Tom Carroll of JLL property services recently penned an article for the UK-based publication Computing that explores the future of intelligent buildings. As Carroll explains, advanced sensors and the ubiquitous adoption of mobile devices, combined with the rapidly burgeoning IoT, will transform the services a building is capable of offering.
Search Results for: IoT
Future smart buildings will adapt and learn
Tom Carroll of JLL property services recently penned an article for the UK-based publication Computing that explores the future of intelligent buildings. As Carroll explains, advanced sensors and the ubiquitous adoption of mobile devices, combined with the rapidly burgeoning IoT, will transform the services a building is capable of offering.
“[This will] optimize energy provision, temperature control, digital wayfinding (using sensors to find deskspace and map surroundings) and, ultimately, deliver a better overall user experience,” wrote Carrol. “[Moreover], the next generation of building management systems (BMS) will function like the building’s operating system, taking in data and making decisions on how to optimize the building’s design and performance.”
In addition, smart building systems will generate, analyze and interpret vast streams of information. This will allow next-gen smart buildings to marry usage data with information about individual staff movements and work habits to help facilitate collaboration between employees.
“By 2030, we predict that the tactical and operational management of workplaces will largely be undertaken by algorithms analyzing millions of data sets,” he stated. “Buildings will be able to link location data with information from corporate databases and social media to engineer interactions between staff members. Offices will soon become part of the management team of any business – for example, notifying one employee working on a project that another specialist is nearby and suggesting a meeting.”
It should be noted that a recent white paper authored by U.S. furniture giant Haworth expressed similar sentiments, as it described how sophisticated sensors deployed in the workplace of the future can help contribute to employee well-being and increased productivity. These smart sensors will be tasked with constantly monitoring environmental conditions as well as the way employee spaces are used. This will enable workspaces to “shape-shift” for maximum efficiency, automatically altering temperature and lighting levels.
“This is an amazing shift in design thinking,” Haworth’s research program manager Mike Bahr elaborated in a statement quoted by Dezeen Magazine. “[We] believe new technologies can make work better by helping people be their best and soon we’ll see employees drawn to the office in their search for increased wellbeing, engagement, and effectiveness. Why? Because their workspace responds to how they work best.”
To be sure, Haworth envisions a workplace of the future where sensor information detailing light intensity and spectrum, sound amplitude and direction, air quality, odor and occupant location and activity are all integrated – feeding critical data to automatic and responsive environmental systems.
“Occupancy sensors that monitor how employees are using a space are already available and give designers the information to create more effective interior layouts. In [the] future they could generate data for a computer system that adapts a space automatically,” Dezeen Magazine explained. “For example, a meeting room might work better as a less formal area without a central boardroom-style table one day, and a video-conference space the next. New advances in sensors that monitor environmental factors like the intensity of light, sound and air quality are also turning these into key tools for better workspaces.”
As we’ve previously discussed on Rambus Press, this is precisely why lensless smart sensors (LSS) are designed to “understand” the movement, presence and patterns of smart office occupants. Indeed, the presence of an individual, the number of occupants and relevant activity are all passively detected with LSS technology.
So, how do Rambus lensless smart sensors work? Well, LSS offers a novel approach to sensing by combining ultra-small diffractive gratings with standard image sensors. Simply put, light passing through the diffractive grating is intelligently spread onto the image sensor below to form an unrecognizable, yet information-rich blob containing the relevant data from a specific scene. This information is combined with application-specific algorithms that can either visually reconstruct a scene, or extract pertinent data, such as the number and location of occupants.
The resulting data is then analyzed – automatically triggering specific systems and functions within a smart building, such as security, heating, cooling and lighting. This smart approach increases the comfort and safety level of the occupants while significantly reducing energy costs. By rethinking the way digital systems ‘see,’ LSS creates an intelligent infrastructure capable of adapting to the ever-changing needs of the individual in the workplace of the future.
With optics approximately the size of a human hair and a power envelope so low that certain applications could run on energy harvested from their environment, Rambus LSS technology offers the potential to positively disrupt the future of smart buildings and cities, as well as wearables, medical equipment, transportation and manufacturing.
