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The purpose of this paper is to develop a model to harness occupancy sensing in a commercial hot-desking environment. Hot-desking is a method of office resource management designed to reduce the real estate costs of professional practices. However, the shortcoming is often in the suitability and appropriateness of allocated work environments. The Internet of Things could produce new data sets in the office at a resolution, speed and validity of which that they could be factored into desk-allocation, distributing seats based on appropriate noise levels, stay length, equipment requirements, previous presence and proximity to others working on the same project, among many others.

The study utilises primary data from a commercial office environment in Central London (numerical building system data and semi-structured interviews) to feed a discrete events simulator. To test the hypothesis, the authors look at the potential for intelligent hot-desking to use “work type” data to improve the distribution of individuals in the office, increasing productivity through the creation of positive “work type environments” – where those working on specific tasks perform better when grouped with others doing the same task. The simulation runs for a typical work day, and the authors compare the intelligent hot-desking arrangement to a base case.

The study shows that sensor data can be used for desk allocation in a hot-desking environment utilising activity-based working, with results that outweigh the costs of occupancy detection. The authors are not only able to optimise desk utilisation based on quality occupancy data but also demonstrate how overall productivity increases as individuals are allocated desks of their preference as much as possible among other enabling optimisations that can be applied. Moreover, the authors explore how an increase in occupancy data collection in the private sector could have key advantages for the business as an organization and the city as a whole.

The research explores only one possible incarnation of intelligent hot-desking, and the authors presume that all data have already been collected, and while not insurmountable, they do not discuss the technical or cultural difficulties to this end. Furthermore, final examination of the productivity benefit – because of the difficulty in defining and measuring the concept – is exploratory rather than definitive. This research suggests that not only human-centric smart building research should be prioritised over energy or space-based themes but also large-scale private sector collection of occupancy data may be imminent, and its potential should be examined.

Findings strongly suggest that the hot-desking may cost more in lost productivity than it gains in reduced rental costs and as such many commercial offices should revaluate the transition, particularly with a view to facilitate intelligent hot-desking. Companies should begin to think strategically about the wider benefits of collecting occupancy data across their real estate portfolio, rather than reviewing use cases in silos. Finally, cities should consider scenarios of widespread collection of occupancy data in the private sector, examining the value these data have to city systems such as transport, and how the city might procure it for these ends.

This paper raises positive and negative social concerns. The value in occupancy data suggested herein, bringing with it the implication it should be collected en mass, has a noted concern that this brings privacy concerns. As such, policy and regulation should heed that current standards should be reviewed to ensure they are sufficient to protect those in offices from being unfairly discriminated, spied or exploited through occupancy data. However, the improved use of occupancy data improving workplaces could indeed make them more enjoyable places to work, and have the potential to become a staple in company’s corporate social responsibility policies.

This paper fulfils an identified need for better understanding the specific uses of occupancy data in the smart building mantra. Several sources suggest the current research focus on energy and rental costs is misguided when the holistic cost of an office is considered, and concepts related to staff – although less understood – may have an order of magnitude bigger impact. This research supports this hypothesis through the example of intelligent hot-desking. The value of this paper lies in redirecting industry and research towards the considering occupancy data in smart building uses cases including – but not limited to– intelligent hot-desking.

The purpose of this case study paper is to demonstrate that, no matter how complex computer security systems are, effort should be concentrated and focused on employees to improve their security awareness. Each employee needs to become a “Security Deputy” to the company's computer security staff and he or she needs to take some responsibility for preventing security breaches – whether inside the workplace or not. It is easy to unwittingly spread a virus, or open security vulnerabilities, and such actions might damage a company's systems perhaps even more than malicious employees, through simple ignorance of security issues.

A series of surveys and questionnaires were designed along with practical exercises and security awareness training sessions.

