EPOS: the Technology behind our IoT

EPOSMote III

The third generation of Motes produced by LISHA consolidates over a decade of industrial and academic deployments. EPOSMote III has a multilayered architecture around a low-power processing and mesh communication module. Additional modules allow for alternative power sources, such as solar and hydraulic; for long-range communication, such as Wi-Fi and 3G; and for numerous sensors, including GPS, gyroscope, accelerometer, temperature, humidity, pressure, turbidite, sound, ultrasound, and many others. It is the driving force behind all projects depicted in this site as 2017.

TSTP - Trustful Space-Time Protocol

The Trustful Space-Time Protoco (TSTP) is a cross-layer protocol designed to deliver authenticated, encrypted, timed, and geo-referenced messages containing SI-compliant data in a resource-efficient way. It pairs SmartData, a novel, data-centric paradigm for programming WSNs and the IoT, in terms of communication. By integrating shared data from multiple networking services into a single communication infrastructure, TSTP is able to eliminate replication of information across services, achieving small overhead in terms of control messages. It relies on symmetric encryption algorithms (i.e. AES + Poly 1305) and distributed key generation (i.e. Elliptical Diffie-Hellmann) to deliver a scenario in which private keys are never exchanged across the network and also not stored somewhere else. A TSTP-IP Fog gateway bridges TSTP secure devices with the cloud using traditional Public Key Infrastructures.

HeCoPS - Heuristic Cooperative Calibration Positioning System

The Heuristic Cooperative Calibration Positioning System (HeCoPS) performs trilateration of range measurements to estimate the position of IoT devices, both indoor and outdoor. Range measurements (rm) can be obtained from the Radio using Received Signal Strength Indicator (RSSI), from ultrasound transceivers, or from a more precise source such as a laser. HeCoPS constantly calculates deviations (dev) of those rm and broadcast them, along with a confidence tag, to its neighbors⁶.

SPTP - Speculative Precision Time Protocol

EPOS Speculative Precision Time Protocol (SPTP) levarages on TSTP timestamps present in each and every message to speculatively synchronize the clock of neighbor nodes to sub-microsecond precision. Clocks are constantly recalibrated to compensate the skew of low-cost mote's clock sources. Calibrations take place in the direction of the sink (Fog gateway), which can be seen as a Master of the original IEEE 1588 PTP. If on-going traffic is too low to support the calibration, then node reaching the half-life of their maximum sync cycle can induce traffic by setting a bit on TSTP header demanding a neighbor to send an empty, to-be-discarded packet.

SmartData

SmartData is a high-level API for sensor networks that aims at leveraging the myriad of features available on such networks while delivering a common, consistent, semantic abstraction for sensed data that facilitates the development of sensing applications without incurring in significant overhead. A SmartData is a piece of data enriched with enough metadata to make it self-contained in terms of semantics, spatial location, timing, and trustfulness. It is meant to be the only application-visible construct in the sensing platform and therefore implicitly mediates all system-level services, including communication, synchronization, and the interaction with transducers and actuators. An application reads a sensor simply by accessing the data contained in a SmartData through its interface. The data is automatically updated from the network according to the parameters specified at instantiation-time. Actuation happens through the same interface: changing a SmartData causes messages to be propagated over the network to command actuators accordingly until the new value is observed. It, therefore, bears the notion of a setpoint for a network-wide controller when seen as a writeable object.

SmartData: Designing Data-Driven Safety-Critical Systems

Data is at the core of the design of modern Safety-Critical Systems. Data is no longer only sensed and processed in the context of the control loops of such systems. It is also secured, stored, and transmitted for the sake of the decision-making processes required for higher levels of autonomy. The task-centered strategies traditionally used to design critical systems consistently support scheduling analysis and verification of tasks execution times as long as periods, deadlines, and execution time estimates are known, but mostly ignore the flow of data across the various components in the system and often assume that data generation time is constant and can be fully encapsulated in the execution time of tasks. These assumptions, however, are not in phase with the design of modern autonomous systems such as smart factories and autonomous vehicles, which are examples of critical systems that are quickly advancing towards autonomy. A Data-driven approach to the design of such systems can more promptly accommodate requirements such as data freshness, redundant data sources, operational safety, and AI-readiness.

Decomposing the problem domain into SmartData considers the modeling of constructs that will abstract the selected entities and their relationships according to the data they produce and consume. The decomposition of the Problem Domain in SmartData follows the principles of Object Orientation. The Problem domain is decomposed into entities representing the data produced and consumed by the system. They are represented as classes that implement the SmartData interface, tagged with either <<Stored>>, <<Sensor>>, <<Transformer>>, or <<Actuator>> stereotypes, and optionally tagged with <<Secure>> and <<Persistent>>. The decomposition starts with identifying the actuation that will be envisioned for the system, followed by the SmartData the actuators are interested in, up to the sensors. For instance, in an autonomous vehicle, one may need to actuate, at a given rate, over throttle, brake, and steering. Each actuation is associated with a specific data input, which must be provided with a specific freshness constraint to avoid consuming expired data. This data dependency will generate Interest in other SmartData, resulting from a transformation or a sensing process. This Interest relation will then carry the timing and security requirements associated with the actuation. If more than one actuation is interested in a SmartData, this SmartData must adapt its period to supply all its consumers accordingly.

The IoT Platform

Build on SmartData, LISHA"s IoT Platform integrates a myriad of wireless communication systems in TSTP segments that are connected to the Internet via trustworthy gateways.

The SmartX Industrial IoT Management Platform

SmartX is building an automated management system for Industrial IoT featuring supervision, tracking, and sophisticated AI algorithms to automatically detect anomalies, from physical faults to security breaches.

References

1. Davi Resner and Antônio Augusto Fröhlich, TSTP MAC: A Foundation for the Trustful Space-Time Protocol, In: Proceedings of the 14th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC 2016), Paris, France, September 2016.
2. Davi Resner and Antônio Augusto Fröhlich, Speculative Precision Time Protocol: submicrosecond clock synchronization for the IoT, In: Proceedings of the 21st IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016), Berlin, Germany, September 2016.
3. Davi Resner and Antônio Augusto Fröhlich, Design Rationale of a Cross-layer, Trustful Space-Time Protocol for Wireless Sensor Networks, In: Proceedings of the 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2015), pages 1-8, Luxembourg, Luxembourg, September 2015.
4. Davi Resner and Antônio Augusto Fröhlich, Key Establishment and Trustful Communication for the Internet of Things, In: Proceedings of the 4th International Conference on Sensor Networks (SENSORNETS 2015), pages 197-206, Angers, France, February 2015.
5. Rodrigo Vieira Steiner, Mohammad Reza Akhavan, Antônio Augusto Fröhlich, and A. Hamid Aghvami, "Performance Evaluation of Receiver Based MAC Using Configurable Framework in WSNs", In: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), pages 884-889, Shanghai, China, April 2013
6. Ricardo Reghelin and Antônio Augusto Fröhlich, "A Decentralized Location System for Sensor Networks Using Cooperative Calibration and Heuristics", In: Proceedings of the 9th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pages 139-146, Torremolinos, Spain, October 2006.
7. José Luis Conradi Hoffmann and Leonardo Passig Horstmann and Matheus Wagner and Felipe Vieira and Mateus Martínez de Lucena and Antônio Augusto Fröhlich, Using Formal Methods to Specify Data-Driven Cyber-Physical Systems, In: Proceedings of the 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), pages 1-8, Anchorage, AK, USA, June 2022. DOI: 10.1109/ISIE51582.2022.9831686.