Water plays a crucial role in the agricultural field for food production and raising livestock. Given the current trends in world population growth, the urgent food demand that must be answered by agriculture highly depends on our ability to efficiently exploit the available water resources. Among critical issues, there is water management. Recently, innovative technologies have improved water management and monitoring in agriculture. Internet of Things, Wireless Sensor Networks and Cloud Computing, have been used in diverse contexts in agriculture. By focusing on the water management challenge in general, existing approaches are aiming at optimizing water usage, and improving the quality and quantity of agricultural crops, while minimizing the need for direct human intervention. This is achieved by smoothing the water monitoring process, by applying the right automation level, and allowing farmers getting connected anywhere and anytime to their farms. There are plenty of challenges in agriculture involving water: water pollution monitoring, water reuse, monitoring water pipeline distribution network for irrigation, drinking water for livestock, etc. Several studies have been devoted to these questions in the recent decade. Therefore, this paper presents a survey on recent works dealing with water management and monitoring in agriculture, supported by advanced technologies. It also discusses some open challenges based on which relevant research directions can be drawn in the future, regarding the use of modern smart concepts and tools for water management and monitoring in the agriculture domain.

Due to large-scale applications and services, cloud computing infrastructures are experiencing an ever-increasing demand for computing resources. At the same time, the overall power consumption of data centers has been rising beyond 1% of worldwide electricity consumption. The usage of renewable energy in data centers contributes to decreasing their carbon footprint and overall electricity costs. Several green-energy-aware resource allocation approaches have been studied recently. None of them takes advantage of the joint migration of jobs and energy in green data centers to increase energy efficiency. This paper presents an optimization approach for energy-efficient resource allocation in mini data centers. The observed momentum around edge computing makes the design of geographically distributed mini data centers highly desirable. Our solution exploits both virtual machines (VMs) and energy migrations between green compute nodes in mini data centers. These nodes have energy harvesting, storage, and transport capabilities. They enable the migration of VMs and energy across different nodes. Compared to VM allocation alone, joint-optimization of VM and energy allocation reduces utility electricity consumption by up to 22%. This reduction can reach up to 28.5% for the same system when integrating less energy-efficient servers. The gains are demonstrated using simulation and a Mixed Integer Linear Programming formulation for the resource allocation problem. Furthermore, we show how our solution contributes to sustaining the energy consumption of old-generation and less efficient servers in mini data centers.

In recent years, the edge computing paradigm has been attracting much attention in the Internet-of-Things domain. It aims to push the frontier of computing applications, data, and services away from the usually centralized cloud servers to the boundary of the network. The benefits of this paradigm shift include better reactivity and reliability, reduced data transfer costs toward the centralized cloud servers, and enhanced confidentiality. The design of energy-efficient edge compute nodes requires, among others, low power cores such as microprocessors. Heterogeneous architectures are key solutions to address the crucial energy-efficiency demand in modern systems. They combine various processors providing attractive power and performance trade-offs. Unfortunately, no standard heterogeneous microcontroller-based architecture exists for edge computing. This paper deals with the aforementioned issue by exploring typical low power architectures for edge computing. Various heterogeneous multicore designs are developed and prototyped on FPGA for unbiased evaluation. These designs rely on cost-effective and inherently ultra-low power cores commercialized by Cortus SA, a world-leading semiconductor IP company in the embedded ultra-low power microcontroller domain. Some microarchitecture-level design considerations, e.g., floating point and out-of-order computing capabilities, are taken into account for exploring candidate solutions. In addition, a tailored and flexible multi-task programming model is defined for the proposed architecture paradigm. We analyze the behavior of various application programs on available core configurations. This provides valuable insights on the best architecture setups that match program characteristics, so as to enable increased energy-efficiency. Our experiments on multi-benchmark programs show that on average 22% energy gain can be achieved (up to 45%) compared to a reference system design, i.e., a system with the same execution architecture, but agnostic of the task management insights gained from the comprehensive evaluation carried out in this work.

Embedded real-time systems consist of hardware and software that controls the behavior of a device or plant. They are ubiquitous in today's technological landscape and found in domains such as telecommunications, nuclear power, avionics, and medical technology. These systems are difficult to design and build because they must satisfy both functional and timing requirements to work correctly in their intended environment. Furthermore, embedded systems are often critical systems, where failure can lead to loss of life, loss of mission, or serious financial consequences. Because of the difficulty in creating these systems and the consequences of failure, they require rigorous and reliable design approaches. The synchronous approach is one possible answer to this demand. Its mathematical basis provides formal concepts that favor the trusted design of embedded real-time systems. The multiclock or polychronous model stands out from other synchronous specification models by its capability to enable the design of systems where each component holds its own activation clock as well as single-clocked systems in a uniform way. A great advantage is its convenience for component-based design approaches that enable modular development of increasingly complex modern systems. The expressiveness of its underlying semantics allows dealing with several issues of real-time design. This article exposes insights gained during recent years from the design of real-time applications within the polychronous framework. In particular, it shows promising results about the design of applications from the avionics domain.

The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy-efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low-power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency.