Resource management in wireless sensor networks using collective intelligence

In wireless sensor networks (WSNs), resource-constrained nodes are expected to operate in highly dynamic and often unattended environments. Hence, support for intelligent, autonomous, adaptive and distributed resource management is an essential ingredient of a middleware solution for developing scalable and dynamic WSN applications. In this article, we present a resource management framework based on a two-tier reinforcement learning scheme to enable autonomous self-learning and adaptive applications with inherent support for efficient resource management. Our design goal is to build a system with a bottom-up approach where each sensor node is responsible for its resource allocation and task selection. The first learning tier (micro-learning) allows individual sensor nodes to self-schedule their tasks by using only local information, thus enabling a timely adaptation. The second learning tier (macro-learning) governs the micro-learners by tuning their operating parameters so as to guide the system towards a global application-specific optimization goal (e.g., maximizing the network lifetime). The effectiveness of our framework is exemplified by means of a target tracking application built on top of it. Finally, the performance of our scheme is compared against other existing approaches by simulation. We show that our two-tier reinforcement learning scheme is significantly more efficient than traditional approaches to resource management while fulfilling the application requirements.

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

Elasticity is a key feature of current cloud computing platforms. Dependent on their demand tenants can dynamically scale up and down their applications. To increase their revenue, cloud providers are used to over-provision their clusters, but they still have to reserve capacity to avoid that services get unresponsive and cause SLO violation during bursts. In this paper, we propose CLOUDFARM, a PaaS architecture with an adaptive SLO-based resource management mechanism. It introduces new flexible SLAs backed with a respective development model and management interface for end-user services. According to their SLAs and the price tenants pay, services can be selectively downgraded to overcome short-term peaks, e.g. While preparing for scale-out. Providers can deploy services optimistically and thus maximize their data center utilization and revenue.

Purpose of ReviewTheoretical frameworks for adaptive natural resource management are quite common, whereas documented examples showing successful implementation of adaptive management and learning through multiple time intervals have remained uncommon. Measures of quality of adaptive natural resource management processes are needed to examine potential factors driving the successful implementation. To address this gap, we developed a multimetric index composed of 22 metrics to assess quality of case studies using quantitative decision support (QDS) to inform adaptive forest management (AFM). Metrics represented three main tasks, including conceptual setup, modeling, and application. We further distinguished these into subtasks: definition of objectives and management options (setup); specifying uncertainty, prediction, and optimization (modeling); and stakeholder involvement along with practice and learning (application). We used a multimetric index to examine temporal and geographic variation in quality of reviewed case studies using QDS to inform AFM. We then conducted a structured literature review of 179 articles, wherein 34 case studies met a priori criteria.Recent FindingsWhen applying the multimetric index to these case studies, we found that over the past decade the index has been intermediate and annual average scores declined by 33% from 4.5 to 3.0 of 10 (where 10 is the highest possible quality score). Aligning with reviews of adaptive natural resource management, reported on-ground application of QDS to inform AFM was rare (n = 2). We also confirmed the expectation that there has been a substantial lack of stakeholder engagement during QDS development tasks.SummaryOur multimetric index provides a novel tool to examine gaps in the use of QDS for adaptive management in diverse domains including but not limited to forests.

Resource management strategy in adaptive cloud provisions the needed resources dynamically to the end-users. To improve the runtime performance of adaptive cloud for service-based applications, two aspects of technical issues are required to be addressed. The first one is the balancing of a large amount of data on existing resources and the second is resource provisioning which can adjust the number of resources optimally to adapt the time-varying workload. As the growth of data is increasing tremendously, efficient resource management is the need in cloud computing. We build a cloud framework to process data in automation with adaptive resource and workload management strategy. Numbers of approaches are reviewed and applied for workload prediction. We developed the model Auto-Regressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning (ARIMA-PERP) and evaluated the results that can satisfy the on-demand need of end-users for efficient resource utilization. To serve the maximum number of user requests, performance metrics of the proposed approach are evaluated. It is observed that our evaluated results achieved an accurate prediction by 91.11%, which meets the efficient resource utilization for the demanded workload. As compared with the exiting approach, we achieved better performance by 0.11% for accurate prediction. The proposed architecture is intended to provide the resources dynamically and efficiently satisfying the demands of the user. To achieve this objective of efficient resource provisioning, algorithms are developed for workload prediction which helps in deciding optimum resource provisioning. Our system uses proactive approach resource management and deployment of the adaptive cloud system. In traditional systems, resources are managed based on demand, availability and the strategy of scheduling which results in delayed response time at large. We configured the ARIMA model to predict the future workload for provisioning the resources dynamically and remove the problem of over-provisioning and under-provisioning in a cloud environment.

