Occupant Privacy Perception, Awareness, and Preferences in Smart Office Environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants' perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people's privacy preferences. The features of the collected modality define data modality features -- spatial, security, and temporal context. In contrast, personal features consist of one's awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people's privacy preferences in smart office buildings helps design more effective measures to improve people's privacy

Dynamic Target Classification in Wireless Sensor Networks

Information exploitation schemes with high-accuracy and low computational cost play an important role in Wireless Sensor Networks (WSNs). This thesis studies the problem of target classification in WSNs. Specifically, due to the resource constraints and dynamic nature of WSNs, we focus on the design of the energy-efficient solutionwith high accuracy for target classification in WSNs. Feature extraction and classification are two intertwined components in pattern recognition. Our hypothesis is that for each type of target, there exists an optimal set of features in conjunction with a specific classifier, which can yield the best performance in terms of classification accuracy using least amount of computation, measured by the number of features used. Our objective is to find such an optimal combination of features and classifiers. Our study is in the context of applications deployed in a wireless sensor network (WSN) environment, composed of large number of small-size sensors with their own processing, sensing and networking capabilities powered by onboard battery supply. Due to the extremely limited resources on each sensor platform, the decision making is prune to fault, making sensor fusion a necessity. We present a concept, referred to as dynamic target classification in WSNs. The main idea is to dynamically select the optimal combination of features and classifiers based on the probability that the target to be classified might belong to a certain category. We use two data sets to validate our hypothesis and derive the optimal combination sets by minimizing a cost function. We apply the proposed algorithm to a scenario of collaborative target classification among a group of sensors which are selected using information based sensor selection rule in WSNs. Experimental results show that our approach can significantly reduce the computational time while at the same time, achieve better classification accuracy without using any fusion algorithm, compared with traditional classification approaches, making it a viable solution in practice

Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper

Implementing work-from-home benefits into the workplace post-COVID-19 pandemic

The COVID-19 pandemic is something the world experienced together, yet altered our day-to-day lives in countless diverse ways. Some examples include how we attend school, how and where we exercise, and how we run our typical, weekly errands. For many, the biggest change caused by the pandemic was the impact on the workplace and its interior environment. Many of these changes directly impacted on full-time employees, for instance, the way work tasks were completed, as well as the location tasks were completed in. The purpose of this study is to recognize ways interior designers, architects, and even employers can help to improve employee satisfaction and productivity levels. The study compares similarities and differences of indoor environmental qualities (IEQ), as well as other interior factors like privacy and biophilic design elements, between home and workplace office environments for employees. The study focused solely on full-time employees over the age of eighteen years old who worked any amount of time at home before returning to their workplace. The method of this study used an online survey platform which made it easy to keep the data organized. Survey participants must be older than eighteen years, as well as a full-time employee. The goals of the survey included identifying positive and negative factors relating to the interior workplace environment in hopes of improving employee satisfaction and productivity. The results of this study have reassured that this topic on improving the post-pandemic workplace to better mimic the benefits found working from home is important and critical in the guaranteed improvements to employee satisfaction and productivity regarding the post-pandemic workplace environment. Keywords: work-from-home, WFH, return-to-office, RTO, return-to-workplace, COVID-19, pandemic, post-pandemic, workplace, satisfaction, production, indoor environmental quality, biophilic desig

Micro-Electro-Mechanical Systems (MEMS) technology

Lecture on Micro Elecrto-Mechanical Systems manufacture. Examples of its application to product manufacture are given

Exploring structure sharing in services and using the principles of product design to conceptualise modular workstations

The object of this thesis is to explore the design requirements of a modular and automated mobile workstation, and conceptualise it by exploring product design principles. During the thesis, a dynamic market survey has been conducted to explore existing competition and understand the user requirements for such a product. A thorough study was conducted to understand the rise and fall of open offices in the professional work environment and its relation to individual productivity. Engineering design principles such as Structure Sharing and Modularisation were effectively explored and utilised during this thesis. Structure sharing as a concept was used to explore sharing that occurs in the organisational structure of an industry providing shared services to the customers. AirBnB, a global shared-hospitality service provider, was used as the primary case-study for this purpose. An approach has been made to understand modularity and inclusive design, and find a common ground to apply the concepts of product development in the field of large-scale distributed construction. The results of this research can now be used for the conceptual design of a workstation for diverse users, by applying structure sharing at an organisational level, as studied for case specific services in this thesis. This design can then be evaluated for resource effectiveness using existing design methodologies or can be used to develop new methodologies

