A review on stochastic approach for dynamic power management in wireless sensor networks <s> Introduction <s> This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems. The survey concludes with a discussion of possible future directions. <s> BIB001 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Introduction <s> Ultra-Low Power Wireless Technologies for Sensor Networks is written for academic and professional researchers designing communication systems for pervasive and low power applications. The main emphasis of the book is on design techniques for low power, highly integrated transceivers. Instead of presenting a single design perspective, this book presents the design philosophies from three diverse research groups, providing three completely different strategies for achieving similar goals. The following transceiver styles are represented: MEMS-assisted super-regenerative transceiver architecture, Ultra-wideband transceiver architecture, Low-IF CMOS transceiver architecture. The design of RF transceiver circuits and architectures is historically (and notoriously) heuristic. By presenting diverse perspectives on transceiver design, Ultra-Low Power Wireless Technologies for Sensor Networks prepares the reader for the countless design decisions they will be making in their own designs. Ultra-Low Power Wireless Technologies for Sensor Networks also includes invited chapters: "Ultra-Low Power Radio in a Package Using Ultra-Wideband Technology" by Julien Ryckaert and Steven Sanders from Interuniversity Microelectronics Centre and "Low Energy Wireless Communication" by Ben Cook and Kris Pister from the University of California, Berkeley. <s> BIB002 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Introduction <s> Recent technological advances in microelectronics and nano-systems technologies have made it feasible to equip wireless sensor nodes with small low-cost cameras to capture and transmit video. Wireless video sensor networks are gaining popularity due to numerous potential applications such as video surveillance, environmental and habitat monitoring, and so on. However, due to the limited battery available in wireless video sensor nodes, provisioning of QoS in such a network is a challenging task. We provide a survey on the major issues related to QoS provisioning in wireless video sensor networks and possible solution approaches. A dynamic power management framework is proposed for a wireless video sensor node to improve energy saving performance so that the lifetime of the sensor node can be increased. This framework considers the video traffic arrival process in the sensor node, the sleep and wakeup processes in the camera and wireless transceiver electronics, the queue status, and the wireless channel condition. Performance analysis results show that the proposed mechanism can achieve considerable energy saving in a sensor node while providing a target level of QoS performance. <s> BIB003 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Introduction <s> Target tracking is an important application of newly developed wireless sensor networks (WSN). Much work has been done on this topic using a plane network architecture. We propose a scheme, namely hierarchical prediction strategy (HPS), for target prediction in hierarchical sensor networks. The network is divided into clusters, which are composed of one cluster-head and many normal nodes, by Voronoi division. For an existing target, cluster-heads only selectively activate nearby sensor nodes to perform tracking. Moreover, Recursive Least Square technique is used to predict the target trajectory and help activate next-round sensor nodes. Extended simulations show the properties of the proposed network architecture and the efficiency of the prediction scheme. <s> BIB004 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Introduction <s> Vehicle-based sensors can be used for traffic monitoring. These sensors are usually set with long sampling intervals to save communication costs and to avoid network congestion. In this paper, we are interested in understanding the traffic-monitoring performance that we can expect from such vehicle-based mobile sensor networks, despite the incomplete information provided. This is a fundamental problem to be addressed. A performance evaluation has been carried out in Shanghai, China, by utilizing the vehicle-based sensors installed in about 4000 taxies. Two types of traffic status-estimation algorithms, i.e., the link-based and the vehicle-based, are introduced and analyzed. The results show that estimations of the traffic status based on these imperfect data are reasonably accurate. Therefore, the feasibility of such an application is demonstrated. <s> BIB005 </s> A review on stochastic approach for dynamic power management in wireless sensor networks <s> Introduction <s> The paper deals with the monitoring system based on Zigbee wireless sensor network technique in details. The system adopts single-chipped microprocessor ZIC2410 radio frequency chip. By the upper computer software and programs of various physiological data acquisition, the physiological data of warded people, such as the pulse, blood pressure, blood oxygen saturation and electrocardiogram, are gathered by the PC software in real time, and then the collected data are transmitted to the remote medical monitoring center through the wireless sensor network. The data information of each node, including the location and health status of the warded people, can be displayed by the monitoring center software. It makes timely and effective medical guidance possible. <s> BIB006
