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How your IP-camera security gets obsolete – fast!

Maintaining an up-to-date IP camera surveillance system requires more than just a passing glance at the monitors. There are significant drawbacks to keeping equipment for too long. Not only is it dated, but there is a chasm between features available on older models, compared to what is available today.

Marc van Jaarsveldt, consultant, The Surveillance Factory, a system integrator, says there are many factors to consider when reviewing your security cameras to determine if an upgrade is required: “There are many factors to consider prior to upgrading and the option to extend the lifespan of your existing equipment must be appraised. The biggest challenge is to decide whether to keep abreast of new technology or to try and keep your system live for as long as possible. This trade-off does have implications for how effective your camera system is as a security tool.”

While getting value out of your initial investment is key, there is no denying the leap that surveillance technology has made in the past five years and what this means in terms of camera technology features and benefits if you do decide to upgrade your system.

When reviewing the system, van Jaarsveldt says that the camera lifespan will generally be impacted by the quality of the equipment purchased at the outset, the current operating environment and maintenance schedule as well as the client’s overall appetite for improvements made to the system.

In a typical surveillance scenario, a quality camera may have a lifespan of between five and ten years, while a less expensive model may only survive for three years. “This will be impacted by the environment as an outdoor camera, for example, will be exposed to harsher elements. There are temperature changes, rain, dust, moving parts (on PTZ cameras) and even electrical surges to contend with, all of which can affect the camera” says van Jaarsveldt.

He cautions that camera lifespans do vary based on the manufacturer. Not all camera brands can survive in the field for ten-years: “The average warranty period for a camera is three years, with a possible extension to five years. Being out of warranty, however, does not mean it doesn’t work, it will simply cost more to repair, should something go wrong.”

If longevity is a goal, then maintenance of the system is critical. Van Jaarsveldt says that while this does not impact the overall lifespan significantly, it can make a difference to its functionality: “Simply cleaning the cameras will help, especially if they are in a harsh environment where they are exposed to sun, dust, water or chemicals from industrial processes. By cleaning the camera housings and lenses you are able to slow down the rate at which the hardware degrades or deteriorates. This can prolong the life of a camera. Also check for and remove nesting insects such as wasps, ants and spiders from camera housings.”

While it is understandable that users want to get the most value out of the system and enjoy a longer lifespan, the biggest influence and challenge to maintaining an up-to-date system is the rapid rate of technology development. While cameras five years ago offered an acceptable 720p resolution (1MP), today’s cameras routinely offer 3MP, 5MP resolutions and even the much talked about 4k, which is 8MP.

“The fact is that cameras five years ago are in no way a comparison to what is currently available. Even the best IP camera then could not compare with what is available now,” says van Jaarsveldt. “This makes the challenge more complicated as newer technology offers so much more value for a security environment where the quality of video footage is so important.”

He says that for some industries, such as retail, this lag in technology poses a significant risk and threat to the business: “There are certain sectors that simply can’t afford to fall behind the technology curve. While the older systems may still work, the reality is that a new system will offer more functionality and significantly more value to the business.”

An example according to van Jaarsveldt is the fact that older generation cameras offer lower quality images due to much lower resolutions and substantially less advanced light management such as WDR: “Earlier generations of cameras don’t offer good resolution with excellent light management, exposure and contrast control and wide dynamic range (WDR). While the new generation IP cameras offer far superior resolutions and most end users tend to accept 2MP or even 3MP as the entry level resolution.”

These higher resolutions offer more detailed images and when the video is analysed for incidents or events, this additional resolution is critically important. Newer IP cameras also offer superior light management, automatically allowing for big variances in contrast to be eliminated by combining multiple images.

“In security environments, where light contrast affects the cameras significantly, this is a very important feature. The camera is now able to produce video footage of a far higher quality and this provides improved security and forensic value,” explains van Jaarsveldt.

He says that while older systems may still be useful, clients need to be aware of the ramifications of keeping the older hardware in the field for too long: “If a complete camera swap out is not affordable, then review your cameras and replace the ones that are used in higher risk areas with newer models. Note that cameras with vastly higher resolutions may affect the performance of the back end recording server or NVR as well.”

By working with reputable system integrators, clients should be made aware of the specific components of their camera system that need to be retired and replaced. “Surveillance systems are generally in place for a good reason, it is imperative that they are upgraded as necessary and maintained appropriately,” says van Jaarsveldt.

