Is it lunchtime?

Use case: intelligent catering

vegan tacos

What’s the issue?

Grabbing a sandwich or coffee at lunchtime or in between lectures is necessary sustenance and also often a social activity. But sometimes the cafe is too busy and students may not have time to wait in the queue before walking to the next lecture. Can data and analytics be used to smooth out demand or respond more effectively in different circumstances? What if the experience of buying and eating was significantly more tailored to the individual, and also perhaps nudging them to a healthier option or encouraging them to environments where they are more comfortable?

What could be done?

Certain times are likely to result in high demand at campus cafes and food outlets, such as lunchtime, but within those times there is flexibility to respond in different ways, or indeed encourage students to arrive at a different time. By using timetabling or event data, increased flow of people to the cafes could be predicted, and real time information on actual location and flow can reinforce and clarify the expected demand.

If the demographics of the individuals were known, special offers or menu choices could be signposted at alternative venues to encourage groups to move elsewhere. For example an event by the Vegan Student Society is just finishing, on the other side of campus from the main canteen which is currently full to capacity. A nearer food outlet that is typically quieter has ordered in some menu items that have been identified as favourites by this demographic using previous purchase history and feedback. Notifications of special offers are sent to students’ mobile phones as their event finishes, encouraging groups of them to divert.

Combining data on where their friends are could introduce a social dimension, and include recommendations and feedback. For example, your friend  Katie has just tried the new pizza and rated it highly, or Meena and Harry are on their way to the Japanese food festival starting in 5 minutes at the students’ union pop up cafe.

Similar strategies more widely could “nudge” students towards different timings (“happy hour” offers on menu items) or different locations, by suggesting alternative menu choices. These could be tailored by knowledge of their specified preferences (eg dietary requirements or healthy eating aims) or purchase history, and triggered by data on people flow. This data might be shared by apps on the individuals’ phones, or through “loyalty cards” but alternative methods include cameras detecting people approaching and targeting them with “adverts” or offers that are designed to attract different demographics (usually through marketing analysis or aggregated purchase history data).

The advantages to the customers could include a more tailored eating experience, cost savings with special offers, opportunities to try out new menus, less queuing, and linking up with friends.

coffee

What examples are there?

Costa Coffee has developed an “intelligent coffee station” that uses cameras to detect eye movement and respond with engaging images. Facial recognition is used to tailor the experience using deduced demographic profiles. For example, different coffee drinks are suggested based on age, although data is kept anonymous and used only for analytics, in part to assess how successful their targeting is. Also calculated is the “dwell time” to see how quickly people make a purchase. Other technologies included are near field communications, telemetry and digital signage, providing facilities including cashless payment.

Door detectors are one of many types of sensor that can count the number of people to assess footfall in the cafe, others include video cameras that recognise people. These are more commonly used in transport although integrated analytics would draw data from multiple sources. There are examples in retail for example used in Manchester, analysing queuing and the popularity of different areas of the store, but also contributing to resourcing strategies such as staffing levels.

The Gebni app https://gebni.com/operates an adaptive pricing methodology, responding to demand not only for the end product but the ingredients used in it. This allows “surge pricing” to make items more expensive when demand is high, but conversely other dishes that don’t share the ingredients of the item in short supply (at any particular location) could be on special offer. This provides some control over the demand and flow of customers, for example incentivising ordering at different times.

Taco Bell uses tablets, kiosks and mobile apps to collect orders. Some of these allow deeper exploration of the menu including different menu options and add-ons, for example to cater for dietary preferences, and some even include games or entertainment. Other examples of enhancing the customer experience include apps that allow customers to choose the type of music they would like to hear and then incorporating it into the cafe’s playlist.

What about ethical and other issues?

Some data used would require consent from the individual, including location, preferences, contacts and social interests, and would need to be part of a clear ethical policy. Where data is only needed in aggregation (such as density of people) or for demographic targeting (such as facial imagery), it must be clear that individuals cannot be identified and data is deleted appropriately. The principles of ‘nudging’ need to be handled with care, with potential negative connotations of manipulation and coercion, for example, is it acceptable to encourage someone to eat elsewhere based on commercial considerations of supply and profit? On the other hand, there is a convenience and customer experience advantage in reduced queuing, increased choice and better tailoring of products, so a careful balance must be struck.

queue

Who needs to be involved?

