DARPA's Ocean of Things (OoT) program aims to achieve maritime situational awareness over large ocean areas through deploying thousands of small, low-cost floats that form a distributed sensor network. Each smart float will have a suite of commercially available sensors to collect environmental and activity data; the later function involves automatically detecting, tracking and identifying nearby ships and – potentially – close aircraft traffic. The floats use edge processing with detection algorithms and then transmit the semi-processed data periodically via the Iridium satellite constellation to a cloud network for on-shore storage. AI machine learning then combs through this sparse data in real time to uncover hidden insights. The floats are environmentally friendly, have a life of around a year and in buys of 50,000 have a unit cost of about US$500 each.
Best known for its Wi-Fi controllers that imbue dumb room air conditioners with smarts, Sensibo has leveraged its expertise to build the Sensibo Pure, a Wi-Fi-connected air purifier for small rooms. The Senisbo Pure can work in conjunction with Sensibo's other products to improve air quality, but it doesn't depend on the presence of one. The tradeoff for this air purifier's relatively small size--it measures 7.68 x 7.68 x 15.28 inches (WxDxH)--is that it can cover rooms only up to 173 square feet (e.g., a room with dimensions of about 13 x 13 feet with a typical 8-foot ceiling). Multiple Sensibo Pure's can be deployed around your home and controlled from its mobile app once they're connected to your Wi-Fi network (2.4GHz only). A feature Sensibo calls Pure Boost can increase that coverage to 294 square feet for a limited time (more on that in a bit).
Google's latest Nest Hub smart display tracks sleep with miniaturised radar without the user having to wear a bracelet or headband. The revamped 7in Google Assistant smart display is being repositioned as a smart alarm clock and health-monitoring device for the bedroom. The camera featured on the larger model has been left out, to protect privacy, and the device instead relies on Google's Soli radar system to detect the presence of people and do all the things other smart displays can such as showing photos, the time, weather and other information. Users will be able to make hand gestures in the air in front of the display to control things such as playback and silencing alarms. But the radar system can also track the movement and breathing during sleep of the person next to the display without requiring extra kit.
Zach Shelby has spent most of the last decade and a half on the front line of the Internet of Things (IoT). His company Sensinode, which was acquired by Arm in 2013, provided enterprise wireless sensor networks to system integrators and product providers. Shelby did lots of interesting work on embedded systems, and incorporating standards such as Bluetooth Low Energy (BLE). But he wanted to go a step further. The company, with Shelby as co-founder and CEO – Jan Jongboom, a colleague at Arm, as co-founder and CTO – is looking to enable developers to create next-gen applications with embedded machine learning (ML).
The integrity of sensors and actuators is critical to the safe and profitable operations of industrial processes. However, the lack of visibility into the heath of those sensors and actuators makes it challenging to ensure their integrity. The slightest sensor variation can have a rippling effect on production rate, scrap, and waste. Sensor integrity affects consumer-facing issues such as safety, customer satisfaction, and higher warranty costs. Nielsen conducted a survey for Advanced Technology Services and founded that the average cost of poor-quality calibration costs manufacturers $1,734,000 each year.
Given the increasing complexity of threats in smart cities, the changing environment, and the weakness of traditional security systems, which in most cases fail to detect serious threats such as zero-day attacks, the need for alternative more active and more effective security methods keeps increasing. Such approaches are the adoption of intelligent solutions to prevent, detect and deal with threats or anomalies under the conditions and the operating parameters of the infrastructure in question. This research paper introduces the development of an intelligent Threat Defense system, employing Blockchain Federated Learning, which seeks to fully upgrade the way passive intelligent systems operate, aiming at implementing an Advanced Adaptive Cooperative Learning (AACL) mechanism for smart cities networks. The AACL is based on the most advanced methods of computational intelligence while ensuring privacy and anonymity for participants and stakeholders. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. Learning is achieved through encrypted smart contracts within the blockchain technology, for unambiguous validation and control of the process. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods, in order to identify anomalies that are usually due to Advanced Persistent Threat (APT) attacks.
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.
We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a.k.a. Hawkes networks using discrete events data. Hawkes networks have become a popular model for statistics and machine learning due to their capability in modeling irregularly observed data where the timing between events carries a lot of information. The problem of detecting abrupt changes in Hawkes networks arises from various applications, including neuronal imaging, sensor network, and social network monitoring. Despite this, there has not been a computationally and memory-efficient online algorithm for detecting such changes from sequential data. We present an efficient online recursive implementation of the CUSUM statistic for Hawkes processes, both decentralized and memory-efficient, and establish the theoretical properties of this new CUSUM procedure. We then show that the proposed CUSUM method achieves better performance than existing methods, including the Shewhart procedure based on count data, the generalized likelihood ratio (GLR) in the existing literature, and the standard score statistic. We demonstrate this via a simulated example and an application to population code change-detection in neuronal networks.
A sleep tech firm has launched an AI mattress that can readjust to ensure the user is in the most comfortable position throughout the night. Called Emma Motion, the bed uses 360 motion sensors to detect even the smallest amount of pressure and reacts by moulding to the sleeper's body. Emma Motion works for anyone regardless of how much they toss and turn to provide'maximum comfort and ultimate ergonomics' and also features a heat-conducting sleep that keeps users cool. The mattress has been launched in France and Belgium this month for 2,499 euros (about £2,200) and will be released in the UK later this year. The world's most advanced smart mattress has been launched by sleep tech brand Emma, set to help address sleep problems The smart mattress has been created by German sleep tech brand Emma.
The need to accurately estimate the speed of road vehicles is becoming increasingly important for at least two main reasons. First, the number of speed cameras installed worldwide has been growing in recent years, as the introduction and enforcement of appropriate speed limits is considered one of the most effective means to increase the road safety. Second, traffic monitoring and forecasting in road networks plays a fundamental role to enhance traffic, emissions and energy consumption in smart cities, being the speed of the vehicles one of the most relevant parameters of the traffic state. Among the technologies available for the accurate detection of vehicle speed, the use of vision-based systems brings great challenges to be solved, but also great potential advantages, such as the drastic reduction of costs due to the absence of expensive range sensors, and the possibility of identifying vehicles accurately. This paper provides a review of vision-based vehicle speed estimation. We describe the terminology, the application domains, and propose a complete taxonomy of a large selection of works that categorizes all stages involved. An overview of performance evaluation metrics and available datasets is provided. Finally, we discuss current limitations and future directions.