标签：#IoT

Scientists have deliberately turned male mouse embryos into females for the first time. A team based in Japan used a CRISPR-based approach to remove the Y chromosome from male cells and create female clones of male mice. “No one has done this before,” says Monika Ward, a reproductive biologist at the University of Hawaii, who…

Startups and Big Ag are using Crispr gene editing to create crops that taste better and grow on a hotter planet. But will they find a market?

Mini human brains are being grown in labs all over the world. Soon, they could outthink neural networks.

How We Built an IoT Platform That Handles 30 Million Concurrent Connections — With a Team of 10 DGIOT is an open-source industrial IoT platform. We run 928 gateways across 16 oil fields, process 652 million data points, and maintain 99.9999% uptime. Here's the architecture that makes it possible. The Problem In 2021, we got a call from Daqing Oil Field — China's largest oil producer. They had a problem: 928 industrial gateways from different vendors 114,809 sensor points speaking 15 different protocols Data collection every 10 minutes (they needed seconds) 15-30 minute end-to-end latency (they needed <3 seconds) False alarm rate above 20% The existing system was a patchwork of vendor-specific tools, each with its own database, UI, and authentication. Operators had to log into 8 different systems just to check if a pump was overheating. They asked: "Can you unify this?" What We Built DGIOT is an Erlang/OTP-based platform that acts as a universal translator for industrial protocols. Think of it as a Rosetta Stone for machines. Modbus ─┐ OPC UA ─┤ MQTT ──┼──→ Unified Pipeline ──→ TDengine ──→ Dashboard IEC104 ─┤ A11 ──┘ The key insight: industrial protocols are just state machines . Once you model each protocol as a gen_statem FSM in Erlang, you can handle hundreds of them concurrently with almost zero overhead. The Architecture: DLAS We designed a four-layer architecture that separates concerns cleanly: Layer 1: DATA — Ingestion Parse Server (23 classes) handles device metadata, user auth, tenant isolation TDengine stores 652M time-series data points with 10:1 compression EMQX handles MQTT message routing at 1M+ msg/sec Mnesia/ETS provides in-memory caching for hot data Layer 2: LOGIC — Ontology Engine This is our secret weapon. We built a 252-entity OWL ontology that models industrial equipment: Pump ⊑ Equipment ⊓ ∃ hasPart.Bearing ⊓ ∃ measures.Pressure Bearing ⊑ Component ⊓ ∃ hasFailureMode.Overheat Overheat → triggers ( Alert ) ∧ reduces ( RemainingLife , 0.8 ) The

The use of AI systems to create viruses opens up new possibilities for combating bacterial resistance. It also raises concerns about the pace at which technology is outstripping regulation.

Consumindo REST APIs no ESP32 sem Estourar a Memória: Conheça o ESP32-HTTP-Client Se você já desenvolveu projetos IoT no ESP32 que se comunicam com APIs REST (seja para enviar dados de sensores para a nuvem, consultar status de serviços ou integrar com Firebase e AWS), provavelmente já enfrentou um destes problemas clássicos: Fragmentação e estouro de heap: O combo padrão HTTPClient + ArduinoJson precisa carregar todo o payload HTTP na RAM como String antes de desserializar o JSON. Em payloads médios ou grandes, isso gera Out of Memory ou travamentos intermitentes. Lentidão em requisições consecutivas: O HTTPClient padrão refaz o handshake TLS/TCP repetidamente, adicionando centenas de milissegundos a cada chamada. Código verboso e boilerplate excessivo: Mais de 15 a 20 linhas de código para instanciar clientes, extrair buffers, checar erros e navegar em nós JSON. Para resolver esses gargalos de forma elegante e moderna, foi criada a biblioteca ESP32-HTTP-Client . O que é o ESP32-HTTP-Client? O ESP32-HTTP-Client é um cliente HTTP/REST moderno, fluente e orientado a objetos para ESP32, projetado especificamente para sistemas embarcados de alta eficiência. Em vez de "fazer download da resposta, guardar na memória e depois processar", ele utiliza Direct Memory Binding (injeção direta) e Stream Parsing : os dados do JSON são lidos diretamente do stream da rede e injetados direto nas suas variáveis ou struct s em C++, sem armazenar o payload inteiro na RAM . // Uma linha. Zero strings intermediárias. Injeção direta em memória. client . get ( "/sensor" ). getBody ( "temperature" , & myFloatVariable ); Benchmark: ESP32-HTTP-Client vs Abordagem Tradicional Em testes controlados com 100 requisições HTTP consecutivas contendo payloads JSON (usando o endpoint /users do JSONPlaceholder), os resultados comprovam a economia de recursos: Métrica / Recurso HTTPClient + ArduinoJson (Padrão) ESP32-HTTP-Client Diferencial Heap alocado por requisição ~58.2 KB ~0.0 KB (15 bytes) ~99.9%

