Dynamic Charging Scheme Problem With Actor–Critic Reinforcement Learning

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.

Existing work on localization in wireless rechargeable sensor networks (WRSNs) assumes that the charger is equipped with an omnidirectional antenna. We notice that the current available COTS (Commercial off-the-shelf) energy harvesting kit such as the one manufactured by Powercast includes wireless charger with directional antenna. We consider the localization problem in a wireless rechargeable sensor network with a mobile wireless charger. We propose two efficient region dividing methods, Angle Division and Grid Division. Both exploit the field angle of the directional charger to localize individual sensor nodes. The Angle Division is more suitable for general convex polygons. Grid Division algorithm is efficient when the nodes are distributed uniformly and the area is approximate to a rectangle. Further, we extend the two methods by combining the charging time to improve the localization precision. To verify the design, we have extensively evaluated our design by both field experiments and large- scale simulations. The experiment and simulation results show that our algorithm can achieve a localization distance error of less than a quarter meter by as less as four stops.

The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes.

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The rapid development of wireless power transfer technology brings forth innovative vehicle energy solutions and breakthroughs utilizing wireless sensor networks (WSNs). In most existing schemes, wireless rechargeable sensor networks (WRSNs) are generally equipped with one or more wireless charging vehicles (vehicles) to serve sensor nodes (SNs). These schemes solve the energy issue to some extent; however, due to off-road and speed limitations of vehicles, some SNs still cannot be charged in time, negatively affecting the lifetime of the networks. Our work proposes a new WRSN model equipped with one wireless charging drone (drone) with a constrained flight distance coupled with several wireless charging pads (pads) deployed to charge the drone when it cannot reach the subsequent stop. Our model solves this charging issues effectively and overcomes energy capacity limitations of the drone. Thus, a wireless charging pad deployment problem is formulated, which aims to apply the minimum number of pads so that at least one feasible routing path can be established for the drone to reach every SN in a given WRSN. Four feasible heuristics, three based on graph theory and one on geometry, are proposed for this problem. In addition, a novel drone scheduling algorithm, the shortest multi-hop path algorithm, is developed for the drone to serve charging requests with the assistance of pads. We examine the proposed schemes through extensive simulations. The results compare and demonstrate the effectiveness of the proposed schemes in terms of network density, region size and maximum flight distance.

To analyze the network of human and non-human actors involved in the computerization of primary health care in the Brazilian federal government. A qualitative study that used as a theoretical reference the actor-network theory and as a methodological reference the cartography of controversies. Data analysis was carried out using Gephi software, and through the extraction of reports, informed by the actor-network theory. We found a network of 288 connections among 33 actors, composed mainly of nonhuman influencers of computerization. These actors are distributed throughout 3 inter-related communities, and manage the network by defining obligations, penalties, conflicts and intentionalities, thus influencing the success of the intended computerization. The network of actors at the federal level generates situations that, in many cases, hamper the successful implementation of a nationwide computerization strategy.

SummaryCharging path planning in wireless sensor networks (WSNs) refers to designing an efficient charging path for sensor nodes in the network. However, most charging schemes mainly consider the planning of charging paths and pay little attention to the impact of uncertainties, such as road conditions and environment on the planning of charging paths, as well as ignoring the charging problem of new nodes in need of charging. Road conditions and the environment directly affect the energy consumption of wireless charging vehicles (WCVs) during traveling. To address the aforementioned challenges, this article proposes an interval many‐objective charging path scheme model, the WCV consumption is an uncertain value, it will change according to the environment, and road conditions, so we represent it as an interval parameter with upper and lower bounds. An interval high‐dimensional multi‐objective model with target energy consumption, path distance, number of dead nodes, and communication delay is constructed. Second, to implement this model, an interval SPEA2 algorithm (I‐SPEA2) that introduces an environmental response mechanism is proposed. I‐SPEA2 treats individual target interval values as ranges of values on a two‐dimensional coordinate axis, forming a quadrilateral, calculates individual size probabilities based on the area to determine the dominant relationship, and combines fixed distance and interval overlap to eliminate redundant individuals. The simulation results show that the interval dynamic model is effective in prolonging the lifecycle of WSN as well as the proposed algorithm reduces the mortality rate of the nodes by 15%, 28%, 13%, 16%, and 21% compared with other algorithms.

Wireless rechargeable sensor network (WRSN) has become the most effective solution to the energy problem in wireless sensor networks (WSNs) by introducing mobile chargers (MCs) to replenish energy for energy‐starved sensor nodes. In the studies of WRSN, there has been growing interest in using lightweight unmanned aerial vehicles (UAVs) as MCs to overcome geographical constraints. Recently, automatic landing pads (PADs) have been introduced in the UAV‐based WRSN, enabling the UAV to automatically replenish energy during the charging flight. Consequently, the UAV’s service range is enlarged, and the UAV can now perform large‐scale charging services. In this paper, we investigate the periodic charging scheduling problem in this novel form of WRSN with the goal of minimizing the charging delay. We present the problem’s definition and model and prove it is NP‐hard. To address this problem, we propose two periodic charging schemes: SNBC (sensor‐node‐based charging scheme) and PBC (PAD‐based charging scheme). We extensively simulated our proposed schemes in a UAV‐based WRSN with PADs, evaluating them based on two key metrics: charging delay and UAV replenishment frequency. The results indicate the efficiency of our approaches. SNBC averaged a 7.2 × 104 s charging delay, while PBC outperformed it with a 14.01% reduction in charging delay and a 13.66% decrease in replenishment frequency.

