A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> With the popularity of portable devices such as personal digital assistants and personal communicators, as well as with increasing awareness of the economic and environmental costs of power consumption by desktop computers, energy efficiency has emerged as an important issue in the design of electronic systems. While power efficient ASIC's with dedicated architectures have addressed the energy efficiency issue for niche applications such as DSP, much of the computation continues to be implemented as software running on programmable processors such as microprocessors, microcontrollers, and programmable DSP's. Not only is this true for general purpose computation on personal computers and workstations, but also for portable devices, application-specific systems etc. In fact, firmware and embedded software executing on RISC and DSP processor cores that are embedded in ASIC's has emerged as a leading implementation methodology for speech coding, modem functionality, video compression, communication protocol processing etc. This paper describes architectural techniques for energy efficient implementation of programmable computation, particularly focussing on the computation needed in portable devices where event-driven user interfaces, communication protocols, and signal processing play a dominant role. Two key approaches described here are predictive system shutdown and extended voltage scaling. Results indicate that a large reduction in power consumption can be achieved over current day solutions with little or no loss in system performance. <s> BIB001 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> We present a system-level power management technique for power saving of event-driven applications. We present a new predictive system shutdown method to exploit sleep mode operations for power saving. We use an exponential-average approach to predict the upcoming idle period. We introduce two mechanisms, prediction-miss correction and pre-wakeup, to improve the hit ratio and to reduce the delay overhead. Experiments on four different event-driven applications show that our proposed method achieves high hit ratios in a wide range of delay overheads, which results in a high degree of power saving with low delay penalties. <s> BIB002 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Dynamic power management schemes (also called policies) can be used to control the power consumption levels of electronic systems, by setting their components in different states, each characterized by a performance level and a power consumption. In this paper, we describe power-managed systems using a finite-state, stochastic model. Furthermore, we show that the fundamental problem of finding an optimal policy which maximizes the average performance level of a system, subject to a constraint on the power consumption, can be formulated as a stochastic optimization problem called policy optimization. Policy optimization can be solved exactly in polynomial time (in the number of states of the model). We implemented a policy optimization tool and tested the quality of the optimal policies on a realistic case study. <s> BIB003 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> This paper introduces a continuous-time, controllable Markov process model of a power-managed system. The system model is composed of the corresponding stochastic models of the service queue and the service provider. The system environment is modeled by a stochastic service request process. The problem of dynamic power management in such a system is formulated as a policy optimization problem and solved using an efficient "policy iteration" algorithm. Compared to previous work on dynamic power management, our formulation allows better modeling of the various system components, the power-managed system as a whole, and its environment. In addition it captures dependencies between the service queue and service provider status. Finally, the resulting power management policy is asynchronous, hence it is more power-efficient and more useful in practice. Experimental results demonstrate the effectiveness of our policy optimization algorithm compared to a number of heuristic (time-out and N-policy) algorithms. <s> BIB004 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Portable systems require long battery lifetime while still delivering high performance. Dynamic power management (DPM) policies trade off the performance for the power consumption at the system level in portable devices. In this work we present the time-indexed SMDP model (TISMDP) that we use to derive optimal policy for DPM in portable systems. TISMDP model is needed to handle the non-exponential user request interarrival times we observed in practice. We use our policy to control power consumption on three different devices: the SmartBadge portable device [18], the Sony Vaio laptop hard disk and WLAN card. Simulation results show large savings for all three devices when using our algorithm. In addition, we measured the power consumption and performance of our algorithm and compared it with other DPM algorithms for laptop hard disk and WLAN card. The algorithm based on our TISMDP model has 1.7 times less power consumption as compared to the default Windows timeout policy for the hard disk and three times less power consumption as compared to the default algorithm for the WLAN card. <s> BIB005 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited). In this paper, we survey several approaches to system-level dynamic power management. We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption. We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components. <s> BIB006 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Energy-efficient design of battery-powered systems demands optimizations in both hardware and software. We present a modular approach for enhancing instruction level