Individuals need to give consent to the sharing of their own data, on which many of these example rely. However, such sharing is commonplace for other apps including social media. University academic services and students union services may need to provide access to data on events and timetabling, and campus services such as facilities management and IT would need to coordinate to maximise the benefits.

Intelligent equipment sharing

Use case: Equipment sharing and co-purchasing

mechanical-1825278_1920

What’s the issue?

Sharing is already a big thing in the digital economy. It can allow individuals and businesses to get more out of their underutilised assets, for example, cars and properties using Uber and Airbnb. Equipment purchased by universities for use in research, teaching or running the campus is often underutilised. This situation could be improved by better coordination of purchasing and sharing.

There is a wide range of benefits to increasing co-purchasing and sharing of equipment, including:

  • Cost savings with higher levels of utilisation and shared purchase costs
  • Collaboration between colleagues, departments and research groups
  • Sharing and improving skills, expertise and knowledge
  • Shared maintenance, insurance and consumables costs
  • Providing access to equipment that is not normally available

However, there are also a number of barriers to overcome such as:

  • Complex charging arrangement
  • Lack of clarity regarding responsibility for maintenance or damage
  • Training and support not available
  • Cultural resistance to sharing – ”this kit belongs to me
  • Increased work due to bureaucracy
  • Lack of knowledge about the existence of the equipment

What are the current solutions?

There are a number of initiatives that aim to provide a solution. They exist at a local/institutional, regional, national or even international level.

Institutional

Most universities hold asset registers, databases and inventories. Many have more than one system. A number have developed sophisticated sharing platforms. For example,Cambridge University is a very large institution with a huge range of research equipment that might be available for sharing. It has developed an equipment sharing database with thousands of items listed. This database is linked to similar services in other universities including Oxford, UCL, Imperial and Southampton (See: www.equipment-sharing.cam.ac.uk)

Strathclyde University’s ULAB platform not only facilitates equipment sharing but also incorporates a booking system simplifying the sharing process and making valuable data available. (see: https://www.ulabequipment.com/)

Regional

A number of regional university organisations have equipment sharing initiatives. They generally bring together equipment and facility databases from a number of universities, these include:

National

Jisc provides the national equipment sharing service “Equipment.Data”. This platform “enables searching across all published UK research equipment databases through one aggregation “portal”, allowing greater accessibility with the aim to improve efficiency and stimulate greater collaboration in the sector.“

Over 50 universities currently contribute their equipment databases to the service. (See: http://equipment.data.ac.uk/)

While very useful, these services are generally only aimed at research equipment. They could be used for all of the equipment on campus including teaching, library, maintenance, estates and catering equipment.

scientific-2040795_1280

Possible broader solutions

In order to make the most of its equipment purchases universities need to merge the data from a wide range of sources. Currently, this data is spread across the institution with little coordination, these include:

  • Insurance equipment databases
  • Maintenance schedules
  • Purchasing units
  • Laboratory inventories
  • Research services
  • Grant and funding bids
  • Procurement plans for all departments

These units will all be holding data on equipment, sometimes the same equipment, but with different data fields. A standard set of data will need to be agreed to harness the possibilities and benefits. Using the Internet of Things (IoT), equipment could even provide and update this data itself, automatically.

Timetabling can be associated with equipment, this is already the case for some high-cost equipment such as specialist laboratory equipment. However, the IoT and the management of increasingly rich data means that increased levels of sharing and co-purchasing becomes possible.

Push notifications services could identify appropriate equipment and current availability in real time. Examples could include sharing digital cameras, printers, scanners, microscopes, vehicles, projectors, sound systems etc. Even the utilisation of estates equipment such as specialist landscaping or building maintenance equipment. However, there needs to be incentives for sharing, high levels of utilisation and to coordinate purchasing across the campus and across departments.