Interested in learning more about Rambus lensless smart sensors? You can check out our LSS product page here.
Rambus talks vehicle security at TU-Automotive
Joe Gullo, the senior director for Rambus automotive strategy and development, recently participated in a TU-Automotive panel that explored the importance of securing next-gen autonomous vehicles. Indeed, the number of threat vectors in the automotive sector have exponentially increased in recent years. This is due to a range of factors, such as more complex software code, ubiquitous connectivity, a greater number of components and broader functionality.
Gullo kicked off his Q&A session by observing that automotive security best practices currently fall into three primary categories: authentication, multi-faceted designs, and flexibility.
“Authentication needs to happen in both directions. In other words, the car has to trust the cloud and the cloud has to trust the car,” he told panel participants and conference attendees. “Unfortunately, I think that authenticating vehicles sometimes gets less attention than it should. This is also true for any IoT device, even refrigerators and washing machines.”
As Gullo pointed out, a multi-faceted design approach is required to address a range of threat vectors, including attacks on the cloud-to-car connection, the in-vehicle network and specific ECUs. However, he emphasized there isn’t a “single, simple solution” that offers optimal security.
“For example, the components for V2X security may not be effective for monitoring and protecting in-vehicle networks. In general, security architectures need to be flexible because future threats are unlikely to resemble our current understanding of threat vectors,” Gullo explained. “These architectures need to have the ability to learn, evolve, and improve ‘in the field’ as new threats emerge. We also need to be thoughtful regarding solution complexity so systems can be adapted quickly as new threats emerge. This means relying on the fundamentals, such as proven algorithms, robust key management, secure boot loaders and constant threat detection, for example.”
As Gullo noted, this is precisely why automotive security architecture needs to evolve from static, simple solutions to a more dynamic framework that is self-learning, easily updatable and multi-faceted to address multiple threat vectors. This progression inevitably brings a number of new issues to the fore, including end-to-end secure data storage for autonomous vehicles.
“There are a host of companies whose core competence is secure, cloud-based data storage. OEMs can and should leverage these companies, although they should make it clear that while partners are tasked with securely storing data, they don’t own it,” Gullo opined. “Analyzing the data, generating insights from the information and acting on those insights is solely within the purview of the OEMs. Also, it goes without saying that a robust key management solution is required to secure the data in the vehicle and during transmission to and from the cloud service.”
To be sure, there are expected to be more than 350 million connected cars on the road by 2020. Google’s autonomous vehicles generate about 1 gigabyte of data every second, while Intel says autonomous vehicle are likely to produce about 2 petabytes of data per year. Information generated by connected and autonomous vehicles includes environmental data, as well as vehicle and driver performance.
“Maintaining the integrity of safety-critical and forensic vehicle data, particularly with respect to V2X, driver performance and vehicle performance, is absolutely critical. While some data should be shared for the ‘common good,’ it will undoubtedly be challenging to reach consensus on precise parameters,” Gullo emphasized. “Whether it’s through the Auto-ISAC or some other consortium, the industry clearly needs to agree on a ‘common good’ data set and ensure that vehicle owners are aware of the requirement to share this information.”
Gullo also described current security standards, specifications and guidelines including the ISO 26262 standard for functional safety and SAE’s J3061 Cybersecurity Guidebook (for Cyber-Physical Vehicle Systems).
“There is also SAE’s pending J3101 standard titled Requirements for Hardware-Protected Security for Ground Vehicle Applications, while UMTRI and the Southwest Research Institute are working on a framework for secure OTA software and firmware upgrades. This space is still evolving, although quite a lot has already been accomplished,” he added.
Solid State Circuits Magazine highlights lensless smart sensors
A Rambus VLSI Symposium paper on lensless smart sensor (LSS) technology has been cited in Solid State Circuits Magazine.
“In an invited paper, Rambus presented an overview of lensless smart sensors that rely on phase-modulated diffraction gratings above a conventional imaging array. Compared to a lens, this More-than-Moore diffraction grating, seen in Figure 6, can be designed for wide wavelength bands and has a lower profile for thinner sensors,” the publication stated.
