Data collection is one of the most important operations in applications of wireless sensor networks (WSNs). In many emerging WSN applications, it is urgent to achieve a guarantee for the latency involved in collecting data. Many researchers have studied collecting data in WSNs with minimum latency but without data collision while assuming that any (or no) data are allowed to be aggregated into one packet. In addition, tree structures are often used for solutions. However, in some cases, a fixed number of data are allowed to be aggregated into one packet. This motivates us to study the problem of minimizing the latency for data aggregation without data collision in WSNs when a fixed number of data are allowed to be aggregated into one packet, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling (MLCAMDAS) problem. The MLCAMDAS problem is shown to be NP-complete here. In addition, a nontree-based method, termed the independent-set-based collision-avoidance scheduling (ISBCAS) algorithm, is proposed accordingly. The ISBCAS is demonstrated via simulations to have good performance.

An Efficient Minimum-Latency Collision-Free Scheduling Algorithm for Data Aggregation in Wireless Sensor Networks

Mobile data gathering and energy harvesting in rechargeable wireless sensor networks

Wireless Rechargeable Sensor Networks (WRSNs) are emerging systems to build data acquisition systems to monitor critical areas. It has better features than Wireless Sensor Networks (WSNs) since in WRSNs, sensors are rechargeable. Wireless Rechargeable Sensor Networks allow sensors with sensing ability to collect and supply data to the operation center (sink). The Wireless Rechargeable Sensor Network appears as a more advanced solution because it can overcome the challenges and disadvantages such as battery limitation. In practical Wireless Rechargeable Sensor Networks, there is a budgetary constraint for the different expenditure categories, and therefore the number of used mobile chargers needs to be minimized. We investigate the problem of scheduling the minimum number of mobile chargers to replenish energy all sensors in a WRSN such that the network can be operated without limitations, and the total number of used chargers is minimized, referred to as the Periodic Energy Replenishment with Minimum Mobile Charger (PERMMC) problem. We design a new algorithm, namely the Mobile Charger Scheduling Algorithm (MCSA) to address the PERMMC problem. Finally, we conduct extensive simulations and provide analyses to demonstrate the performance of MCSA.

WRSNs: Toward an Efficient Scheduling for Mobile Chargers

Energy constraint is one of the main design issues of wireless sensor networks. Over the past few years, the concept of the wireless mobile charger (WMC) has been proposed in order to improve the energy efficiency of the networks. In such schemes, optimizing the moving trajectory and charging time of WMC are considered as two main problems. Several schemes attempt to optimize the moving trajectory of WMC without considering the balanced energy depletion time of nodes, which leads to degrading the energy efficiency and network lifetime. Thus, a route optimization of WMC algorithm is proposed to determine the optimal trajectory of WMC to move along sensor nodes in such a way that the balanced energy depletion time of the nodes is achieved. Moreover, in most of the existing schemes, WMCs charge the nodes as long as their batteries are fully charged. However, not considering the remaining lifetime of the other nodes leads to premature network death and decreases the network performance. Therefore, a charge time optimization of WMC algorithm is proposed to overcome the mentioned problem. Simulation results demonstrate that the proposed scheme remarkably enhance the network performance in terms of different evaluation metrics.

Energy-Efficient Tour Optimization of Wireless Mobile Chargers for Rechargeable Sensor Networks

The Industrial Internet of Things uses intelligent sensors to collect the physical properties of objects placed on a large area. It provides innovative service and network functioning, such as time-bounded data delivery and efficient sensor sleep cycle management. Intelligent scheduling is crucial to assisting the situation. The periodic scheduling of radio transceivers of sensor nodes into sleep or active mode helps accomplish efficient energy consumption. Such an extensive scale network work on data collection on multihop fashion. Our proposal provides the algorithm to form the node’s backbone, which task is to collect the data from sensors scatted in the area. Further, the heuristic algorithm is proposed to assist the backbone node in balancing the incoming traffic for scheduling. In Time Division Multiple Access (TDMA) driven Medium Access Control (MAC), the nodes are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. Our proposed work is named aggressive scheduling medium access control (AS-MAC). If the owner node does not have data to forward, then the corresponding slot will be aggressively scheduled by other nodes. Therefore, that slot and all consecutive slots if the corresponding owner nodes do not use will go to the needy nodes. The proposed system model presents a unique instance of interference for successfully delivering control and data packets. Therefore, a probabilistic estimation has given for the packet delivery rate for the raised specific condition. Our proposal’s distinctive feature is that the performance is better than Time Division Multiple Access for any given load. Finally, the packet delivery rate, packet drop, energy consumption, and time frame requirement for a different number of sensor node has been calculated and compared with the current state-of-the-art proposals.

Load-balance scheduling for intelligent sensors deployment in industrial internet of things

Recently, many studies have investigated scheduling mobile devices to recharge and collect data from sensors in wireless rechargeable sensor networks (WRSNs) such that the network lifetime is prolonged. In reality, because mobile devices are more powerful and expensive than sensors, the cost of the mobile devices often consumes a high portion of the budget. Due to a limited budget, the number of mobile devices is often limited. Some sensors in WRSNs may not be operated without time limits due to the limited number of mobile devices. Therefore, sensors in a sensing field must be weighted by their importance, that is, the more important an area covered by a sensor, the higher the weight of the sensor. Therefore, in this paper, the problem of scheduling limited mobile devices for energy replenishment and data collection in WRSNs with multiple sinks such that the total weight of the recharged sensors that can be operated without time limits is maximized, termed the periodic energy replenishment and data collection with limited mobile devices (PERDCLMD) problem, is studied. In addition, the greedy scheduling algorithm (GSA) is proposed for the PERDCLMD problem accordingly. Simulation results show that the GSA provides a good performance in terms of the total weight of the recharged sensors.

Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks

High sensitivity, excellent linearity, and wireless monitoring are strongly desired for the practical application of flexible temperature sensors in real‐time wearable health care. Especially the multichannel body temperature monitoring system has high requirements on the performance of the sensor because it needs to monitor a large amount of data at the same time. Herein, a flexible temperature sensor based on a porous graphene/polydimethylsiloxane sensing layer is developed. The prepared sensor exhibits high sensitivity of 5.203% °C−1 for temperature sensing between 30 and 70 °C, and excellent linearity (R2 = 0.996) in the temperature range from 30 to 70 °C. Based on its excellent performance, the proposed high‐performance temperature sensors can be applied in practical applications such as body temperature monitoring and human breath monitoring under different states including slow breath, normal breath, and fast breath. Moreover, a high‐throughput wireless body temperature monitoring system including wireless sensor modules, a cloud server, and a portable electronic device is constructed, which can achieve a remote and multichannel body temperature monitoring efficiently.

Chen-Yong Huang

Papers

Network Under Limited Mobile Devices: A New Technique for Mobile Charging Scheduling With Multiple Sinks

High Throughput In–Situ Temperature Sensor Array with High Sensitivity and Excellent Linearity for Wireless Body Temperature Monitoring