Adaptive resource management (ARM) is an approach to managing that allows decision makers to learn about a system and subsequently change management actions based on new information about system processes (i.e., adapt) to better meet fundamental objectives. This is not to be confused with changing management actions when the state of the system changes. For example, changing a harvest regulation when populations decline is not ARM. This dynamic decision making may be fortuitously optimal, but if the effect of harvest is uncertain then changing regulations may be suboptimal—for example, weather may have caused the decline. Adaptive resource management can be implemented along a spectrum of passive to active to reduce system uncertainty. Active ARM is when explicit hypotheses are posited then implemented to test them, and monitoring occurs to elucidate whether the effect of the management action achieved a given result. Passive ARM uses current management practices, natural variation in the system, and monitoring to reduce uncertainty. Even though northern bobwhite (Colinus virginianus) have been studied for a century, uncertainty regarding optimal management strategies still exists. The Albany Quail Project (AQP) has used both modes of ARM to learn about northern bobwhite populations to better meet the hunting objectives of stakeholders. Between 1992 and 2019 the AQP radio-tagged 5,182 unique individuals and banded an additional 5,008 birds on the primary study area near Albany, Georgia, USA. Additionally, 1,724 nests have been monitored and population surveys conducted in the spring and autumn. Active ARM occurred with tests of supplemental feeding, hardwood cleanup, and predator control whereas passive ARM was used to learn about prescribed fire regimes, brood habitat preferences, small mammal cycles, and raptor migrations. We built an integrated population model (IPM) that combined known-fate survival data, nesting records, dead recoveries from harvest, and population surveys to model the system. The preliminary results of the IPM demonstrated that populations have increased during the project. The 5 most abundant autumn populations have occurred in the most recent 10 years of the project when bobwhite populations throughout their range have continued to decline. Unlike typical ARM applications, the AQP has not had an explicit model to predict consequences of future management actions; nonetheless, the essence of ARM has been followed. Since its inception uncertainty has been reduced, management actions have been changed, and bobwhite populations have responded. Improvements to AQP and similar projects could include explicit hypotheses and predictive models about the system

In the last few years, interest in wireless sensor networks has increased considerably. These networks can be useful for a large number of applications, including habitat monitoring, structural health monitoring, pipeline monitoring, transportation, precision agriculture, supply chain management, and many more. Typically, a wireless sensor network consists of a large number of simple nodes which operate with exhaustible batteries, unattended. Manual replacement or recharging the batteries is not an easy or desirable task. Hence, how energy is utilized by the various hardware subsystems of individual nodes directly affects the scope and usefulness of the entire network. This paper provides a comprehensive assessment of state-of-the-art of dynamic power management (DPM) in wireless sensor networks. It investigates aspects of power dissipation in a node and analyses the strength and limitations of selective switching, dynamic frequency, and voltage scaling.

Recently, there has been an increased reliance on computing systems supported by a multi-tier architecture. In multi-tier computing systems, it is important to appropriately manage resource allocation to ensure fairness of a QoS (quality of service) level avoiding overload conditions in tiers. This paper proposes an adaptive resource management algorithm for multi-tier computing systems in order that all clients have the same QoS level. We introduce a computing architecture which consists of multiple tiers, a group of resource managers, and an arbiter. Each tier is specialized to execute each subtask of clients and hosts virtual machines on its server pool. Each resource manager handles resource allocation of each client and updates the resources by locally exchanging a QoS level of its client with some other resource managers. Then, the resource managers request the resources to the arbiter. The arbiter compensates the requested resources to avoid overload conditions in tiers. Based on the compensation by the arbiter, each resource manager reallocates the resources to the subtasks of its client. We show sufficient conditions for the proposed resource management algorithm to achieve a fair QoS level avoiding overload conditions in all tiers at each time.