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Now IBM’s Watson joins IoT revolution in agriculture

Global expansion of the Watson Decision Platform taps into AI, weather and IoT data to boost production

IBM has announced the global expansion of Watson Decision Platform for Agriculture, with AI technology tailored for new crops and specific regions to help feed a growing population. For the first time, IBM is providing a global agriculture solution that combines predictive technology with data from The Weather Company, an IBM Business, and IoT data to help give farmers around the world greater insights about planning, ploughing, planting, spraying and harvesting.

By 2050, the world will need to feed two billion more people without an increase in arable land [1]. IBM is combining power weather data – including historical, current and forecast data and weather prediction models from The Weather Company – with crop models to help improve yield forecast accuracy, generate value, and increase both farm production and profitability.

Roric Paulman, owner/operator of Paulman Farms in Southwest Nebraska, said: “As a farmer, the wild card is always weather. IBM overlays weather details with my own data and historical information to help me apply, verify, and make decisions. For example, our farm is in a highly restricted water basin, so the ability to better anticipate rain not only saves me money but also helps me save precious natural resources.”

New crop models include corn, wheat, soy, cotton, sorghum, barley, sugar cane and potato, with more coming soon. These models will now be available in the Africa, U.S. Canada, Mexico, and Brazil, as well as new markets across Europe and Australia.

Kristen Lauria, general manager of Watson Media and Weather Solutions at IBM, said: “These days farmers don’t just farm food, they also cultivate data – from drones flying over fields to smart irrigation systems, and IoT sensors affixed to combines, seeders, sprayers and other equipment. Most of the time, this data is left on the vine — never analysed or used to derive insights. Watson Decision Platform for Agriculture aims to change that by offering tools and solutions to help growers make more informed decisions about their crops.” 

The average farm generates an estimated 500,000 data points per day, which will grow to 4 million data points by 2036 [2]. Applying AI and analysis to aggregated field, machine and environmental data can help improve shared insights between growers and enterprises across the agriculture ecosystem. With a better view of the fields, growers can see what’s working on certain farms and share best practices with other farmers. The platform assesses data in an electronic field record to identify and communicate crop management patterns and insights. Enterprise businesses such as food companies, grain processors, or produce distributors can then work with farmers to leverage those insights. It helps track crop yield as well as the environmental, weather and plant biologic conditions that go into a good or bad yield, such as irrigation management, pest and disease risk analysis and cohort analysis for comparing similar subsets of fields.

The result isn’t just more productive farmers. Watson Decision Platform for Agriculture could help a livestock company eliminate a certain mold or fungus from feed supply grains or help identify the best crop irrigation practices for farmers to use in drought-stricken areas like California. It could help deliver the perfect French fry for a fast food chain that needs longer – not fatter – potatoes from its network of growers. Or it could help a beer distributor produce a more affordable premium beer by growing higher quality barley that meets the standard required to become malting barley.

Watson Decision Platform for Agriculture is built on IBM PAIRS Geoscope from IBM Research, which quickly processes massive, complex geospatial and time-based datasets collected by satellites, drones, aerial flights, millions of IoT sensors and weather models. It crunches large, complex data and creates insights quickly and easily so farmers and food companies can focus on growing crops for global communities.

IBM and The Weather Company help the agriculture industry find value in weather insights. IBM Research collaborates with start up Hello Tractor to integrate The Weather Company data, remote sensing data (e.g., satellite), and IoT data from tractors. IBM also works with crop nutrition leader Yara to include hyperlocal weather forecasts in its digital platform for real-time recommendations, tailored to specific fields or crops. IBM acquired The Weather Company in 2016 and has since been helping clients better understand and mitigate the cost of weather on their businesses. The global expansion of Watson Decision Platform for Agriculture is the latest innovation in IBM’s efforts to make weather a more predictable business consideration. Also just announced, Weather Signals is a new AI-based tool that merges The Weather Company data with a company’s own operations data to reveal how minor fluctuations in weather affects business.

The combination of rich weather forecast data from The Weather Company and IBM’s AI and Cloud technologies is designed to provide a unique capability, which is being leveraged by agriculture, energy and utility companies, airlines, retailers and many others to make informed business decisions.

[1] The UN Department of Economic and Social Affairs, “World Population Prospects: The 2017 Revision”

[2] Business Insider Intelligence, 2016 report: https://www.businessinsider.com/internet-of-things-smart-agriculture-2016-10


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What if Amazon used AI to take on factories?