simulators with cycle-accurate simulation of energy dissipation in embedded systems. Our methodology has tightly coupled component models thus making our approach more accurate. Performance and energy computed by our simulator are within a 5% tolerance of hardware measurements on the SmartBadge. We show how the simulation methodology can be used for hardware design exploration aimed at enhancing the SmartBadge with real-time MPEG video feature. In addition, we present a profiler that relates energy consumption to the source code. Using the profiler we can quickly and easily redesign the MP3 audio decoder software to run in real time on the SmartBadge with low energy consumption. Performance increase of 92% and energy consumption decrease of 77% over the original executable specification have been achieved. <s> BIB007 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Portable systems require long battery lifetime while still delivering high performance. Dynamic voltage scaling (DVS) algorithms reduce energy consumption by changing processor speed and voltage at run-time depending on the needs of the applications running. Dynamic power management (DPM) policies trade off the performance for the power consumption by selectively placing components into low-power states. In this work we extend the DPM model presented in [2, 3] with a DVS algorithm, thus enabling larger power savings. We test our approach on MPEG video and MP3 audio algorithms running on the SmartBadge portable device [1]. Our results show savings of a factor of three in energy consumption for combined DVS and DPM approaches. <s> BIB008 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Dynamic power management (DPM) is a design methodology aimed at reducing power consumption of electronic systems by performing selective shutdown of idle system resources. The effectiveness of a power management scheme depends critically on accurate modeling of service requests and on computation of the control policy. In this work, we present an online adaptive DPM scheme for systems that can be modeled as finite-state Markov chains. Online adaptation is required to deal with initially unknown or nonstationary workloads, which are very common in real-life systems. Our approach moves from exact policy optimization techniques in a known and stationary stochastic environment and extends optimum stationary control policies to handle the unknown and nonstationary stochastic environment for practical applications. We introduce two workload learning techniques based on sliding windows and study their properties. Furthermore, a two-dimensional interpolation technique is introduced to obtain adaptive policies from a precomputed look-up table of optimum stationary policies. The effectiveness of our approach is demonstrated by a complete DPM implementation on a laptop computer with a power-manageable hard disk that compares very favorably with existing DPM schemes. <s> BIB009 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> With power dissipation becoming an increasingly vexingproblem across many classes of computer systems, measuringpower dissipation of real, running systems has becomecrucial for hardware and software system research and design.Live power measurements are imperative for studiesrequiring execution times too long for simulation, such asthermal analysis. Furthermore, as processors become morecomplex and include a host of aggressive dynamic powermanagement techniques, per-component estimates of powerdissipation have become both more challenging as well asmore important.In this paper we describe our technique for a coordinatedmeasurement approach that combines real totalpower measurement with performance-counter-based, per-unitpower estimation. The resulting tool offers live totalpower measurements for Intel Pentium 4 processors, andalso provides power breakdowns for 22 of the major CPUsubunits over minutes of SPEC2000 and desktop workloadexecution. As an example application, we use the generatedcomponent power breakdowns to identify program powerphase behavior. Overall, this paper demonstrates a processorpower measurement and estimation methodology andalso gives experiences and empirical application resultsthat can provide a basis for future power-aware research. <s> BIB010 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> An effective method of dynamic power management,based on wavelet and Kalman analysis,is proposed to extend life of wireless sensor network by making economical use of energy.Wavelet,Kalman filter and AR are used to forecast the time when next event will happen,and the state in which wireless sensor should lie is decided based on the time.Total energy consumption is decreased under fraction of event missed as low as posible.Experimental results show the method is effective. <s> BIB011 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Energy is a limited resource in wireless sensor networks. In fact, the reduction of power consumption is crucial to increase the lifetime of low power sensor networks. Several approaches on dynamic power management have contributed to reduce the power consumption, but few of them consider the application constraints to optimize it. In this paper, we propose a new application-driven power management approach, where we model the sensor node operation and the application constraints using the hybrid automata framework. We also model a real sensor network application for fire detection and we show the performance of our approach in terms of energy drop, comparing it to an Ideal Model and a Naive approach. <s> BIB012 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Wireless sensor networks play a key role in monitoring remote or inhospitable physical environments. One of the most important constraints is the energy efficiency problem. Power conservation and power management must be taken into account at all levels of the sensor networks system