A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

M2M Communications for E-Health and Smart Grid: An Industry and Standard Perspective

An overview of several standardization activities for machine-to-machine (M2M) communications is presented, analyzing some of the enabling technologies and applications of M2M in industry sectors such as Smart Grid and e-Health. This summary and overview of the ongoing work in M2M from the industrial and standardization perspective complements the prevalent academic perspective of such publications to date in this field

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Novel QoS-aware proactive spectrum access techniques for cognitive radio using machine learning

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%

Contribution to the integration, performance improvement, and smart management of data and resources in the Internet of Things

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones.[ENG] This doctoral dissertation has been presented in the form of thesis by publication. The IoT has seen a tremendous growth in the last few years. Not only due to its potential to transform societies, but also as an enabling technology for many other technological advances. Unfortunately, the IoT is a relatively recent paradigm that lacks the maturity of other well-established (not so recent) revolutions like the internet itself or Wireless Sensor Networks; upon which the IoT is built. The presented Thesis contributes to this maturation process by researching on the underlying communication mechanisms that enable a truly ubiquitous and effective IoT. As a Thesis by compilation, 5 relevant articles are introduced and discussed. Each of such articles delve into different key aspects that, in their own way, help closing the gap between what the IoT is expected to bring and what the IoT actually brings. As thoroughly commented throughout the main text, the comprehensive approach taken in this Thesis ensures that multiple angles of the same plane --the communication plane-- are analyzed and studied. From the mathematical analysis of how electromagnetic waves propagate through complex environments to the utilization of recent Machine Learning techniques, this Thesis explore a wide range of scientific and researching tools that are shown to improve the final performance of the IoT. In the first three chapters of this document, the reader will be introduced to the current context and state-of-the-art of the IoT while, at the same time, the formal objectives of this Thesis are outlined and set into such a global context. In the next five chapters, the five corresponding articles are presented and commented. For each and every of these articles: a brief abstract, a methodology summary, a highlight on the results and contributions and final conclusions are also added. Lastly, in the two last chapters, the final conclusions and future lines of this Thesis are commented.Los artículos que componen la tesis son los siguientes: 1. R. M. Sandoval, A.-J. J. Garcia-Sanchez, F. Garcia-Sanchez, and J. Garcia-Haro, \Evaluating the More Suitable ISM Frequency Band for IoT-Based Smart Grids: A Quantitative Study of 915 MHz vs. 2400 MHz," Sensors, vol. 17, no. 1, p. 76, Dec. 2016. 2. R. M. Sandoval, A.-J. J. Garcia-Sanchez, J.-M. M. Molina-Garcia-Pardo, F. Garcia-Sanchez, and J. Garcia-Haro, \Radio-Channel Characterization of Smart Grid Substations in the 2.4-GHz ISM Band," IEEE Trans. Wirel. Commun., vol. 16, no. 2, pp. 1294{1307, Feb. 2017. 3. R. M. Sandoval, A. J. Garcia-Sanchez, and J. Garcia-Haro, \Improving RSSI-based path-loss models accuracy for critical infrastructures: A smart grid substation case-study," IEEE Trans. Ind. Informatics, vol. 14, no. 5, pp. 2230{2240, 2018. 4. R. M. Sandoval, A.-J. Garcia-Sanchez, J. Garcia-Haro, and T. M. Chen, \Optimal policy derivation for Transmission Duty-Cycle constrained LPWAN," IEEE Internet Things J., vol. 5, no. 4, pp. 1{1, Aug. 2018. 5. R. M. Sandoval, S. Canovas-Carrasco, A. Garcia-Sanchez, and J. Garcia-Haro, \Smart Usage of Multiple RAT in IoT-oriented 5G Networks: A Reinforcement Learning Approach," in 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K), 2018, pp. 1-8.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías de la Información y las Comunicaciones por la Universidad Politécnica de Cartagen