This study describes a sensor node powered by an energy harvesting method based on the watermill principle. This method is suitable whenever a sensor node has to be deployed in the nearby of an underground water line, such as a drainage system or an aqueduct. The operating scenario for whom this solution has been developed and employed is a wireless sensor network for the monitoring of the environmental conditions of the so-called ‘Bottini’ in Siena, Italy. The ‘Bottini’ is a network of medieval aqueducts dug in the underground of the historic centre of the city, in which water still flows nowadays. Using the proposed energy harvesting system the sensor nodes are able to operate independently, minimising the maintenance and allowing the real-time monitoring of environmental parameters, thanks, in order to manage the preservation of this ancient site. The entire system is composed of three parts: the power generation system, the data acquisition system and the wireless transmission system. The whole architecture has been tested in the operating scenario, precisely in ‘Fontebranda’, one of the biggest fountains in the ‘Bottini’ network.

Transmission power control is an effective method to reduce energy consumption for wireless sensor networks. However, the current algorithms of power control have relatively low accuracy. At the same time, the parameters cannot be adjusted dynamically. In order to improve the energy utilisation as well as data transmission efficiency and balance the load, therefore, a fuzzy power-optimised clustering routing algorithm is proposed in this study. The algorithm optimises the iteration radius and classifies the sensor nodes into different categories according to their node degree. Then select the cluster head by multi-parameter iteration adaptively among the same category, and optimise the cluster structure with a comprehensive consideration of parameters such as degree of centralisation, distance between node and base station and so on. Finally, fuzzy control is used to adjust the transmission power of cluster nodes dynamically to minimise the energy consumption. Simulation results show that when the average cluster radius , weight of election parameters , adjustment parameter of cluster radius , compared with other similar algorithms, the proposed algorithm prolongs the lifetime by at least 42.2% and increases the amount of data packets by at least 40.1%.

Most of researches on wireless sensor network (WSN) are focused on how to reduce energy consumption to increase the network lifetime. There are few researches solving the problem of energy optimization ensuring delay constraint. Whereas, the delay is important factor for applications that require delay-sensitive data. Several works have been published to balance the energy consumption and delay. They achieved many different results but each proposal has certain limitations. Some proposals have high computational and messaging complexity. Some others have not found the optimal solution. In this study, we investigate in finding solution to improve the energy efficiency of sensor nodes satisfying end-to-end delay to transmit data from sensor nodes to sink in multi-hop WSNs. Based on Lagrange relaxation method, we propose an aggregate cost function between energy consumption and delay as well as an efficient method to find the optimal multiplier for that objective function. We provide two algorithms to find paths with least energy consumption while maintaining end-to-end delay requirement from any sensor node to sink. Besides analyzing the complexity and convergence of the algorithm, the simulation results also show that the proposed algorithm achieved good balance between energy consumption and delay compared with the previous proposals.

Wireless sensor network (WSN) has proposed requirements more than just data collection and transmission. Different types of traffics call for different quality of service (QoS) demands. However, for the nature of uncertainty of wireless link and unpredictability of traffic transmission, it is very hard to distribute resources to different traffics precisely ahead of time. Based on the cascade self-tuning control, this study proposes the cascade self-tuning architecture for FD-MAC (CSFD-MAC) architecture. Taking the advantage of feedback theory, CSFD-MAC decouples contention window (CW) adaption and active time (AT) adaption into two separated cascading control loops. CW controller aims for the absolute node-to-node delay to a fixed value and AT controller guarantees the relative delay ratio. The software simulations illustrate the validity of controller design and the hardware experiments demonstrate the ability of CSFD-MAC in the absolute delay control as well as the proportional delay differentiation. More important, CSFD-MAC which takes AT as a controlled variable, not only has a much more superior performance in the metrics of delay and throughput compared with other QoS-aware MAC, but also has the advantage of efficient power management.

Nowadays, Internet of things (IoT) and robotic systems are key drivers of technological innovation trends. Leveraging the advantages of both technologies, IoT-aided robotic systems can disclose a disruptive potential of opportunities The present contribution provides an experimental analysis of an IoT-aided robotic system for environmental monitoring. To this end, an experimental testbed has been developed. It is composed of: (i) an IoT device connected to (ii) an unmanned aerial vehicle (UAV) which executes a patrolling mission within a specified area, where (iii) an IoT network has been deployed to sense environmental data. An extensive experimental campaign has been carried out to scavenge pros and cons of adopted technologies. The key results of the authors analysis show that: (i) the UAV does not incur any significant overhead due to onboard IoT equipment and (ii) the overall quality of service expressed in terms of network joining time, data retrieval delay and packet loss ratio satisfies the mission requirements. These results enable further development in larger-scale environment.