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nodes have a certain probability to share at least one common key By con guring the size of the key pool and the size of the key ring, which includes the keys a sensor node selects, this scheme provides good security properties with reasonable overhead For convenience, we call this scheme basic probabilistic key predistribution The basic probabilistic key predistribution scheme consists of three phases: key predistribution, shared key discovery, and path-key establishment The key predistribution phase is performed to generate the key pool and distribute keys to sensor nodes After being deployed, if two sensor nodes need to establish a pairwise key, they rst attempt to do so through shared key discovery, during which they discover whether they share a common key.

As a rst step in evaluating these interventions, the criteria for empirically supported treatments in psychology have been applied to pediatric psychology through a series of review papers in the Journal of Pediatric Psychology (1999, 2000, 2001) The basic standards used to establish empirically supported treatments in clinical psychology, developed by a taskforce within Division 12 (Clinical Psychology) of the American Psychological Association, guided the identi cation of effective treatments in pediatric psychology (see Chambless & Hollon, 1998) Following the Division 12 guidelines, a well-established treatment must have been evaluated in at least two good between group design experiments that showed that the treatment was superior to a placebo or alternate treatment or equivalent to an already established treatment in studies with adequate statistical power (sample size of 30 per group).

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If they do, there is no need to start path-key establishment; otherwise, these two nodes start the path-key establishment phase to establish a pairwise key with the help of other sensor nodes Eschenauer and Gligor studied the connectivity between sensor nodes resulting from the basic key predistribution as random graphs [3] Consider a graph of n sensor nodes where there is an edge between two nodes if and only if they share a common key Let p be the probability that two sensor nodes have a shared key, and n be the number of sensor nodes in a network This graph can be modeled as a random graph G(n, p) of n nodes for which the probability that an edge exists between two nodes is p.

A series of nine or more welldesigned case studies, in which the treatment is contrasted with another treatment, is also acceptable Treatment manuals and a complete description of the sample were also required Finally, to be deemed well-established, the ndings must have been reported by two different laboratories A probably ef cacious treatment requires two experiments that show that the treatment was more effective than a waitlist control group or one or more experiments meeting the wellestablished criteria, but without replication by independent research teams The movement to establish criteria for effective treatments is critical to demonstrate that psychological interventions are powerful and meet relatively rigorous scienti c scrutiny of their merit Nonetheless, setting standards for effective treatments has generated discussion of the short- and long-term implications of categorizing treatment approaches by effectiveness, particularly in areas in which intervention research.

According to the classic results on random graph theory by Erdos and Renyi, there exists a value of p such that the probability that a random graph is fully connected moves from 0 to 1 in a large random graph To further consider the fact that a sensor node has limited communication range, the preceding random graph can be modi ed to have an edge between two nodes only if they are neighbors This requires a larger p to ensure that the random graph of the sensors is fully connected The additional analysis by Eschenauer and Gligor also indicates that there are two critical parameters during the phase of key predistribution: the size of the key pool (P), and the size of each key ring (k).

All currently used data-transmission systems are synchronized in phase and symbol rate in some manner Start stop synchronization has already been discussed All fully synchronous transmission systems have timing generators or clocks to maintain stability The transmitting device and its companion receiver at the far end of the circuit must maintain a timing system In normal practice, the transmitter is the master clock of the system The receiver also has a clock that in every case is corrected by some means to its transmitter s master clock equivalent at the far end Another important timing factor is the time it takes a signal to travel from the transmitter to the receiver This is called propagation time With velocities of propagation as low as 20,000 mi/ s, consider a circuit 200 mi in length The propagation time would then be 200/ 20,000 s or 10 ms.