Extend System Lifetime Research Articles

Power-saving system designs for hexagonal cell based wireless sensor networks with directional transmission

This paper examines the lifetimes of alternative system configurations for a wireless sensor network (WSN) in a circular region. The coverage region is divided into hexagonal cells, and the information from each cell is combined and transmitted to the sink or to other cells. Sensors are equipped with directional antennas that improve transmit/receive power efficiency and potentially reduce interference. Simultaneous transmission to multiple cells is permitted, and the power level of different transmissions can be controlled to different levels. Under the assumptions of uniform information density and uniform sensor distribution, we show that for larger systems (with 800 cells or more) lifetime can be prolonged by more than 10 times by introducing a limited number of transmission lines or line sinks to facilitate information transfer to the main sink. Alternatively, introducing up to six secondary sinks can increase the system lifetime by a factor of about 4.5 for the same size region. Our results imply that incorporating line or secondary sinks may provide simple, low-cost solutions for extending system lifetime for larger WSN’s.

Energy and environmental impacts of a 37.57 MW dc ground-mounted large-scale photovoltaic system in Malaysia: A life-cycle approach

This paper estimated the environmental performance and primary energy consumption of a newly installed large-scale solar photovoltaic system of 37.57 MW dc in Bidor, Malaysia using the process-based life-cycle assessment approach. The mid-point and end-point impact categories were evaluated, and seven relevant indicators related to energy and global warming were determined. The global warming impact and energy payback time obtained were 30.95 g CO2-eq/kWh and 3.43 years, respectively. Furthermore, the global warming impact mitigation potential attained was 811 kilo metric tonne (ktonne) CO2-eq throughout the system lifetime. Analyses of contribution, sensitivity and uncertainty were performed to i) identify photovoltaic system components that substantially consumed primary energy and impacted the environment, ii) evaluate the effect of four key parameters, namely irradiation, performance ratio, system lifetime and electricity mix on the energy and global warming-related indicators, and iii) estimate the uncertainty range of the evaluated indicators. The results highlighted that the main contributor to the indicators is the photovoltaic module. Other significant contributors are the civil work and operational and maintenance but the contribution from these components is much lower than the photovoltaic module. Besides that, the cumulative energy demand, energy return on investment, global warming impact and global warming impact mitigation potential showed an improvement when extending system lifetime from 15 to 40 years, i.e. from 0.8136 to 0.3677 MJ, 4.61 to 11.25, 55.37 to 24.99 g CO2-eq/kWh and 407.36 to 1055.78 g CO2-eq respectively, except for energy payback time and global warming payback time, i.e. from 3.26 to 3.55 years and 1.42 to 1.71 years respectively.

Comparison of different performance recovery procedures for polymer electrolyte membrane fuel cells

To both extend system lifetime and enable reliable performance benchmarking, recovery procedures are of great importance to distinguish and mitigate the reversible and irreversible performance degradation of polymer electrolyte membrane fuel cells. In this work, three common recovery procedures available in the literature (JRC-based protocol, DOE-based protocol, and overnight rest) being part of a load cycling durability test are characterized by polarization curves and electrochemical impedance spectroscopy. Such direct comparison using same conditions and material has not been published so far. To compare the relative recovery of the three recovery procedures the recovery related to the last operation period, to the beginning of the whole test, and the non-recovered performance loss within each operation period are assessed. Moreover, the mechanisms leading to the various recovery effects are analyzed. Generally, with the contribution of gas purging to water removal, all three recovery procedures reduce greatly mass transfer resistance and recover most of the performance loss in the high current density range. At lower current density, the three procedures differ substantially. In the case of JRC-based protocol, the kinetic losses are recovered by the reduction of Pt oxides and structure change of ionomer by reducing the cathode potential and fuel cell temperature, respectively. The overnight rest results in similar recovery of the performance loss. The DOE-based protocol leads to relatively low recovery of losses in the kinetic region of the polarization curves. Additionally, the effect of the cell operating history is considered.

A Hybrid Model for Data Prediction in Real-World Wireless Sensor Networks

Data prediction is proposed in wireless sensor networks (WSNs) to extend system lifetime by avoiding transmissions of redundant messages. Existing prediction-based approaches can be classified into two types. One focuses on historical data reconstruction and proposes backward models, which incur uncontrollable delay. The other focuses on the future data prediction and proposes forward models, which require additional transmissions. This letter proposes a hybrid model with the capabilities of both historical data reconstruction and future data prediction to avoid additional transmission and control delay. Two algorithms are proposed to implement this model in real-world WSNs. One is a stagewise algorithm for sensor nodes to build optimal models. The other is for the sink to reconstruct and predict sensed values. Two WSN applications are simulated based on three real data sets to evaluate the performances of the hybrid model. Simulation results demonstrate that the proposed approach has high performance in terms of energy efficiency with controllable delay.