What started as a vision for mapping urban gardens has evolved into something much bigger. Over the past weeks, we've built a complete decentralized ecosystem that connects IoT sensors, robots, and communities using Monero (XMR) and MYZ tokens. The Journey: From Gardens to Streets It all began with a simple idea: create a map for urban gardens. But we quickly realized that a map alone wasn't enough. We needed a full system that could: Monitor soil health in real-time Automate irrigation and analysis Enable private, decentralized payments Connect communities and institutions Here's what we built. 🗺️ The Urban Garden Map Using Leaflet.js and a REST API, we created an interactive map where anyone can register their urban garden. The map supports: Geolocation with /nearby endpoint Full CRUD operations for gardens Search by name and city Check it out: Live Demo 📡 Arduino Sensors for Smart Agriculture We integrated Arduino sensors to monitor soil conditions in real-time: pH (0-14 scale) EC (Electrical Conductivity) Temperature and Humidity The data flows through Node.js APIs and is stored in MongoDB, making it accessible for analysis and reporting. 🦾 The Robot Ecosystem We built a family of software robots that can receive payments automatically in MYZ and XMR: 1. AgricoloBot - The Garden Assistant Monitors soil health Generates automatic reports Provides recommendations for farmers 2. Robot Arm - The Physical Gardener 4 DOF (Degrees of Freedom) Controlled via WebSocket Can water, plant, analyze, and harvest 3. CleanStreetBot - Street Cleaning Robot Reports waste with geolocation Automates zone cleaning Generates reports for municipalities 4. RecicloBot, PuliziaBot, CompostBot - Recycling and Waste Management Monitor containers and optimize collection routes Track composting and organic waste management 💰 Decentralized Payments with Monero and MYZ All robots receive automatic payments through an escrow system: 85% → Robot owner 2% → MyZubster platform 8% → Bosco Community

* *The purpose of this light version threat model is to demonstrate how STRIDE can be applied to an ECG device. It is intended for readers learning system decomposition and threat modelling techniques. The example includes a simplified set of components, threats, and mitigations for educational purposes and is not intended to represent a comprehensive medical device cybersecurity assessment or any regulatory submission. **Assumption: This example models a typical ECG device, which may include network connectivity in a clinical environment. Trust Boundaries: Trust boundaries exist between the ECG device, hospital network, and external clinical systems. System Definition: ECG is the abbreviation for an Electrocardiogram. It is used to detect electrical activity of the heartbeat in the form of P wave, QRS complex and T wave to identify and diagnose irregularities in heartbeat. Electrodes are placed on patient’s limbs and chest to measure the electrical potentials. It translates tiny electrical signals into digital wave patterns. These waveforms are used by the doctors to evaluate the heart rhythm and check for cardiac damage. Components • Electrodes • Lead wires • Amplifier and filters • Analogue-to-Digital Converter (ADC) • Main processing unit • Display/printer • Local storage • Network interface (Ethernet/Wi-Fi/Bluetooth), if supported. Data Flow Diagram: Electrodes → Lead wires → Amplifier and filters → Analogue-to-Digital Converter (ADC) → Main processing unit → Display / Printer / Local storage / Network interface (if supported) |TRUST BOUNDARY|→ Electronic Health Record (EHR) / Clinical Information System 2. STRIDE Threats: Threats Description Spoofing in general ** - Spoofing is the act of impersonating a legitimate user, device, or system to gain unauthorized access to resources or services. Violates authentication. * Spoofing in ECG * - An attacker may impersonate an authorized clinician, connected medical device, or trusted clinical system to gain unauthoriz

A startup has created beagles without the gene that causes runny noses and watery eyes for allergy sufferers.

