Ad hoc wireless networks are expected to be deployed in a wide variety of civil and military applications. The increasing use of collaborative applications and wireless devices may further add to the needs and usage of ad hoc networks. The communicating nodes might be distributed randomly and are assumed to have the capacity of packet forwarding to communicate with each other over a shared radio channel. Building such networks poses a significant technical challenge because of the constraints imposed by the characteristics of the ad hoc networks. Resources, including energy, bandwidth, processing capacity and memory, that are relatively abundant in wired environments, are strictly limited and must be preserved.
The emergence of real-time applications and the widespread use of wireless devices have generated the need to provide quality-of-service (QoS) support in wireless ad hoc networking environments. QoS is usually defined as a set of service requirements that need to be met by the network while transporting a packet stream from a source to its destination(s). The network needs are governed by the service requirements specified by the end user applications. The network is expected to guarantee a set of measurable pre-specified service attributes to the users in terms of end-to-end performance, such as delay, bandwidth, probability of packet loss, delay variance, etc. [1]. The QoS metric bandwidth is more difficult to guarantee in wireless ad hoc networks, because the wireless bandwidth is the scarce resource and always shared among adjacent nodes. This requires extensive collaboration between the nodes, both to establish the route and to secure the resources necessary to provide the QoS.
Since wireless nodes are generally dependent on finite battery source, the routing protocol for QoS provisioning must also consider the residual battery power and the rate of battery consumption in order to increase longevity of such networks [2 and 3]. Thus all the techniques for QoS provisioning should be power-efficient. On the other hand, the ability to provide QoS is heavily dependent on how well the resources are managed at the MAC layer. A QoS routing protocol developed for one type of MAC layer does not generalize to others easily. Among the QoS routing protocols proposed so far, some use generic QoS measures and are not tuned to a particular MAC layer [4, 5 and 6]. Some use CDMA to eliminate the interference between different transmissions [7 and 8]. In [9], the authors develop a QoS routing protocol for ad hoc networks using TDMA in small networks. The protocol is based on AODV [10], and builds QoS routes only as needed.
Future networks must be adequately equipped to handle multipoint communication in a fast and economical manner. When the network is modeled as a weighted, undirected graph, the problem is that of finding a minimal Steiner tree for the graph, given a set of destinations. The problem is known to be NP-complete. Consequently, several heuristics exist which provide approximate solutions to the Steiner problem in networks [41]. In [42], the authors present a random neural network (RNN) model can be used to significantly improve the quality of the Steiner trees delivered by the best available heuristics that are the minimum spanning tree heuristic and the average distance heuristic.
The recent proliferation of QoS-aware group applications over the wireless ad hoc networks has accelerated the need for efficient multicast support. In this paper, we only consider “bandwidth” as the QoS and present a constraint formulation model for the QoS-MEM (QoS-aware Minimum Energy Multicast) problem in a TDMA-based ad hoc network. In general, “bandwidth” in time-slotted network system is measured in terms of the amount of “free” slots. Consequently, in order to establish a bandwidth guaranteed QoS multicast tree from a source to all destinations, we have the following goals for this optimization problem:
1. The bandwidth allocated on each link of the multicast tree should meet the bandwidth requirement.2. A suitable scheduling of free slots for each link of the multicast tree can be also obtained from this model.3. The total RF energy consumption on the bandwidth-guaranteed multicast tree is minimized.
Clearly, such a joint power-minimization and scheduling is a challenging optimization problem. In fact, either the scheduling problem with even a single power level or the best-effort minimum energy multicast problem, is by itself known to be an NP-hard problem [32 and 36]. Our simulation results show that an optimal solution of the QoS-MEM problem using our model can always be obtained in a timely manner for networks with no more than 20 nodes. The remaining of this paper is organized as follows. In Section 2, we overview related work concerning QoS unicast/multicast routing and minimum energy multicast routing in wireless ad hoc networks. In Section 3, we give a network model and the definition of BCMT (bandwidth-constrained multicast tree). Section 4 derives the linear constraint formulation for Problem QoS-MEM systematically in a form of Mixed Integer Linear Programming (MILP), and proves that it produces the optimal solutions. Computational results assessing the performance are given in Section 5. Section 6 summarizes our finding and points out several future research problems.
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