Following their involvement in the exercises and awareness training, employees demonstrated improvement in security awareness. Users were made explicitly aware of the realities of IT security with pertinent questions asked in order to force them evaluate their own reactions to a situation which may escalate into a security incident.

The research was undertaken in a typical medium‐large sized company within the energy business sector, but it is possible that results may be different in other sectors.

It is clear that security technologies alone cannot prevent incidents and therefore employees need good quality security awareness training in order to protect the organisation.

It is becoming increasingly important that employees are taken through a more rigorous security‐awareness training programme, in order to protect business computer systems and to “protect them from themselves”.

The purpose of this paper is to provide an in-depth overview of the security requirements and challenges for Internet of Things (IoT) and discuss security solutions for various enabling technologies and implications to various applications.

Security requirements and solutions are analysed based on a four-layer framework of IoT on sensing layer, network layer, service layer, and application layer. The cross-layer threats are analysed followed by the security discussion for the enabling technologies including identification and tracking technologies, WSN and RFID, communication, networks, and service management.

IoT calls for new security infrastructure based on the new technical standards. As a consequence, new security design for IoT shall pay attention to these new standards. Security at both the physical devices and service-applications is critical to the operation of IoT, which is indispensable for the success of IoT. Open problems remain in a number of areas, such as security and privacy protection, network protocols, standardization, identity management, trusted architecture, etc.

The implications to various applications including supervisory control and data acquisition, enterprise systems, social IoT are discussed. The paper will serve as a starting point for future IoT security design and management. The security strategies for IoT should be carefully designed by managing the tradeoffs among security, privacy, and utility to provide security in multi-layer architecture of IoT.

The paper synthesizes the current security requirements for IoT and provides a clear framework of security infrastructure based on four layers. Accordingly, the security requirements and potential threats in the four-layer architecture are provided in terms of general devices security, communication security, network security, and application security.

The Android pattern lock screen (or graphical password) is a popular user authentication method that relies on the advantages provided by the visual representation of a password, which enhance its memorability. Graphical passwords are vulnerable to attacks (e.g. shoulder surfing); thus, the need for more complex passwords becomes apparent. This paper aims to focus on the features that constitute a usable and secure pattern and investigate the existence of heuristic and physical rules that possibly dictate the formation of a pattern.

The authors conducted a survey to study the users’ understanding of the security and usability of the pattern lock screen. The authors developed an Android application that collects graphical passwords, by simulating user authentication in a mobile device. This avoids any potential bias that is introduced when the survey participants are not interacting with a mobile device while forming graphical passwords (e.g. in Web or hard-copy surveys).

The findings verify and enrich previous knowledge for graphical passwords, namely, that users mostly prefer usability than security. Using the survey results, the authors demonstrate how biased input impairs security by shrinking the available password space.

The sample’s demographics may affect our findings. Therefore, future work can focus on the replication of our work in a sample with different demographics.

The authors define metrics that measure the usability of a pattern (handedness, directionality and symmetry) and investigate their impact to its formation. The authors propose a security assessment scheme using features in a pattern (e.g. the existence of knight moves or overlapping nodes) to evaluate its security strengths.

This paper is premised upon an analysis of 26 cities within the UK regarding their smart city projects. Each city was analyzed through news articles, reports and policy documents to ascertain the level of each city's development as a smart city. Each was coded by separating the projects into five types, which were ranked on a scale from 0 (no plans for use) to 5 (project type in use). The most common types are the provision of open data and the creation of business ecosystems as the primary driver of the smart city. However, many councils and enterprises proclaim smartness before the technology is actually in use, making it difficult to separate what is utilised and what is under development. Therefore, this paper further carried out an analysis of 20 cities and their intended plans to usher in the smart city, to observe the expected emergence of smart city technology. This was achieved by interrogating various roadmaps and policy documents produced by the respective cities. It was found that the most prevalent form of emergent smart city technology is the rollout of 5G and increased educational programmes alongside a proliferation of internet of things and electric vehicle usage.