Prolong System Lifetime Research Articles

Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts

Spare parts redundancy is an effective way to improve system reliability and prolong system lifetime. Generally, preventive maintenance and inventory management of a standby system can significantly reduce operating and maintenance costs, particularly crucial in industrial systems. Additionally, during storage, spare parts may experience degradation failure due to internal mechanisms and sudden failure due to external shocks, complicating the health management of the standby system. Unfortunately, few existing studies consider the impact of spare parts’ competing failures in standby systems. To address this gap, this paper proposes a novel method for predicting the lifetime and optimizing the maintenance policy of nonlinear degradation standby systems, considering both degradation and sudden failures of spare parts. Firstly, we established the degradation model under the competing failure of spare parts and derived the analytical formula for life prediction of the standby system in the presence of competing failures, assuming that sudden failure follows the Weibull distribution. Furthermore, we developed a joint optimization model for replacement maintenance and inventory management based on predictive information, aiming to minimize the expected cost, in which the block replacement interval and the number of spare parts are treated as decision variables. Finally, the effectiveness and potential application value of the proposed method are verified through numerical simulation and a case study.

Edge Computing of Online Bounded-Error Query for Energy-Efficient IoT Sensors.

Since the power of transmitting one-bit data is higher than that of computing one thousand lines of code in IoT (Internet of Things) applications, it is very important to reduce communication costs to save battery power and prolong system lifetime. In IoT sensors, the transformation of physical phenomena to data is usually with distortion (bounded-error tolerance). It introduces bounded-error data in IoT applications according to their required QoS2 (quality-of-sensor service) or QoD (quality-of-decision making). In our previous work, we proposed a bounded-error data compression scheme called BESDC (Bounded-Error-pruned Sensor Data Compression) to reduce the point-to-point communication cost of WSNs (wireless sensor networks). Based on BESDC, this paper proposes an online bounded-error query (OBEQ) scheme with edge computing to handle the entire online query process. We propose a query filter scheme to reduce the query commands, which will inform WSN to return unnecessary queried data. It not only satisfies the QoS2/QoD requirements, but also reduces the communication cost to request sensing data. Our experiments use real data of WSN to demonstrate the query performance. Results show that an OBEQ with a query filter can reduce up to 88% of the communication cost when compared with the traditional online query process.

Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence

Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many real-world applications, such as IEI (IoT of environmental intelligence), the required sensing data usually have limited error tolerance according to the QoS2 (quality of sensor service) or QoD (quality of decision-making) required. However, the bounded-error-pruned sensor data can achieve higher data correlation for better compression without jeopardizing QoS2/QoD. Moreover, the sensing data in widely spread sensors usually have strong temporal and spatial correlations. We thus propose a BESDC (bounded-error-pruned sensor data compression) scheme to achieve better leverage between the bounded error and compression ratio for sensor data. In this paper, our experiments on a sensor network of two-tier tree architecture consider four different environmental datasets including PM 2.5, CO, temperature and seismic wave with different scales of bounded errors. With the bounded errors required by the given IEI applications, our BESDC can reduce the total size of data transmission to minimize both energy consumption in sensor-tier devices and storage of fog-tier servers. The experimental results demonstrate that our BESDC can reduce transmission data by over 55% and save 50% energy consumption when assigning 1% of error tolerance within QoS2/QoD requirement. To the best of our knowledge, the proposed BESDC scheme can help other energy-efficient IoT schemes applying network topologies and routing protocols to further enhance energy-efficient IoT services.

Can Dark Silicon Be Exploited to Prolong System Lifetime?

Besides stringent power and thermal constraints, a dark silicon chip is also subjected to various reliability threats. This article illustrates how the dark silicon can be exploited to improve the chip’s lifetime through efficient utilization of computational resources and power budget, while still performing in a similar way. – Muhammad Shafique, Vienna University of Technology

Adaptive multi-path routing based on an improved leapfrog algorithm

Congestion in wireless sensor networks (WSNs) can result in the phenomenon of packet loss, which in turn reduces throughput and wastes energy. Therefore, congestion in WSNs needs to be controlled to achieve the goals of high energy efficiency, prolonged system lifetime, and better fairness and quality of service. We propose an adaptive multi-path approach based on an improved leapfrog algorithm to solve the transmission-congestion problem in WSNs. Specifically, this paper establishes a path-satisfaction model that considers the predicted degree of congestion, the remaining energy, and the minimum number of hops. The algorithm is updated discretely during the local optimization process, and a variable learning factor is introduced. The memetic information of individual frogs is optimized with a threshold-selection strategy that encourages leaping from weaker individuals to better ones. In global optimization, the search orientation of each memeplex is based on exchanging and recombining information with other memeplexes, and a multi-path routing idea is introduced to select the optimal path. Simulation results show that this method performs well in real time, significantly improves energy efficiency, and prolongs the network lifetime.