“Results were presented in the context of point range finding, eye tracking and occupancy detection applications. While the raw images appear incomprehensible to the human eye, image reconstruction is possible, but the end application information can also be derived directly from the raw data itself, using the known point-spread function.”
As we’ve previously discussed on Rambus Press, lensless smart sensors enable a new approach to optical sensing that delivers on package, power and price by replacing traditional lenses with tiny diffractive optics. In addition, LSS operates in visible and thermal wavelengths, offering significant size and cost advantages versus standard thermal imaging modules. With the addition of these new capabilities, LSS can replace traditional thermal lenses with optical gratings that are significantly less expensive, enabling adoption of LSS thermal and visible sensing into a broad range of IoT applications including automotive, virtual and augmented reality and smart home use cases.
In terms of the latter category, smart buildings and homes are steadily moving beyond traditional structures and evolving into complex, connected systems designed to optimize efficiency, productivity, comfort and safety for their occupants. With its tiny form factor, low power, low cost and wide field of view, LSS is an ideal sensing solution for building automation systems and can be easily integrated into smart LED bulbs, commercial light fixtures, or an unobtrusive discrete sensor pack to feed.
Moreover, LSS is capable of detecting and interpreting activity within a space at a size and performance previously unattainable with existing building sensing technologies, all without compromising privacy. The data about the general activity and number of occupants in the area can be used to intelligently trigger environmental systems, monitor traffic flows and optimize area usage, reducing the environmental impact, along with operating and maintenance costs.
Interested in learning more about lensless smart sensors? You can check out our LSS product page here and our article archive on the subject here.
Looking beyond Dennard Scaling
Robert H. Dennard co-authored his now-famous paper for the IEEE Journal of Solid State Circuits way back in 1974. Essentially, Dennard and his engineering colleagues observed that as transistors are reduced in size, their power density stays constant. Meaning, power use stays in proportion with area, as both voltage and current scale (downward) with length.
There is a general industry consensus that the laws of Dennard scaling broke down somewhere between 2005-2007. Because threshold and operating voltage cannot be scaled any longer, it isn’t possible to maintain a constant power envelope from generation to generation – while simultaneously achieving the performance gains historically associated with reducing transistor size. Nevertheless, according to Yakun Sophia Shao and David Brooks, the semiconductor industry has already begun to adapt to the loss of Dennard Scaling as the end of Moore’s Law also looms large on the near-term horizon.
“This will likely lead to the additional consolidation in the semiconductor industry and fabrication companies will rely on ‘More-than-Moore’ to prove differentiation,” Shao and Brooks explained in their 2015 book titled Research Infrastructures for Hardware Accelerators. “Without either kind of scaling, there is also a risk of stagnation in the overall computing industry.”
However, Shao and Brooks emphasize that technology disruption often means new opportunities for innovation at the design and architecture level.
“Companies will increasingly differentiate their products based on vertically integrated solutions that leverage new applications mapped to innovative hardware architectures,” they stated. “In this context, application and domain specific hardware accelerators are one of the most promising solutions for improving computing performance and energy efficiency in a future with little benefit from device technology innovation.”
Steven Woo, VP of Systems and Solutions at Rambus, concurs with the assessment offered by Shao and Brooks.
“Although Moore’s Law has facilitated the creation of more transistors per chip for decades, clock speeds are plateauing due to power and thermal limitations. Similarly, improvements in Instructions Per Clock cycle have plateaued as well,” he explained.
“With the traditional paths for improving system performance no longer yielding gains at their historic rates, the industry must focus on rethinking system architectures to drive large improvements in performance and power efficiency.”
Further complicating matters, says Woo, is the fact that traditional performance and power efficiency bottlenecks in systems have shifted over the years due to the evolution of both architecture and applications. Put simply, the relentless progression of Moore’s Law and clock speed scaling prevalent throughout the 1990s and early 2000s so effectively improved computation capabilities that processing bottlenecks have moved to other areas.