I didn't put Shelly Plus 1PM and 2PM behind my wall switches for the app or the cloud. I put them there because each one quietly does two jobs at once: it switches a circuit locally, and it measures exactly what that circuit draws. The moment I had them wired in, every light group and every roller shutter in the flat became both controllable AND measurable in Home Assistant — with no separate energy meter and, deliberately, no cloud in the loop. This is the part most Shelly write-ups skip: the metering isn't a bonus, it's the reason to use them. This is how I set them up, kept them fully local, wired the physical switches so they still work like switches, and pulled per-channel energy into the HA Energy Dashboard. Why the Plus series for in-wall use The Plus 1PM and 2PM are Gen2 devices. That matters: they speak a local RPC API over HTTP and WebSocket, they're supported by the native Home Assistant Shelly integration out of the box, and nothing about them needs the internet once they're on your Wi-Fi. The 1PM is a single relay with power metering — I use it behind light circuits. The 2PM has two relays with metering and a dedicated cover/roller-shutter mode — that's what sits behind my shutters. The catch you plan for up front: both need a neutral wire in the switch box. Older installations often don't have one at the switch, so check before you order. If the neutral is there, the rest is easy. The first thing I change: kill the cloud, pin the IP Straight out of the box I do two things in the Shelly web UI (just browse to the device's IP). First, I disable Bluetooth and Cloud under Settings — I don't want the device reaching out to anything, and Home Assistant talks to it directly on the LAN. Second, I give it a fixed address via a DHCP reservation on my router, so the native integration never loses it after a reboot. That's the whole hardening story. No firmware flashing, no third-party firmware — stock Shelly firmware with the cloud switched off is already a fully

I used to laugh at people who panicked over data limits. Then my carrier switched my "unlimited" plan...

Most conversations about CRA, DORA, and NIS2 compliance for IoT hardware boil down to one uncomfortable binary: redesign the board around newer, security-capable silicon, or accept that your existing product line falls out of compliance on a fixed deadline. For a product with years left in its lifecycle and a BOM that took months to qualify, "just redesign it" is rarely a real answer. There's a third option that gets far less attention than it deserves: pair the legacy chip with a modern security co-chip that absorbs the cryptographic boundary, while the legacy part keeps doing exactly what it already does well - application logic, peripherals, display, sensor polling. Call it a soft fade-out. The old silicon stays in service until its natural end-of-life; the compliance gap gets closed by a second, much cheaper part sitting next to it, not by replacing it. The Three Gaps a Legacy Chip Has - and Why a Co-Chip Fixes Them The regulatory pressure driving all of this isn't abstract. NIST finalized its post-quantum cryptography standards in 2024, and IR 8547 sets real dates: ECDSA and RSA are deprecated after 2030, disallowed after 2035. Germany's BSI has gone further - TR-02102-1 (2026 edition) sets a stricter 2030 deadline for high-protection-need data, and treats the migration as "alternativlos" (without alternative) rather than a recommendation. Older embedded silicon typically lacks three things simultaneously: a hardware-isolated key store (TEE/APM), side-channel countermeasures (DPA protection) strong enough for physical-access threat models, and enough RAM/compute headroom to run lattice-based PQC algorithms in software without starving the rest of the firmware. Redesigning the whole board to fix all three at once is expensive and slow. But none of those three gaps require touching the part that's already doing its job - they're all boundary problems. A second, purpose-built chip can own the boundary. Three concrete pairings Using the ESP32 family as a worked exa

Kris DeVault is desperate. His son, Brody, was born in March 2023. It wasn’t long before he started to show signs of developmental delay, says DeVault. As time went on, Brody started missing key milestones in speech, movement, and coordination, he says. When Brody was around two and a half years old, a genetic test…

The health tech data giant, which handles vast amounts of patients' medical data, said hackers struck one of its protected health data stores.

As of this week in Montana, any biotech company with an experimental drug has a clear path to selling it to consumers. Companies whose drugs have been through preliminary testing—sometimes in as few as 10 healthy people—can pay $12,500 to apply to a newly established review board for approval. Once its treatment is rubber-stamped, the…

Why this combination keeps winning for IoT telemetry backends — not in a benchmark, but against flaky gateways, replayed data, and firmware that never quite agrees with itself. If you’ve ever built the backend for a fleet of IoT devices — sensors, gateways, industrial equipment reporting temperature, humidity, GPS, battery, signal strength — you’ve faced the same fork in the road early on: what do you build the ingestion layer with, and what do you store the data in? After building a telemetry backend from scratch for a real fleet of LoRa/BLE sensors and gateways — handling dual ingestion paths, binary and JSON payload formats, automatic recovery of lost data, and a growing set of operational dashboards — I keep coming back to the same combination: Python (FastAPI + asyncio) for the API, MQTT for device transport, and PostgreSQL for storage. Here’s why that combination holds up so well for this specific problem, not just “in general.” Full Article: https://medium.com/@jackpelorus/python-postgresql-and-mqtt-the-boring-stack-that-actually-survives-a-real-device-fleet-9297146cbe8d?sharedUserId=jackpelorus

