A review on stochastic approach for dynamic power management in wireless sensor networks <s> Basic power management system model <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> BIB001 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Basic power management system model <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> BIB002 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Basic power management system model <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> BIB003 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Basic power management system model <s> Dynamic power management aims at extending battery life by switching devices to lower-power modes when there is a reduced demand for service. Static power management strategies can lead to poor performance or unnecessary power consumption when there are wide variations in the rate of requests for service. This paper presents a hierarchical scheme for adaptive dynamic power management (DPM) under nonstationary service requests. As the main theoretical contribution, we model the nonstationary request process as a Markov-modulated process with a collection of modes, each corresponding to a particular stationary request process. Optimal DPM policies are precalculated offline for selected modes using standard algorithms available for stationary Markov decision processes (MDPs). The power manager then switches online among these policies to accommodate the stochastic mode-switching request dynamics using an adaptive algorithm to determine the optimal switching rule based on the observed sample path. As a target application, we present simulations of hierarchical DPM for hard disk drives where the read/write request arrivals are modeled as a Markov-modulated Poisson process. Simulation results show that the power consumption of our approach under highly nonstationary request arrivals is less than that of a previously proposed heuristic approach and is even comparable to that of the optimal policy under stationary Poisson request process with the same arrival rate as the average arrival rate of the nonstationary request process. <s> BIB004 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Basic power management system model <s> Energy constraint is a critical problem for practical applications in wireless sensor networks.Focused on the energy efficiency problem of wireless sensor networks,a dynamic power optimization method is proposed with target prediction. Particle filter is introduced to predict target state,which is adopted to awaken wireless sensor nodes so that their sleep time is prolonged.With the distributed computing capability of nodes,distributed genetic algorithm and simulated anneal is proposed to optimize the energy consumption of measurement. Considering the application of target tracking,target position prediction,node sleep scheduling and optimal sensing node selection are implemented.Moreover,a routing scheme of for- warding nodes is presented to achieve extra energy conserva- tion.Experimental results of target tracking verified that en- ergy-efficiency is enhanced by dynamic power optimization with target prediction in wireless sensor networks. <s> BIB005
The operating system level dynamic power management schemes take the knowledge of application requirement and present states of power manageable devices and then switch the devices into different power modes for the next state. The unused components turn off if idle period is large. Today, DPM trends a Markov process in which stochastic models can analyze the power and performance in the system. The system model consists of a power manager (PM), a service provider (processor), a service requestor (input from sensors) and a service request queue (memory) as shown in Figure 2 . The activity of different devices in WSN may be in discrete time, continuous time and event driven also that affect the battery lifetime. The temporal event behavior over the entire sensing region, R, can be assumed as a Poisson process with an average event rate. The power manager tracks the states of service provider, service requester, service queue and gives command signals to control the power modes of the system and power management components those are idle and waiting for the next command. The power manager can be a component of the software or hardware module. We can implement a suitable stochastic modelling approach at the Operating System (OS) level to achieve better energy efficiency at lower cost. Stochastic scheme or Markov Decision Policy (MDP) models a system and its workload BIB002 BIB005 . Here, consider the workload is the arrival of the request and the information associated with it. Existing analysis and modelling of DPM schemes BIB001 BIB004 have been accepted with the length of the queue of waiting tasks as a parameter of latency measurement. However, the deadline of task execution is important latency or performance parameter in Markov models. Dynamic power management obtains the correct power switching time after observing the workload and system component states at a performance cost BIB003 . Therefore, the basic need of DPM is embedded operating system (micro-operating system in case of sensor node) the power management components which support different power states, e.g. active, sleep and idle. The switching between different power modes increases latency and degrades performance, which results the power and latency trade-offs. The authors presented the basic idea behind dynamic power management and the resulting power and performance trade-off space in wireless sensor network. Ideally, a power manageable component with more than two power down states and its switching to deep sleep state can reduce more power consumption. Moreover, switching from power down to power up states and vice versa, require finite transition time and overhead of storing processor state before turning the device into low power mode or off. Deeper sleep state requires larger waking up time and which in turn increases the latency. Hence, there is a great need of an optimized DPM scheme that can reduce power consumption with performance constraints and gain performance with power constraints for power source limited applications in wireless sensor networks.