This paper describes ongoing investigations at Microsoft Research into algorithms for adaptive real-time scheduling and resource management. We are implementing a system which will allow sets of independently authored real-time applications to be scheduled, providing both initial resource negotiation and dynamic feedback to applications about their resource consumption. Instead of relying on human beings to pre-compute scheduling and resource requirements, applications will be able to dynamically adapt their run-time behavior to the resources available. Under overload conditions, user preferences and policies will be applied in order to bring the total resource requirements within available resources. This will permit chosen applications to continue executing perfectly even while others execute with dynamically renegotiated resource grants or are gracefully shut down, as per user preferences. We believe that this approach will lead to real-time scheduling and resource management algorithms which are appropriate for workstation and home multi-media environments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Wireless ad hoc sensor networks have the advantage of spanning a large geographical region and being able to collaboratively detect and track non-local spatio-temporal events. This paper presents a dual-space approach to event tracking and sensor resource management in sensor networks. The dual-space transformation maps a non-local phenomenon, e.g., the edge of a half-plane shadow, to a single point in the dual space, and maps locations of distributed sensor nodes to a set of lines that partitions the dual space. The detection problem becomes finding and tracking the cell that contains the point in the arrangement defined by these lines. This mechanism can be effectively used for power management of the sensor network - nodes that will not be immediately visited by an event can be turned off to save energy required for sensing, processing, and communication. The approach has been successfully demonstrated on a laboratory testbed built using the UC Berkeley motes sensors. An implemented application of detecting and tracking light shadow edges moving over a sensor field is described.

In South Australia’s Eyre Peninsula, groundwater provides 85% of the region’s reticulated water supply. Fresh groundwater resides within shallow karstic limestone aquifers recharged by incident rainfall. Water levels are very responsive to short-term climate variability and are at risk of sustained decline due to long-term drying trends and the further rainfall declines indicated by projections of future climate, thereby increasing risk to water security and groundwater-dependent ecosystems. In 2009, a new adaptive resource management approach was enabled through legislative reform that better addresses climate variability, particularly where aquifer robustness is low. This allows the volume of water available for licensed allocations to be varied annually depending on the current condition of the aquifer resources. A three-tiered trigger level policy varies the rate at which water allocations are limited in proportion to monitored changes in groundwater storage. The three trigger thresholds are specified for each discrete groundwater resource, based on levels of risk. We now have more than five years of observations and practice of this approach to learn of its efficacy and consequences for water users, the water resources, and the environment. It has proved to be an effective way to deal with the uncertainties in how and when climate may change and how water management principles can effectively respond. Our case study provides an example of the importance of legislative reform to enable adaptive water resource management to effectively tackle the challenges of water planning in a drying climate.

Adaptive optimization of renewable natural resources: Solution algorithms and a computer program

Compared with conventional phased array radar, co-located MIMO radar can effectively control transmitting beam width by means of sub-array division, providing greater flexibility in system resource management. To realize the effective resource management for the co-located MIMO radar in multi-target tracking, the resource management optimization model is proposed, where four working parameters can be controlled, including sub-array number, sampling period, transmitting waveform energy and working mode. The objective function integrates the average time and energy resource consumption of the system, and constraints are used to ensure that targets can be effectively detected. When solving the optimization problem, consider fully the influence of sub-array number on the other working parameters, the originally four-dimensional optimization problem is decomposed into several three-dimensional optimization problems, and finally the adaptive resource management algorithm for the co-located MIMO radar is obtained. The simulation results demonstrate that the proposed algorithm can control the working parameters of the co-located MIMO radar effectively. Furthermore, with the proposed adaptive resource management algorithm the average resource consumption of the co-located MIMO radar can be reduced effectively.

