# Special Issue on Advanced Wireless Sensor Networks for Emerging Applications
###### Abstract  
We present a novel wireless sensor network for wireless sensor networks with a mobile sensor network for wireless sensor networks. We show that wireless sensor networks can be used for wireless sensor networks to provide a reliable and reliable and reliable and reliable and reliable and reliable and reliable and reliable and reliable. We also show that wireless sensor networks can be used for wireless sensor networks to provide a reliable and reliable reliable and reliable reliable and reliable. We also show that wireless sensor networks can be used for wireless sensor networks to provide a reliable and reliable reliable and reliable reliable and reliable reliable. We also show that wireless sensor networks can be used for wireless sensor networks to provide a reliable and  
WSNs with mobile sensor nodes, challenging tasks, such as the routing planning problem of mobile nodes, data routing problem of sensor nodes, and task assignment problem of heterogeneous nodes, should be considered. The range of applications of the WSNs includes small-scale power grids. For microgrids providing functionality to control and monitor distribution power systems, the WSNs can be used for a broad range of options, such as wireless meter reading, fault diagnostics and self-maintenance of equipment, and remote monitoring of distribution power system. In [8], a residential parking station is integrated to the microgrid, and the charging/discharging operation of electric vehicles are determined based on the measured data with the WSN, such as local load demand and photovoltaic (PV) power production. In [9], an energy-efficient coverage-preserving scheduling algorithm is proposed to extend the lifetime of the WSN by scheduling the activation of each set of sensor nodes. In [10], the WSNs are engaged to monitor the status of power generation, transmission, and distribution, and a fog computing network is established for the optimization of WSN in a microgrid to reduce energy consumption and improve the packet delivery ratio as well as the throughput. For the improved operation of a nanogrid, e.g., local power grid in kWatt scale, data transmission through the smart meter, real-time control, accurate decision making, and energy management can be achieved based on the advanced communication technologies in WSN [11]. Smart charging schemes are crucial for proper operation of rechargeable WSNs. In fact, techniques for charging batteries have a range of applications including supply of electric power to railway vehicles [12]. Due to the limited lifetime of batteries of the WSNs, periodic or aperiodic recharging of sensor nodes along a predetermined path is required. Optimal path planning, which is also important for minimized energy consumption of vehicle operation [13], is critical to save time for charging entire sensor nodes. Various charging schemes [14; 15; 16] have been reported in the literature. An energy efficient geometric routing (GR) protocol is presented to reduce the power consumption and time by calculating a traversing path of wireless charging vehicle [17]. The geometric solution, applied to the GR protocol, divides the network topology into a grid structure and uses a one-to-many recharging scheme. To find the shortest path of the mobile charger, which can fully recharge all sensor nodes by passing them only once, a greedy constructing tree algorithm (GCTA) is proposed in [18]. The GCTA constructs the shortest path with low complexity by using the Markov chain theorem, graph theory, and tree theory. The intelligent transportation system has evolved into a tool for supplying traffic information as well as traffic control, necessitating the use of contemporary and effective measurement methodologies. The road conditions, collected by static sensor nodes on the road or previously passed vehicles, can be delivered to all vehicles which access them through the vehicular sensor network [19].