hierarchy. DPM (dynamic power management) technology has been widely used in sensor networks. In this paper, we propose a new energy-efficient DPM, which is a modified sleep state policy developed by Simunic and Chdrakasan (2001) and combined with optimal geographical density control (OGDC) by Zhang and Hou (2004) to keep a minimal number of sensor nodes in the active mode in wireless sensor networks. Implementing dynamic power management with considering the battery status, probability of event generation and OGDC will reduce the energy consumption and prolong the whole lifetime of the sensor networks <s> BIB013 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Wireless sensor networks operating on limited energy resources need to be power efficient to extend the system lifetime. This is especially challenging for video sensor networks due to the large volumes of data they need to process in short periods of time. Towards this end, this paper proposes two coordinated power management policies for video sensor networks. These policies are scalable as the system grows and flexible to video parameters and network characteristics. In addition to simulation results, our prototype demonstrates the feasibility of implementing these policies. Finally, the analytical framework we provide gives an upper bound for the achievable sleep fraction and insight into how adjusting select parameters will affect the performance of the power management policies. <s> BIB014 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Energy is a limited resource in wireless sensor networks. The reduction of energy consumption is crucial to prolong the lifetime of wireless sensor networks. Dynamic power management (DPM), which is to reduce power dissipation by putting the sensor node into different states, should be carefully taken into account in wireless sensor networks. In this paper, a new method of DPM is proposed. In this method, the next eventpsilas time which is a non-stationary series is predicted as accurate as possible by wavelet neural networks. Nodes in deeper sleep states consume lower energy while asleep, but incur a longer delay and higher energy cost to awaken. So the nodes state is decided to move through the predictable time associated with the threshold time and residual power. The simulation results show that the energy consumption is significantly reduced and the whole lifetime of the wireless sensor networks is greatly prolonged with the proposed method. <s> BIB015 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> In a wireless sensor network (WSN), nodes are power constrained. As a consequence, protocols must be energy efficient to prolong the network lifetime. Network simulators are usually used to develop and analyze WSN protocols. However, most simulators employ the linear battery model to simplify the simulation process, leading to wrong conclusions about the network lifetime, because this model does not take into account the battery capacity recovery effect. In this work we consider a more realistic battery model (i.e., Rakhmatov-Vrudhula battery model) when applying the dynamic power management with scheduled switching modes (DPM-SSM) approach to a blind flooding protocol (i.e., FLOOD). The battery model is implemented in the network simulator NS2, and the FLOOD protocol is modified to use the DPM-SSM technique. Extensive simulation results show that the FLOOD protocol can still achieve good performance results while saving up to 30% on energy consumption. <s> BIB016 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Power consumption of computing systems has become an important issue in industrial design areas. Dynamic power management (DPM) is an effective way to low the system power. This paper presents a new expert-based heuristic algorithm with fine-grained delay constraints (FDC-DPM), which selects the best policy from a set of well-known policies dynamically. Three rules are presented for FDC-DPM to make a choice. FDC-DPM gives a flexible way to make a good tradeoff between energy consumption and fine-grained delay constraint. Compared with the machine learning method in [2], FDC-DPM is simpler and it can achieve comparable energy savings with the same delay constraints under different workloads. <s> BIB017 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Limited energy resources in wireless sensor networks (WSNs) call for a comprehensive cross-layer analysis of energy consumption in a multi-hop network. In this paper, we provide a stochastic analysis of the energy consumption in a random network environment. Accordingly, a comprehensive cross-layer analysis framework, which employs a stochastic queueing model in realistic channel environments, is developed. This framework accurately predicts the distribution of energy consumption for nodes in WSNs during a given time period. We show that when the time duration is long, the energy consumption asymptotically approaches a Normal distribution. Using the distribution of energy consumption, the distribution of node lifetime is also investigated. With the help of this probabilistic model, a case study with an anycast protocol is conducted to show how the developed framework can analytically predict the distribution of energy consumption and lifetime. Comprehensive simulations and testbed experiments are provided to validate the developed model. The cross-layer framework is also used to identify relationships between the distribution of energy consumption and network parameters, such as network density, duty cycle, and traffic rate. To the best of our knowledge, this is the first work to investigate probabilistic distribution of energy consumption in WSNs. <s> BIB018 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> This paper studies the sleep scheduling problem for large scale wireless sensor networks (WSNs), which have hundreds to thousands of sensors. Sensors are extremely sensitive