List of Titles

Adaptive low-power wireless sensor network architecture for smart street furniture-based crowd and environmental measurements

- Nassar, Mohammed, Luxford, Len, Cole, Peter, Oatley, Giles, Koutsakis, Polychronis, IEEE

Authors: Nassar, Mohammed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis , IEEE
Date: 2019
Type: Text , Conference proceedings
Relation: 2019 IEEE 20th International Symposium on a World of Wireless, Mobile and Multimedia Networks
Full Text: false
Reviewed:
Description: Street furniture such as bins, seats and bus shelters can become "smart" with the inclusion of wireless sensor nodes, which consist of environmental sensors, wireless modules, processors and microcontrollers. One of the most crucial challenges for smart street furniture is how to manage power consumption efficiently without affecting data freshness. In this work, we propose a novel Wireless Sensor Network (WSN) architecture for smart street furniture. Unlike existing WSNs which are based on a one-way communication model between wireless sensor nodes and the server, the proposed architecture employs a two-way communication model and a dynamic adaptation of the time interval of measurements to balance between power consumption and data updates. Our approach also provides a real-time low-power design for wireless sensor nodes which efficiently communicate the updated data instead of sending the same data on a regular basis. To the best of our knowledge, this is the first work in the relevant literature which extends the functionality of the wireless module in wireless sensor nodes to act not only as a station sending environmental data but also as soft Access Point (AP), sensing MAC addresses and WiFi signal strengths from surrounding WiFi-enabled devices. We have conducted experiments on the Murdoch University campus and our results show that our proposal improves lifetime of wireless sensor nodes up to 293% compared to static architectures similar to the ones that have been proposed in the literature. Moreover, network bandwidth is improved up to 38% without affecting data freshness. Finally, storage space for the database at the server is reduced up to 99%.
Description: E1

Real-time localisation system for GPS-denied open areas using smart street furniture

- Nassar, Mohamed, Luxford, Len, Cole, Peter, Oatley, Giles, Koutsakis, Polychronis

Authors: Nassar, Mohamed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
Date: 2021
Type: Text , Journal article
Relation: Simulation Modelling Practice and Theory Vol. 112, no. (2021), p.
Full Text: false
Reviewed:
Description: Wifi-based localisation systems have gained significant interest with many researchers proposing different localisation techniques using publicly available datasets. However, these datasets are limited because they only contain Wifi fingerprints collected and labelled by users, and they are restricted to indoor locations. We have generated the first Wifi-based localisation datasets for a GPS-denied open area. We selected a busy open area at Murdoch University to generate the datasets using so-called “smart bins”, which are rubbish bins that we enabled to work as access points. The data gathered consists of two different datasets. In the first, four users generated labelled WiFi fingerprints for all available Reference Points using four different smartphones. The second dataset includes 2450865 auto-generated rows received from more than 1000 devices. We have developed a light-weight algorithm to label the second dataset from the first and we proposed a localisation approach that converts the second dataset from asynchronous format to synchronous, applies feature engineering and a deep learning classifier. Finally, we have demonstrated via simulations that by using this approach we achieve higher prediction accuracy, with up to 19% average improvement, compared with using only the fingerprint dataset. © 2021 Elsevier B.V.

The current and future role of smart street furniture in smart cities

- Nassar, Mohamed, Luxford, Len, Cole, Peter, Oatley, Giles, Koutsakis, Polychronis

Authors: Nassar, Mohamed , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
Date: 2019
Type: Text , Journal article
Relation: IEEE Communications Magazine Vol. 57, no. 6 (2019), p. 68-73
Full Text: false
Reviewed:
Description: Recently, street furniture, including bins, seats, and bus shelters, has become smart as it has been equipped with environmental sensors, wireless modules, processors, and microcontrollers. Accordingly, smart furniture is expected to become a vital part of the IoT infrastructure and one of the drivers of future smart cities. This work focuses on how smart street furniture can be exploited within the IoT architecture as a basis of recommender systems, toward achieving smart cities' different components. We present and discuss recent relevant work as well as the key challenges and opportunities for future research. We explain that much work is still required when it comes to combining scalability, real-time processing, smart furniture, and recommender systems.

Wifi-based localisation datasets for No-GPS open areas using smart bins

- Nassar, Mohamed, Hasan, Mahmud, Khan, Md, Sultana, Mirza, Hasan, Md, Luxford, Len, Cole, Peter, Oatley, Giles, Koutsakis, Polychronis

Authors: Nassar, Mohamed , Hasan, Mahmud , Khan, Md , Sultana, Mirza , Hasan, Md , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
Date: 2020
Type: Text , Journal article , Data article
Relation: Computer Networks Vol. 180, no. (2020), p. 1-5
Full Text: false
Reviewed:
Description: In recent years, Wifi-based localisation systems have gained significant interest because of the lack of Global Positioning System (GPS) signal in indoor and certain open areas. Over the past decade, many datasets have been introduced to enable researchers to compare different localisation techniques. Existing datasets, however, have failed to cover open areas such as parks in cases where GPS is still unavailable, and there is a lack of Wifi access points. Also, the existing datasets only focus on getting Wifi fingerprint collected and labelled by users. To the best of our knowledge, no dataset provides Received Signal Strengths (RSS) collected by Wireless Access Points (APs). In this work, we offer two datasets publicly. The first is the Fingerprint dataset in which four users generated 16,032 accurate and consistently labelled WiFi fingerprints for all available Reference Points (RPs) in a central and busy area of Murdoch University, known as Bush Court. The second is the APs dataset that includes 2,450,865 auto-generated records received from 1000 users' devices, including the four users, associated with Wifi signal strengths. To overcome the Wifi coverage problem for the Bush Court, we attached our previously designed Wireless Sensor Nodes (WSNs) to existing garbage bins, enabling them to provide real-time environmental sensing and act as soft APs that sense MAC addresses and Wifi signals from surrounding devices.

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