Dynamic Power Management in Wireless Sensor Network
The specific objectives are to:
- Design a dynamic power management technique that considers the applications constraints to exploit active and idle
- Simulate a wireless sensor network using a protocol that can distribute the energy consumption across all nodes equally.
This chapter reviews several important concepts that are related to the work presented in this research work. These include wireless sensor network, simulation of WSNs, sensor network global view and requirement, application of WSNs, power management in sensor network, battery model, comparison of features for WSNs and wireless ad hoc network (WAHNs), Wireless LAN and IEEE 802.11b Standard, OPNET simulator, and finally on this chapter literatures related to our work were reviewed in order to have better understanding of the concept.
Overview of Wireless Sensor Network
Emergence of the concept of multi-hop ad-hoc wireless networks, low-power electronics, low-power, short-range wireless communication radios, and intelligent sensors is considered the major technological enabler for deployment of sensor networks (SNs). The goal of this survey is to identify key architectural and design issues related to sensor networks, critically evaluate the proposed solutions, and outline the most challenging research directions. The evaluation has three levels of abstraction:
- Individual components on SN nodes (processor, communication, storage, sensors and/or actuators, and power supply)
- Node level
- Distributed networked system level
Special emphasis is on architecture, system software, to some extent, and new challenges related to using new types of components in networked systems. The evaluation is guided by anticipated technology trends and current and future applications. The main conclusion of the analysis is that the architectural and synthesis emphasis will be shifted from computation and, to some extent communication, components to sensors, actuators, and different types of sensors and applications that require distinctly different architectures at all three levels of abstraction (Sadler, 2005)
What is a Sensor?
A general definition of a sensor is ―a device that produces measurable response to a change in a physical or chemical condition.‖ More specifically, a sensor is “a device that responds to a stimulus, such as heat, light, or pressure, and generates a signal that can be measured or interpreted.” The Sensor Network community often (but not always) defines a sensor node as a small, wireless device, capable of responding to one or several stimuli, processing the data and transmitting the information over a short distance using a radio link. Sensor nodes employ electronic circuits that minimize power consumption. Typically sensors are thought of as measuring light, sound and temperature. However, sensors can measure other variables, such as electromagnetic fields or vibrations. Sensors transmit values wirelessly to one or several sinks (Singh et al., 2010)
A Sensor Network is a wireless, ad-hoc network, made of a large number (hundreds or thousands) of nodes, whose positions occur randomly. The OSI model and the classic layered view of communication networks may or may not apply directly to sensor networks. Other models of sensor network communications include a protocol stack model that includes physical, medium access control, network, transport and application layers as well as power management, mobility management and task management planes. However, no model is used universally (Singh et al., 2010).
What are Wireless Sensor Networks?
Wireless sensor networks consist of distributed, wirelessly enabled embedded devices capable of employing a variety of electronic sensors. Each node in a wireless sensor network is equipped with one or more sensors in addition to a microcontroller, wireless transceiver, and energy source. The microcontroller functions with the electronic sensors as well as the transceiver to form an efficient system for relaying small amounts of important data with minimal power consumption (Culler and Clark, 2004).
The most attractive feature of wireless sensor network is their autonomy. When deployed in the field, the microprocessor automatically initializes communication with every other node in range, creating an ad-hoc mesh network for relaying information to and from the gateway node. This negates the need for costly and ungainly wiring between nodes, instead relying on the flexibility of mesh networking algorithms to transport information from node to node. This allows nodes to be deployed in almost any location. Coupled with the almost limitless supply of available sensor modules, the flexibility offered by wireless sensor networks offers much potential for application-specific solutions. The diagram in Figure 2.1 and 2.2 shows a wireless sensor and MicroStrain’s line of smart, wireless sensor nodes (Culler and Clark, 2004).
SYSTEM ANALYSIS AND DESIGN
This chapter gives us a break down of the simulation tool, energy consumption in WSNs, power management, source of power consumption, dynamic power optimization at the node level and energy consumption of the node.
There are many simulators that have been created. However, a lot of them were written for specific purposes testing just one network component or protocol.
OPNET IT Guru is an application that allows you to model a wide variety of networks and situations. The application can be used to test the performance of a modelled network configured with predefined parameters. After model construction, a simulation can be run to gather user-defined statistics. Results are presented as graphs for easy evaluation. The OPNET Modeller software package is among the most popular and most comprehensive tools available in the market for modelling new communication technologies and protocols. OPNET Modeller includes a vast model library of communications devices, communication mediums, and cutting-edge protocols. OPNET simulator is selected for this research work (Kottapalli, 2003)
Energy Consumption in WSNS
As a microelectronic device, the main task of a sensor node is to detect phenomena, carry out data processing timely and locally and transmit or receive data. A typical sensor node is generally composed of four components; a power supply unit, a sensing unit, a computing unit and a communicating unit.
The sensing node is powered by a limited battery, which is impossible to replace or recharge in most application scenarios. Except for the power unit, all other components will consume energy when fulfilling their task. Extensive study and analysis of energy consumption in WSNs are available (Najm, 1994).
