The Internet of Things (IoT) concept has met requirements for security and reliability in domains like automotive industry, food industry, as well as precision agriculture. Furthermore, System of Systems (SoS) expands the use of local clouds for the evolution of integration and communication technologies. SoS devices need to ensure Quality of Service (QoS) capabilities including service-oriented management and different QoS characteristics monitoring. Smart applications depend on information quality since they are driven by processes which require communication robustness and enough bandwidth. Interconnectivity and interoperability facilities among different smart devices can be achieved using Arrowhead Framework technology via its core systems and services. Arrowhead Framework is targeting smart IoT devices with wide applicability areas including smart building, smart energy, smart cities, smart agriculture, etc. The advantages of Arrowhead Framework can be underlined by parameters such as transmission speed, latency, security, etc. This paper presents a survey of Arrowhead Framework in IoT/SoS dedicated architectures for smart cities and smart agriculture developed around smart cities, aiming to outline its significant impact on the global performances. The advantages of Arrowhead Framework technology are emphasized by analysis of several smart cities use-cases and a novel architecture for a telemetry system that will enable the use of Arrowhead technology in smart agriculture area is introduced and detailed by authors.

In the precision viticulture domain, data recorded by monitoring devices are large-scale processed to improve solutions for grapes’ quality and global production and to offer various recommendations to achieve these goals. Soil-related parameters (soil moisture, structure, etc.) and atmospheric parameters (precipitation, cumulative amount of heat) may facilitate crop diseases occurrence; thus, following predictive analysis, their estimation in vineyards can offer an early-stage warning for farmers and, therefore, suggestions for their prevention and treatment are of particular importance. Using remote sensing devices (e.g., satellites, unmanned vehicles) and proximal sensing methods (e.g., wireless sensor networks (WSNs)), we developed an efficient precision agriculture telemetry platform to provide reliable assessments of atmospheric phenomena periodicity and crop diseases estimation in a vineyard near Bucharest, Romania. The novelty of the materials and methods of this work relies on providing comprehensive preliminary references about monitored parameters to enable efficient, sustainable agriculture. Comparative analyses for two consecutive years illustrate an excellent correlation between cumulative and daily heat, precipitation quantity, and daily evapotranspiration (ET). In addition, the platform proved viable for wine-grapes disease estimation (powdery mildew, grape bunch rot, and grape downy mildew) and treatment recommendations based on the elaborated phenological calendar. Our results, together with continuous monitoring for the upcoming years, may be used as a reference to perform productive, sustainable smart agriculture in terms of yield and crop quality in Romania. In the Conclusion section, we show that farmers and personnel from cooperatives can use this information to make assessments based on the correlation of the available data to avoid critical damage to the wine-grape.

Along with the ubiquity of various Internet of Things (IoT) applications, data collection in harsh environments with the help of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) appears as a popular approach. However, from a networking and transmission perspective, how unmanned vehicles collaborate with each other and with end IoT devices as well as the propagation characteristics for such data transmissions are less exploited. In this article, we develop a long-range wide-area network (LoRaWAN)-based data collection framework which enables UAV and/or USV-assisted data acquisition in harsh environments. Within this framework, end devices are placed in hard-to-reach locations and data acquisition is performed in an on-demand manner by unmanned vehicles acting as a gateway or peer for end devices (EDs). The framework also includes a step-by-step procedure for data collection and analytics, from parameter configuration to model validation. To reveal the transmission characteristics, we identify three scenarios in a mountainous region in Romania and perform extensive real-life measurements for data collection. Based on the collected datasets, we develop four propagation models and demonstrate that our empirical models outperform the theoretical reference models. Based on the developed empirical models, the coverage of LoRaWAN for any spreading factor (SF) can be estimated with high precision.

Multiuser detection technique implies joint demodulation of overlapping data streams of data sent by different users, improving their individual performances based on the knowledge of some key parameters of all users. Each user is identified by its own signature code, which should be, in the ideal case, orthogonal on all the others. Several multiuser detectors have been developed for synchronous CDMA systems. This paper analyzes the performances of MMSE and conventional multiuser detectors. The results are compared in terms of Bit Error Rate (BER) as function of Signal to Noise Ratio (SNR). Extensive Monte Carlo simulations have been performed and the results obtained lead to several interesting conclusions that are highlighted in the last section.