Mobil Ad Hoc Networks Protocol Design Part 16 ppt

Mobile Ad-Hoc Networks: Protocol Design 592 Fig. 10. Average delay with varying the maximum waiting time as 0.05sec and 0.35sec [taken from (Jaegal & Lee, 2008)] Fig. 11. Flooding completion time with varying the maximum waiting time as 0.05sec and 0.35sec [taken from (Jaegal & Lee, 2008)] difference occurs in case when W max = 0.35sec. Latency and completion time of FONIAH are considerably lower than those of geoflood as nodes are densely deployed. EEPA achieves rapid delivery throughout the network as fast as blind flooding and it noticeably alleviates the number of transmissions. 5. Conclusion In this chapter, we discussed the issues behind supporting efficient broadcasting for MANET and previously published broadcasting schemes. The key argument of efficiency in broadcasting is reducing the amount of overhead introduced during the propagation of a packet to nodes in the network. The reason is that MANET is one of resource-constrained networks such as mobile networks and wireless sensor networks. More precisely, collision and contention are likely to occur due to wireless resource sharing under the condition that the resource is strictly limited. In addition to the problems, energy consumption is an important consideration. Broadcasting in Mobile Ad Hoc Networks 593 The optimal reliable broadcasting is known as NP-complete even if each node has the global topology information. Hence, many of the broadcasting schemes require each node to listen to redundant packets during a short waiting time to examine the necessity of transmission. Since the waiting time may be a factor increasing end-to-end delay, some broadcasting schemes employs the concept of a hybrid approach to alleviate delay granting a priority to help a node rebroadcast immediately. With the enhancement of the broadcasting approach, the performance has been considerably improved. Unfortunately, most broadcasting schemes presented here barely ensure the feasibility and practically in the real world because of the underlying assumptions such as static network model and error-free communication. Moreover, using extra devices such as ranging measurements and GPS is costly and power-intensive. Therefore, significant research effort is needed with consideration of high mobility and energy conservation. 6. References Abramson, N. (1970). The Aloha System-Another Alternative for Computer Communications, Proceedings of the Fall Joint Computer Conference (AFIPS’70), pp. 281-285, Montvale, Nov. 1970 Arango, J.; Degermark, M.; Efrat, A. & Pink, S. (2004). An Efficient Flooding Algorithm for Mobile Ad-hoc Networks, Proceedings of IEEE Workshop on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt’04), Cambridge, Mar. 2004 Doherty, L.; Pister, K. S. J. & Ghaoui, L. E. (2001). Convex Position Estimation in Wireless Sensor Network, Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1655-1663, ISBN 0-7803-7016-3, Anchorage, Apr. 2001 Getting, I. A. (1993). Perspective/navigation-The Global Positioning System. IEEE Spectrum, Vol.30, Iss.12, Dec. 1993, pp. 36-47, ISSN 0018-9235 Ho, C.; Obraczka, K.; Tsudik, G. & Viswanath, K. (1999). Flooding for Reliable Multicast in Multi-hop Ad Hoc Networks, Proceedings of the 3rd International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (DIAL- M’99), pp. 64-71, Seattle, Aug. 1999 Jaegal, C. & Lee, C. (2008). An Efficient Flooding Algorithm for Position-based Wireless Ad Hoc Networks, Proceedings of the International Conference on Convergence and Hybrid Information Technology, pp. 13-20, ISBN 978-0-7695-3407-7, Busan, Nov. 2008 Jetcheva, J. G.; Hu, Y.; Maltz, D. A. & Johnson, D. B. (2001). A Simple Protocol for Multicast and Broadcast in Mobile Ad Hoc Networks. Internet Draft of the Internet Engineering Task Force (IETF): draft-ietf-manet-simple-mbcast-01.txt, Jul. 2001 Johnson, D. B. & Maltz, D. A. (1996). Dynamic Source Routing in Ad Hoc Wireless Networks, In: Mobile Computing, Tomasz Imielinski & Henry F. Korth, pp. 153-181, Kluwer Academic Publishers, ISBN 0792396979, Boston Karp, B. & Kung, H. T. (2000). GPSR: Greedy Perimeter Stateless Routing for Wireless Networks, Proceedings of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’00), pp. 243-254, Boston, Aug. 2000 Lee, S. & Ko, C. (2006). An Efficient Neighbor Based Broadcasting for Mobile Ad Hoc Networks, Proceedings of the International Conference on Computational Science (ICCS’06), pp. 1097-1100, ISBN 978-3-540-34381-3, Reading, May 2006 Mobile Ad-Hoc Networks: Protocol