Joint optimization of mission abort and component switching policies for multistate warm standby systems

Safety-critical systems have been widely applied in various practical engineering fields to perform specific missions. Failures of such systems will lead to significant damage, thus, mission abort polices can be implemented to enhance system survivability. Warm standby is one of the most commonly used techniques in mission-critical and safety-critical systems to reduce failure risk during mission execution. Switching between working component and standby component is a feasible way to balance component degradation and extend system lifetime. Motivated by these results, we propose a joint optimization model that captures both component switching and mission abort policies for multistate warm standby systems. The optimal component switching and mission abort policies based on component condition are obtained to balance the trade-off between mission success probability and system survivability and to minimize the long-run expected total economic loss. A numerical study of virtual machine system is conducted to demonstrate the proposed model and obtained results.

Optimum reassignment of degrading components for non-repairable systems

The components in a system can degrade differently, due to the operational loads or environmental conditions, or both, in their positions being different. Therefore, reassignment of the functionally exchangeable components to the positions at appropriate time can increase system reliability and extend system lifetime. In this article, a new component reassignment problem is proposed, and a mixed binary nonlinear programming model is built to determine the optimal reassignment time and optimal reassignment of degrading components with the objective of maximizing system lifetime. The model integrates continuous optimization and combinatorial optimization, and provides a framework for optimizing the component reassignment decisions for various degradation models and system structures. Furthermore, the optimization model and analytical results are derived for k-out-of-n systems of linearly degrading components or exponentially degrading components. The analytical results reduce the complexity of solving the optimization model and are used to design an efficient solution method. Finally, numerical examples in a power supply system demonstrate applications of the optimization model and further provide managerial insights for the component reassignment problem of degrading components.

Energy and power awareness in hardware schedulers for energy harvesting IoT SoCs

The recent growth of applications in the emerging Internet of Things field is posing new challenges in the long-term deployments of sensing devices. Currently, system designers rely on energy harvesting to reduce battery size and extend system lifetime. While some system functions need constant power supply, others can have their service adapted dynamically to the available harvested energy and harvesting power. Our proposed Torpor is a power-aware hardware scheduler which continuously monitors harvesting power and in combination with its software runtime, dynamically activates system functions depending on the available energy and its rate of change. By performing a few key functions in hardware, Torpor incurs a very low power overhead during continuous monitoring, while the software runtime provides a high degree of flexibility to enable different scheduling policies. We implemented Torpor on a FPGA-based prototype and demonstrated that dynamic scheduling policies which take the harvesting power into account can have a 2× or more improvement in execution rates compared to static (input-power-independent) policies, while dynamic policies that are aware also of the system's power consumption can achieve 1.5× improvement in the execution rates compared to the ones that do not. The power consumption of Torpor's always-on hardware integrated on chip is estimated to be less than 4 μW, making it a very promising power-management add-on for microprocessors used in IoT nodes.

A Systematic Review on Recent Advances in mHealth Systems: Deployment Architecture for Emergency Response.

The continuous technological advances in favor of mHealth represent a key factor in the improvement of medical emergency services. This systematic review presents the identification, study, and classification of the most up-to-date approaches surrounding the deployment of architectures for mHealth. Our review includes 25 articles obtained from databases such as IEEE Xplore, Scopus, SpringerLink, ScienceDirect, and SAGE. This review focused on studies addressing mHealth systems for outdoor emergency situations. In 60% of the articles, the deployment architecture relied in the connective infrastructure associated with emergent technologies such as cloud services, distributed services, Internet-of-things, machine-to-machine, vehicular ad hoc network, and service-oriented architecture. In 40% of the literature review, the deployment architecture for mHealth considered traditional connective infrastructure. Only 20% of the studies implemented an energy consumption protocol to extend system lifetime. We concluded that there is a need for more integrated solutions specifically for outdoor scenarios. Energy consumption protocols are needed to be implemented and evaluated. Emergent connective technologies are redefining the information management and overcome traditional technologies.

A >78%-Efficient Light Harvester over 100-to-100klux with Reconfigurable PV-Cell Network and MPPT Circuit.

Energy harvesting is an attractive solution to extend system lifetime for internet of everything (IoE) nodes. Ambient light is a common energy source that can be harvested by photovoltaic (PV) cells. However, light intensity varies widely depending on location, ranging from ∼10s of lux in dim indoor conditions to ∼100klux under direct sunlight. Designing a fully integrated light harvester that spans such a wide range of light intensity with high efficiency is challenging, especially since typically low PV cell voltage requires a high upconversion ratio and PV-cell voltage/current characteristics change significantly with light intensity. Boost DC-DC converters are a typical energy-harvesting solution with high conversion efficiency, but they require a large off-chip inductor and hence cannot be fully integrated, increasing system size [1–3]. Recently, switched-capacitor (SC) DC-DC converters have been actively researched to enable fully-integrated energy harvesting using on-chip capacitors [4–6]. However, their efficiency has typically been limited to the 40-to-55% range at low input power levels (≤1µW) due to conduction/switching losses.