By ANTONY BOURNE, IFS Global Industry Director for Manufacturing

Amazon recently announced record profits of $3.03bn, breaking its own record for the third consecutive time. However, Amazon appears to be at a crossroads as to where it heads next. Beyond pouring additional energy into Amazon Prime, many have wondered whether the company may decide to enter an entirely new sector such as manufacturing to drive future growth, after all, it seems a logical step for the company with its finger in so many pies.

At this point, it is unclear whether Amazon would truly ‘get its hands dirty’ by manufacturing its own products on a grand scale. But what if it did? It’s worth exploring this reality. What if Amazon did decide to move into manufacturing, a sector dominated by traditional firms and one that is yet to see an explosive tech rival enter? After all, many similarly positioned tech giants have stuck to providing data analytics services or consulting to these firms rather than genuinely engaging with and analysing manufacturing techniques directly.

If Amazon did factories

If Amazon decided to take a step into manufacturing, it is likely that they could use the Echo range as a template of what AI can achieve. In recent years,Amazon gained expertise on the way to designing its Echo home speaker range that features Alexa, an artificial intelligence and IoT-based digital assistant.Amazon could replicate a similar form with the deployment of AI and Industrial IoT (IIoT) to create an autonomously-run smart manufacturing plant. Such a plant could feature IIoT sensors to enable the machinery to be run remotely and self-aware; managing external inputs and outputs such as supply deliveries and the shipping of finished goods. Just-in-time logistics would remove the need for warehousing while other machines could be placed in charge of maintenance using AI and remote access. Through this, Amazon could radically reduce the need for human labour and interaction in manufacturing as the use of AI, IIoT and data analytics will leave only the human role for monitoring and strategic evaluation. Amazon has been using autonomous robots in their logistics and distribution centres since 2017. As demonstrated with the Echo range, this technology is available now, with the full capabilities of Blockchain and 5G soon to be realised and allowing an exponentially-increased amount of data to be received, processed and communicated.

Manufacturing with knowledge

Theorising what Amazon’s manufacturing debut would look like provides a stark learning opportunity for traditional manufacturers. After all, wheneverAmazon has entered the fray in other traditional industries such as retail and logistics, the sector has never remained the same again. The key takeaway for manufacturers is that now is the time to start leveraging the sort of technologies and approaches to data management that Amazon is already doing in its current operations. When thinking about how to implement AI and new technologies in existing environments, specific end-business goals and targets must be considered, or else the end result will fail to live up to the most optimistic of expectations. As with any target and goal, the more targeted your objectives, the more competitive and transformative your results. Once specific targets and deliverables have been considered, the resources and methods of implementation must also be considered. As Amazon did with early automation of their distribution and logistics centres, manufacturers need to implement change gradually and be focused on achieving small and incremental results that will generate wider momentum and the appetite to lead more expansive changes.

In implementing newer technologies, manufacturers need to bear in mind two fundamental aspects of implementation: software and hardware solutions. Enterprise Resource Planning (ERP) software, which is increasingly bolstered by AI, will enable manufacturers to leverage the data from connected IoT devices, sensors, and automated systems from the factory floor and the wider business. ERP software will be the key to making strategic decisions and executing routine operational tasks more efficiently. This will allow manufacturers to keep on top of trends and deliver real-time forecasting and spot any potential problems before they impact the wider business.

As for the hardware, stock management drones and sensor-embedded hardware will be the eyes through which manufacturers view the impact emerging technologies bring to their operations. Unlike manual stock audits and counting, drones with AI capabilities can monitor stock intelligently around production so that operations are not disrupted or halted. Manufacturers will be able to see what is working, what is going wrong, and where there is potential for further improvement and change.

Knowledge for manufacturing

For many traditional manufacturers, they may see Amazon as a looming threat, and smart-factory technologies such as AI and Robotic Process Automation (RPA) as a far off utopia. However, 2019 presents a perfect opportunity for manufacturers themselves to really determine how the tech giants and emerging technologies will affect the industry. Technologies such as AI and IoT are available today; and the full benefits of these technologies will only deepen as they are implemented alongside the maturing of other emerging technologies such as 5G and Blockchain in the next 3-5 years. Manufacturers need to analyse the needs which these technologies can address and produce a proper plan on how to gradually implement these technologies to address specific targets and deliverables. AI-based software and hardware solutions will fundamentally revolutionise manufacturing, yet for 2019, manufacturers just have to be willing to make the first steps in modernisation.

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