SIMULATION, RESULT AND DISCUSSION
With the development of microelectronic mechanical system (MEMS), wireless networking, and embedded microprocessor, wireless sensor networks (WSNs) have been deployed in environmental, military, and commercial areas. WSNs consist of thousands of small nodes capable of sensing, processing, and communicating. In many sensor network applications, each node periodically samples its sensors and transmits the data (in multi-hop fashion) to the base station, where it will be further delivered to the end users through long-haul links (e.g., the Internet) (Monks et al., 2003).
There are several sources of power wastage in the MAC layer, such as collision, overhearing, protocol overhead, and idle listening. Among those for many applications, idle listening is often the major source of energy inefficiency. MAC protocols for WSNs nodes save energy by going to sleep if they do not have data to sense, receive, or transmit. When sensor nodes sleep, they turn off the microcontroller and the radio. However, the existing results are based on the protocol specific assumptions (e.g piggybacking) (Monks et al., 2003).
The energy consumption of ad hoc nodes taking into account the interactions of the IEEE 802.11 MAC protocol and the packet forwarding performed on the ad hoc multi-hop networks. This is done based on the fraction of time that the interfaces spend in each operational state and on the capacity of the ad hoc networks. Ad hoc networks assume the use of IEEE 802.11 wireless LAN interfaces. Nevertheless, IEEE 802.11 interfaces operating in ad hoc mode have some peculiarities that are frequently disregarded (Monks et al., 2003).
SUMMARY, CONCLUSION AND RECOMMENDATION
The objective of this project was to use a dynamic power management technique that considers the applications constraints to exploit active and idle states. We also simulated a wire sensor network using a protocol that can distribute the energy consumption across all nodes equally. We model Energy Consumption of the Nodes by calculating the average power () consumed by the interface and we
calculated the node lifetime, which represents the time before the energy of the node reaches zero. From overall analysis of the results obtained after running the regression analysis, power management could be represented by:
Pm = C0 + C1Qsl + C2Qid + C3Qrx + C4Qtx . . . (5.1)
Generally, lifetime of wireless sensor node is correlated with the battery current usage profile. By being able to estimate the power consumption of the sensor nodes, applications and routing protocols are able to make informed decisions that increase the lifetime of the sensor network. As most WSN nodes are battery powered, their lifetime is highly dependent on their power consumption. This research work studied and analyzed the effect of power management in 802.11b wireless LAN in ad- hoc mode. From the results generated, it is easy to compute the power management (Pm).
A critical factor of the wireless sensor network operation is the energy consumption of the portable devices. Typically, wireless nodes are battery- powered and the capacity of these batteries is limited by the weight and volume restrictions of the equipment. Consequently, it is recommended to reduce the energy consumption of the nodes in the ad hoc network. Moreover, in multi hop ad hoc networks each node may act as a router. Thus, the failure of a node due to energy exhaustion may impact the performance of the whole network.
In order to improve on this research, some areas below ought to be explored.
- The scope of this thesis could be improved to cover the evaluated, performance of two simple time synchronization algorithms suitable for wireless sensor
- This thesis could be expanded to reduce the time taken to send message and receive response from the WSNs and Security protocol for wireless sensor network
- Integration of cryptographic primitives into the attributes of these WLAN models will greatly ease the evaluation of the impact of security mechanisms on the performance and energy consumption characteristics of the network as well as the entire system.
- Akyildiz, W. Su, Sankarasubramaniam, Y. and Cayirci, E. (2002) ―Wireless sensor networks: A survey,‖ IEEE Computer, vol. 38, pp. 393–422.
- Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, (2002) ―A survey on sensor networks,‖ IEEE Communication Magazine, vol. 40, pp. 102–116.
- Alonso, J. A. Dunkels, and T. Voigt, (2004) ―Bounds on the energy consumption of routings in wireless sensor networks,‖ in Proc. of WIOPT.
- Anna H. and Anastasi (2006), Wireless sensor Network Design John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England pp 44-59
- Annapolis Micro Systems. (2010), http://www.annapmicro.com/products.html Asada, G.(1998) ―Wireless integrated network sensors: Low power systems on
- a chip,‖ in Proc. of the 24th European Solid-State Circuits Conference, (The Hague, Netherlands). Pp 5-14
- Benini and G.D. Micheli, (2002). Dynamic Power Management: Design Techniques and CAD Tools, Norwell, MA, Kluwer, pp. 23-34
- Bhardwaj M. and A. P. Chandrakasan, (2008), ―Bounding the lifetime of sensor networks via optimal role assignments,‖ in Proc. of INFOCOM, pp. 1587– 1596.
- Bhardwaj, M. T. Garnett, and A. P. Chandrakasan, (2001) ―Upper bounds on the lifetime of sensor net- works,‖ in Proc. of ICC, pp. 785–790.
- Buettner, M. E. Anderson, and John, (2006) ―X-mac: A short preamble mac protocol for duty-cycled wireless sensor networks,‖ in Proc. of the 4th ACM Conference on Embedded Network Sensor Systems (Sensys’06).
- Chung, E. L. Benini, and Micheli, G. D. (1999) Dynamic power management using adaptive learning tree, ICCAD, pp 2-9
- Clark, D. Culler D. (2004) ―Encryption advances to meet Internet challenges,‖ IEEE Computer online magazine, http://www.computer.org/computer/articles/August/technews800.htm
- Dharma , P. A., & Quin-an, Z. (2011), Introduction to Wireless and Mobile Systems. Stamford: Cenage Learnin, pp 4-16