In Network Processing and Data Aggregation in

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1 IntroductionRecently, the technological advancements in hardware have led to the design ofextremely powerful chips. Gordon E. Moore stated in 1965 that the complexity ofintegrated circuits, with respect to minimum component cost, doubles every 24 months.This “law” had hold for several years. However, on April 13 2005, Gordon Moorehimself stated in an interview that the law may not hold valid for too long, sincetransistors may reach the limits of miniaturization at atomic levels. Now that thecomputational speed is bounded by physical limitations, the interest of hardware expertshas been attracted to miniaturization of devices. The new trend of compacting thesystems enabled the development of coin-sized computing devices that are capable ofproducing digital representations of real-world phenomena. These devices are widelyknown as wireless sensors and they usually consist of sensing, data processing, andcommunicating components. The ability to communicate with each other throughwireless interfaces enables the deployment of a big number of sensor nodes in a fieldforming a wireless sensor network (WSN). In order for sensors to be both small andinexpensive, they have several resource constraints:Low bandwidth communication – The bandwidth of wireless links is usually limitedto a few hundred Kbps. Network doesn’t provide quality of service, the latency is highlyvariable and the loss of a packet is a frequent phenomenon.Power consumption – Most of the times sensors are battery powered. The batteriesthat are used are non rechargeable, irreplaceable and very small. Therefore, whiletraditional networks aim to achieve high quality of service, sensor network protocolsfocus primarily on power conservation. Prolonging the network’s lifetime is the maingoal. In addition to that a WSN must support parameterized trade-off mechanisms thatprovide the end user with the option of prolonging network lifetime at the cost of lowerthroughput or higher transmission delay.Computation – The node has limited computational power but it is usually adequate tocover the needs of the communication, application and sensing activities.Sensing accuracy – Signal processing functions convert physical events into internaldata representations. Sensed data, due to limitations of the sensor, may contain
environmental noise inducing this way uncertainty in readings. A damaged sensor mightalso generate inaccurate data.In Figure 1 a typical example of a sensor node (Berkeley MICA Mote) is illustrated sothat the reader is able to realize its tiny size. Table 1 provides us with the hardwarecharacteristics of the MICA Mote.ProcessorStorageRadio BandComm. RangeData RateTx PowerReceive Power4Mhz, 8bit MCU(ATMEL)512KB916Mhz33 m40 Kbits/sec12 mA1.8 mAFigure 1. The Berkeley MICA MoteTable 1. Mica Mote’s characteristicsWireless sensor networks can be used in awide range of application domains:• Environmental monitoring (coordinated sensing or information gathering in adisaster area).• Supervising items in a factory warehouse.• Intelligent building management• Organization of vehicle traffic in a large city (traffic routing).• Military (target recognition and tracking).• Medical and health care.Each one of the above applications has its own network, hardware and nodedeployment requirements. WSNs lack of standardized solutions because they areapplication specific networks, i.e., design requirements of a sensor network change dependingon the application. However, no matter the kind of the application, the communicationscenario is always identical (Figure 2). A user sends several tasks in the form of queriesto the network. The user can either be directly attached to a gateway node or he can alsobe connected remotely through an intermediate network (e.g. internet or via satellite).After the query reaches the WSN (sink), it is automatically translated into an internal
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Page 53 and 54: REFERENCES[1] W. Ye, J. Heidemann,

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