In the future, the use of Blockchain technology could make co-purchasing and sharing of equipment and facilities much simpler and more common. Blockchain, combined with IoT, can help to overcome some of the major barriers that exist such as the issues of trust, cost sharing, location and maintenance scheduling.

While the use of Blockchain technology and the IoT to enable equipment sharing is still in its infancy there are a few examples emergingin other fields.Notably, in Australia, a group of farmers have worked with their bank to co-purchase and share high-value equipment. The equipment was fitted with sensors producing large quantities of data that could help with the provision of an equitable sharing arrangement, as well as optimal performance and maintenance regimes. The participants in this scheme feel that digitised business assets, connected to the IoT and managed with smart contracts and blockchain technology, will become commonplace in the future. (See:CBA’s Smart Assets Experiment).

Intelligent energy

Use case: generating, storing and using energy in an intelligent way

lightbulb

What’s the Issue?

All universities and colleges have become increasingly aware of their energy use. The increasing cost and reducing their carbon footprint are major driving forces. Most plans and initiatives focus on:

  • Savingenergy
  • Generating energy
  • Managing and storing energy

A common factor in all three cases is the ability to monitor and measure energy use.

What Are the Possible Solutions?

Saving energy

The simplest and most practical, if rather mundane, way of improving energy use is to save it. Simple actions like:

  • Turning off lights
  • Shutting down computers after use
  • Using low energy equipment and appliances
  • Having appropriate heating temperature levels
  • Having self closing doors

To encourage, or “nudge” towards, this behavior a number of universities and colleges have introduced rewards, targets and competitions. Examples include:

  • The University of Stuttgart’s “Initiative 1000” aimed, successfully, to 1,000 megawatt hours of electricity and 1,000 megawatts of heat in just 6 months.
  • Tulane University’s “Tulane Unplugged” is a competition that challenges students to reduce the energy use in their halls of residence. The scheme is promoted by “Energy Advocates”, trained student volunteers and uses the campus “Building Dashboard” to monitor electricity, gas and chilled water use in real time.
  • A competition at the University of California called “Energy Smack Down” encourages its 9 campuses to see who can save the most energy. It was promoted under the slogan “Do it in the dark” and has proved so successful that it is now competed for at 186 campuses, nationwide.

he opportunity to save energy is much greater in new buildings. The Edge building, partly occupied by the Amsterdam University of Applied Sciences, claims to be the greenest in the world. Not only do its solar panels generate all the building power needs, it uses a range of other energy saving techniques including:

  • The innovative use of natural light
  • Use of wall as thermal mass
  • Louvers for shading
  • Ethernet-poweredLED lighting
  • Use of IoT with 30,000 sensors measure daylight, occupancy levels, movement, humidity, temperature and CO2.
  • Customised occupant app to locally regulate light and climate
  • Rainwaterharvesting for toilets and terrace irrigation

pylons

Generating energy

There are a range of technologies available for the provision of locally generated energy.

A number of universities around the world are using these to help with their energy needs. Examples include:

Other universities are investigating the use of ground source heat, fuel cell and wave technologies to generate power.

Managing and storing energy

It’s in the management and storage of energy that we perhaps see the greatest opportunities for the application of intelligence. The ability to use data from a number of sources, combined with the use of the Internet of Things and an array of monitoring devices, provides a range of new possibilities.

Energy systems are generally designed to cope with peak energy needs. These peaks may only take place once (or less) per day but the generation and delivery infrastructures need to be designed to cope with this. It means that that costly and inefficient surplus capacity has to be available.

To overcome this issue some universities aim to store energy during periods of low need for later use. One high profile scheme  at the University of New South Wales has led to the installation of a 500kWh battery to store solar energy for release during peak use periods.

Some universities are replacing their fleets of campus vehicles with EVs (Electric Vehicles). There are a number of projects looking at how that energy stored in their batteries can be made available to the campus grid during periods of high electricity use. Indeed some schemes are looking at whether power companies may provide payments to those making this stored power available. One notable scheme at the University of California San Diego the Triton Rides Programme uses V2G (vehicle-to-grid) technology, with bi-directional EV charging stations, to provide zero emission transport and an energy storage system.

