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We saw earlier in this chapter that providing a secure communication service relied on holding at least some information secret. Trying to keep a long, complex private key in our heads is not feasible. It is possible to store this information in a PC, but there is always the risk that someone will use the machine illicitly when you are not there. There is also the inconvenience of not being able to work on a different location. Ideally, it would be better to be able to carry the secret information around with us, in a secure and compact form. One way to do this is to use a smart card. As shown in Figure 2.29, smart cards are computers encapsulated within credit-card sized plastic bodies. The processors are designed to run rather slowly but thereby consuming little electrical power even when running at full speed, and the entire card consumes pico amps (for memory retention) when not in use. The card has xed, read-only memory, which contains programming instructions. For security applications these could comprise the currently favoured encryption algorithm or algorithms. The read-only memory may also hold data including the user s private key. An alternative approach is to implement the algorithm on special purpose hardware, such as an encryption chip. A number of

Fara Warner, Think Lean, Fast Company, (February 2002), 40. All references to Bedbury and his theories appear in Scott Bedbury, Nine Ways to Fix a Broken Brand, Fast Company, (February 2002), 72 77. Lardner and Repetto, NYPD: A City and Its Police, xiv. Markey, A Breath of Fresh Growth, 35 36. Dana Beth Ardi, Knowledge Workers Unite!, Forbes ASAP (March 25, 2002), 29. Henry, The COMPSTAT Paradigm, 281. Ann Quigley, Fast Companies, Continental Airlines magazine (Winter 2002), 56. Mayor Tony Williams was the guest speaker at the monthly meeting of the New York City Crime Commission, January 23, 2002, New York City. The author was in attendance. Ridley, Worth Repeating, 41.

and dilute keywords are also introduced to spatially group the nodes. The where keyword is for de ning selection conditions according to available power and/or time and/or amplitude and/or location. The group by eld is used to specify the set of tasks for which the aggregation of the sensed data will be carried out. The based on keyword is followed by the parameters required for the aggregation and dilution algorithm run by the sensor nodes. In [22], the distributed algorithm that process the queries in this format is explained. 4.4.6. Data aggregation Since WSNs are comprised of large numbers of energy-constrained, inexpensive devices deployed in possibly inaccessible areas, battery re-charge or replacement is not a viable option. For this reason, energy-ef cient protocols have to be developed to minimize device energy consumption, hence maximizing the amount of time during which the network can be fully operational (denoted as network lifetime in the following). In such resource constrained devices, the dominant component in terms of energy consumption is the wireless transceiver. This imposes the development of an energy-ef cient protocol stack. In general terms, an energy-ef cient communication protocol is a protocol designed to minimize the energy-consumption associated with its operations without signi cantly degrading other relevant metrics performance (such as delay, throughput, etc.). Energy-conservation often involves trade-offs (energy vs. accuracy, energy vs. delay, etc.) that have to be considered when in the protocol design. The basic building blocks to energy-ef cient solutions for WSNs are the following. The wireless interface consumes a xed amount of energy for receiving (due to the transceiver circuitry), while the cost for transmitting is comprised of two components: the rst is xed and is due to the transceiver circuitry, and the second component