Design 594 Lim, H. & Kim, C. (2001). Flooding in Wireless Ad Hoc Networks. Computer Communications Journal, Vol.24, No.3, Feb. 2001, pp. 353-363, ISSN 0140-3664 Lou, W. & Wu, J. (2002). On Reducing Broadcast Redundancy in Ad Hoc Wireless Networks. IEEE Transactions on Mobile Computing, Vol.1, Iss.2, Apr Jun. 2002, pp. 111-122, ISSN 1536-1233 Ni, S.; Tseng, Y.; Chen, Y. & Sheu, J. (1999). The Broadcast Storm Problem in a Mobile Ad Hoc Network, Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom’99), pp. 151-162, ISBN 1-58113-142-9, Seattle, Aug. 1999 Niculescu, D. & Nath, B. (2001). Ad-hoc Positioning System (APS), Proceedings of IEEE GLOBECOM 2001, pp. 2926-2931, ISBN 0-7803-7206-9, San Antonio, Nov. 2001 Park, V. D. & Corson, M. S. (1997). A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks, Proceedings of the 16th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’97), pp. 1405-1413, ISBN 0-8186- 7780-5, Kobe, Apr. 1997 Pearlman, M. R. & Hass, Z. J. (1999). Determining the Optimal Configuration for the Zone Routing Protocol. IEEE Journal on Selected Areas in Communications, Vol.17, Iss.8, Aug. 1999, pp. 1395-1414, ISSN 0733-8716 Peng, W. & Lu, X. (2000). On the Reduction of Broadcast Redundancy in Mobile Ad Hoc Networks, Proceedings of the 1st Annual International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’00), pp.129-130, ISBN 0-7803-6534-8, Boston, Aug. 2000 Perkins, C. E. & Royer, E. M. (1999). Ad-hoc On-demand Distance Vector Routing, Proceedings of 2nd IEEE Workshop on Mobile Computing Systems and Applications, pp. 90-100, ISBN 0-7695-0025-0, New Orleans, Feb. 1999 Qayyum, A.; Viennot, L. & Laouiti, A. (2002). Multipoint Relaying for Flooding Broadcast Messages in Mobile Wireless Networks, Proceedings of the 35th Annual Hawaii International Conference on System Sciences, pp. 3866-3875, ISBN 0-7695-1435-9, Big Island, Jan. 2002 Ryu, J.; Kim, M.; Hwang, S. & Han, K. (2004). An Adaptive Probabilistic Broadcast Scheme for Ad-Hoc Networks, Proceedings of the 7th IEEE International Conference on High Speed Networks and Multimedia Communications (HSNMC’04), pp. 646-654, ISBN 978- 3-540- 22262-0, Toulouse, Jun Jul. 2004 Saadawi, T. & Ephremides, A. (1981). Analysis, Stability, and Optimization of Slotted ALOHA with a Finite Number of Buffered Users. IEEE Transactions on Automatic Control, Vol.26, Iss.3, Jun. 1981, pp. 680-689, ISSN 0018-9286 Shang, Y. & Ruml, W. (2003). Improved MDS-based Localization, Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’04), pp. 2640-2651, ISBN 0-7803-8355-9, Hong Kong, Mar. 2004 Tseng, Y.; Ni, S. & Shih, E. (2001). Adaptive Approaches to Relieving Broadcast Storms in a Wireless Multihop Mobile Ad Hoc Network, Proceedings of the 21st International Conference on Distributed Computing Systems, pp. 481-488, ISBN 0-7695-1077-9, Mesa, Apr. 2001 Williams, B. & Camp, T. (2002). Comparison of Broadcasting Techniques for Mobile Ad Hoc Networks, Proceedings of the 3rd ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 194-205, ISBN 1-58113-501-7, Lausanne, Jun. 2002 THE CMU MONARCH GROUP. Wireless and Mobility Extensions to ns-2. http://www.monarch.cs.cmu.edu/cmu-ns.html, Oct. 1999 0 Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks Song Gao 1 , Lijun Qian 1 , and D.R. Vaman 2 1 Prairie View A&M University, 2 CeBCom Research Center U.S.A 1. Introduction Recent technological advances have resulted in the development of wireless ad hoc networks which are envisioned to provide rapid on-demand network deployment due to their self-configurability and lack of pre-deploy infrastructure requirements. These devices generally have small form factors, and have embedded storage, processing and communication ability I. F. Akyildiz (2009). With the growing proliferation of such wireless devices, the spectrum is increasingly getting congested. However, it has also been pointed out in several recent measurement reports that the spectrum are highly under-utilized FCC (2002). In order to achieve much better spectrum utilization and viable frequency planning, Cognitive Radios (CRs) are under development to dynamically capture the unoccupied spectrum J. Mitola (1999). Many challenges arise with such dynamic and hierarchical means of accessing the spectrum, especially for the dynamic resource allocation of CR users by adapting their transmission and reception parameters to the varying spectrum condition while adhering to power constraints and diverse quality of service (QoS) requirements (see, for example, S. Tao (2006); Q. Zhao (2007)). In