On a wider scale the adoption of Smart Grids, often combined with energy storage, can greatly increase campus energy efficiently. The European Union defines the Smart Grid as a:

” ..network that can cost efficiently integrate the behaviour and actions of all users connected to it – generators and consumers – in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety…”it” employs innovative products and services together with intelligent monitoring, control, communication, and self-healing technologies…”

Smart Grids allow the two-way flow of power for transmission and distribution. They enable real time collection data monitoring generation, consumption, maintenance and efficiency. This allows controls, computers and other equipment to work together to respond to changing demand.

Many universities are at the forefront of Smart Grid and Micro-grid development. They integrate the local generation of power (solar, wind etc.), local storage, (such as batteries), national grid connectivity, management of building requirements, loading monitoring and prediction, and weather monitoring, The LUT University in Finland  has developed a Green Campus Smart Grid to pilot the monitoring of electricity usage and integrate its wind turbine, solar panels and storage batteries.

In the UK, Newcastle University’s Smart Grid provides a test bed for a range of technologies including large scale battery storage.

As with many of the technologies being used in the Intelligent Campus the issue of energy use is likely to be addressed not through a single solution but through the integration of a range of technologies and data collection. For example, the linking of weather forecasting data, timetabling information and event management with energy control, will allow developments such as intelligent use of energy to heat and light buildings. The forecast of an impending cold snap could allow buildings, that will be used the next day, to be warmed in advance, not only making them more comfortable but also allowing the use of “off peak” energy. Similarly, when the weather is warming, heating can be reduced beforehand. If there is a regular, predicted, energy peak for a short time, such as during a major event, power being used for air conditioning in another building might be diverted to the event location for an hour or two, smoothing out that peak demand.

wind farm

Who needs to be involved?

While many of the issues relating to the Intelligent Campus’ energy use will be seen as an estate department issues, there will be major and long term, policy, investment and planning decisions that will involve senior management. The more prosaic, power saving schemes will involve the buy-in of the student and staff body.

Building the Intelligent Campus #Digifest19

This week it’s time for Jisc’s Digifest and I am doing a one hour interactive presentation on the Intelligent Campus.

The smart campus is already here; the technology, sensors and data analysis capability is all available, but it isn’t all joined up and so has limited scope in terms of what we can learn and how we can use the knowledge.

In order to enhance the student experience, allow for more effective and efficient use of space, could we take the smart campus and make it intelligent?

Universities and colleges spend billions on their campuses, yet they are frequently underutilised and are often a frustrating experience for students. In this session, I will describe the campus of the future. How does a traditional campus become a smart campus? What are the steps to make a smart campus, an intelligent campus? We have an opportunity to provide our members with a service that can help them address that problem. If we extend our learning analytics infrastructure to collect data from a wider range of institutional software and devices then we can deliver novel insights to institutional managers to help them make their campuses more efficient, improve student experience and deliver higher quality teaching.

The future intelligent campus service aims to find effective ways to use data gathered from the physical estate and combine it with learning and student data from student records, library systems, the virtual learning environment (VLE) and other digital systems. This session will describe what data can be gathered, how it can be measured and explore the potential for enhancing the student experience. It will demonstrate and explain to the delegates what the exciting future of the intelligent campus. Importantly I will also ask delegates to consider the ethical issues when implementing an intelligent campus as well as the legal requirements.

The one hour session takes place on there 12th March, at 11:30 in hall 9.

chairs

To read more about what is an intelligent campus have a read of this article, What makes an intelligent campus?, in Educational Technology written by me.

To find out more about the Jisc Intelligent Campus project – Using data to make smarter use of your university or college estate – see this webpage.

Jisc have published a guide for universities and colleges who are interested in venturing into the Intelligent Campus space.

In order to understand the potential of the Intelligent Campus space we have published a series of use cases.

Congestion ahead

Use case: people flows and congestion

people

What’s the issue?

The flow of people through campus and beyond is complex and not well understood outside of known peak times such as class changes or lunchtime. The density of people at any one place and time, and the speed of their movement, can have a big impact on how easily people can get in and around campus buildings and facilities. Knowledge about pedestrian movements, and ways to respond to them, could be valuable to help provide better services and a tailored experience, within campus and connecting to the wider local environment.