Energy-Neutral Design Framework for Supercapacitor-Based Autonomous Wireless Sensor Networks

To design autonomous wireless sensor networks (WSNs) with a theoretical infinite lifetime, energy harvesting (EH) techniques have been recently considered as promising approaches. Ambient sources can provide everlasting additional energy for WSN nodes and exclude their dependence on battery. In this article, an efficient energy harvesting system which is compatible with various environmental sources, such as light, heat, or wind energy, is proposed. Our platform takes advantage of double-level capacitors not only to prolong system lifetime but also to enable robust booting from the exhausting energy of the system. Simulations and experiments show that our multiple-energy-sources converter (MESC) can achive booting time in order of seconds. Although capacitors have virtual recharge cycles, they suffer higher leakage compared to rechargeable batteries. Increasing their size can decrease the system performance due to leakage energy. Therefore, an energy-neutral design framework providing a methodology to determine the minimum size of those storage devices satisfying energy-neutral operation (ENO) and maximizing system quality-of-service (QoS) in EH nodes, when using a given energy source, is proposed. Experiments validating this framework are performed on a real WSN platform with both photovoltaic cells and thermal generators in an indoor environment. Moreover, simulations on OMNET++ show that the energy storage optimized from our design framework is utilized up to 93.86%.

Performance Evaluation of Token Bucket based Congestion Control Protocol for Wireless Sensor Networks

The distinctive characteristics of WSN such as for instance coherent nature of traffic to base station that happens through its many-to-one topology and collision in physical channel are main factors behind congestion in wireless sensor networks. Also when sensor nodes inject sensory data into network the congestion is possible. Congestion affects the continuous flow of data, loss of information, delay in the arrival of data to the destination and undesirable usage of large amount of energy in the nodes. Therefore Congestion in wireless sensor networks (WSN) must be controlled to be able to prolong system lifetime, improve fairness, high energy-efficiency, and improve quality of service (QoS). In this paper, main focus is on improving the PASCCC further by utilizing token bucket algorithm in place of priority queues. This work in addition has dedicated to the inter cluster data aggregation to boost the outcome further. The objective is to stop congestion utilizing the token bucket algorithm.

Location-free Minimum Coverage Determination in a Heterogeneous Wireless Sensor Network

Abstract The minimum connected cover problem is one of the most fundamental issues in wireless sensor networks, which directly affects the capability and efficiency of the wireless sensor network (WSN). Constructing a minimum connected coverage has a great relevance for network services, such as prolonged system lifetime by utilizing redundant deployment of sensor nodes where the main challenge in the design of sensor networks is the limited battery power of the sensors and the difficulty of replacing and/or recharging these batteries. Thus, it is necessary that the sensors be densely deployed and energy-efficient protocols be designed to maximize the network lifetime while meeting the specific application requirements in terms of coverage and connectivity. In this paper, we propose a new distributed algorithm to find the minimum connected cover of the queried region by discovering the redundant sensors for heterogeneous sensors, each with arbitrary sensing range and is not aware of its location or relative direction of its neighbor. We provide performance metrics to analyze the performance of our approach and the simulation results show that our approach clearly improves the network lifetime over existing algorithms.

A Literature Survey of Topology Control and Its Related Issues in Wireless Sensor Networks

Issues of Topology control (TC) have captured more attentions in Wireless Sensor Networks (WSN). While WSN applications are normally optimized by the underlying network topology. Now a day's WSNs is one of the most interesting areas of research and are universally being used and deployed or implements to monitor the surrounding physical environments. A number of approaches have been invested in wireless sensor networking, such as topology directed routing, sensor coverage based TC and network connectivity based TC. Many schemes have proved to be able to provide a better network monitoring and communication performance with prolonged system lifetime. In this survey paper, it provides a view of the studies in the area of WSN with different topology issues. By summarizing previous achievements and analyzing existed problems, we provide some idea within this field and also point out some research direction for the future.

