 25% better latency results than the Ed448-based digital signature counterparts targeting Cortex-M4 platform.