Micro Scale Energy Harvesting For Ultra-Low Power Systems

Ultra-low power systems such as Wireless sensor network (WSN) nodes have emerged as an active research topic due to their vast application areas. Such WSNs would be able to perform their sensing functions and wireless communication without any supervision, configuration, or maintenance. These systems have to cope with severe power supply constraints. The need shared by most WSNs for long lifetimes and small form factors does not match up well with the power density of available battery technology. This could limit the use of WSNs due to the need for large batteries. It is not expected that better batteries for small devices will become available in the near future. Energy harvesting could therefore be a solution to making WSNs autonomous and could thus enable widespread use of these systems in many applications. Energy harvesting is becoming more and more popular for micro-power applications where the environmental energy is used to power up the systems. As sensors have become smaller, cheaper, and increasingly abundant, there have been commensurate reductions in the size and cost of computation and wireless communication. In context of micro scale solar energy harvesting systems, the design of ecient energy conversion unit and accurate maximum power point tracking(MPPT) unit becomes a tremendous challenge due to area constraint and very low (W) output power. This thesis presents a novel MPP tracking method including a charge pump based DC-DC converter for extracting energy from a tiny single PV cell (open circuit voltage 0.4V). We have used a feed-forward (FF) unit to track maximum power point. The design of FF MPP is derived from the operating point of solar cell under dierent solar intensity. This scheme consumes very little power and is faster when compared to other methods. This method eliminates the use of current sensor and other power hungry elements in the MPPT unit. The proposed method tracks the MPP with less than 2 % error and gives eciency of 63.50% through FF MPPT. The complete circuit has been simulated using 0.18 m CMOS process

The Design of Medium Access Control (MAC) Protocols for Energy Efficient and QoS Provision in Wireless Sensor Networks

This thesis work focuses on innovative design of media access control (MAC) protocols in wireless sensor networks (WNSs). The characteristics of the WSN inquire that the network service design considers both energy efficiency and the associated application requirement. However, most existing protocols address only the issue of energy efficiency. In this thesis, a MAC protocol has been proposed (referred to as Q-MAC) that not only minimized the energy consumption in multi-hop WSNs, but also provides Quality of Service (QoS) by differentiating network services based on priority levels prescribed by different applications. The priority levels reflect the state of system resources including residual energy and queue occupancies. Q-MAC contains both intra- and inter- node arbitration mechanisms. The intra-node packet scheduling employs a multiple queuing architectures, and applies a scheduling scheme consisting of packet classification and weighted arbitration. We introduce the Power Conservation MACAW (PC-MACAW), a power-aware scheduling mechanism which, together with the Loosely Prioritized Random Access (LPRA) algorithm, govern the inter-node scheduling. Performance evaluation are conducted between Q-MAC and S-MAC with respect to two performance metrics: energy consumption and average latency. Simulation results indicate Q-MAC achieves comparable performance to that of S-MAC in non-prioritized traffic scenarios. When packets with different priorities are introduced, Q-MAC yields noticeable average latency differentiations between the classes of service, while preserving the same degree of energy consumption as that of S-MAC. Since the high density nature of WSN may introduce heavy traffic load and thus consume large amount of energy for communication, another MAC protocol, referred to as the Deployment-oriented MAC (D-MAC)has been further proposed. D-MAC minimalizes both sensing and communication redundancy by putting majority of redundant nodes into the sleep state. The idea is to establish a sensing and communication backbone covering the whole sensing field with the least sensing and communication redundancy. In specific, we use equal-size rectangular cells to partition the sensing field and chose the size of each cell in a way such that regardless of the actual location within the cell, a node can always sense the whole cell and communicate with all the nodes in neighboring cells. Once the sensing field has been partitioned using these cells, a localized Location-aware Selection Algorithm (LSA) is carried out to pick up only one node within each cell to be active for a fixed amount of period. This selection is energy-oriented, only nodes with a maximum energy will be on and the rest of nodes will be put into the sleep state once the selection process is over. To balance the energy consumption, the selection algorithm is periodically conducted until all the nodes are out of power. Simulation results indicated that D-MAC saves around 80% energy compared to that of S-MAC and Q-MAC, while maintaining 99% coverage. D-MAC is also superior to S-MAC and Q-MAC in terms of average latency. However, the use of GPS in D-MAC in identifying the nodes within the same cell, would cause extra cost and complexity for the design of sensor nodes