Smart Brain Interaction Systems for Office Access and Control in Smart City Context

Over the past decade, the term “smart cities” has been worldwide priority for city planning by governments. Planning smart cities implies identifying key drivers for transforming into more convenient, comfortable, and safer life. This requires equipping the cities with appropriate smart technologies and infrastructure. Smart infrastructure is a key component in planning smart cities: smart places, transportation, health and education systems. Smart offices present the concept of workplaces that respond to user’s needs and allow less commitment to routine tasks. Smart offices solutions enable employees to change status of the surrounding environment upon the change of user’s preferences using the changes in the user’s biometrics measures. Meanwhile, smart office access and control through brain signals is quite recent concept. Hence, smart offices provide access and services availability at each moment using smart personal identification (PI) interfaces that responds only to the personal thoughts/preferences issued by the office employee not any other person. Hence, authentication and control systems could benefit from the biometrics. Yet these systems are facing efficiency and accessibility challenges in terms of unimodality. This chapter addresses those problems and proposes a prototype for multimodal biometric person identification control system for smart office access and control as a solution

Multi-agent Communication Protocols with Emergent Behaviour

The emergent behaviour of a multiagent system depends on the component agents and how they interact. A critical part of interaction between agents is communication. This thesis presents a multi-agent system communication model for physical moving agents. The work presented in this thesis provides all the tools to create a physical multi-agent communication system. The model integrates different agent technologies at both the micro and macro level. The micro structure involves the architecture of the individual components in the system whilst the macro structure involves the interaction relationships between these individual components in the system. Regarding the micro structure of the system, the model provides the description of a novel hybrid BDI-Blackboard architectured agent that builds-in a hybrid of reactive and deliberative agent. The macro structure of the system, provided by this model, provides the operational specifications of the communication protocols. The thesis presents a theory of communication that integrates an animal intelligence technique together with a cognitive intelligence one. This results in a local co-ordination of movements, and global task coordination. Accordingly, agents are designed to communicate with other agents in order to coordinate their movements via a set of behavioural rules. These behavioural rules allow a simple directed flocking behaviour to emerge. A flocking algorithm is used because it satisfies a major objective, i.e. it has a real time response to local environmental changes and minimises the cost of path planning. A higher level communication mechanism is implemented for task distribution that is carried out via a blackboard conversation and ii negotiation process with a ground based controller. All the tasks are distributed as team tasks. A novel utilization of speech acts as communication utterances through a blackboard negotiation process is proposed. In order to implement the proposed communication model, a virtual environment is built that satisfies the realism of representing the agents, environment, and the sensors as well as representing the actions. The virtual environment used in the work is built as a semi-immersive full-scale environment and provides the visualisation tools required to test, modify, compare and evaluate different behaviours under different conditions. The visualization tools allow the user to visualize agents negotiations and interacting with them. The 3D visualisation and simulation tools allow the communication protocol to be tested and the emergent behaviour to be seen in an easy and understandable manner. The developed virtual environment can be used as a toolkit to test different communication protocols and different agent’s architecture in real time

Context-Aware Gossip-Based Protocol for Internet of Things Applications

This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol’s ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority. Keywords: Internet of Things (IoT); wireless sensor network (WSN); gossiping protocol; context-aware; content-aware; routing protocolKing Saud University (RG-1438-002

Analyzing Passive BCI Signals to Control Adaptive Automation Devices

Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and responds to users’ feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers’ comfort levels; (b) an application that analyzes workers’ feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers’ attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices

Optimizing E-Learning Cognitive Ergonomics Based on Structural Analysis of Dynamic Responses

Smart Assistive Technologies (SAT) can be a powerful tool in supporting education environments and inclusion for learners with visual/hearing impairments. For example, while captions in videos are a necessity for deaf users, audio reading is inevitable for blind ones. Including such technologies into a smart e-learning environment provide huge opportunities to customize the content presentation to needs and ability of learners. Despite the number of models being introduced during the last decade, acceptance model and behavioral model are, yet, exhibiting design drawbacks for learners with visual and hearing impairments. Meanwhile, the e-learning initiatives in the universities have paid great efforts in order to optimize usability of conventional e-learning systems. However, optimizing assistive e-learning systems is not covered in the recent research. Central to e-learning optimization is the learners’ realization problem; in terms of the size of gap between learners’ expectations and real interaction measures. This paper presents a study of measure the usability of assistive e-learning systems and modeling better interaction based on adjusted Fitt’s Law to consider time of movement for assistive technologies embedded in e-learning systems. The proposed usability evaluation considers the hardness of mental operations during e-learning various activities