As software developers, we often spend most of our time building APIs, databases, authentication systems, and web applications. That's certainly been my focus recently, especially working with Go, JWT authentication, and backend services. Last week, however, I had the opportunity to participate in the KijaniSpace Hackathon , held at Zone01 Kisumu , and it introduced me to an entirely different side of software development. Our challenge was to build solutions using: Geographic Information Systems (GIS) The Copernicus API IoT devices where applicable It was an opportunity to see how software can interact with our physical world. What is GIS? GIS (Geographic Information Systems) is a technology used to collect, analyze, visualize, and manage data that has a geographic location. Imagine not just storing information like: Temperature Population Vegetation Buildings Roads ...but also knowing exactly where that information exists on Earth. That location data allows developers to build intelligent systems capable of answering questions like: Which farms are experiencing drought? Which roads are likely to flood? Which areas are losing forest cover? Where should new infrastructure be built? GIS transforms ordinary data into meaningful geographic insights. Discovering the Copernicus Program Before this hackathon, I had heard very little about Copernicus. Copernicus is the European Union's Earth Observation Programme. It provides free satellite imagery and environmental data collected by the Sentinel satellite missions. Through its APIs, developers can access information about: Land cover Vegetation health Weather patterns Water bodies Air quality Climate changes Disaster monitoring What amazed me most is that much of this data is openly available for developers to build impactful applications. Where IoT Fits In Some teams also explored Internet of Things (IoT) solutions. IoT devices can collect real-world information through sensors measuring: Soil moisture Temperature Humidi

Modern electrical grids increasingly rely on distributed sensors installed across conductors, towers, poles, substations, and remote line sections. These devices can measure: Conductor temperature Current and voltage Mechanical tension Line sag Vibration Weather conditions Fault passage Switch and recloser states Collecting these measurements is relatively straightforward. Building a reliable data pipeline around them is much harder. Power infrastructure often operates in locations with unstable connectivity, limited bandwidth, and strict requirements for alarm delivery. A useful architecture must therefore do more than move telemetry from sensors to a cloud database. It must determine which data is urgent, validate measurements, preserve event order, survive network outages, and integrate the results with operational utility systems. This article explores how to design that pipeline. The Basic Architecture A practical grid-monitoring data flow may look like this: Field Sensors | v Protocol Adapters | v Edge Data Model | +----> Local Rules and Fault Detection | +----> Local Time-Series Buffer | +----> Event Queue | v Central IoT or Utility Platform | +----> SCADA +----> GIS +----> OMS +----> Analytics +----> Maintenance Systems The edge gateway sits between field equipment and central applications. Its job is not limited to protocol conversion. It also acts as a local data-processing and reliability layer. Why Cloud-Only Processing Is Risky Imagine a utility operating 5,000 field sensors. Each device reports one measurement every second. That produces: 5,000 measurements per second 300,000 measurements per minute 18,000,000 measurements per hour Most of those measurements will describe normal operating conditions. Sending every individual value to a central platform creates unnecessary: Bandwidth consumption Storage growth Processing overhead Communication costs Dependence on network availability More importantly, cloud-only logic can stop working when the connectio

Evidence First, Answer Second: Building an Observable Industrial AI Agent with SigNoz Most AI systems are designed to give an answer. That is useful in a chatbot. On a factory floor, it can be dangerous. While building Industrial IoT Anomaly Control , I kept coming back to one question: What should an AI agent do when it detects a real problem but does not have enough evidence to explain the cause safely? My answer was simple: it should stop, show what it knows, and send the case to a human. This project is a real-time monitoring system for a simulated water-treatment plant. It streams live sensor data from six industrial assets, detects unusual behaviour, searches a knowledge base for similar incidents, and then chooses between two paths: recommend a safe action when the evidence is strong; escalate for human review when the evidence is weak. SigNoz is what makes this decision process visible. Instead of seeing only the final AI response, I can inspect the full path from the incoming sensor reading to anomaly detection, knowledge retrieval, policy checks, agent explanation, and recovery. The problem with traditional alarms Factories already collect large amounts of telemetry such as vibration, temperature, humidity, sequence numbers, and timestamps. The problem is not missing data. The problem is turning that data into a useful decision. A traditional threshold alarm may say: Vibration is above the configured limit. That still leaves the operator with several questions: Is the machine actually failing? Is the sensor or gateway sending bad data? Has this pattern happened before? Is there enough evidence to recommend maintenance? Why did the AI reach this conclusion? Repeated threshold alerts can also create alarm fatigue. If one fault produces dozens of alerts, operators may start treating them as noise. I wanted the system to create one investigation instead of another flood of alarms. What I built The demo represents six assets in a water-treatment plant. A TCP si

This week, I covered a fascinating effort to preserve organs outside the body. There’s a huge shortage of donor organs, and one of the main reasons is time—they survive only a matter of hours outside the body, even when they’re kept on ice. Doctors dream of organ banks—stores of human organs that can be preserved…