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Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the interactions among these devices. Nonetheless, automatically inferring the semantics of these devices using data-driven methods provides an ideal solution to the problems brought about by this heterogeneity. In this paper, we undertake a multi-dimensional study to address the problem of inferring the semantics of IoT devices using machine learning models. Using two datasets with over 67 million data points collected from IoT devices, we developed discriminative models that produced competitive results. Particularly, our study highlights the potential of Image Encoded Time Series (IETS) as a robust alternative to statistical feature-based inference methods. Leveraging just a fraction of the data required by feature-based methods, our evaluations show that this encoding competes with and even outperforms traditional methods in many cases. [ABSTRACT FROM AUTHOR]

The smart devices in Internet of Things (IoT) need more effective data storage opportunities, as well as support for Artificial Intelligence (AI) methods such as neural networks (NNs). This study presents a design of new associative memory in the form of impulsive Hopfield network based on leaky integrated-and-fire (LIF) RC oscillators with frequency control and hybrid analog–digital coding. Two variants of the network schemes have been developed, where spiking frequencies of oscillators are controlled either by supply currents or by variable resistances. The principle of operation of impulsive networks based on these schemes is presented and the recognition dynamics using simple two-dimensional images in gray gradation as an example is analyzed. A fast digital recognition method is proposed that uses the thresholds of zero crossing of output voltages of neurons. The time scale of this method is compared with the execution time of some network algorithms on IoT devices for moderate data amounts. The proposed Hopfield algorithm uses rate coding to expand the capabilities of neuromorphic engineering, including the design of new hardware circuits of IoT. [ABSTRACT FROM AUTHOR]

The huge spreading of Internet of things (IoT)-oriented modern technologies is revolutionizing all fields of human activities, leading several benefits and allowing to strongly optimize classic productive processes. The agriculture field is also affected by these technological advances, resulting in better water and fertilizers' usage and so huge improvements of both quality and yield of the crops. In this manuscript, the development of an IoT-based smart traceability and farm management system is described, which calibrates the irrigations and fertigation operations as a function of crop typology, growth phase, soil and environment parameters and weather information; a suitable software architecture was developed to support the system decision-making process, also based on data collected on-field by a properly designed solar-powered wireless sensor network (WSN). The WSN nodes were realized by using the ESP8266 NodeMCU module exploiting its microcontroller functionalities and Wi-Fi connectivity. Thanks to a properly sized solar power supply system and an optimized scheduling scheme, a long node autonomy was guaranteed, as experimentally verified by its power consumption measures, thus reducing WSN maintenance. In addition, a literature analysis on the most used wireless technologies for agri-food products' traceability is reported, together with the design and testing of a Bluetooth low energy (BLE) low-cost sensor tag to be applied into the containers of agri-food products, just collected from the fields or already processed, to monitor the main parameters indicative of any failure or spoiling over time along the supply chain. A mobile application was developed for monitoring the tracking information and storing conditions of the agri-food products. Test results in real-operative scenarios demonstrate the proper operation of the BLE smart tag prototype and tracking system. [ABSTRACT FROM AUTHOR]

The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances' readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection. [ABSTRACT FROM AUTHOR]

In recent years, Internet of things (IoT) devices are playing an important role in business, education, medical as well as in other fields. Devices connected to the Internet is much more than the number of world population. However, it may face all kinds of attacks from the Internet easily for its accessibility. As we all know, most attacks against IoT devices are based on Web applications. So protecting the security of Web services can effectively improve the situation of IoT ecosystem. Conventional Web attack detection methods highly rely on samples, and artificial intelligence detection results are uninterpretable. Hence, this article introduced a supervised detection algorithm based on benign samples. Seq2Seq algorithm is been chosen and applied to detect malicious web requests. Meanwhile, the attention mechanism is introduced to label the attack payload and highlight labeling abnormal characters. The results of experiments show that on the premise of training a benign sample, the precision of proposed model is 97.02%, and the recall is 97.60%. It explains that the model can detect Web attack requests effectively. Simultaneously, the model can label attack payload visually and make the model "interpretable." [ABSTRACT FROM AUTHOR]

With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in large ecosystems. [ABSTRACT FROM AUTHOR]

