Department Publications

This paper proposes a secure and cost-effective communication and management approach for distributed disaster early warning networks to ensure reliable, low-latency alert dissemination. While decentralized architectures reduce latency by enabling direct device communication, they are highly vulnerable to attacks due to their exposure in public community settings and the challenges posed by Network Address Translation (NAT) and firewalls. Traditional NAT traversal techniques often lack the built-in security required for such sensitive infrastructure. We identify Nebula, an open-source overlay networking tool, as a viable solution because it is lightweight enough for low-resource devices such as the Raspberry Pi typically found in decentralized early warning systems, supports NAT traversal, and provides endto-end encryption. Preliminary experiments conducted in an AWS environment—designed to simulate real-world Raspberry Shake sensor deployments—confirm Nebula’s ability to establish direct peer-to-peer (P2P) connectivity with a single hop and effectively encrypt traffic

Voice communication is essential in post-disaster scenarios where users may be unable to type due to injury, stress, or environmental constraints. While Meshtastic provides a decentralized LoRa-based mesh network for long-range messaging, it does not natively support voice and is limited by low data rates and strict regulatory duty-cycle requirements. This paper investigates two methods for enabling voice over such networks: waveform-based audio packetization and a semantic speech-to-text (STT)/text-to-speech (TTS) approach. Preliminary experimental evaluations demonstrate that waveform-based methods, even when using low-bitrate codecs like Codec2, are impractical for urgent communication; delivering a 3-second voice message requires hundreds of packets and several minutes due to ransmission latency and duty-cycle constraints. In contrast, the proposed offline STT/TTS method transcribes speech locally, transmits it as a standard text payload, typically in a single packet and reconstructs the audio at the receiver. This semantic approach reduces delivery time from minutes to seconds, while maintaining high reliability and regulatory compliance. The study concludes that semantic communication is the most viable solution for voice interaction in bandwidth-constrained LoRa mesh environments.

Semi-grant-free non-orthogonal multiple access (SGF-NOMA) schemes group one grant-based (GB) user with multiple grant-free (GF) users into one time/frequency resource block (RB) to enhance spectral efficiency. Due to the sporadic traffic of GF users and the stringent quality of service (QoS) requirement of the GB user, the access collision problem becomes severe in SGF-NOMA. To solve this problem, this paper firstly designs an RB-based power pool (PP), which directs GF users to adjust their transmit power without disrupting the ongoing transmission of the internal GB user. After that, this work proposes an efficient multi-agent deep reinforcement learning (MA-DRL) framework to jointly optimize the PP and access strategy for maximizing the network throughput. In particular, this work exploits the fast-response feature of the traditional competitive MA-DRL and the increased-performance feature of the …

Fire is a serious threat to both human life and property, and fire safety is crucial. Many traditional methods have been developed to detect fires. However, inspired by past research that used Wi-Fi signals for monitoring environments and recognizing activities, this approach aims to use Wi-Fi signals to detect fire that utilize existing wireless communication systems for non-intrusive sensing. The idea is that fire and smoke can interfere with Wi-Fi signals passing through an environment, then Wi-Fi CSI could provide detailed information about the wireless channel between a Wi-Fi transmitter and a receiver. This research demonstrates the effectiveness of using Wi-Fi for fire detection, utilizing Raspberry Pi and ESP32 devices with ESP32 CSI tool. It analyses the temporal shift in Wi-Fi CSI Amplitude before, during, and after the ignition of a flame. The results showed that fire causes a noticeable variation in the CSI …

Substantial data losses occur during transmission between the sensor nodes of a wireless sensor network due to various causes such as the instability of wireless communication links and network congestion. Most of the data recovery methods proposed are computationally expensive or rely on additional data from neighboring sensors or other types of correlated data which may not always be available. In this paper, we instead propose a lightweight real-time method of missing data recovery based on chromatic derivatives (CDs), a noise robust alternative to standard derivatives that enables high-fidelity local signal approximation. Using the bandlimited nature of the measured signals, our method interpolates the missing data using CDs-based non-uniform sample reconstruction. In contrast to many state-of-the-art methods, our approach relies solely on the received data stream from the transmitting sensor for …

Unmanned aerial vehicles (UAVs)-assisted communication systems are considered as a promising technology in diverse verticals. This paper studies the deployment of UAVs in wireless sensor network (WSN) systems. Considering the energy-constrained nature of the wireless sensors, we have proposed a multi-UAV deployment algorithm that minimizes the maximum power transmitted among the sensor nodes (SN) for given minimum data collection rate, minimum data-transferring rate, maximum power and height constraints. The problem is divided into three subproblems in order to reduce the complexity involved in solving them as a single problem. The subproblems are UAV-SN association, 2D positioning of the UAVs and the altitude optimization of the UAVs. Each subproblem is optimized by fixing other parameters as constant. First, the UAV-SN association is addressed using a customized Gale–Shapley …