this chapter, an energy constrained wireless CR ad hoc network is considered, where each node is equipped with CR and has limited battery energy. One of the critical performance measures of such networks is the network lifetime. Additionally, due to the infrastructureless nature of ad hoc networks, distributed resource management scheme is desired to coordinate and maintain communications between each transmitting receiving pair. In this context, the present chapter provides a framework of distributed energy efficient spectrum access and resource allocation in wireless CR ad hoc networks that employ orthogonal frequency division multiple access (OFDMA) K. Fazel (2003); A. Pandharipande (2002) at the physical layer. OFDMA is well suited for CR because it is agile in selecting and allocating subcarriers dynamically and it facilitates decoding at the receiving end of each subcarrier J. Bazerque (2007). In addition, multi-carrier sensing can be exploited to reduce sensing time I. F. Akyildiz (2006). Each emerging CR user will select its subcarriers and determine its transmission parameters individually by solving an optimization problem. The optimization objective is to minimize its energy consumption per bit 1 while satisfying its QoS requirements and power limits. 1 which is defined as the ratio of the total transmission and reception power consumption over available subcarrier set to its achieved throughput 28 2 Theory and Applications of Ad Hoc Networks Fig. 1. Block diagram of the proposed distributed resource allocation algorithm Compared with the power minimization with respect to target data rate constraints S. Tao (2006) or throughput maximization under power upper bound Q. Zhao (2007), this objective function, which measures the total energy consumed for reliable information bits transmitted, is particularly suitable for energy constrained networks where the network lifetime is a critical metric. Although the emerging CR users will not cause harmful interference to the existing users, they may choose the same subcarriers in the same time slot independently, and thus co-channel interference may be introduced. In this work, we allow multiple new users to share the same subcarriers as long as their respective Signal-to-Interference-and-Noise-Ratio (SINR) is acceptable. This may be achieved by distributed power control R. Yates (1995), which converges very fast. The flow chart of the proposed distributed energy efficient spectrum access and resource allocation scheme is highlighted in Fig. 1, where step 2 corresponds to the constrained optimization performed by each emerging user individually. More detailed illustrations of the flow chart are given in section 4. Resource allocation problem in wireless ad hoc networks has been extensively investigated in the literature. In G. Kulkarni (2005), the resource allocation problem is explored for OFDMA-based wireless ad hoc network by directly adopting distributed power control scheme for the power and bits allocation on all subcarriers to improve power efficiency. A greedy algorithm is proposed for best subcarrier selection in CR networks employing multicarrier CDMA Q. Qu (2008), and distributed power control is performed thereafter to resolve co-channel interference. An Asynchronous Distributed Pricing (ADP) scheme is proposed in J. Huang (2006), where the users need to exchange information indicating the interference caused by each user to others. In the context of CR enabled wireless sensor network (WSN) S. Tao (2006), a two-step algorithm is proposed to tackle the allocation problem: channel assignment with objective of minimizing transmission power and channel contention to reserve the subcarrier set for transmission by intended transmitters, while the interference spectrum mask is assumed to be known a priori. The authors of Q. Zhao (2007) address the opportunistic spectrum access (OSA) problem in WSN, in which a distributed channel allocation problem is modeled by a partially observable Markov decision process 596 Mobile Ad-Hoc Networks: Protocol Design Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 3 framework (POMDP) while assuming the transition probability of each channel is known. In Y. T. Hou (2007), the CR spectrum sharing problem is formulated in multi-hop networks with objective to minimize the space-bandwidth product (SBP). However, the transmission power allocated on each subcarrier is assumed to be the same which may lead to significant performance loss. The effect of power control is analyzed in a subsequent