This paper presents a duty cycle-based, dual-mode simultaneous wireless information and power transceiver (SWIPT) for Internet of Things (IoT) devices in which a sensor node monitors the received power and adaptively controls the single-tone or multitone communication mode. An adaptive power-splitting (PS) ratio control scheme distributes the received radio frequency (RF) energy between the energy harvesting (EH) path and the information decoding (ID) path. The proposed SWIPT enables the self-powering of an ID transceiver above 20 dBm input power, leading to a battery-free network. The optimized PS ratio of 0.44 enables it to provide sufficient harvested energy for self-powering and energy-neutral operation of the ID transceiver. The ID transceiver can demodulate the amplitude-shift keying (ASK) and the binary phase-shift keying (BPSK) signals. Moreover, for low-input power level, a peak-to-average power ratio (PAPR) scheme based on multitone is also proposed for demodulation of the information-carrying RF signals. Due to the limited power, information is transmitted in uplink by backscatter modulation instead of RF signaling. To validate our proposed SWIPT architecture, a SWIPT printed circuit board (PCB) was designed with a multitone SWIPT board at 900 MHz. The demodulation of multitone by PAPR was verified separately on the PCB. Results showed the measured sensitivity of the SWIPT to be −7 dBm, and the measured peak power efficiency of the RF energy harvester was 69% at 20 dBm input power level. The power consumption of the injection-locked oscillator (ILO)-based phase detection path was 13.6 mW, and it could be supplied from the EH path when the input power level was high. The ID path could demodulate 4-ASK- and BPSK-modulated signals at the same time, thus receiving 3 bits from the demodulation process. Maximum data rate of 4 Mbps was achieved in the measurement. [ABSTRACT FROM AUTHOR]

Communicating Things Network (CTN) is the latest paradigm in the development of smart technologies. CTN comprises a network of physical devices capable of extracting and sharing digital information. The aim of CTN is to develop smart appliances that boost productivity and provide real-time data rapidly than any structure or a network that is dependent on human interference. Interconnected physical objects in the network communicate with each other and facilitate intelligent decision-making by monitoring and analysing their surroundings. In today's era, CTNs are playing a significant role in daily activities by providing a substantial reduction in costs with increased visibility and efficiency in all aspects of businesses and individuals. In this manuscript, we have considered an important application of CTNs (i.e., IoT) and have proposed a secure Hybrid Industrial IoT framework using the Blockchain technique. We have used a hybrid industrial architecture where different branches of a company are located in more than one country. Although IoT devices are used in many organizations and assist in reducing their production costs along with improving quality, several threats can occur in IoT devices initiated by various intruders. Intruders may compromise IoT devices with the purpose of performing malicious activities. For example, a company's employee may steal some product or may rest during working hours. To prevent these issues, the Blockchain technology is considered as the best technique that provides secrecy and protects the control system in real-time conditions. In this manuscript, we have used a Blockchain mechanism to extract information from IoT devices and store extracted records into Blockchain to maintain transparency among various users located at different places. Furthermore, the experimentation of the proposed framework has been performed against the internal communication of Blockchain where IoT devices are compromised by several intruders. Results have been analysed against the conventional approach and validated with improved simulated results that offer an 89% success rate over user request time, falsification attack, black hole attack, and probabilistic authentication scenarios because of the Blockchain technology. [ABSTRACT FROM AUTHOR]

In this paper, we present an implementation work of sensing and actuation capabilities for IoT devices using the oneM2M standard-based platforms. We mainly focus on the heterogeneity of the hardware interfaces employed in IoT devices. For IoT devices (i.e., Internet-connected embedded systems) to perform sensing and actuation capabilities in a standardized manner, a well-designed middleware solution will be a crucial part of IoT platform. Accordingly, we propose an oneM2M standard-based IoT platform (called nCube) incorporated with a set of tiny middleware programs (called TAS) responsible for translating sensing values and actuation commands into oneM2M-defined resources accessible in Web-based applications. All the source codes for the oneM2M middleware platform and smartphone application are available for free in the GitHub repositories. The full details on the implementation work and open-source contributions are described. [ABSTRACT FROM AUTHOR]

Cognitive capabilities are indispensable for the Internet of Things (IoT) not only to equip them with learning, thinking, and decision-making capabilities but also to cater to their unprecedented huge spectrum requirements due to their gigantic numbers and heterogeneity. Therefore, in this paper, a novel unified channel management framework (CMF) is introduced for cognitive radio sensor networks (CRSNs), which comprises an (1) opportunity detector (ODR), (2) opportunity scheduler (OSR), and (3) opportunity ranker (ORR) to specifically address the immense and diverse spectrum requirements of CRSN-aided IoT. The unified CMF is unique for its type as it covers all three angles of spectrum management. The ODR is a double threshold based multichannel spectrum sensor that allows an IoT device to concurrently sense multiple channels to maximize spectrum opportunities. OSR is an integer linear programming (ILP) based channel allocation mechanism that assigns channels to heterogeneous IoT devices based on their minimal quality of service (QoS) requirements. ORR collects feedback from IoT devices about their transmission experience and generates special channel-sensing order (CSO) for each IoT device based on the data rate and idle-time probabilities. The simulation results demonstrate that the proposed CMF outperforms the existing ones in terms of collision probability, detection probability, blocking probability, idle-time probability, and data rate. [ABSTRACT FROM AUTHOR]