What could be done?

Pedestrian flow could affect the time for journeys between classes, waiting times at cafes or sudden changes in how busy the library is. Location trackers such as used by mobile phones can provide data on flow, and also people counters, such as using video systems, can be placed around campus to collect data on the numbers of people in that location at any time. Such data can have a number of applications, including combining with other contexts to improve services, such as:

  • Monitoring the increasing numbers of people towards a known destination could anticipate potential problems with congestion and queueing. For example, students heading towards the cafeteria could indicate an unusually high demand for food and trigger staffing or stocking changes to cope with higher numbers
  • Timetabling data indicates when classes are scheduled to end, but real time data on movement could indicate that some classes finish earlier or later, leading to changing patterns in availability of services. Usage data could show that the library is already busy when one class ends, and students could be directed towards other study areas or computer rooms that have more availability
  • Where campuses interact with local towns and cities, for example crossing roads, using transport services or local shops and cafes, the changing flow of people could be used to increase the capacity/timing of pedestrian crossings, frequency of transport services or adding extra staff on to the tills and counters
  • Over time the data may suggest interesting patterns of behaviour that could be used to further predict, anticipate and respond to congestion. One example might be the impact of weather – on sunny days more take away coffee might be ordered, and campus cafes could expand capacity using picnic tables and pop up kiosks.
  • Similarly if rain is starting as lectures finish, students might be re-routed away from slippy paths, and may be expected to congregate in sheltered areas. Using room utilisation data, spare rooms could be opened up to accommodate social interaction and refreshment breaks, or pop up library or IT services could be opened
  • It would also be possible to combine individual preferences (based on specified likes and dislikes or modelled on their behaviour) to anticipate more specifically what an individual is likely to do next. From the above examples, it might be known what drink is normally ordered from the cafe, what bus route is taken and when an individual tends to do their work in the IT suites. Combining this data across multiple individuals provides a greater level of detail in planning what bus services to run, what drinks to stock and the usage of computer rooms.

What examples are there?

Many of the existing examples are from “Smart cities”, involving vehicular and pedestrian traffic, to aid safety, improve health and environmental concerns, and also inform retail and business. However, such applications can be easily applied to campus routes and facilities.

Google maps is one of the best known examples of tracking the location of mobile devices (typically in cars) to show congestion on traffic routes. The mapping service then can suggest the best/quickest route for the traffic conditions at the time and provide alternatives if congestion is estimated to lead to a slower journey time.

Other methods of “people counting” include video cameras, which can also combine with CCTV, recognising an image of a person and transmitting the numbers (usually not the images). Such systems are used on some buses and trains for example to tally against fare income or check usage of different routes.

In Las Vegas, not only do they track vehicles through a junction but also count the number of pedestrians crossing the streets and also “jaywalking”, and then re-routing vehicular traffic when the numbers of pedestrians is high.

A prototype LED smart pedestrian crossing in London only reveals the crossing when pedestrians appear and can expand to cope with higher numbers of people, or to move the stop lines further back if weather conditions are poor, to allow for increased stopping distances of vehicles.

People counters are used in business and retail areas for example in Manchester to better understand queuing time and which areas of a store are popular. The data also contributes to strategies to improve walkability and transport, understand the impact of events and marketing campaigns, and assist businesses and community services in adopting appropriate staffing and security arrangements.

What about ethical and other issues?

In principle, data on people movement tends to be aggregated to use the total numbers and changes to those numbers rather than knowledge about a specific individual. This is similar to the way google uses your location to provide mapping data, and is widely accepted. However, images of individuals may be being captured along with their movements and this information could be used inappropriately without strict controls and clear consent rules. Similarly, as data becomes combined, it begins to create a picture of a person’s behaviour that could be considered more of an invasion of privacy – for example which cafe are they going to, who else is there and what do they drink?

Who needs to be involved?

