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

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

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

Wireless Power Transfer and Data Collection in Wireless Sensor Networks

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semi-decentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular Technolog

Kablosuz sensör ağlarinda yönlü antenlerle enerji̇ veri̇mli̇ yönlendi̇rme

Without measurements, sustainable development effort can not progress in the right direction. Wireless sensor networks are vital for monitoring in real time and making accurate measurements for such an endeavor. However small energy storage in the sensors can become a bottleneck if the wireless sensor network is not optimized at the hardware and software level. Directional antennas are such optimization technologies at the hardware level. They have advantages over the omnidirectional antennas, such as high gain, less interference, longer transmission range, and less power consumption. In wireless sensor networks, most of the energy is consumed for communication. Considering the limited energy in small scale batteries of the sensors, energy efficient (aware) routing, is one of the most important software optimization techniques. The main goal of the technique is to improve the lifetime of the wireless sensor networks. In the light of these observations, it is desirable to do a coupled design of directional antennas with network software, for fully exploiting the advantages offered by directional antenna technology. In this thesis, the possibilities of doing such integrated design are surveyed and improvements are suggested. The design of the proposed microstrip patch antenna array is discussed and the performance characteristics are assessed through simulations. In the benchmarks, the proposed routing method showed improvements in energy usage compared to the existing approaches.Ölçümler olmadan sürdürülebilir kalkınma çabaları doğru yönde ilerleyemez. Bu tür çabalar için, kablosuz sensör ağları, gerçek zamanlı olarak izleme ve kesin ölçümler yapmak için vazgeçilemez unsurdur. Ancak, sensör ağı, donanım ve yazılım düzeylerinde optimize edilmemişse, sensörlerde enerji yetersizliği görülebilinir. Yönlü antenler, donanım düzeyinde uygulanan optimizasyon teknolojilerinden biri olmakla birlikte, çok yönlü antenlerden farklı olarak, yüksek kazanç, daha az parazit, daha uzun iletim mesafesi ve daha az güç tüketimi sağlarlar. Kablosuz sensör ağlarında enerjinin çoğu iletişim için tüketilir. Sensörlerdeki limitli enerjili küçük ölçekli piller göz önüne alındığında, yazılım düzeyindeki önemli metodlardan biri olan enerji verimli (duyarlı) yönlendirme protokolü, kablosuz sensör ağının genel enerji kullanımını optimize etmek ve ömrünü uzatmak için gereklidir. Bu gözlemlerin ışığında, yönlü anten teknolojisinin sunduğu potansiyel avantajlardan tam olarak yararlanmak için, yönlü antenlerin ağ yazılımıyla birlikte entegre tasarımını yapmak arzu edilir. Bu tezde, böyle bir entegre tasarımın yapılma olasılıkları araştırılmış ve iyileştirmeler önerilmiştir. Tezde, küçük şeritli yamalı anten dizisinin tasarımı tartışılmış ve performans karakteristikleri simulasyonlarla ölçülmüştür. Önerilen yönlendirme algoritması, diğer yönlendirme algoritmaları ile karşılaştırıldığında, enerji kullanımında iyileştirmeler göstermiştirM.S. - Master of Scienc