Two-phase data traffic optimization of wireless sensor networks for prolonging network lifetime

Wireless network sensing and control systems are becoming increasingly important in many application domains due to advent of nanotechnology. The size of a wireless sensor network can easily reach hundreds or even thousands of sensor nodes. Since these types of networks usually have limited battery resources, power consumption optimization for prolonging system lifetime of such networks have received a great attention by the researchers in this field in recent years. In this paper, a centralized approach for clustering and data transmission mechanism is proposed that optimizes the power consumption and hence lifetime of the network. The mechanism is comprised of two phases. In the first phase, a mechanism based on a centralized cluster head selection that utilizes information such as nodes residual energies and their locations in the network is proposed in order to select the most appropriate candidates as cluster heads. In the second phase, the concept of a “window size” is introduced where minimization of the number of cluster head changes of a node and consequently maximization of the network lifetime is considered. Simulation results validate that the proposed mechanism does effectively reduce data traffic and therefore increases network lifetime.

QoS aware Routing Scheme for AODV Protocol based on Traffic Priority in Wireless Sensor Networks

The most predicament issue that happens in WSN is Congestion. Congestion causes packet loss, which in turn reduces throughput and energy efficiency. Therefore congestion in WSN’s needs to be controlled for high energyefficiency, to prolong system lifetime, improve fairness, and improve quality of service (QoS) in terms of throughput (or link utilization) and packet loss ratio along with the packet delay. The main focus of this paper is to provide modification in an existing system working with DSR (Dynamic Source Routing).This paper proposes modifications in the protocol of the existing system as AODV (Ad-Hoc on Demand Distance Vector). Proposed protocol provides an efficient way to overcome congestion in wireless cluster sensor network. Proposed algorithm provides priority both on data and location.

Tracking Heterogeneous Dynamic Sensor Node using Fuzzy Logic to Prolong System lifetime in WSN

Mobility of sensor node in WSN is one of the key advantages of wireless over fixed communication system. But to track the sensor node in the heterogeneous network is more challenging and difficulties. In heterogeneous system, generally power consumption is more then homogeneous system. Thus, tracking the location of sensor node is not only one of the challenges for location management but to prolong the system lifetime is also very much important in WSN. Fuzzy application is a new era in communication system. Using fuzzy in heterogeneous system, can easily track the sensor node and consequently prolong the system lifetime. In this paper we introduce a movement pattern learning strategy system to track the node's movement using adaptive fuzzy logic. Every node of different category identified as a cell in a location. Here fuzzy inferences system extracts pattern from the past data records as occupying cell number, date and time of sensor node of particular type. Here in this paper this strategy has been implemented and we propose a mathematical model, that model has been verified with real time data. This mechanism reduces sensor node's location tracking cost and simultaneously decreases call-loss rates. All together overall it prolong the system lifetime Here in this paper we have discussed and proposed a mathematical model to find an optimal solution to optimize energy consumption of the sensor node and to maximize system life time. Through an extensive simulation results show that the proposed model has good performances in the aspects of energy consumption and efficiency of the system network to prolong the system life time.

Ranging Energy Optimization for Robust Sensor Positioning Based on Semidefinite Programming

Sensor positioning is an important task of location-aware wireless sensor networks. In most sensor positioning systems, sensors and beacons need to emit ranging signals to each other. Sensor ranging energy should be low to prolong system lifetime, but sufficiently high to fulfill prescribed accuracy requirements. This motivates us to investigate ranging energy optimization problems. We address ranging energy optimization for an unsynchronized positioning system, which features robust sensor positioning (RSP) in the sense that a specific accuracy requirement is fulfilled within a prescribed service area. We assume a line-of-sight (LOS) channel exists between the sensor and each beacon. The positioning is implemented by time-of-arrival (TOA) based two-way ranging between a sensor and beacons, followed by a location estimation at a central processing unit. To establish a dependency between positioning accuracy and ranging energy, we assume the adopted TOA and location estimators are unbiased and attain the associated Cramer-Rao bound. The accuracy requirement has the same form as the one defined by the Federal Communication Commission (FCC), and we present two constraints with linear-matrix-inequality form for the RSP. Ranging energy optimization problems, as well as a practical algorithm based on semidefinite programming are proposed. The effectiveness of the algorithm is illustrated by numerical experiments.

