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In an external project, under conditions of competitive bidding, the process of achieving a contract can be extremely difficult In most cases, price competition is fierce As we have noted previously, it is not unheard of for one company to literally buy a contract to keep their employees working, sacrificing any possibility of profit to stay in the business during down cycles How can effective project-based firms compete under intense global pressure How do we get a project contract The basic corner stones of a contract between the customer and the contractor are scope of work, project execution plan, price, and commitment (Figure 17) Deciding what is the magnitude, level of detail, and order of priority of these elements in any particular contract depends very much on the situation In some circumstances, the key feature the customer is looking for may be simply price The customer may view alternative project options as essentially interchangeable, that is, undifferentiated When one option is basically as good as the next, it is usually appropriate to look no further than price in making a contract award decision General Electric Company has a sterling reputation for the efficiency of its global outsourcing operations For many of the products it outsources, it looks for the optimal price and awards the contract on that basis

Linear estimator sn 1 lin Rn (V, 2) 2 = 2 2s+1 1 (s r + 1 )n 2 1 2(s 1 + 1 )+1 r 2 Rlin (V, 2) 2 = 2

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72121 Hop Count-Based Schemes Hop count-based localization techniques use a mechanism similar to classical distance vector routing An illustrative example of this technique is the DV-hop localization scheme [150] Several anchor nodes are assumed to exist in the network, as shown in Figure 75 An anchor node broadcasts a beacon, which will be ooded in the entire network The beacon contains the location of the anchor node and also has a parameter called the hop count, which is initialized to one Each node that receives this beacon will copy the value of the hop count from the source of the beacon into its own database while also incrementing the hop count value The beacon is then further transmitted Beacons from the same source that are received with a higher hop count value than that maintained by the node are ignored As a result all the nodes in the network will receive the shortest distance to multiple anchor nodes in terms of number of hops In order to convert the hop count into physical distance, the system estimates the average distance per hop The anchors in the network estimate the average distance per hop by making use of the location and the hop count information for all the anchors inside the network Using the average distance per hop and the number of hops to a locator, a node can calculate the estimated distance to an anchor Once a node has the estimated distance from three or more anchors, it uses trilateration (see next section) to estimate its own location It is obvious here that obtaining the correct hop counts between the system nodes and every anchor node is critical The more anchors that a node can hear from, the more precise the localization can be A similar approach is also taken in [151] The latter proposes a scheme called amorphous localization In this case each system node uses a mechanism similar to DV-hop to obtain the hop distance A different approach using mathematical formulae is proposed for estimating the average distance of a single hop An adversary (who can be either an external attacker or an internal attacker) can try to either manipulate the hop count measurement or the translation from hop count to physical distance Note that manipulating the hop count measurement will also result in an incorrect translation from hop count to physical distance The hop count measurements can be manipulated using physical layer attacks such as increasing/decreasing the transmission power or using network layer attacks such as jamming the area between two nodes (this might result in beacons taking a longer route), forming a wormhole (this will result in.

For some estimators, the availability of the optimal rate of convergence is conditioned to the knowledge of information about the signal, such as its regularity This constraint is a drawback in applications In this context we try to develop adaptive estimators DEFINITION 116 X is an adaptive estimator for the loss Lp and the set {F , A} if for all A, there exists a constant c > 0:

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