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de ne a few up-front objectives and capabilities. Over time, the elded system/product requires new capabilities as problem/opportunity spaces evolve. Some systems, such as computers, become obsolete in a very short period of time and are discarded. From a business perspective, the cost to upgrade and maintain the devices is prohibitive relative to purchasing a new computer. In contrast, some Users, driven by decreasing budgets and slow changes in the external environments, may use systems and products far beyond their original intended service lives. Consider the following example: EXAMPLE 27.1



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The US Air Force B-52 aircraft is postured to achieve the longest service life of any aircraft. During its lifetime, the aircraft s systems and missions have evolved from their initial operational capability (IOC) when the aircraft was rst introduced to the present via capability upgrades. The projected service lifespan far exceeds what the aircraft s innovators envisioned.

Whenever a routing message or a data packet is received, the timer is set or refreshed to the maximum of what is left for the current timer and the value associated with the received message Thus, the timer is maintained on a per-node basis When the timer expires, the node switched to the active state Simulation results show that a reduction of energy consumption near 50% can be achieved compared to networks without power management..





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The Evolutionary Development Model is based on the premise that a system s capabilities evolve over time via a series upgrades.

Conceptually, the Evolutionary Development Model may be suited for some applications; however, it, too, has its fallacies. Dr. Boehm (p. 63) notes the following points: Fallacy 1: The Evolutionary Development Model is dif cult to delineate from the build, test, and x approaches whose spaghetti code and lack of planning were the initial motivation for the waterfall model. Fallacy 2: The Evolutionary Development Model stemmed from the often unrealistic assumption that the system would always provide the exibility to accommodate unplanned evolution paths. Regarding fallacy 2, Dr. Boehm (p. 63) states This assumption is unjusti ed in three primary circumstances: 1. Circumstance in which several independently evolved applications must subsequently be closely integrated. 2. Information sclerosis cases, in which temporary work-around for software de ciencies increasingly solidify into unchangeable constraints on evolution, . . . 3. Bridging situations, in which the new software is incrementally replacing a large existing system. If the system is poorly modularized, it is dif cult to provide a good sequence of bridges between old software and the expanding increments of new software.

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1154 Open Issues Traditional minimum-energy routing protocols are proved to be inef cient in ad hoc networks where nodes are of equal importance to the operation of the network; hence energy-ef cient routing protocols that maximize the system lifetime of network are desired Most previous works that target this problem rst formulate it as an optimization problem and solve it with some optimization techniques, or propose some heuristics These algorithms typically require a large amount of message passing However, the energy consumption of control overhead is always ignored, and whether these proposals still work in real network is an open issue Moreover, most algorithms assume that the energy consumed for transmitting one packet along a link is xed This assumption does not hold if the underlying MAC layer employs power control on a per-packet basis.

Data Modeling The data model defines the entities about which data needs to be collected. The entities are usually identified while using the process mapping technique. Entities such as customer, product, and invoice can be found in the data flows between different business processes. The data model then defines the properties or attributes that must be known about an entity. For example, the entity called customer has attributes such as customer number, name, address, and credit limit. Data modeling also produces a visual diagram called an entity relationship diagram or ERD. Like the process flow diagrams produced by the process mapping technique, the ERDs provide another visual method for

Because of inherent aws in the Evolutionary Development Model, coupled with a lack of understanding, maturity, and risk in system requirements up front, Dr. Boehm introduced the Spiral Development Model illustrated in Figure 27.2. Spiral development employs a series of highly iterative development activities whereby the deliverable end product of each activity may not be the deliverable system. Instead, the evolving set of knowledge and subsequent system requirements that lead to development of a deliverable

Risk Analysis Risk Analysis Risk Analysis Risk Prototype Analysis 1 Requirements Plan Life Cycle Plan

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