Many applications, such as smart buildings, crowd flow, action recognition, and assisted living, rely on occupancy information. Although the use of smart cameras and computer vision can assist with these tasks and provide accurate occupancy information, it can be cost prohibitive, invasive, and difficult to scale or generalise to different environments. An alternative solution should bring similar accuracy while minimising the listed problems. This work demonstrates that a scalable wireless sensor network with CO₂-based estimation is a viable alternative. To support many applications, a solution must be transferable and must handle not knowing the physical system model; instead, it must learn to model CO₂ dynamics. This work presents a viable prototype and uses the captured data to train machine learning-based occupancy estimation systems. Models are trained under varying conditions to assess the consequences of design decisions on performance. Four different learning models were compared: gradient boosting, k-nearest neighbours (KNN), linear discriminant analysis, and random forests. With sufficient labelled data, the KNN model produced peak results with a root-mean-square error value of 1.021.

With the rapid implementation and expansion of the Internet of things (IoT) technologies in smart cities, network congestion control and energy-efficient routing in wireless sensor and actuator networks (WSANs) for virtualising IoT have emerged as a crucial area of research in recent years. This study implements the particle swarm optimisation (PSO) algorithm to realise network congestion control and energy-efficient routing in the transport layer of WSANs. This algorithm is based on the flocking behaviour of birds. The simulation results show that the proposed PSO-based approach provides better performance in terms of network lifetime and packet drop ratio compared with the ant colony optimisation and the artificial bee colony algorithm.

The growing trend of ageing has created many challenges. One of these challenges is the rehabilitation of individuals who take time, resources and manpower. The motivation for this study is to propose an ontology-based automatic design methodology for use in intelligent rehabilitation systems in the Internet of things (IoTs). For interconnection, all health system resources in a network use IoT technology. In order to provide fast and effective rehabilitation for different patients, IoT is combined with ontology. The experimental results obtained from rehabilitation of the lower limbs show that the IoT-based rehabilitation system works properly and an ontology-based approach can help create an effective rehabilitation strategy, configure and quickly deploy all available resources with the requirements given. In this study, three types of biosensors have been selected. These biosensors are used for blood, saliva and breathing tests. The expansion of the growth and maturity of technology based on the Fisher-Pry model is based on patent and bibliometrics analysis. The analysis of intellectual property rights according to their number indicates that blood vital sensors reached their turning point in 2009, but the vital sensors of saliva and respiration will reach their turning point and maturity with a delay of 8–14 years.

Today, mobile phones have become smarter than ever before and people are always carrying them. Mobile phones are not only references for computing and communications, but also a great option for gathering information about individuals and their surroundings. This study investigates the problem of mapping air pollution by leveraging a crowd of people that are equipped with smartphones. The proposed system uses mobile cloud computing as well, in order to collect and aggregate air pollution data. At the layer of mobile devices, air pollution is measured by local portable sensors through the exposure of users to the surrounding environment. Afterwards, these pieces of local information generated by the crowd of users are aggregated in the cloud layer. The proposed system is implemented in two components for mobile device and cloud. Furthermore, the scenario-based approach is used to evaluate the functionality of the system.

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

Wireless sensor network (WSN) has proved to be a hardcore desideratum for the continuous monitoring of hostile areas suffering from devastating forest fires, sudden volcanic eruptions, floods and many more. It can be contemplated by reviewing the state-of-the-art routing strategies, network instability and the delay incurred in data reception at the user end may lead to deplorable circumstances. In this study, a design of a novel routing architecture is proposed for harsh environment monitoring in heterogeneous WSN. It aims to ameliorate the stability period and network lifetime by shortening the communicative distance of nodes from the gateway node and by mitigating the hot-spot problem in the network. This architecture comprises of a network with multiple gateway nodes (MGNs) (four in this case), placed at equidistant from each other, outside the monitoring area. MGN is enriched with unlimited resources of energy, computation and coverage capabilities. The proposed MGN-based routing architecture (MRA) also improves the cluster head selection by incorporating node density factor along with energy and distance. Simulations show that MRA outperforms the state-of-the-art protocols, i.e. threshold-sensitive energy-efficient delay-aware routing protocol, Stable and Energy Efficient Clustering Protocol (SEECP), and Distance based Residual Energy-Efficient Stable Election Protocol (DRESEP) for two different cases of ‘nodes and energy’ fractions at various performance metrics.

Smart or intelligent sensors are integrated physical units embedded with sensors, processors, and communication devices. The sensors also known as edge nodes form the lower-most tier in the internet of things architecture. These devices rely on cryptographic technique to ensure ‘root of trust’ for the users. The implementation attacks namely side-channel attacks (SCAs) pose a dangerous threat for the cryptographic implementation in the edge nodes since the attacks are undetectable by nature. Among the different categories of SCAs proposed in the literature, power analysis attacks (PAAs) are vastly studied and widely employed because it can be mounted with relatively inexpensive equipment. In this study, the different categories of PAAs along with the countermeasures are reviewed in detail. The impact of the SCA on the edge nodes is examined along with a case study on medical sensor nodes.