A wireless sensor network (WSN) consists of mostly tiny, resource-constrained, selforganized, low power, low cost and simple sensor nodes which are organized in a cooperative manner. The sensor nodes can sense, communicate and control the surrounding environment. They can provide interaction between the users (human being), physical environment and embedded computers in order to perform some specific operation. They follow IEEE 802.15.4 as basis standard for lower layers (physical and medium access control) and other standards like ZigBee, ISA100.11a etc. as upper layer (application, routing) protocols. WSNs have wide application bandwidth emerging the areas of agricultural, medical, military, environmental, industrial control, monitoring, civil and mechanical, etc. Target tracking and continuous monitoring in WSNs are important energy hungry problems with a large spectrum of applications, such as surveillance BIB001 , natural disaster relief BIB004 , traffic monitoring BIB005 and pursuit evasion games, and so forth. A wide range of wireless sensor network applications helps in transforming human lives in various aspects of intelligent living technology. These attractive applications have generated a great interest amongst industrialists and researchers BIB006 . Several multifunctional sensor nodes in specified area work for any specified tasks continuously without any internal interruption. Collectively sensor nodes connect with the pan-coordinator or sink nodes through the gateway and helps in forwarding the information to users through the network as depicted in Figure 1 . Usually, a sensor node receives then processes and route the data to the next level nodes. The power manager as a part of micro-operating system (μ. OS), controls and manage the on/off and power down states of the components of sensor node (e.g. Embedded processor, memory, RF transceiver) to enable power management. DPM is referred to as an operating system-level algorithm/technique which is used to control the power and performance parameters of a low power system, by increasing idle time slots of its devices and switching the devices to low power mode. Broadly, the ultra-low-power components of the sensor node make the node energy efficient by implementing dynamic power management policies to achieve longer lifetime BIB002 . The total power dissipation of sensor node can be modelled with static and dynamic power dissipation. Static power consumption is the result of leakage current flow in ultra-low power components of sensor node that can be reduced at design time by using static techniques. The static power can be reduced using synthesis and compilation at design time, whereas the dominate part such as dynamic power is the result of switching power consumption that can be reduced by selectively switching or shutting down hardware components on a sensor node. The switching or dynamic power consumption decreases quadratically with supply voltage (i.e. power gating) and linearly with the reduction in the working frequency (i.e. clock gating). Further, more the power modes of a component more is the saving of energy in a sensor node. To maximize the lifetime of the battery, the power consumption of sensor node components should reduce. Hence, the power management problem minimizes switching power or the power consumption of components. Switching power is defined as the power required for the component to change over from low power mode to high power mode and vice versa. The stochastic modelling is used for studying DPM in a wireless sensor network for power management  BIB003 . Emphasis on the system model for power management shows the basic requirements for sensor node components and demands for power management. The rest of this paper is organized as follows. Next section illustrates the abstract system model for dynamic power management and the system component requirements are identified. The section 3 classifies the major policies such as greedy schemes, timeout schemes, predictive schemes, stochastic schemes, dynamic scaling schemes, switching and scheduling schemes with their pros and cons. A brief discussion on other state of the art on discrete time and continuous time techniques is also presented. In section 4, the power consumption issues in continuous time Markov model and semi Markov model have been analyzed in detailed case studies 1 & 2 respectively. It also provides some background about existing stochastic models for dynamic power management as described in the literature, aiming at their stiff and weak points in wireless sensor network applications  . Apart from this, it highlights the important key areas, challenges and lacuna in managing the power dynamically using power aware stochastic modelling techniques, which gives future direction for research in the WSN area. Finally, section 5 concludes the entire paper and states about the Author's future interest and investigation.