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In wireless ad hoc networks, the resource allocation strategy faces a roadblock at a very fundamental level [11]: Estimation of available resources is a nontrivial task even for simplest multihop ad hoc networks Wireless resources are shared by multiple nodes that do not have an inherent coordination infrastructure The contention resolution and resource bidding procedures usually propagate over long distances and affect far-away nodes The lack of isolation of resources and the dynamic nature of the network make resource estimation and allocation very challenging tasks This fact, coupled with the weak motivation for QoS-demanding applications for ad hoc networks, have limited the acceptance of QoS-based communication proposals for ad hoc networks 1323 QoS in Wireless Sensor Networks A WSN can be regarded as a special type of ad hoc network with very resourceconstrained nodes, lower mobility, and larger scale.

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, NAMs) during which the SMAP is unavailable and performing power saving Figure 127 shows an SMAP superframe consisting of a power-saving sleep (ie, S) subframe followed by a contention period (ie, CP), which are separated by a movable boundary For convenience the intervals of two adjacent CP and S subframes will be normalized to 1 During the CP time interval the SMAP updates the movable boundary between the two subframes tCP (i) and tS (i) are de ned to be the durations of the CP and S subframe intervals in the ith instance of this subframe, where tCP (i) + tS (i) = 1 For superframe i we de ne an error signal to be [6] e(i) = (U(i) Uth )tCP (i) (123).

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With these additional constraints, it is easy to dismiss QoS provisioning in WSNs as implausible However, there is one important difference between ad hoc networks and WSNs WSNs are de ned.

by applications they are deployed for A large set of WSN applications, including security and surveillance, requires QoS guarantees from the network Note that QoSbased applications were not integral parts of ad hoc networks and were proposed as additional applications that could run in parallel with non-QoS applications Hence, QoS provisioning is a requirement, and not an optional feature, for WSNs Being multihop networks, WSNs potentially suffer under the same shortcomings and problems as ad hoc networks if similar QoS provisioning mechanisms are adopted Furthermore, considering very limited resources in sensor nodes and the large scale of WSNs, per ow resource reservation-based approaches are especially ill-suited for WSNs The two important differences between both network types suggests new directions in QoS provisioning: First, the mobility of WSNs is very limited when compared with ad hoc networks.

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where U(i) is the achieved CP usage and Uth is a target usage based on a normalized utilization threshold that has been set, Uth . In reference 6 an update mechanism based on least mean square (LMS) adaptive control was used to dynamically update the boundary between CP and S subframes. According to the LMS optimization criterion, we want to minimize (e(i))2 , and it can be shown that this leads to e(i) e(i)2 = 2(U(i) Uth )tCP (i) tCP (i) tCP (i) (12.4)

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Resource availability in WSNs uctuates as a function of the offered load and not as a function of network connectivity over long periods of time Therefore, communication decisions do not need to be updated very frequently Second, the large scale of WSNs can easily be used as an advantage to eliminate explicit resource allocation Distribution of communication responsibility over larger areas provides gains through diversity and allows local decisions to be made, leading to end-to-end QoS guarantees QoS provisioning in WSNs is directly geared toward satisfying application requirements In Figure 131, a generalized framework for information ow in a WSN is depicted The main components of this framework are the sink, source locality, and relay nodes The information ow starts with assignment of a particular task to sensor nodes Upon information retrieval through sensors, source and other nodes nearby preprocess the information.

Due to the dif culty in evaluating the partial derivative in this term, it is incorporated into a single constant denoted by K [10]. The value of K is then appropriately tuned for the situation being considered. Using this result and assuming that we do not let tCP (i) drop below a minimum value tmin , the following update equation can be used: tCP (i + 1) = max {min(tCP (i)[1 + K(U(i) Uth )], 1), tmin } . (12.5)

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