Peter Cole

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.

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.

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.

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%.

Top-down sanitation programs that promote a specific sanitation technology based on the presumptions of 'outside experts' have been criticised for endorsing unsustainable, expensive and inappropriate technologies. In response to these failings, a new era of demand-led sanitation programs (including community-led total sanitation and sanitation marketing) encourage greater participation of users to create appropriate sanitation technologies. This paper examines the use of participatory design sessions with local builders and householders in three rural districts in Malawi. The paper provides an account of the participatory design methodology and critically reflects on the processes and challenges in relation to power, creativity and ownership. The designs created during the sessions are presented with recommendations for further testing and structural refinement.

This paper presents the Weighted Random Early Detection (WTRED) strategy for congestion handling in TCP networks. WTRED provides an adjustable weight parameter to increase the sensitivity of the average queue size in RED gateways to the changes in the actual queue size. This modification, over the original RED proposal, helps gateways minimize the mismatch between average and actual queue sizes in router buffers. WTRED is compared with RED and FRED strategies using the NS-2 simulator. The results suggest that WTRED outperforms RED and FRED. Network performance has been measured using throughput, link utilization, packet loss and delay.

This work presents the Risk Threshold RED (RTRED) congestion control strategy for TCP networks. In addition to the maximum and minimum thresholds in existing RED-based strategies, we add a third dropping level. This new dropping level is the risk threshold which works with the actual and average queue sizes to detect the immediate congestion in gateways. Congestion reaction by RTRED is on time. The reaction to congestion is neither too early, to avoid unfair packet losses, nor too late to avoid packet dropping from time-outs. We compared our novel strategy with RED and ARED strategies for TCP congestion handling using a NS-2 simulation script. We found that the RTRED strategy outperformed RED and ARED.

Mobile phones nowadays are equipped with multiradio interfaces which allow radio link connections to an array of wireless networks. The presence of these interfaces however, introduces serious concerns. These interfaces consume huge amounts of energy thus can quickly drain their batteries and reduce the length of usage of the mobile phones. In this paper, a new activation service is proposed to reduce the energy consumption in mobile phones. The basic idea behind this proposal is to introduce an auxiliary receiver that is able to receive a special signal from the access point and activate the wireless interfaces on the mobile phone. During idle times, the wireless interfaces are switched off to reduce its idle power - the energy a device consumes in `standby' state. When call or data arrives, the auxiliary receiver will receive notification from an access point and activate the relevant wireless interface on the mobile phone to receive the data or call. Through this activation service, we believe that power consumption can be reduced thus increase the longevity of mobile phone usage.

This work presents the Weighted Random Early Detection (WTRED) strategy for congestion handling in TCP networks. The strategy dynamically adjusts RED's maximum threshold, minimum threshold and weight parameters to increase network performance. This work describes RED and FRED implementations and highlights their disadvantages. Using the NS-2 simulator, we compare WTRED with these classic congestion control strategies. The simulation results demonstrate the shortcomings of RED and FRED. The results also show that WTRED achieves greater link utilization and throughput than RED and FRED.

Network congestion is a phenomenon caused by the extreme demand of restricted network resources. Various congestion control strategies have been proposed to increase network performance. This study suggests that there is a mismatch between the microscopic and macroscopic behavior in (Random Early Detection) RED’s queue management mechanism. This work investigates this problem and propose QSRED (Queue Sectors RED) to avoid unsatisfactory performance. QSRED is simulated against RED and ERED (Effective RED) by measuring: throughput, link utilization, packets loss and average delay using the NS2 simulator. The results suggest that Queue Sectors RED (QSRED) helps RED overcome the mismatch between microscopic and macroscopic behavior of queue length dynamics.