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1351 PEGASIS: Chain of Reporting Nodes Lindsey, Raghavendra, and Sivalingam [35] proposed a framework for energyef cient data gathering algorithms in wireless sensor networks Their poweref cient gathering in sensor information system (PEGASIS) protocol [35] rst organizes sensors into a chain, by a centralized algorithm (eg, the sink can decide about ordering of reports) Thus, sensors are initialized as c0, c1, , cn21 Data gathering is performed in rounds In round k, rst nd i k mod n Each round consists of n iterations In each iteration, only one sensor is sending a message, containing data gathered by that sensor Iterations are performed as follows: c0 sends to c1, c1 to c2, , ci21 to ci Then cn21 sends to cn22, cn22 to cn23, , ci 1 to ci Finally, cisends the gathered data to the MC.

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The distance to the MC is assumed to be larger than the distances between the sensors Chains can be dif cult to construct in multihop sensor networks For single-hop networks, initialization algorithm needs to run, or the MC needs to assign reporting indices to individual sensors Once constructed, when sensors change activity status (from active to passive) or stop functioning, the order scheme needs to run again, or a maintenance procedure is needed The scheme is also not sensitive to the energy levels of the sensors, as different sensors consume different amount of energy, depending mainly on their distances to the MC 1352 LMST- and Geocasting-Based Data Gathering Several localized solutions are proposed in refs [33] and [34] One is a localized algorithm that rst constructs LMST (or other sparse structure such as the relative neighborhood graph (RNG)).

Instead of creating a chain, a token is circulated in the network The node currently having the token will send it to one of its LMST neighbors This can be done in different ways Nodes can forward with equal probability of sending to one of its neighbors (not returning to the neighbor it came from) Since the average degree of LMST is about 204 [18], there is on average one such neighbor to forward the token The forwarding probability may depend on node densities Neighbors with more LMST neighbors should have a smaller probability of getting the token (since they may get tokens from more neighbors in the process) Next, neighbors with more energy left may have a higher probability of getting the token Finally, in the case of monitoring an event that can be.

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2 E x [Var(Y | X)] =1 . (20.12) Var(Y ) Var(Y )

IEEE 802.15.3 WIRELESS PANs Frame Format, Channel Access, and Piconet Operation of IEEE 802.15.3 Wireless PANs

Logical channel assignment (Ref. 1).

RESONANCE [Refs. 1 and 4]

emotions in large heterogeneous samples, rather than being rare or exotic abnormalities Guilt and resentment have meaningful correlates; some examples include perception of the existing inequalities as unjust, cognitions that one s own higher standard of living and the lower standard of living of others are causally related, or cognitions that the inequalities could be reduced by redistribution and preference for the need-based principle of distributive justice (over the contribution-proportional principle) It has been shown that both guilt and resentment dispose people to perform prosocial activities in favor of the disadvantaged (guilt is more closely associated with personal sacri ces, resentment with political protest) It could also be proved that guilt and resentment were not reducible to self-interests of the privileged which was also assessed in these studies namely, in terms of fear of losses through forced redistribution and anger at the disadvantaged because of their lack of self-help.

However, as enlightenment gradually spread even to the world of groundwater engineering, pressure mounted for more logical legal codes, in which the lateral continuity of aquifers and the principle of prior rights (ie rst come, rst served ) are taken into account Nevertheless, in most underdeveloped countries laissez-faire approaches to groundwater abstraction rights are still the norm, often to the ultimate dis-.