Water resource management in Sri Lanka is hindered by inefficient water distribution systems in reservoirs and tanks, resulting in water scarcity, wastage, and negative impacts on agriculture, hydropower generation, and drinking water. Despite the global progress in Machine Learning (ML)-based predictive models for hydrological forecasting, Sri Lanka lacks the application of such techniques in its reservoir water management systems. This work proposes a novel approach that integrates ML and Internet of Things (IoT) technologies to develop a demand prediction and smart water distribution system tailored to the Sri Lankan context. The proposed system employs a hybrid model combining Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks to accurately predict reservoir inflow, release, and water levels. Sensory data from ESP32 microcontrollers is collected and transmitted to a cloud …

Real-time pedestrian sensing at crossings is essential for Intelligent Transportation Systems (ITS). Yet traditional vision-based solutions and wearable devices face privacy, cost, and deployment challenges. While Wi-Fi channel state information (CSI) has shown promise in indoor environments, its potential for dynamic outdoor environments remains largely unexplored. This paper presents a device-free, CSI-based system for detecting human movement and walking direction at crossings and alert oncoming vehicles to enhance pedestrian safety. A twostage classification framework is adopted with noise suppression and Doppler-sensitive features. Classical and deep learning models are evaluated across four outdoor locations and seven subjects, achieving 96.9% accuracy for movement detection and up to 62% accuracy for classification of movement direction. These findings demonstrate the feasibility of …

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab’s EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1–6 s warning window, slightly behind the 1.5–6.75 s of the internet-based performance of CRISiSLab’s system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration.

This research presents an efficient fruit monitoring and orchard management system to replace the existing paper-based manual process. The new method is based on five state-of-the-art technologies, namely Radio Frequency IDentification (RFID), Wi-Fi network, Mobile App, Coud database/server, and Web Application. This paper presents the proper integration of these technologies to provide an effective, worker-friendly, and cost-effective solution to the problem. The proposed method starts by attaching RFID tags to each tree and each fruit and registering them in the cloud database. The cloud server visualizes the status of the orchard and implements inventory management. Workers use a hand device for tasks such as bagging, spraying, and plucking. The orchard manager carries out task assignments and worker deployment on the web application. Each worker gets notified of the assigned tasks on his hand …

This paper addresses the problem of deploying unmanned aerial vehicles (UAVs) to assist data collection and transmission in wireless sensor networks (WSNs). A multi-UAV deployment algorithm is proposed for an energy-constrained network to minimize the maximum transmit power of the sensor node (SN), while satisfying data rate and transmit power constraints. The problem is divided into two subproblems, namely, UAV-SN association, and UAV positioning. First, the UAV-SN association is addressed using a customized Gale-Shapley algorithm. Then, the 2D positions of the UAVs are decided using a modified pattern search algorithm. The two subproblems are solved alternatively in an iterative manner to obtain a near-optimal solution. Compared to baseline algorithms, the proposed solution results in at least 4-fold and 4.5-fold reductions in the number of iterations required for convergence, and the maximum …

Blockchain is an innovative technology which enabled new applications for solving numerous problems in distributed environments such as the internet of things (IoT) equipped food supply chains (FSCs). In FSCs, the large volume of IoT data such as audio, video, images, and sensor data will be transferred to ensure the traceability of the food to its source. When blockchain technology is used in an FSC, the storage requirements in the nodes will grow with time, since blockchain only allows information adding, without deleting the already stored information. Therefore, it is evident that offchain storage offers more flexibility than onchain storage in IoT equipped supply chains. This paper proposes a scalable storage scheme using offchain storage, where the data from IoT devices in the supply chain will store offchain. To reduce the growth of offchain storage, we exploit the fact that some information regarding a …

The emergence of Industry 4.0 has resulted in a rapid increase in the demand for bandwidth due to the proliferation of the industrial Internet of Things (IIoT). Increased utilization of the network also gives rise to undesirable consequences such as high latency and increased likelihood of data loss through packet drops. Encoding inputs using event-based sampling is a potential solution for decreasing network traffic generated when continuous input variables are sampled. The problem addressed by this article is the fact that currently there is no effective method for comparing different encoders. The resulting contribution is the use of mutual information to compare the input and encoded output in terms of accuracy for the currently most efficient encoders that use either memory based event triggering (MBET) or deadband error modulation (DEM). This allows a practitioner to select a suitable encoder for a given input …

Indoor positioning by wireless means is of significant interest in a wide range of applications; Wi-Fi and Bluetooth Low Energy (BLE) are popular candidate wireless technologies for this. We present a novel BLE beacon placement algorithm that complements existing Wi-Fi infrastructure in a hybrid Wi-Fi/BLE positioning system. Composite signal strength measurements thus obtained, are used to derive machine learning models for location estimation. Our results show that positioning estimates at table-level granularity (0.9 m × 1.8 m) in a computer laboratory can be achieved with 97% accuracy. This is an improvement of 20% and 30% compared to Wi-Fi and BLE only techniques respectively.

Dielectric heaters are mainly used in industry for various applications. Due to generation of near field energy from stray electromagnetic fields, these machines were selected for analysis as a source for RF energy harvesting. In this research, the energy harvester was designed using a ferrite core loop antenna, rectifying and smoothing circuit and an ultra-low power management system in order to provide energy to a temperature and humidity sensor node. This application was tested within the footwear manufacturing industry due to availability of dielectric heaters (RF Welding Machine) and temperature and humidity are continuously measured during the production process.