“For example, the rise of the Internet of Things (IoT), Big Data analytics, in-memory computing and machine learning has resulted in ever-larger amounts of data being generated and analyzed,” he continued. “In many systems today, so much data is transferred across networks that data movement is itself becoming a critical performance bottleneck. Moreover, the very act of moving data is consuming a significant amount of power, so much so that it’s often more efficient to move the computation to the data instead.”
Consequently, there is an industry-wide effort to re-examine the architecture of conventional computing platforms by reducing and even eliminating certain modern bottlenecks.
“There are a number of recent developments in the industry that address modern HPC and data center bottlenecks such as Near Data Processing. These include the use of various accelerators including GPUs, FPGAs, and specialized processors,” Woo stated. “These industry efforts are focusing on both the hardware and the software infrastructure that ultimately will allow applications to achieve large gains in performance and power efficiency.”
Perhaps most important, says Woo, is to realize that traditional architectures may not be the best choice for certain data intensive workloads because they don’t address key power efficiency and data movement bottlenecks.
“Traditional processors coupled with acceleration hardware such as FPGAs, along with technologies to minimize data movement, offer new approaches to improving performance and power efficiency in modern systems. We believe FPGAs, alongside other acceleration silicon, will continue to play an important role in helping to evolve computing platforms by enabling flexible acceleration and near data processing,” he added.
Semiconductor Engineering highlights side-channel attacks
Brian Bailey of Semiconductor Engineering has written an article that highlights the danger side-channel attacks pose to connected devices and systems.
“As the world begins to take security more seriously, it becomes evident that a device is only as secure as its weakest component. No device can be made secure by protecting against a single kind of attack,” Bailey explained. “Encryption and root of trust can add additional layers of protection. But even then, the system may not be secure.”
This is because every electronic device emits information about what it is doing, says Bailey, and that information can be used to pry open its defenses. This technique is generally referred to as a side-channel attack. Essentially, side-channel attacks, which include Simple Power Analysis (SPA) and Differential Power Analysis (DPA), can be exploited to analyze characteristics such as power, radiation and timing to infer what a system or chip is doing.
According to Bailey, a Rambus paper written by Gilbert Goodwill confirms that an unprotected AES128 algorithm running on a generic processor can be cracked with only 4 minutes of sample data collected and 10 minutes of analysis.
“When the same algorithm was implemented in an FPGA board, it increased the collection time to 50 minutes plus 12 minutes for analysis,” he noted. “Using that same board, but with a DPA-protected implementation, they were not able to crack it even after obtaining 3 hours of trace data. The statistics they collected also indicated that obtaining more traces would not enable them to crack the device.”
As Bailey points out, there are still many connected devices that have yet to be hacked.
“Lightbulbs never had to have security built into them, but they do now. Security didn’t matter until they become connected,” he added. “Now they provide a way into your network. One can only hope that more companies take hacking seriously, but early indications are that it is still an afterthought.”
As we’ve previously discussed on Rambus Press, all physical electronic systems routinely leak information about their internal process of computing. In practical terms, this means attackers can exploit various side-channel techniques to gather data and extract secret cryptographic keys from IoT endpoints. Regardless of specific instruction set architecture (ISA), most industry security solutions on the market today can be soundly defeated by side-channel attacks. Even a simple radio is capable of gathering side-channel information by eavesdropping on frequencies emitted by electronic devices. In some cases, secret keys can be recovered from a single transaction clandestinely performed by a device several feet away.
Worryingly, millions, if not billions, of connected IoT endpoints are powered by chips that are vulnerable to side-channel attacks. Such unprotected silicon can be found in a wide range of electronic devices including wearables, medical equipment, vehicles, smart appliances and rapidly evolving smart city infrastructure. Fortunately, specific DPA countermeasure strategies can be employed to protect IoT devices and related infrastructure. These include techniques to minimize information leakage, generating noise to drown out leakage signals, the use of randomness to mask computational intermediates, algorithm and implementation obfuscation as well as the use of protocols designed to preserve secrecy even in the presence of (some) leakage.
Interested in learning more about protecting silicon from side-channel attacks? You can check out our DPA countermeasures page here and our article archive on the subject here.
