Lifetime-enhancement Schemes for Power-constrained Routing in Wireless Sensor Networks: A Survey

Notwithstanding the broad applicability of wireless sensor network (WSN), the successful development and deployment in various real-world applications is currently challenging. The node-devices are powered by constrained-batteries that are typically impossible to substitute after deployments. Therefore, WSNs are energy-constrained and liable to early failure while expected to run for long-terms without battery replenishments.Diverse researchers have recently presented specific lifetime enhancement routing solutions based on data aggregation technology targeted at reducing energy-consumption,and improving the communication reliability of various WSNs.This paper presents a brief survey of the research field of data aggregation-ware routingin energy-constrained WSN. The paper overviews and classifies various existing routing solutions based on their delivery models and also identifies someof their majordrawbacks.

Review of distributed energy storage aggregation technology under multi-energy interconnection

At present, with the rapid growth of intermittent renewable energy, volatile power supply is replacing controllable power supply, and the difficulty of real-time balance between supply and demand is increasing year by year. In the future, the continuous development and utilization of this new energy will make the development and innovation of distributed energy storage technology become the general trend. Firstly, this paper briefly introduces the principle of distributed energy storage and the basic principle of multi energy coordinated operation, and analyzes its advantages and disadvantages from the perspective of economic optimization and energy utilization maximization. Secondly, it analyzes the application status and shortcomings at home and abroad, and puts forward the direction and expectation of possible breakthrough. Aiming at the problem that distributed energy storage can not participate in peak load regulation, the feasibility of multi energy interconnection distributed energy storage aggregation technology is discussed.

Decentralized and Dynamic Band Selection in Uplink Enhanced Licensed-Assisted Access: Deep Reinforcement Learning Approach

Enhanced licensed-assisted access (eLAA) is an operational mode that allows the use of unlicensed band to support long-term evolution (LTE) service via carrier aggregation technology. The extension of additional bandwidth is beneficial to meet the demands of the growing mobile traffic. In the uplink eLAA, which is prone to unexpected interference from WiFi access points, resource scheduling by the base station, and then performing a listen before talk (LBT) mechanism by the users can seriously affect the resource utilization. In this paper, we present a decentralized deep reinforcement learning (DRL)-based approach in which each user independently learns dynamic band selection strategy that maximizes its own rate. Through extensive simulations, we show that the proposed DRL-based band selection scheme improves resource utilization while supporting certain minimum quality of service (QoS).

A Distributed Collision-Free Data Aggregation Scheme for wireless sensor network

Due to the advantages of large-scale, data-centric and wide application, wireless sensor networks have been widely used in nowadays society. From the physical layer to the application layer, the multiply increasing information makes the data aggregation technology particularly important for wireless sensor network. Data aggregation technology can extract useful information from the network and reduce the network load, but will increase the network delay. The non-exchangeable feature of the battery of sensor nodes makes the researches on the battery power saving and lifetime extension be carried out extensively. Aiming at the delay problem caused by sleeping mechanism used for energy saving, a Distributed Collision-Free Data Aggregation Scheme is proposed in this article to make the network aggregate data without conflicts during the working states periodically changing so as to save the limited energy and reduce the network delay at the same time. Simulation results verify the better aggregating performance of Distributed Collision-Free Data Aggregation Scheme than other traditional data aggregation mechanisms.

Some Hesitant Fuzzy Linguistic Muirhead Means with Their Application to Multiattribute Group Decision-Making

The proposed hesitant fuzzy linguistic set (HFLS) is a powerful tool for expressing fuzziness and uncertainty in multiattribute group decision-making (MAGDM). This paper aims to propose novel aggregation operators to fuse hesitant fuzzy linguistic information. First, we briefly recall the notion of HFLS and propose new operations for hesitant fuzzy linguistic elements (HFLEs). Second, considering the Muirhead mean (MM) is a useful aggregation technology that can consider the interrelationship among all aggregated arguments, we extend it to hesitant fuzzy linguistic environment and propose new hesitant fuzzy linguistic aggregation operators, such as the hesitant fuzzy linguistic Muirhead mean (HFLMM) operator, the hesitant fuzzy linguistic dual Muirhead mean (HFLDMM) operator, the hesitant fuzzy linguistic weighted Muirhead mean (HFLMM) operator, and the hesitant fuzzy linguistic weighted dual Muirhead mean (HFLWDMM) operator. These operators can reflect the correlations among all HFLEs. Several desirable properties and special cases of the proposed operators are also studied. Furthermore, we propose a novel approach to MAGDM in a hesitant fuzzy linguistic context based on the proposed operators. Finally, we conduct a numerical experiment to demonstrate the validity of our method. Additionally, we compare our method with others to illustrate its merits and superiorities.