Method to Identify Dependability Objectives for Use in Multiobjective Optimization Problem

Intelligent mechatronic systems, such as self-optimizing systems, allow an adaptation of the system behavior at runtime based on the current situation. To do so, they generally select among several pre-defined working points. A common method to determine working points for a mechatronic system is to use model-based multiobjective optimization. It allows finding compromises among conflicting objectives, called objective functions, by adapting parameters. To evaluate the system behavior for different parameter sets, a model of the system behavior is included in the objective functions and is evaluated during each function call. Intelligent mechatronic systems also have the ability to adapt their behavior based on their current reliability, thus increasing their availability, or on changed safety requirements; all of which are summed up by the common term dependability. To allow this adaptation, dependability can be considered in multiobjective optimization by including dependability-related objective functions. However, whereas performance-related objective functions are easily found, formulation of dependability-related objective functions is highly system-specific and not intuitive, making it complex and error-prone. Since each mechatronic system is different, individual failure modes have to be taken into account, which need to be found using common methods such as Failure-Modes and Effects Analysis or Fault Tree Analysis. Using component degradation models, which again are specific to the system at hand, the main loading factors can be determined. By including these in the model of the system behavior, the relation between working point and dependability can be formulated as an objective function. In our work, this approach is presented in more detail. It is exemplified using an actively actuated single plate dry clutch system. Results show that this approach is suitable for formulating dependability-related objective functions and that these can be used to extend system lifetime by adapting system behavior.

A Fault Tolerant Approach for FPGA Embedded Processors Based on Runtime Partial Reconfiguration

The ever increasing adoption of field programmable devices in various application domains for building complex embedded systems based on FPGA processors along with the reliability issues having emerged for FPGA devices built with the latest nanometer technologies, have raised the need for new fault tolerant techniques in order to improve dependability and extend system lifetime. In addition, the runtime partial reconfiguration technology highly mature in the modern FPGA families along with the availability of unused programmable resources in most FPGA designs provide new and interesting opportunities to build advanced fault tolerance mechanisms. In this paper, we exploit the dynamic reconfiguration potential of today's FPGA architectures and the advances in the related design support tools and we propose a fault-tolerant approach for FPGA embedded processors based on runtime partial reconfiguration. According to the proposed methodology, the processor core is partitioned into reconfigurable modules and each module is duplicated to implement a concurrent error detection mechanism. Precompiled configurations containing spare resources are generated for each duplicated module and are used to repair at runtime the defective modules. Also, a fault tolerance scheme for the proxy logic of the reconfigurable modules, which cannot move in the alternative configurations along with the rest logic, is proposed. Moreover, a compression method for the alternative partial bitstreams, which significantly reduces the high storage space requirements of the proposed approach, is presented. Two different hardware decompression schemes have been implemented in a Virtex-5 device and compared in terms of area overhead and decompression latency. Furthermore, a thorough examination has been performed, regarding how the percentage of the spare resources and their allocation in the reconfigurable regions affect the compression efficiency and the processor performance. Finally, the proposed approach has been demonstrated in three different components --- ALU, multiplier-accumulator, and instruction-fetch unit --- of an open-source embedded processor.

Energy‐efficient Path Planning for Solar‐powered Mobile Robots*

We explore the problem of energy‐efficient, time‐constrained path planning of a solar‐powered robot embedded in a terrestrial environment. Because of the effects of changing weather conditions, as well as sensing concerns in complex environments, a new method for solar power prediction is desirable. We present a method that uses Gaussian Process regression to build a solar map in a data‐driven fashion. Using this map and an empirical model for energy consumption, we perform dynamic programming to find energy‐minimal paths. We validate our map construction and path‐planning algorithms with outdoor experiments, and we perform simulations on our solar maps to further determine the limits of our approach. Our results show that we can effectively construct a solar map using only a simple current measurement circuit and basic GPS localization, and this solar map can be used for energy‐efficient navigation. This establishes informed solar harvesting as a viable option for extending system lifetime even in complex environments with low‐cost commercial solar panels.

Wireless Body Area Network for Medical Healthcare

A body area network is a wireless network of biomedical sensors that are attached to a human body. The aim of wireless body area network (WBAN) is to facilitate continuously recording and monitoring of a person’s health condition, if needed, over a long-distance communication network. A sensing system is to be worn by the individual for a long duration. The hardware must be compact and light. This limits the size of the battery. These factors have made energy the most critical resource in WBAN and extending system lifetime has become a priority to fully realize the capabilities of WBAN. This paper presents design and system integration of WBAN technology along with issues and technical challenges of WBAN.

