Internet of things (IoT) is creating new opportunities for developing innovative applications by leveraging on existing and new technologies. In recent years, a variety of consumer and industrial IoT applications have been developed and deployed. Despite much progress, developing IoT applications is still a complex, time-consuming and challenging activity. This is because IoT systems involve a wide range of hardware and software components, depending on a variety of communication and distributed system technologies. Many IoT application frameworks of varying approaches have been developed to manage the complexities of developing IoT applications. However, there remains a paucity of surveys on these IoT application development frameworks. This study presents a comprehensive review and a comparative analysis of existing IoT application development frameworks and toolkits, illustrating their strengths and weaknesses. This study will assist in finding the most appropriate IoT application development paradigm for the desired IoT application. Finally, future research directions are highlighted to improve existing and future frameworks and toolkits for IoT applications.

In wearable health monitoring system, the energy consumption is dominated by the transmitter. These systems generally use proprietary acquisition platforms that are incompatible with each other which makes this even more challenging. This study presents a compressive sensing-based biopotential acquisition unit to reduce the overheads of wirelessly transmitting and storing the data. The instrumentation amplifier (INA) in the system defines the quality of the acquired biopotential signals. At the heart of the system is an analogue-to-information converter (AIC) to enable the random under-sampling operation. AIC is used to digitise the output of the biopotential INA. Both INA and AIC are implemented in 65 nm CMOS technology. To confirm stable operation under different operating conditions, the design is simulated under different process, voltage and temperature (PVT) corners. The simulation results show that the proposed INA has a common mode rejection ratio of 100.18 dB and noise of 35.89 pV/sqrt (Hz). AIC achieves a sampling rate of 0.5 kS/s, an effective number of bits 9.54 bits, figure of merit 187 fj/conv-step, and consumes 69.33 nW from 1 V power supply.

Localisation of objects and people in indoor environments has been widely studied due to security issues and because of the benefits that a localisation system can provide. Indoor positioning systems (IPSs) based on more than one technology can improve localisation performance by leveraging the advantages of distinct technologies. This study proposes a multi-sensor IPS able to estimate the three-dimensional (3D) location of stationary objects using off-the-shelf equipment. By using radio-frequency identification (RFID) technology, machine-learning models based on support vector regression (SVR) and artificial neural networks (ANNs) are proposed. A k-means technique is also applied to improve accuracy. A computer vision (CV) subsystem detects visual markers in the scenario to enhance RFID localisation. To combine the RFID and CV subsystems, a fusion method based on the region of interest is proposed. We have implemented the authors’ system and evaluated it using real experiments. On bi-dimensional scenarios, localisation error is between 9 and 29 cm in the range of 1 and 2.2 m. In a machine-learning approach comparison, ANN performed 31% better than SVR approach. Regarding 3D scenarios, localisation errors in dense environments are 80.7 and 73.7 cm for ANN and SVR models, respectively.

Graphene-based heterostructures provide a viable platform to implement optoelectronic devices that can operate in the terahertz (THz) band. In this study, the authors focus on multilayer (ML) graphene as the building block to implement high-frequency and low-energy plasmonic interconnects for on-chip signalling in next-generation systems. Two specific plasmonic interconnect geometries are analysed: single waveguide (SWG) and parallel-plate waveguide (PPWG). While SWG interconnects support propagating surface plasmons that are polarised in the transverse magnetic direction, in PPWG interconnects, nearly dispersion-less quasi-transverse electromagnetic modes are supported. The dispersion characteristics are derived by solving Maxwell's equations in the device setup in which ML graphene presents an impedance boundary condition. The effects of number of layers, electrostatic screening, and Fermi level are included in the model of intra-band dynamical surface conductivity of ML graphene. The authors also develop analytical models of energy-per-bit and bandwidth density for both SWG and PPWG interconnects. The energy dissipation includes the effect of plasmon generation, detection, and modulation circuitry within a thermal- and shot-noise-limited transmission of information. They quantify optimal interconnect length scales for which plasmonic interconnects provide lower energy and higher bandwidth when compared against their electrical (copper/low-κ) counterparts at the 2020 ITRS technology node.

Advanced metering infrastructure (AMI), one of the prime components of the smart grid, has many benefits like demand response and load management. Electricity theft, a key concern in AMI security since smart meters used in AMI are vulnerable to cyber attacks, causes millions of dollar in financial losses to utilities every year. In light of this problem, the authors propose an entropy-based electricity theft detection scheme to detect electricity theft by tracking the dynamics of consumption variations of the consumers. Relative entropy is used to compute the distance between probability distributions obtained from consumption variations. When electricity theft attacks are launched against AMI, the probability distribution of consumption variations deviates from historical consumption, thus leading to a larger relative entropy. The proposed method is tested on different attack scenarios using real smart-meter data. The results show that the proposed method detects electricity theft attacks with high detection probability.

Many successful and innovative information technology applications use gestures as input. These programs span a wide variety of genres, platforms and input technologies, from the touch screen of a smart phone to the full-motion, the natural input of devices like the Kinect Sensor. Visual Gesture Builder, a data-driven machine-learning solution for gesture detection, was used to capture useful yoga gestures with high accuracy. This gesture analysis technology is being explored for incorporation into exergames for personalised medical interventions. The research goal is to test whether a machine learning algorithm in a basic computer video exergame can assess yoga skill acquisition in targeted select populations as a means to promote healthy physical activity.