Highly flexible FPAA and FPGA based universal sensor node design targeting researchers is discussed in this paper. The reconfigurable system is capable of supporting up to three low power analog sensors and two digital sensors in parallel. This facilitates dynamic configurability and remote monitoring by connecting the device to the internet. The design can be utilized in development of remote sensing devices to be establish at locations where frequent repairing and maintenance is difficult. Compensability for aging, adjustability to different environments, flexibility are key advantages of this system.

A smart city is a vision for urban development which integrates multiple information and communication technology (ICT) and Internet of Things (IoT) solutions to manage a city's assets and services. Urban population is constantly growing, and this implies an inevitable increase in the resource consumption within the city. The realization of intelligent solutions is therefore essential to optimize the usage of resources, minimize the environmental impact and improve the quality of life of the city's inhabitants.

Everyone is moving towards smart era rapidly with new technologies. This paper focuses on monitoring a building from a remote place to ensure the activities happening in particular locations. Here we have proposed a sensor-based wireless distributed system with people counting and activity detection for a building. A novel approach is used to people count using one motion sensor. A System model is built and experimented in the department of Electronic and Telecommunication Engineering (ENTC), University of Moratuwa. This enables monitoring activities in the lecture hall, lecturer room and lab with high precision.

The DV-Hop algorithm is an attractive option for the localization of nodes in a wireless sensor network due to its simplicity. However it suffers from poor accuracy. This paper presents a novel algorithm to enhance the accuracy of this algorithm. The idea is to use the DV-Hop algorithm to estimate positions of the anchors which are already known. The estimation errors so obtained are then minimized through an optimization algorithm which finds the optimum correction to the hop size. The locations of the unknown nodes are then estimated with the modified hop size using least squares estimation. We examine the performance of the algorithm for varying node numbers, anchor ratios and radio range. The results show that the proposed algorithm has better performances in localization precision and stability than the original DV-Hop algorithm.

A novel wireless sensor network architecture designed for gathering and managing onsite dynamic information in emergency situations is presented. The system can operate in two different states-normal and emergency, where switching between states occurs according to perception from the environment. The main functionalities of the system include perceiving, processing and sharing the gathered information within a wireless sensor network which is deployed in a multi-story indoor environment. Algorithms for sensor localization, network self-configuration as well as data filtering and prediction are studied in the characterized emergency environment. The proposed architecture will act to reduce risk in rescue operations following an emergency as well as generating early warnings of possible emergencies.

The Australian construction industry is identified as one among five industries to receive priority attention under the Australian Work Health and Safety Strategy 2012-2022. Among the recorded construction fatalities in 2012, vehicle incidents accounted for 15% and being hit by moving objects accounted for 10%. Tracking of individuals on-site could provide a technological solution to improve safety. This paper proposes a novel, non-tagging-based, wireless networking solution (called device-free localization) to enhance safety on site. Device free localization techniques use radio signal propagation effects such as multi-path fading and shadowing caused by the obstruction(s). The proposed solution defines zones in the construction site. Zone identification is based on the risk associated with the location map of the site. Defining risk profiles considers the activities associated with the geographic location, such as …

Device free detection and localization is a promising and a very practical research area where installation of expensive sensors and/or tagging the intruder is not feasible. This paper reports a mesh network developed for device free trespasser detection. We use the Received Signal Strength Indicator (RSSI) measurements of the wireless links within the mesh as the sole sensing mechanism. A fingerprint of RSSI measurements characterizes the environment under normal conditions including multi-path effects. A technique to differentiate RSSI variations due to trespassers from those due to multi-path effects is demonstrated. The results of the experiments conducted in an outdoor environment have verified that the proposed method can be used to detect trespassers successfully. The reliability of the detection is improved by cross-correlating RSSI variations among the multiple links in the mesh. The ability to extend …

Transferring computational tasks from user-worn devices to everyday objects allows users to freely focus on their regular non-computing tasks. Identifying micro-activities (short-repetitive-activities that compose the macro-level behavior) enables the understanding of subtle behavioral changes and providing just-in-time information without explicit user input. In this paper, we propose the concept of micro-activity recognition of augmented-everyday-objects and evaluate the applicability of machine learning algorithms that are previously used for macro-level activity recognition. We outline a few proof-of-concept application scenarios that provide micro-activity-aware just-in-time information.

Localization is a significant consideration in a Wireless Sensor Network (WSN), as it is essential to correlate sensor information with the geographic location it originates from. The DV-Hop algorithm, being a range-free, distributed algorithm, is an attractive option in this regard, due to its simplicity. In this paper, we present two algorithms to improve the localization accuracy of the DV-Hop algorithm. The Hop Size derived through a distributed fashion by the DV-Hop algorithm is optimized using the knowledge of the position of the anchor nodes. The locations of the unknown nodes are then estimated using the optimum hop size. We examine the performance of the proposed algorithms through simulations in environments with varying node numbers, anchor ratios and radio range. The results show that the proposed algorithms have improved performance in localization accuracy and stability compared to the original …