Electrostatic Energy-Harvesting and Battery-Charging CMOS System Prototype

The self-powering, long-lasting, and functional features of embedded wireless microsensors appeal to an ever-expanding application space in monitoring, control, and diagnosis for military, commercial, industrial, space, and biomedical applications. Extended operational life, however, is difficult to achieve when power-intensive functions like telemetry draw whatever little energy is available from energy-storage microdevices like thin-film lithium-ion batteries and/or microscale fuel cells. Harvesting ambient energy overcomes this deficit by continually replenishing the energy reservoir and indefinitely extending system lifetime. In this paper, a prototyped circuit that precharges, detects, and synchronizes to a variable voltage-constrained capacitor verifies experimentally that harvesting energy electrostatically from vibrations is possible. Experimental results show that, on average (excluding gate-drive and control losses), the system harvests 9.7 nJ/cycle by investing 1.7 nJ/cycle, yielding a net energy gain of approximately 8 nJ/cycle at an average of 1.6 ¿W (in typical applications) for every 200 pF variation. Projecting and including reasonable gate-drive and controller losses reduces the net energy gain to 6.9 nJ/cycle at 1.38 ¿W.

Design and optimization of distributed sensing coverage in wireless sensor networks

For many sensor network applications, such as military surveillance, it is necessary to provide full sensing coverage to a security-sensitive area while, at the same time, minimizing energy consumption and extending system lifetime by leveraging the redundant deployment of sensor nodes. In this paper, we propose a surveillance service for sensor networks based on a distributed energy-efficient sensing coverage protocol. In the protocol, each node is able to dynamically decide a schedule for itself to guarantee a certain degree-of-coverage (DOC) with average energy consumption inversely proportional to the node density. Several optimizations and extensions are proposed to enhance the basic design with a better load-balance feature and a longer network lifetime. We consider and address the impact of the target size and the unbalanced initial energy capacity of individual nodes to the network lifetime. Several practical issues such as the localization error, irregular sensing range, and unreliable communication links are addressed as well. Simulation shows that our protocol extends system lift-time significantly with low energy consumption. It outperforms other state-of-the-art schemes by as much as 50% reduction in energy consumption and as much as 130% increase in the half-life of the network.

Probabilistic Power Management for Wireless Ad Hoc Networks

Extending system lifetime by effectively managing power on participating nodes is critical in wireless ad hoc networks. Recent work has shown that, by appropriately powering off nodes, energy may be significantly saved up to a factor of two, especially when node density is high. Such approaches rely on the selection of a virtual backbone (i.e., a connected dominating set) of the topology to forward ongoing traffic, coupled with algorithms to manually and periodically recompute such a backbone for load balancing purposes. The common drawback of such schemes is the need to involve periodic message exchanges and to make additional restrictive assumptions. This paper presents Odds1, an integrated set of energy-efficient and fully distributed algorithms for power management in wireless ad hoc networks. Odds build on the observation that explicit and periodic re-computation of the backbone topology is costly with respect to its additional bandwidth overhead, especially when nodes are densely populated or highly mobile. Building on a fully probabilistic approach, Odds seek to make a minimum overhead, perfectly balanced, and fully localized decision on each node with respect to when and how long it needs to enter standby mode to conserve energy. Such a decision does not rely on periodic message broadcasts in the local neighborhood, so that Odds are scalable as node density increases. Detailed mathematical analysis, discussions and simulation results have shown that Odds are indeed able to achieve our objectives while operating in a wide range of density and traffic loads.

Description of EDCS technology clusters

Evolutionary Systems are those that are capable of accomodating change over an extended system lifetime with reduced risk and cost/schedule impact. Most of our complex defense systems depend on software for their successful operation and, as a result, the software in those systems is the prmary vehicle for adapting to change. The EDCS (Evolutionary Design of Complex Software) Program is providing for the development and experimental application of new software technologies which can enable significant improvements in miltary mission effectiveness and information superiority. The goal is the capability to produce software intensive military systmes that are highly flexible and adaptable to meet changing requirements --- evolutionary systems.In addition to DARPA, the EDCS Program is co-sponsored by USAF Rome Laboratory, USAF Wright Laboratory, US Army Missile Command, and the National Science Foundation (NSF). Technical management is provided jointly by the DoD Software Engineering Institute (SEI) and Rome Laboratory. Rome Laboratory is DARPA's primary contracting agent.EDCS is organized into 5 Technology Clusters:1. Rationale Capture and Software Understanding2. Architecture and Generation3. High Assurance / Real-Time4. Information Management5. Dynamic LanguagesThis paper contains a short description of each cluster.