Internet of Things (IoT) does not stop integrating an important number of components and objects that are characterised by their complexity and heterogeneity. Such constraints make the existing routing protocols unsuitable for IoT communications in a smart cities environment. To accomplish all the expected tasks and satisfy the user services, it is important to guarantee a quality of communication that answers to the requirements of smart cities applications in terms of data and processing. This study addresses the problem of the performance degradation of IoT communications in smart cities and proposes a new routing algorithm to improve them. The proposed algorithm is called SSRA (smart and self-organised routing algorithm), which can select the best route for the packets. SSRA allows a self-organising routing process according to a new situation of the network and devices parameters recently detected. When using SSRA, the communication records an improvement in performance in terms of packet delivery rate, throughput, end-to-end delay and overhead packets. SSRA extends also the devices lifetime by allowing fair and efficient energy consumption.

The aim of this study is to describe the authors' efforts to accomplish the green vehicular ad hoc network (GVANET) project. The goal is to realise the concept of a self ‘renewably’ powered communication infrastructure appropriate for intelligent transportation systems. The work in this project was started in 2009 with the intention of representing the necessary steps must be taken to achieve such system and to declare the usefulness of adopting environmental energy resources in building mission critical systems. The other objectives of this project are presenting a reasonably cost, reliable, secured and easy to install communication infrastructure. Their plan to implement the GVANET was achieved after passing two design levels: device level and system level. In the device level, they suggest that the architecture of a road side unit (RSU) must be enhanced to maintain its robustness and availability against the different failure events which may occur due to many reasons such as power supply shortage or denial of service attacks. The design philosophy in the system level concentrates on building a sustainable, secured, reliable and scalable communication infrastructure to solve the different systematic problems raised when implementing the RSU as a member of the GVANET.

The Internet of things (IoT) has attracted a great deal of research and industry attention recently and is envisaged to support diverse emerging domains including smart cities, health informatics, and smart sensory platforms. Operating system (OS) support for IoT plays a pivotal role in developing scalable and interoperable applications that are reliable and efficient. IoT is implemented by both high-end and low-end devices that require OSs. Recently, the authors have witnessed a diversity of OSs emerging into the IoT environment to facilitate IoT deployments and developments. In this study, they present a comprehensive overview of the common and existing open-source OSs for IoT. Each OS is described in detail based on a set of designing and developmental aspects that they established. These aspects include architecture and kernel, programming model, scheduling, memory management, networking protocols support, simulator support, security, power consumption, and support for multimedia. They present a taxonomy of the current IoT open-source OSs. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT devices/applications based on their functional and non-functional requirements. They remark that this is the first such tutorial style paper on IoT OSs.

Estimating longitudinal vehicle speed is required in a wide range of applications from road safety to vehicular emissions modelling. Each vehicle speed estimation method has specific challenges including the high cost of measurement equipment, the small range of vehicle models accessible for performing tests and the low resolution of data in time and space. Thus, the goals of this study are (a) to investigate the use of smartphones’ integrated sensors as a convenient, reliable and powerful means of vehicle speed data collection which would mitigate the issues observed in previous methods, and (b) to propose a post-processing pipeline for generating vehicles’ speed profiles based on the raw data captured by the proposed data collection method. Accordingly, a smartphone application is developed to facilitate the collection of vehicles’ movement data, particularly inside tunnels and high-density urban areas, where state-of-the-art global positioning system (GPS) applications fail to record the desired data. Moreover, a post-processing pipeline is proposed for calculating vehicles’ speed profiles and emissions from the raw smartphone acceleration data, and the full system is evaluated through a series of controlled experiments as well as simulations.

The identification of elderly activities through intelligent sensors in a smart home can effectively monitor the abnormal movements of residents in everyday life without adding supplementary loads caused by wearable sensors. To meet the question of societal stake where the need for a discrete and ambulatory follow-up is required by the gerontologists, we propose the development of a new inside surveillance system based on multi-sensor detection. Here, we propose a new mechanism for locating and detecting the elderly activities in a smart home. Moreover, the goal of our mechanism is to improve the supervision of areas and locate people effectively within wireless sensor networks. The contribution of this work is threefold: first, two different technologies of detection combined using the fuzzy logic method are used to minimize the error during the detection process. Second, the number of messages diffused to the base station is reduced through dynamic clustering method. Third, an optimal method for the selection of moving sensors is proposed for the localisation phase. We discuss in depth the proposed monitoring algorithm performance in terms of energy consumption, execution time, and location error. Furthermore, experiment results and relevant performance comparisons with related works are presented.