Pedestrian flow information could be valuable to a wide range of audiences, both within campus and in the wider community. The providers of academic services (such as timetabling), social facilities (bars and cafes) and retail businesses could all be users of the data. In addition, integration with events planning and transport services could impact on local authorities, transport providers and the police.

It’s too noisy!

Use case: noise and sound

radio

What’s the issue?

A significant amount of teaching is still delivered aurally. Face to face learning activities, for example collaborative group work, also typically use forms of communication between students that rely on a conducive acoustic environment. In addition, many students live in close proximity to each other, and engage in social activities that can create noise. So sounds are both an important element of learning and the university experience for most students and also a source of annoyance.

High levels of noise can be damaging to health, and noise pollution or noise nuisance reflect unreasonable amounts of noise within an environment. These can sometimes result in the local authority taking action against those creating the noise. However, in most cases noise is more of a distraction or annoyance, affecting your ability to carry out the activity you intended. The impacts can include disruptions to sleep, increased stress or anxiety, or simply poor concentration or productivity. Historically, responses to noise problems are infrequent and complaint driven, so have limited impact on the situation at the time.

What can be done?

The level of “annoyance” experienced as a result of the noise is subjective, and can be moderated by a number of factors including sensitivity to noise, the ability to control the noise, the predictability of the noise event, and other environmental “comfort” factors.

The Intelligent campus offers opportunities to provide control and information that can reduce the amount of annoyance. Equally, responding in real time to triggers of certain noise levels could result in additional mechanisms to reduce the noise at its source or by insulating the sound from those who are impacted.

The idea of quiet study spaces already exists, and even some halls of residence are designated as quieter spaces, although they are typically static, not responding to environmental changes, and relying on self-regulation or human monitoring and intervention. What would be more effective would be to tailor spaces to the individual or group’s activities and preferences, and respond immediately to noises that impact negatively.

Environmental sensors can continuously monitor the acoustic levels of both indoor and outdoor spaces, and report this information through a variety of interfaces including mobile devices. For example apps could show you the current noise levels in busy bars, study rooms or computer suites, enabling the individual to choose a location appropriate to their activity.

Newer sensors also sometimes have processing capability, including being able to identify and categorise different types of noise, for example recognising the difference between traffic noise, live music from a band in the student union, or the sound of shouting and screaming. This allows for a differentiated approach to various acoustic triggers.

Making it intelligent

Integrating different types of data can provide a rich source of information about noise events and their context, allowing more informed responses. Knowledge of event timings (eg music concerts), and the movement and congregation of people across campus (through location information), can start to build a picture of where noise might build up and what neighbouring groups or activities might be impacted (people sleeping, or doing quiet study).

Nudging strategies, for example recommending different routes or transport options could help to disperse noisy crowds, suggest less busy bars, or direct crowds away from exam rooms. Less direct than alternative routing could be signposting special offers or “things you like” nearby to entice you elsewhere – food in the cafe, a lunchtime club attended by people you know, a book you wanted just arrived in the library… Knowledge of the intended timing (and the actual happening) of noisy events or quiet study activities could also influence the schedule of maintenance works.

Noise cancelling technology exists in more sophisticated headphones, and could be applied to larger environments such as study rooms where external noise is detected and unwanted. The data identifying noise events could be combined with data on scheduled quiet events to effectively target noise cancelling, or even respond to mobile controls by individual users. Weather could also be incorporated into the decision strategies, for example if windows are more likely to be open on a hot day, allowing noise to carry further.

headphones

Are there any examples?

A research partnership between the City of Calgary and The University of Calgary in Canada used acoustic sensors to autonomously detect and categorise acoustic events. The technology was able to pinpoint the location and time of the noise, and use machine learning to differentiate between different noise types including music and construction. The authorities were notified when legal levels were exceeded, allowing action to be taken, and noise patterns over time were mapped and correlated to better understand acoustic impacts of different activities.

In Louisville Kentucky, similar technology allows a drone response to gunshots. Thankfully such incidents are rare in the UK, however other applications could be imagined, for example providing monitoring and aid to individuals in distress or subject to attacks.

