Quality Of Service Enabled Cross-Layer Multicast Framework For Mobile Ad Hoc Networks.

Rangkaian ad hoc bergerak merupakan suatu rangkaian tanpa wayar yang boleh dibentuk secara bebas, dinamik serta disusunatur dan ditadbir dalam bentuk topologi rangkaian sementara dan arbitrari. Mobile ad hoc networks (MANETs) are wireless networks that can freely and dynamically be created, organized and administered into arbitrary and temporary network topologies

Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods

Copyright @ 2010 Elsevier B.V. All rights reserved.In this paper, the quality of service multicast routing and channel assignment (QoS-MRCA) problem is investigated. It is proved to be a NP-hard problem. Previous work separates the multicast tree construction from the channel assignment. Therefore they bear severe drawback, that is, channel assignment cannot work well with the determined multicast tree. In this paper, we integrate them together and solve it by intelligent computational methods. First, we develop a unified framework which consists of the problem formulation, the solution representation, the fitness function, and the channel assignment algorithm. Then, we propose three separate algorithms based on three representative intelligent computational methods (i.e., genetic algorithm, simulated annealing, and tabu search). These three algorithms aim to search minimum-interference multicast trees which also satisfy the end-to-end delay constraint and optimize the usage of the scarce radio network resource in wireless mesh networks. To achieve this goal, the optimization techniques based on state of the art genetic algorithm and the techniques to control the annealing process and the tabu search procedure are well developed separately. Simulation results show that the proposed three intelligent computational methods based multicast algorithms all achieve better performance in terms of both the total channel conflict and the tree cost than those comparative references.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

Minimum-cost multicast over coded packet networks

We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast (where membership of the multicast group remains constant for the duration of the connection) and dynamic multicast (where membership of the multicast group changes in time, with nodes joining and leaving the group). For static multicast, we reduce the problem to a polynomial-time solvable optimization problem, and we present decentralized algorithms for solving it. These algorithms, when coupled with existing decentralized schemes for constructing network codes, yield a fully decentralized approach for achieving minimum-cost multicast. By contrast, establishing minimum-cost static multicast connections over routed packet networks is a very difficult problem even using centralized computation, except in the special cases of unicast and broadcast connections. For dynamic multicast, we reduce the problem to a dynamic programming problem and apply the theory of dynamic programming to suggest how it may be solved

Robust minimum energy wireless routing for underwater acoustic communication networks

Marine robots are an increasingly attractive means for observing and monitoring the ocean, but underwater acoustic communications remain a major challenge. The channel exhibits long delay spreads with frequency-dependent attenuation; moreover, it is time-varying. We consider the minimum energy wireless transmission problem [MET], augmented by the practical condition that constraints on link power must be satisfied in probability. For this, we formulate the robust counterpart of the multicommodity mixed-integer linear programming (MILP) model from Haugland and Yuan [1], and derive scaled power levels that account for uncertainty. Our main result is that the deterministic formulation with these scaled power levels recovers exactly the optimal robust solution in the absence of correlations, and therefore allows for efficient solution via MILP. This approach achieves significant power improvements over heuristics, and naturally lends itself to vehicle networks.United States. Office of Naval Research (Grant N00014-09-1-0700

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

Improving Multicast Communications Over Wireless Mesh Networks

In wireless mesh networks (WMNs) the traditional approach to shortest path tree based multicasting is to cater for the needs of the poorest performingnode i.e. the maximum permitted multicast line rate is limited to the lowest line rate used by the individual Child nodes on a branch. In general, this meansfixing the line rate to its minimum value and fixing the transmit power to its maximum permitted value. This simplistic approach of applying a single multicast rate for all nodes in the multicast group results in a sub-optimal trade-off between the mean network throughput and coverage area that does not allow for high bandwidth multimedia applications to be supported. By relaxing this constraint and allowing multiple line rates to be used, the mean network throughput can be improved. This thesis presents two methods that aim to increase the mean network throughput through the use of multiple line rates by the forwarding nodes. This is achieved by identifying the Child nodes responsible for reducing the multicast group rate. The first method identifies specific locations for the placement of relay nodes which allows for higher multicast branch line rates to be used. The second method uses a power control algorithm to tune the transmit power to allow for higher multicast branch line rates. The use of power control also helps to reduce the interference caused to neighbouring nodes.Through extensive computer simulation it can be shown that these two methods can lead to a four-fold gain in the mean network throughput undertypical WMN operating conditions compared with the single line rate case

DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

The optimal multicast tree problem in the Software-Defined Networking (SDN) multicast routing is an NP-hard combinatorial optimization problem. Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence. In this paper, an SDN intelligent multicast routing algorithm based on deep hierarchical reinforcement learning is proposed to circumvent the aforementioned problems. First, the multicast tree construction problem is decomposed into two sub-problems: the fork node selection problem and the construction of the optimal path from the fork node to the destination node. Second, based on the information characteristics of SDN global network perception, the multicast tree state matrix, link bandwidth matrix, link delay matrix, link packet loss rate matrix, and sub-goal matrix are designed as the state space of intrinsic and meta controllers. Then, in order to mitigate the excessive action space, our approach constructs different action spaces at the upper and lower levels. The meta-controller generates an action space using network nodes to select the fork node, and the intrinsic controller uses the adjacent edges of the current node as its action space, thus implementing four different action selection strategies in the construction of the multicast tree. To facilitate the intelligent agent in constructing the optimal multicast tree with greater speed, we developed alternative reward strategies that distinguish between single-step node actions and multi-step actions towards multiple destination nodes