Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.

The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the security problems within the 5G-enabled IoT networks. This paper proposes a Multi layer Blockchain Security model to protect IoT networks while simplifying the implementation. The concept of clustering is utilized in order to facilitate the multi-layer architecture. The K-unknown clusters are defined within the IoT network by applying techniques that utillize a hybrid Evolutionary Computation Algorithm while using Simulated Annealing and Genetic Algorithms. The chosen cluster heads are responsible for local authentication and authorization. Local private blockchain implementation facilitates communications between the cluster heads and relevant base stations. Such a blockchain enhances credibility assurance and security while also providing a network authentication mechanism. The open-source Hyperledger Fabric Blockchain platform is deployed for the proposed model development. Base stations adopt a global blockchain approach to communicate with each other securely. The simulation results demonstrate that the proposed clustering algorithm performs well when compared to the earlier reported approaches. The proposed lightweight blockchain model is also shown to be better suited to balance network latency and throughput as compared to a traditional global blockchain.

Air pollution poses significant risk to environment and health. Air quality monitoring stations are often confined to a small number of locations due to the high cost of the monitoring equipment. They provide a low fidelity picture of the air quality in the city; local variations are overlooked. However, recent developments in low cost sensor technology and wireless communication systems like Internet of Things (IoT) provide an opportunity to use arrayed sensor networks to measure air pollution, in real time, at a large number of locations. This paper reports the development of a novel low cost sensor node that utilizes cost-effective electrochemical sensors to measure Carbon Monoxide (CO) and Nitrogen Dioxide (NO2) concentrations and an infrared sensor to measure Particulate Matter (PM) levels. The node can be powered by either solar-recharged battery or mains supply. It is capable of long-range, low power communication over public or private LoRaWAN IoT network and short-range high data rate communication over Wi-Fi. The developed sensor nodes were co-located with an accurate reference CO sensor for field calibration. The low cost sensors’ data, with offset and gain calibration, shows good correlation with the data collected from the reference sensor. Multiple linear regression (MLR) based temperature and humidity correction results in Mean Absolute Percentage Error (MAPE) of 48.71% and R2 of 0.607 relative to the reference sensor’s data. Artificial Neural Network (ANN) based calibration shows the potential for significant further improvement with MAPE of 38.89% and R2 of 0.78 for leave one out cross validation.

This paper reports the development of a practical visible light positioning (VLP) system using received signal strength. The indoor localization system is accurate and easy to train and calibrate despite using fingerprinting technique. The VLP system consists of cheap photodiode-based receiver and consumer grade LED luminaires. The impact of distance metrics used to compute the weights of the weighted K-nearest neighbor (WKNN) algorithm on the localization accuracy of the VLP is investigated. Experimental results show that square chord distance is the most robust and accurate metric and significantly outperforms the commonly used Euclidean distance metric. A room-scale implementation shows that a mean error of 2.2 cm and a 90-percentile error of 4.9 cm within a 3.3 m × 2.1 m 2-D floor space are achievable. However, the high localization accuracy comes at the cost of requiring 187 offline measurements to construct the fingerprint database. A method for estimating an optical propagation model using only a handful of measurements is developed to address this problem. This leads to the creation of a dense and accurate fingerprinting database through fabricated data. The performance of the VLP system does not degrade noticeably when the localization is performed with the fabricated data. A mean error of 2.7 cm and a 90-percentile error of 5.7 cm are achievable with only 12 offline measurements.

Accurate, reliable indoor localization or positioning is the key enabler for location-based services. Indoor localization can be broadly classified into two distinct categories. Active localization entails tracking a tag attached to or carried by the target. Passive localization, on the other hand, involves positioning a device free or untagged target. While passive or device free localization is comparatively more difficult to achieve, it is the preferred option for many applications. Vision-based techniques can accurately localize an un-tagged target. However, privacy is a significant concern and thus have limited usability for many applications, especially in non-commercial and residential settings. Passive localization using RF or wireless sensing is an unobtrusive option and has seen extensive research efforts in recent years leading to a saturated research field but no consensus solution. Researchers have been investigating alternative solutions that can facilitate robust passive localization. The rapid proliferation of Internet of Things (IoT) is bringing ubiquitous networked devices and ambient sensors into modern buildings. The consequential pervasive ambient intelligence and signals of opportunity can enable unobtrusive device free positioning. This article presents a comprehensive review of non-RF solutions covering visible light-, infrared-, physical excitation-and electric field sensing-based techniques. Limitations of the state of the art and potential future research directions are also outlined.

Visible Light Positioning (VLP) is a promising indoor localization method as it provides high positioning accuracy and allows for leveraging the existing lighting infrastructure. Photodiode (PD)- based receiver is a commonly used tag for VLP. However, a tag employing single PD requires three or more luminaires to be visible. This paper presents a VLP system that uses a custom-made tag utilizing multiple PDs. It applies Received Signal Strength (RSS)-based fingerprinting using Weighted k-Nearest Neighbor (WkNN) algorithm for localization. Experimental results show that it is possible to localize using less than three luminaires with high accuracy. Manhattan and Matusita distance metrics are found to provide lower localization accuracy than the Euclidean metric for the WkNN algorithm. The creation of a dense fingerprinting database through 2-D interpolation is presented as a method to reduce the cost of time and labour. The localization performance of the VLP system does not degrade noticeably with the fabricated database. The localization accuracy of the WkNN algorithm is shown to be better than that of a Multi-Layer Perceptron (MLP)-based regressor. The developed VLP system is also experimentally benchmarked against the HTC Vive showing comparable performance.