In Singapore, at Nanyang Technological University, researchers have applied noise control technology (such as used in headphones) to cancel external noise. They formed a grid-like array of units on an open window, which reduced up to 50% of noise from traffic and construction. It detects the noise and models it in real time, producing an inverted waveform to cancel out the noise as it happens.

Ethical issues

In some of the real examples above, the perception of the introduction of “surveillance” was opposed by some, but the advantages of accurately targeting serious incidents were argued as being better for privacy than blanket CCTV coverage. As with all examples involving sharing of data, the collection and interpretation of information about individuals, such as their location or behaviours, is sensitive and needs to follow a strict ethical code.

Who needs to be involved?

With the increasing integration of data to monitor, model and respond to acoustic events and the wider environment, a broad range of stakeholders and data could be involved. This includes academic services such as timetabling; estates services including catering outlets and buildings maintenance; student social activities such as clubs and events; campus events including concerts; local authorities for roadworks, construction activity, traffic management and transport operators.

Intelligent Campus Community Event – University of Birmingham – 7 May 2019

Photo by Peter Clarkson on Unsplash

If you are working in the area of the Intelligent campus and are interested in work being undertaken in this space by others, then we would like to invite you to attend one of our community events.

The community of practice gives people an opportunity to network, share practice, hear what various institutions are doing and what Jisc is doing in this space.

  • Smart City
  • Smart Campus
  • Wayfinding
  • Wi-Fi Heat Mapping
  • Mapping
  • Space Utilisation
  • Smart Buildings
  • RFID tracking
  • Wi-Fi tracking
  • Facial recognition
  • Chatbots
  • Robots
  • Artificial Intelligence
  • Learning Spaces

The fourth of these events is being hosted and  taking place at the University of Birmingham’s Conference Centre, Edgbaston Park Hotel in
Birmingham on the 7 May 2019 from 10:00 to 4:00, and lunch will be provided.

Please put this date in your diary, you can book onto the event using this link

https://www.jisc.ac.uk/events/intelligent-campus-community-event-07-may-2019

You will have the opportunity to discover more about the Jisc project that is being undertaken in the Intelligent Campus space as well as hear from others about their work in this exciting topic. There will be plenty of opportunities for discussion and networking.

Take part in our Internet of Things competition – Deadline 8 March

Following our recent collaboration with The Things Network (TTN) in equipping the Janet Network with a dedicated infrastructure and a portal for long range wide area network (LoRaWAN) technology, we have started a new IoT programme to raise awareness about potentials of this technology among our community. We want to hear new ideas and existing challenges that can be addressed by LoRaWAN technology. Under Jisc and Digital Catapult initiatives, up to ten winners will receive LoRaWAN Gateways on a long-term loan. These can be used to test your idea as a small-scale pilot.

We want to hear from you if you have an idea that could improve:

  • Teaching
  • Learning
  • Campus operation
  • Student experience
  • How a university or college plays a role in enhancing citizens’ quality of life

Find out more

https://www.jisc.ac.uk/rd/get-involved/take-part-in-our-iot-competition

The deadline is the 8 March 2019.

Reflections on the Intelligent Campus Community Event at City

The Oliver Thompson Lecture Theatre at City, University of London was the venue for the third of our Intelligent Campus community events. These events give people a chance to network, share practice and hear what various institutions are doing. You will have the opportunity to discover more about our intelligent campus project and our work in this space. We had over a hundred people turn up for the event, for many of whom this was their first community event.

After a joint welcome with Dom Pates from City, Jisc’s James Clay gave an introduction to the Intelligent Campus, explaining where the project came from, what we were doing and where we intended to go. Continue reading

Consultation for the Intelligent Campus Data Protection Impact Assessment Toolkit

Andrew Cormack, Jisc’s Chief Regulatory Adviser has been working on a Data Protection Impact Assessment Toolkit for the Intelligent Campus.

DPIA Toolkit v0-11

This is a draft of a Toolkit for applying Data Protection Impact Assessments to Intelligent Campus applications. This has been derived from an RFID Toolkit approved by European Regulators in 2011, with input from other experts within Jisc.

We are seeking input from the community on the toolkit and welcome feedback.

Please either e-mail feedback to Andrew Cormack, or post in the comments.