Wireless sensor networks (WSNs) are typically used with dynamic conditions of task-related environments for sensing(monitoring) and gathering of raw sensor data for subsequent forwarding to a base station. In order to deploy WSNs in real environments, a variety of technical challenges must be addressed. With traditional techniques developed for a specific task, it is hard to react in dynamic situations beyond the scope of the intended task. As a solution to this problem, machine learning (ML) techniques that are able to handle dynamic situations with successful learning process have been applied lately in WSNs. Particularly, deep learning (DL) techniques, a class of ML techniques characterized by the use of deep neural network, are used for WSNs to extract higher level features from raw sensor data. A range of benefits obtained from ML techniques applied to WSNs can be described as reduced computational complexity, increased feasibility in finding optimal solutions, increased energy efficiency, etc. On the other hand, it is found from our survey that large training time and large dataset to get acceptable performance are accompanied with large energy consumption which is not favorable for resource-restrained WSNs. Reviews on the applications of ML techniques in WSNs appeared in the literature. However, few reviews have dealt with the applications of DL techniques in WSNs. In this review, recent developments of ML techniques for WSNs are presented with much emphasis on DL techniques. The DL techniques developed for various applications in WSNs are addressed together with their respective deep neural network architectures.

In reinforcement learning (RL), a reinforcement signal may be infrequent and delayed, not appearing immediately after the action that triggered the reward. To trace back what sequence of actions contributes to delayed rewards, e.g., credit assignment (CA), is one of the biggest challenges in RL. This challenge is aggravated under sparse binary rewards, especially when rewards are given only after successful completion of the task. To this end, a novel method consisting of key-action detection, among a sequence of actions to perform a task under sparse binary rewards, and CA strategy is proposed. The key-action defined as the most important action contributing to the reward is detected by a deep neural network that predicts future rewards based on the environment information. The rewards are re-assigned to the key-action and its adjacent actions, defined as adjacent-key-actions. Such re-assignment process enables increased success rate and convergence speed during training. For efficient re-assignment, two CA strategies are considered as part of proposed method. Proposed method is combined with hindsight experience replay (HER) for experiments in the OpenAI gym suite robotics environment. In the experiments, it is demonstrated that proposed method can detect key-actions and outperform the HER, increasing success rate and convergence speed, in the Fetch slide task, a type of task that is more exacting as compared to other tasks, but is addressed by few publications in the literature. From the experiments, a guideline for selecting CA strategy according to goal location is provided through goal distribution analysis with dot map.

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.

Wireless power transfer (WPT) is being developed to supply electric power to electric trains, using a source coil in/on the railway track and a pick-up coil on the train. A number of benefits can be obtained by eliminating the catenary and pantograph currently used for railway electric power supply. However, the WPT employs coils, and the electromagnetic field generated by the WPT can interfere with conventional train detection methods, such as track circuits and RFID, installed on the tracks. Train position information is critical for railway operation, especially for high speed trains. This paper proposes a novel system which provides train position information using a source coil segment. It consists of onboard sensor coils, ferrite blocks designed using specific position information, and a detector. The proposed system can be implemented using the source coil and load coil of the WPT. Information about the train's relative position is obtained by the detection of ferrite blocks distributed within the source coil segment. The train position information provided by the ferrite components is detected by onboard sensor coils and a detector. The proposed ferrite position identification (FPID) system provides accurate train position information and is not affected by WPT electromagnetic interference. The operating principles of the FPID system are presented in detail. The performance of the proposed FPID system was measured by simulations and experiments.

In this paper, we consider an overlay cognitive radio network, in which a secondary transmitter (ST) is willing to relay the information of a primary transmitter toward a primary receiver. In return, ST can access the licensed band to send its own information superimposed with the primary signal to a secondary receiver. The power-limited ST uses a power splitting protocol to harvest energy from its received signal to increase its transmit power. We analyze the performance of the primary and the secondary systems under independent Nakagami-m fading by deriving their corresponding outage probabilities in integralbased expressions. In addition, by considering the high signalto- noise ratio, we obtain very tight closed-form approximations of the outage probabilities. Thereafter, by further analyzing the approximations, we reveal novel insights on the diversity orders and coding gains of the two systems. Our analytical results are validated through extensive Monte-Carlo simulations.

Provision of energy to wireless sensor networks is crucial for their sustainable operation. Sensor nodes are typically equipped with batteries as their operating energy sources. However, when the sensor nodes are sited in almost inaccessible locations, replacing their batteries incurs high maintenance cost. Under such conditions, wireless charging of sensor nodes by a mobile charger with an antenna can be an efficient solution. When charging distributed sensor nodes, a directional antenna, rather than an omnidirectional antenna, is more energy-efficient because of smaller proportion of off-target radiation. In addition, for densely distributed sensor nodes, it can be more effective for some undercharged sensor nodes to harvest energy from neighboring overcharged sensor nodes than from the remote mobile charger, because this reduces the pathloss of charging signal due to smaller distances. In this paper, we propose a hybrid charging scheme that combines charging by a mobile charger with a directional antenna, and energy trading, e.g., transferring and harvesting, between neighboring sensor nodes. The proposed scheme is compared with other charging scheme. Simulations demonstrate that the hybrid charging scheme with a directional antenna achieves a significant reduction in the total charging time required for all sensor nodes to reach a target energy level.