Innovations Diffusion: A Spatial Sampling Scheme for Distributed Estimation and Detection

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We consider a wireless network with distributed processing capabilities for estimation or detection applications. Due to limited communication resources, the network selects only a subset of sensor measurements for estimation or detection as long as the resulting fidelity is tolerable. We present a distributed sampling scheme based on the concept of <emphasis emphasistype="italic">innovations diffusion</emphasis> to select the sensor nodes. In the proposed scheme, sensor selection is accomplished through local communication and signal processing. In order to conserve energy and prolong system lifetime, the proposed algorithm selects a nearly minimum number of active sensors to ensure a desired fidelity for each working period. Extensive simulations illustrate the effectiveness of the proposed sampling scheme. </para>

Efficient power-consumption-based load-sharing topology control protocol for harsh environments in wireless sensor networks

Energy conservation and network performance are critical issues in wireless sensor networks. The authors present a novel efficient communication topology control protocol, called quorum-based load-sharing control protocol (QLSCP). QLSCP is a quorum-based communication protocol, which chooses appropriate communication nodes, adjusts the service loads of critical nodes and performs adaptive sleep management. QLSCP is suitable for harsh environments without a central control server calculating the locations of sensors, using the factor of the remaining power to build the system topology. The proposed protocol divides the topology operation into topology formation, adjustment and execution phases. The topology formation phase builds the backbone of an enhanced tree. In the topology adjustment phase, the enhanced tree is adjusted by an optimal balance of critical nodes in the backbone. In the topology execution phase, the efficient surplus-energy consuming (ESC) mechanism is proposed to efficiently exhaust the energy of each node. The ESC mechanism is designed to efficiently exhaust the latest remaining electronic power of sensor nodes. Simulation results of QLSCP demonstrate that it efficiently achieves to prolong system lifetime in harsh environments.

Upstream congestion control in wireless sensor networks through cross-layer optimization

Congestion in wireless sensor networks not only causes packet loss, but also leads to excessive energy consumption. Therefore congestion in WSNs needs to be controlled in order to prolong system lifetime. In addition, this is also necessary to improve fairness and provide better quality of service (QoS), which is required by multimedia applications in wireless multimedia sensor networks. In this paper, we propose a novel upstream congestion control protocol for WSNs, called priority-based congestion control protocol (PCCP). Unlike existing work, PCCP innovatively measures congestion degree as the ratio of packet inter-arrival time along over packet service time. PCCP still introduced node priority index to reflect the importance of each sensor node. Based on the introduced congestion degree and node priority index, PCCP utilizes a cross-layer optimization and imposes a hop-by-hop approach to control congestion. We have demonstrated that PCCP achieves efficient congestion control and flexible weighted fairness for both single-path and multi-path routing, as a result this leads to higher energy efficiency and better QoS in terms of both packet loss rate and delay.

Impact‐induced hydrothermal activity on early Mars

We report on numerical modeling results of postimpact cooling of craters with diameters of 30, 100, and 180 km in an early Martian environment, with and without the presence of water. The effects of several variables, such as ground permeability and the presence of a crater lake, were tested. Host rock permeability is the main factor affecting fluid circulation and lifetimes of hydrothermal systems, and several permeability cases were examined for each crater. The absence of a crater lake decreases the amount of circulating water and increases the system lifetime; however, it does not dramatically change the character of the system as long as the ground remains saturated. It was noted that vertical heat transport by water increases the temperature of localized near‐surface regions and can prolong system lifetime, which is defined by maximum near‐surface temperature. However, for very high permeabilities this effect is negated by the overall rapid cooling of the system. System lifetimes, which are defined by near‐surface temperatures and averaged for all permeability cases examined, were 67,000 years for the 30‐km crater, 290,000 years for the 100‐km crater, and 380,000 for the 180‐km crater. Also, an approximation of the thermal evolution of a Hellas‐sized basin suggests potential for hydrothermal activity for ∼10 Myr after the impact. These lifetimes provide ample time for colonization of impact‐induced hydrothermal systems by thermophilic organisms, provided they existed on early Mars. The habitable volume reaches a maximum of 6,000 km38,500 years after the impact in the 180‐km crater model.