A Low-Cost Calibration Method for Low-Cost MEMS Accelerometers Based on 3D Printing.

A ubiquitous sensor in embedded systems is the accelerometer, as it enables a range of applications. However, accelerometers experience nonlinearities in their outputs caused by error terms and axes misalignment. These errors are a major concern because, in applications such as navigations systems, they accumulate over time, degrading the position accuracy. Through a calibration procedure, the errors can be modeled and compensated. Many methods have been proposed; however, they require sophisticated equipment available only in laboratories, which makes them complex and expensive. In this article, a simple, practical, and low-cost calibration method is proposed. It uses a 3D printed polyhedron, benefiting from the popularisation and low-cost of 3D printing in the present day. Additionally, each polyhedron could hold as much as 14 sensors, which can be calibrated simultaneously. The method was performed with a low-cost sensor and it significantly reduced the root-mean-square error (RMSE) of the sensor output. The RMSE was compared with the reported in similar proposals, and our method resulted in higher performance. The proposal enables accelerometer calibration at low-cost, and anywhere and anytime, not only by experts in laboratories. Compensating the sensor's inherent errors thus increases the accuracy of its output.

Lightweight Authentication Protocol for M2M Communications of Resource-Constrained Devices in Industrial Internet of Things.

The Industrial Internet of Things (IIoT) consists of sensors, networks, and services to connect and control production systems. Its benefits include supply chain monitoring and machine failure detection. However, it has many vulnerabilities, such as industrial espionage and sabotage. Furthermore, many IIoT devices are resource-constrained, which impedes the use of traditional security services for them. Authentication allows devices to be confident of each other's identity, preventing some security attacks. Many authentication protocols have been proposed for IIoT; however, they have high computing requirements not viable to resource-constrained devices, or they have been found insecure. In this paper, an authentication protocol for resource-constrained IIoT devices is proposed. It is based on the lightweight operations xor, addition, and subtraction, and a hash function. Also, only four messages are exchanged between the principals to authenticate. It has a low execution-time and communication-cost. Its security was successfully assessed with the formal methods Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Burrows-Abadi-Needham (BAN) logic, together with an informal analysis of its resistance to known attacks. Its performance and security were compared with state-of-the-art protocols, resulting in a good performance for resource-constrained IIoT devices, and higher security similar to computational expensive schemes.

A Lightweight Cipher Based on Salsa20 for Resource-Constrained IoT Devices.

The Internet of Things (IoT) paradigm envisions a world where everyday things interchange information between each other in a way that allows users to make smarter decisions in a given context. Even though IoT has many advantages, its characteristics make it very vulnerable to security attacks. Ciphers are a security primitive that can prevent some of the attacks; however, the constrained computing and energy resources of IoT devices impede them from implementing current ciphers. This article presents the stream cipher Generador de Bits Pseudo Aleatorios (GBPA) based on Salsa20 cipher, which is part of the eSTREAM project, but designed for resource-constrained IoT devices of Class 0. GBPA has lower program and data memory requirements compared with Salsa20 and lightweight ciphers. These properties allow low-cost resource-constrained IoT devices, 29.5% of the embedded systems in the market, to be able to implement a security service that they are currently incapable of, to preserve the user's data privacy and protect the system from attacks that could damage it. For the evaluation of its output, three statistical test suites were used: NIST Statistical Test Suite (STS), DIEHARD and EACirc, with good results. The GBPA cipher provides security without having a negative impact on the computing resources of IoT devices.

Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs.

Mobile ad-hoc networks (MANETs) are dynamic by nature; this dynamism comes from node mobility, traffic congestion, and other transmission conditions. Metrics to evaluate the effects of those conditions shine a light on node's behavior in an ad-hoc network, helping to identify the node or nodes with better conditions of connection. In this paper, we propose a relative index to evaluate a single node reliability, based on the link disconnection entropy disorder using neighboring nodes as reference. Link disconnection entropy disorder is best used to identify fast moving nodes or nodes with unstable communications, this without the need of specialized sensors such as GPS. Several scenarios were studied to verify the index, measuring the effects of Speed and traffic density on the link disconnection entropy disorder. Packet delivery ratio is associated to the metric detecting a strong relationship, enabling the use of the link disconnection entropy disorder to evaluate the stability of a node to communicate with other nodes. To expand the utilization of the link entropy disorder, we identified nodes with higher speeds in network simulations just by using the link entropy disorder.

A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals.

The increasing use of mobile devices in indoor spaces brings challenges to location methods. This work presents a hybrid intelligent method based on data mining and Type-2 fuzzy logic to locate mobile devices in an indoor space by zones using Wi-Fi signals from selected access points (APs). This approach takes advantage of wireless local area networks (WLANs) over other types of architectures and implements the complete method in a mobile application using the developed tools. Besides, the proposed approach is validated by experimental data obtained from case studies and the cross-validation technique. For the purpose of generating the fuzzy rules that conform to the Takagi-Sugeno fuzzy system structure, a semi-supervised data mining technique called subtractive clustering is used. This algorithm finds centers of clusters from the radius map given by the collected signals from APs. Measurements of Wi-Fi signals can be noisy due to several factors mentioned in this work, so this method proposed the use of Type-2 fuzzy logic for modeling and dealing with such uncertain information.