Organisations’ reliance on Information Technology (IT) is rapidly increasing. IT strategy is developed and implemented for particular purposes by different organizations. We should therefore expect that there will be network of actors within the computing environment, and that such network of actors will be the key to understanding many otherwise unexpected situations during the development and implementation of IT strategy. This network of actors has aligned interests. Many organizations are developing and implementing their IT strategy, while little is known about the network of actors and their impacts, which this paper reveals. This paper describes how Actor-Network Theory (ANT) was employed to investigate the impact of network of actors on the development and implementation of IT strategy in an organisation. ANT was used as it can provide a useful perspective on the importance of relationships between both human and non-human actors. Another example: design and implementation of a B-B web portal, is offered for comparison.

Recent advancement in the wireless power transfer technology has motivated the development of a wireless rechargeable sensor network (WRSN). In WRSNs, the formation of an optimal recharging schedule for each wireless charger vehicle is a well known NP-complete problem. To determine the optimal recharging schedule for each wireless charger vehicle, this paper presents a coverage-aware recharge scheduling scheme (CRS) where ACO-based metaheuristic algorithm is employed. In order to provide fast recharging in WRSN, the proposed scheme employs multiple wireless charger vehicles to perform the charging task. Performance analysis of the proposed scheme confirms its superiority in terms of charging latency.

The limited energy of the sensor nodes (SNs) has always been a major hindrance for wireless sensor networks (WSNs). Wireless charging of the SNs is a promising alternative to solve the energy constraint problem in WSNs. The charging paradigm with such rechargeable SNs is known as wireless rechargeable sensor network (WRSN). While plenty research efforts have been made to improve the charging performance in a WRSN, little has been done to address the scheduling problem of the MCs having limited capacity. By scheduling problem of the MCs, we want to mean that how efficiently the capacity of multiple MCs can be utilized so that minimum number of MCs are used in order to fulfill the energy demands of the SNs. To fill this research lacuna, we propose three novel on-demand charging schemes which are termed as Pcharge, Bcharge, and Fcharge in this paper. The effectiveness of the proposed schemes is validated by simulation results which reveal that the proposed schemes can achieve promising performance in terms of tour length and capacity utilization of the MCs as compared to a state-of-the-art scheme.

This paper explores the use of a new pedagogy, the rock art stability index (RASI), to engender deeper understanding of weathering science concepts by students. Owing to its dynamic nature, RASI represents a quintessential actor network for weathering science, because it links task in the landscape with an active material practice and an alternative materialistic world-view recently called for in positivistic science, to create place. Using concept maps as an assessment tool, 571 college undergraduate students and 13 junior high school integrated science students (ages 12—13) were evaluated for increased learning potential between pre- and post-field experiences. Further, this article demonstrates that when students use RASI to learn the fundamental complex science of weathering they make in-depth connections between weathering form and process not achieved through traditional, positivistic weathering pedagogy. We argue that RASI draws upon inherent actor networks which allow students to link weathering form and process to an animate conceptualization of landscape. Conceptualizing landscape as sentient actor networks removes weathering science disciplinary connections and their inherent pedagogic practices. Our focus in this paper is not to challenge weathering ontology and epistemology, but rather to argue that there is a need for a pedagogical paradigm shift in weathering science.

Provision of mobile banking services from an actor–network perspective: Implications for convergence and standardization

SUMMARYLocation estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks. The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring relies on an accurate position estimation to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth, and geographical features. The actors are not equipped with positioning adaptors, and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actors, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the nodes can be efficiently positioned using a multi‐hop approach with local information exchange only. The introduced approach is also applied to a scenario, where monkey swarm monitoring is simulated, to test the generalizability of the algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

Maintaining inter-actor connectivity is extremely crucial in mission-critical applications of Wireless Sensor and Actor Networks (WSANs), as actors have to quickly plan optimal coordinated responses to detected events. Failure of a critical actor partitions the inter-actor network into disjoint segments besides leaving a coverage hole, and thus hinders the network operation. This paper presents a Partitioning detection and Connectivity Restoration (PCR) algorithm to tolerate critical actor failure. As part of pre-failure planning, PCR determines critical/non-critical actors based on localized information and designates each critical node with an appropriate backup (preferably non-critical). The pre-designated backup detects the failure of its primary actor and initiates a post-failure recovery process that may involve coordinated multi-actor relocation. To prove the correctness, we construct a formal specification of PCR using Z notation. We model WSAN topology as a dynamic graph and transform PCR to corresponding formal specification using Z notation. Formal specification is analyzed and validated using the Z Eves tool. Moreover, we simulate the specification to quantitatively analyze the efficiency of PCR. Simulation results confirm the effectiveness of PCR and the results shown that it outperforms contemporary schemes found in the literature.