to energy consumption because they are powered by batteries. In this paper, we propose stochastic sleep scheduling, a generic duty- cycling scheduling method based on stochastic theory. It lets each sensor fall asleep based on certain stochastic processes. This strategy requires no clock synchronization, and a little coordination amongst sensors. In order to characterize this design, we analyze its end-to-end communication delay and energy consumption using stochastic methods. According to the analysis, this method actually has a reasonably small delay due to the redundancy in WSNs. Simulation studies are used to verify the analytical results. We also apply stochastic sleep scheduling to S-MAC, a state-of- the-art duty-cycled sensor MAC protocol. Simulation results verify the energy consumption of the new MAC protocol. <s> BIB019 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> In recent years, many approaches and techniques have been explored for the optimization of energy usage in Wireless Sensor Networks (WSN). It is well recognized that a proper energy consumption model is the foundation for developing and evaluating a power management scheme in WSN. In this paper, we propose a new complete information Markov Decision Process (MDP) model to characterize sensors energy levels. We also propose and compare several centralized power control policies to select the more efficient policy that optimizes throughput and energy consumption. <s> BIB020 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> We investigate the Kalman filtering problem via wireless sensor ::: networks over fading channels. When part or all of the observation ::: measurements are lost in a random fashion, we obtain the conclusion that the ::: packet dropout probabilities depend upon the time-varying channel gains and ::: the transmission power levels used by the sensors. We develop a satu- rated power ::: controller which trades off sensor energy expenditure versus state estimation ::: accuracy. The latter is measured by the expected value of ::: the future covariance matrices provided by the associated time-varying Kalman filter. ::: We study the statistical convergence properties of the error covariance ::: matrix and pointed out the existence of the admissible packet arrival ::: probability bound. <s> BIB021 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Focused on energy efficiency issues under tracking targets within wireless sensor networks (WSNs), a new dynamic power management (DPM) method for tracking distributed targets is proposed in this paper combining with the network energy consumption model and wake-up mechanism. With precedent location information of maneuvering target, the algorithm involved both cancelling noise by wavelet filter and predicting target state by autoregressive transformation is introduced to awaken wireless sensor nodes so that their sleep time is prolonged and energy consumption is reduced. According to the current location of maneuvering target, related nodes in an appointed cluster of WSNs constitute a distributed dynamic tracking unit, and the cluster head is responsible for collecting the measurement information from the nodes in the tracking unit. Simulation results show that: The dynamic energy optimization method and tracking algorithms presented in this paper can effectively in reducing the energy cost of the node to extend the life of node and network, which are fully applicable to the battlefield maneuvering target tracking on the ground. <s> BIB022 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Wireless sensor networks (WSN) are used extensively to monitor a wide range of physical and environmental parameters. The collected data can then be used to make various decisions and control actions. The individual sensors nodes of a WSN typically consist of a processor, sensors, and transceiver. As the monitoring device of WSN is increasingly mobile, the need for efficient power management (PM) techniques is becoming crucial in order to extend the life of the battery. <s> BIB023 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Dynamic power management <s> Abstract We analyze an N -policy of a discrete time Geo/G/1 queue with disasters. We obtain the probability generating functions of the queue length, the sojourn time, and regeneration cycles such as the idle period and the busy period. We apply the queue to a power saving scheme in wireless sensor networks under unreliable network connections where data packets are lost by external attacks or shocks. We present various numerical experiments for application to power consumption control in wireless sensor networks. We investigate the characteristics of the optimal N -policy that minimizes power consumption and derive practical insights on the operation of the N -policy in wireless sensor networks. <s> BIB024
Some important factors that affect the sensor node life are the power consumption in different modes of operation, switching cost to power down modes and time duration of the processor in each mode  . A class of major dynamic power management policies such as greedy schemes, time-out schemes, predictive schemes, stochastic schemes, dynamic scaling schemes, switching and scheduling schemes respectively have been presented in Figure 3 . The greedy (always ON) and timeout policies are heuristic policies. The timeout scheme shuts down a power manageable component after a fixed inactive time (T). Time out policy outperforms only when the idling duration is very large, but it costs for waiting period dissipation BIB014 . Predictive scheme is another type of heuristic scheme. It first predicts the future arrival time and nature, then directs the system for next state and makes it idle if the predicted time duration is more than break-even time BIB001 BIB002 . Thus, predicted time determines the switching of the components to the low power state. The authors have introduced