Adaptive water resources management recognizes the centrality of learning to its effective implementation.However, the literature on learning in Adaptive Management (AM) is disjointed and lacks rigorous treatment.The purpose of this paper is to clarify how learning associated with AM is distinctive and to explain the importance of successfully integrating it into policy design.Because adaptive learning holds a unique position within the adaptive management and assessment process, it should serve as the basis for a systematic redesign of water policy processes.Adaptive environmental assessment and management processes, which highlight the role of adaptive learning, are built upon in this paper.For the purpose of this paper, adaptive learning is defined as "an ongoing process of inquiry that incorporates new knowledge with the aim to continually improve management policies".In order for adaptive learning to live with and profit from nature's dynamism, it is argued that it must be problem-centered, decision relevant, reflective, and inherently transformative.When this kind of learning becomes a fully integrated component of AM, water resource institutions have a greater likelihood of maturing into actual "learning systems".The specific objectives of this paper are to: 1) redefine what adaptive learning is, 2) explain how this particular type of learning is distinctive and 3) outline the role of adaptive learning in policy formation.Regarding policy design, the paramount issue is determining how to respond to the speed, scale, and complexity of the planetary challenges.This paper draws on a number of related areas of research to present propositions for increasing the adaptive capacity of water organizations.These research topics include: demonstrating how adaptive learning should be integrated into policy design; establishing new standards of practice that are based on adaptive learning rather than achieving 'optimal' end-game solutions; using the breakthrough views in order to transform learning into effective action; framing management problems in non-reductionist ways so that nature may serve as an ally and pilot; using shadow networks to inspire innovation, encourage institutional learning, and improve governance rules; and creating a new social reality that is more future-responsive to problems and more hospitable to new ways of thinking about water management.

Seasonal hydrological forecasts are of critical importance for the effective management of water resources, particularly in complex and vulnerable basins such as the Mediterranean area which have both a deficitary water regimen and a high anthropogenic pressure. Nevertheless, inaccuracies in meteorological inputs can propagate through hydrological models, amplifying uncertainties in flow predictions. It is imperative to rectify these forecasts, whether meteorological or hydrological, to enhance prediction reliability and provide robust data for informed decision-making. This study proposes an advanced framework for seasonal hydrological forecasting that integrates raw and corrected weather forecasts with distributed hydrological modeling and sophisticated post-processing techniques to enhance flow prediction accuracy in the J&amp;#250;car River in Spain as a representative case study of the Mediterranean area.The catchment hydrological model was implemented using the model TETIS v9.1 wich was calibrated and validated using observed records from 1981 to 2019 at seven control points. To do this, a split sample test was conducted using the period 2009&amp;#8211;2019 for calibration, and the rest of the time series for validation (1981&amp;#8211;2008). The process involved refining parameter maps to ensure good or acceptable performance at all control points. Meteorological data were sourced from the W5E5 dataset, downscaled to a 0.09&amp;#176; resolution using ERA5-Land. This improved the spatial and temporal resolution of the hydrological model. Once we have established an acceptable hydrological model,, the seasonal forecast hindcasts were evaluated using meteorological inputs from global forecasting systems, including ECMWF-SEAS5, CMCC_SPSv35, DWD_GCFS21, and MeteoFrance System8. To address uncertainties in the meteorological forecasts and their propagation to hydrological outputs, two complementary correction strategies were implemented. First, artificial intelligence(fuzzy logic) was applied to correct meteorological inputs before integration into the hydrological model, assuming errors originate solely from meteorological data and treating the hydrological model as a &amp;#8220;perfect&amp;#8221; simulator. Second, a hydrological error model was developed to identify and adjust discrepancies between simulated and observed flows, addressing systematic biases and errors in the hydrological simulation.The results demonstrated that forecasts based on corrected meteorological inputs exhibited significant accuracy improvements compared to those using unprocessed inputs. The hydrological error model further enhanced prediction reliability by mitigating systematic biases. These findings underscore the effectiveness of combining meteorological forecasts with AI-driven corrections to address uncertainties, thereby improving the robustness of seasonal hydrological predictions. This study highlights the potential for integrating advanced correction techniques into seasonal hydrological forecasting frameworks, offering a replicable methodology for other basins with similar complexities. The proposed framework enhances both the reliability and applicability of forecasts, ensuring their relevance for effective decision-making in complex hydrological systems as the Mediterranean area. The improved accuracy of these forecasts provides a sound scientific support for adaptive water resource management, particularly in the face of increasing climatic variability and environmental changes.Acknowledgments: This study was funded by the Colombian Ministry of Science, Technology, and Innovation (MINCIENCIAS) through the Call for Doctorates Abroad 885-2; by the Valencian Regional Government through the WATER4CAST 2.0 (CIPROM/2023/5) research project; and Spanish Ministry of Science and Innovation through the research project TETISPREDICT (PID2022-141631OB-I00).&amp;#160;