Figure 14.3. Number of cooperating nodes versus node density. in VS .NET

Draw Quick Response Code in VS .NET Figure 14.3. Number of cooperating nodes versus node density.
Figure 14.3. Number of cooperating nodes versus node density.
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toward lower densities, while the operational costs for each detection event will drive it toward higher densities. Thus, for a given cooperation protocol source model, delity requirement there will be an optimal deployment density for at least some cost functions in this family. A broad set of such problems remains to be more fully explored.
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14.3 FIDELITY IN HETEROGENEOUS NETWORKS The basic set of assumptions regarding network con guration in the previous section might be termed sensor network classic: a dense set of nodes of the same type (sensor, radios, processors) usually with highly limited resources (especially energy) but which are inexpensive and can be deployed in large numbers to form a multihop network. Apart from the challenge of data extraction at minimal resource cost, other dif cult problems for networks of this type that have attracted the attention of the research community will be discussed in Section 14.4. For the remainder of this chapter we will also consider heterogeneous networks, including the possibility of mobile elements and hierarchical networks that have nodes with greater capabilities (e.g., sensing or radio range, more storage, position-location ability, reliable energy supply, etc.). In this section we explore the implications for the delity problem, rst with mobility and then with hierarchical networks. 14.3.1 Mobility In MANETs, mobility is one of the major contributors to overhead from the protocol stack, thereby reducing communication ef ciency [73 77]. More recently, however, mobility has been found to increase the capacity of wireless ad hoc networks [78 81]. Here, the networks have a mixture of static and mobile nodes. The type of mobility that mobile agents can possess falls into one of the following three categories [43]: r Random Mobility. The nodes are assumed to move in an arbitrarily random pattern typically modeled as uniform Brownian motion for analytical convenience [78 81]. This model has also been used in data mule work [82, 83]. r Predictable Mobility. This model assumes that the pattern of mobility of the mobile nodes is known, and this knowledge can be exploited to route data [84 86]. The mobile agents are not moving for the purpose of data transfer and hence their paths may not coincide with the routing requirements. r Controlled Mobility. Here the mobility pattern of the mobiles is completely under the control of the network. Prototypes of such networks have been produced [87 90]. Controlled mobility could also be implemented by providing infrastructure to move the nodes. This infrastructure can then serve additional purposes such as logistical support to the network [158]. There are fundamental differences in the throughput and delay properties when the mobility is controlled as opposed to when the mobility is random or predictable, as
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we now detail. In reference 81, it is assumed that all nodes are randomly mobile within a unit disc area. The data traf c pattern is assumed to be random as in reference 40. Data travels over only two wireless hops, from the source node to a mobile node that acts as a relay and then from the relay to the destination. With this model the throughput was found to be (1). A similar result was obtained for mobility constrained to one dimension in [79]. The delay for the above scenarios was found in reference 80, and algorithms were discussed for trading delay and capacity. Delay D(n) is related to throughput T(n) by D(n) = (nT (n)) for the wireless network scenario of [29]. For the model in reference 81 when the nodes are randomly mobile with average velocity v(n), the delay scales as ( n/v(n). As noted in reference 80, three important features that in uence the throughput and delay in ad hoc networks are the number of hops, the transmission range, and the node mobility and velocity. In reference 80, schemes are proposed that exploit these three features to different degrees to obtain different points on the throughput-delay curve in an optimal way, enabling achievement of the results in references 29 and 81 as limiting cases of a larger achievable region. Another scenario for a network with mobile nodes was considered in reference 78. The network consisted of n static nodes, which acted as sources and destinations for data. However, the network also had m randomly mobile nodes which were used as relays. For this model, using the routing scheme proposed in reference 78, the throughout is (m/n log3 n) with an average delay of 2d/v where v is the velocity of the mobile nodes. Now consider the network scenario as in reference 81 where all nodes are mobile except that their motion is controlled rather than random. Here a na ve communi cation strategy is for each source to move to its destination and communicate at almost zero range. Hence interference among simultaneous transmissions is zero, and each sender receiver pair can utilize the full available bandwidth W. The pernode throughput is W with constant delay. The delay depends on the traveling time of mobile nodes to reach their destinations, which is constant as network area is constant. This compares to the worst-case delay in reference 81, which is in nite. Clearly, controlled mobility has the potential to yield fundamentally different throughput and delay limits compared to those achieved with random mobility in references 78 and 81. Further details, including mobile routing protocols, are presented in reference 43. We note that, of course, hybrid routing strategies are possible, with some combination of multihopping in the static network within close neighborhoods of mobile nodes, in which case the mobile nodes serve the role of a higher level of communication infrastructure. In a network where most nodes are static, a reasonable way to view the mobile nodes is as a form of infrastructure for the support of the static elements. One consequence of providing long-range data transport is that the mobile nodes can help save energy in the static nodes, since these must no longer relay data from other nodes over long multihop wireless routes. The extra energy overhead of mobility may not be a major concern because the mobile nodes can periodically recharge themselves. The reliability of transmission across a network also depends strongly on the mobility assumptions. One of the common problems arising in ad hoc networks
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