adaptive filters for workload prediction to make the state changes of the power manageable devices on the basis of the predicted time. They have used workload traces, taking different processors for different time slots in a daytime. A performance metric is defined for efficiency of observations. A workload prediction scheme does not deal with a generalized system model and they are not suitable for providing an accurate tradeoff between power saving and performance reduction. Therefore, stochastic techniques are required to mitigate the limitations of predictive techniques. The stochastic policies are better in terms of the power delay trade-off than heuristic policies BIB015 . Static approach has a priori knowledge of different power manageable component states and stationary workload. Adaptive approach accounts for non-stationary nature of the workloads and uses policy pre-characterization, parameter learning and policy interpolation in taking the determination for power down states of parts BIB017 . Adaptive Markov Control Process for non-stationary workloads are online adaptation DPM schemes for non-stationary workloads or real-life systems BIB009 . In discrete time Markov decision policy (DTMDP), the power manager takes the decision for next state at discrete time intervals regardless of the input nature. A discrete time, finite state Markov decision model for power-managed systems are proposed for giving the exact solution using linear optimization problem in polynomial time BIB003 . This discrete time Markov model takes decision for the next state at every defined discrete interval of time. Therefore, it is not suitable for continuous monitoring or event driven processes. In discrete finite horizon Markov decision process (MDP), the sensor's battery discharge process as an MDP is modelled stochastically and characterized the optimal transmission strategy BIB020 . The scheme can be generalized for the decentralized case with the stochastic game theory technique. Generally, network lifetime analysis models in the literature assume average node power consumption. A discrete time Markov model for a node with a Bernoulli distribution of arrival process and phase-type distribution of service time has been validated for single and multi-hop network BIB018 . Observations tell that, these schemes have limitations in terms of architectural modification. A continuous time Markov decision policy (CTMDP) is event driven and the decision taken can change only at event occurrence. A wrong decision can increase energy overhead than energy saving. The continuous time Markov models BIB004 need all the stochastic processes as exponential processes. These models overcome the problems in discrete time methods. Continuous time modelling is complex and does not give good results for real time systems. Therefore, semi-Markov model reduces the need of strict exponential distribution. Time indexed semi Markov Decision Process (TISMDP) combines the advantages of event driven Semi Markov Decision process model with discrete time Markov decision process (DTMDP) model, but limited to non-exponential arrival distribution coupled with non-uniform transition distribution BIB005 . The multiple nonexponential processes require a more flexible model and the Time-indexed semi-Markov models can be one of its solution. A model with multiple non-exponential processes increases the model as well as system complexity. Nevertheless, the non-stationary nature of work loads can save more power because of its adaptive nature scheme BIB007 . The N-policy gives accumulation of N events and then processing to make the system more energy efficient. Other techniques such as dynamic scaling BIB008 , decreases the power consumption of the system components by operating them at different low voltage and frequency levels during active phase. The switching and scheduling schemes help in power saving by controlling idle periods of the system  . The sleep state policy also improves energy efficiency to the greatest extent BIB013 . The anticipated workload of the different subsystems and the estimation of the task arrival rate at the scheduler are crucial preconditions for a DPM technique. Various cases of filters as estimation techniques are investigated BIB011 BIB021 . These filters are predicting based which trace the past N number of tasks at scheduler and define the work load of the microcontroller for the next observation point. In a more precise estimation process, the workload of every hardware component is first observed and then the future load on the sensor node can be determined. This is particularly useful for selective switching. The scheduler can give the appropriate workload information regarding input events and event counter counts frequency and time-period of hardware employed by each task BIB010 . A stochastic sleep scheduling (SSS) with and without adaptive listening scheme is proposed and proved better in terms of energy consumption and delay reduction at the network level for S-MAC. This scheme is applicable for high node scalability and stochastic sensor sleep period BIB019 . The energy efficiency can be improved in the routing layer to enhance the network lifetime. Several energy efficient routing algorithms for wireless sensor network are discussed which assume arrival of the input as a stochastic process  . The authors have shown improvement in wireless sensor network lifetime introducing dynamic power management policy in broadband routing BIB016 , target tracking BIB022 and other applications BIB012 BIB023 BIB024  . It is remarked that instead of energy efficiency improvement in routing protocols, power management can also improve energy efficiency. An OS-directed, event based, predictive power management technique BIB006 is proposed for single node energy efficiency improvement. PowerTOSSIM  , mTOSSIM  and eSENSE  are easily available platform for sensor node life estimation.