work Y. Shi (2007). Dynamic Frequency Hopping Community (DFHC) is proposed in W. Hu (2007) for the spectrum sharing in CR based IEEE 802.22 wireless regional area networks (WRANs) to ensure QoS satisfaction and reliable protection to licensed users. In this chapter, a new constrained optimization problem is formulated and solved that minimizing energy per bit across users subject to QoS and power constraints in a multi-user ad hoc network. A novel concept, “energy-efficient waterfilling”, is given in this section that is fundamentally different from the rate-adaptive waterfilling or margin-adaptive waterfilling 2 .In this case the optimal point is located in the constraint interval rather than on the boundary. In fact, the rate-adaptive and marg in-adaptive waterfilling can be considered as special cases of the energy-efficient waterfilling presented in this work. The results obtained provide a valuable insight that the optimal solution of energy efficient resource allocation is not best subcarrier selection for multiple transmitting receiving pairs in an OFDMA network S. Gao (2008). The proposed distributed subcarrier selection and power allocation scheme provides an efficient and practical solution for dynamic spectrum access in CR wireless ad hoc networks employing OFDMA. By combining the optimal resource allocation of individual users and distributed power control, the proposed method guarantees fast convergence speed, computational efficiency and implementation simplicity. Motivated by iterative waterfilling (IWF) algorithm in W. Yu (2002), another distributed solution may be obtained by solving the multi-user distributed channel and power allocation problem iteratively. However, it may take many steps for the iterative algorithm to converge if it converges at all and the delay may be too large to be tolerable. The cost of the additional computation complexity is high. On the contrary, the proposed optimal resource allocation of individual users is easy to obtain and distributed power control algorithm has well-known fast convergence speed. Furthermore, it will be shown that the proposed distributed algorithm performs closely to the global optimal point. 2. System model We consider an energy constrained CR OFDMA network of N communicating pairs. Both transmitter i and receiver j is indexed by N := { 1,2, , N } .Ifj = i, receiver j is said to be the intended receiver of transmitter i. The transmission system is assumed to be a time-slotted OFDMA system with fixed time slot duration T S . Slot synchronization is assumed to be achieved through a beaconing mechanism. Before each time slot, a guard interval is inserted to achieve synchronization, perform spectrum detection as well as resource allocation (based on the proposed scheme). Inter-carrier interference (ICI) caused by frequency offset of the side lobes pertaining to transmitter i is not considered in this work (which can be mitigated by windowing the OFDM signal in the time domain or adaptively deactivating adjacent subcarriers T. Weiss (2004)). A frequency selective Rayleigh fading channel is assumed at the physical layer, and the entire spectrum is appropriately divided into M subcarriers to guarantee each subcarrier 2 The optimal allocation strategy with objective to minimize power or maximize throughput is named margin-adaptive and rate-adaptive waterfilling over frequency channels, respectively. 597 Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 4 Theory and Applications of Ad Hoc Networks experiencing flat Rayleigh fading S. Kondo (1996). We label the subcarrier set available to the transmitter receiver pair i after spectrum detection by L i ⊂ { 1,2, , M } .LetG :=  G k i,j ,i, j ∈N,k ∈L i  denote the subcarrier fading coefficient matrix, where G k i,j stands for the sub-channel coefficient gain from transmitter i to receiver j over subcarrier k. G k i,j = |H k i,j ( f )| 2 , where |H k i,j ( f )| is the transfer function. It is assumed that G adheres to a block fading channel model which remains invariant over blocks (coherence time slots) of size T S and uncorrelated across successive blocks. The noise is assumed to be additive white Gaussian noise (AWGN), with variance σ 2 i,k over subcarrier k of receiver i.WedefineP :=  p k i , p k i ≥ 0, i ∈N,k ∈L i  as the transmission power allocation matrix for all users in N over the entire available subcarrier set  i∈N L i ,wherep k i is the power allocated over subcarrier k for transmitter i.Foreach transmitter i,thepower vector can be formed as p p p i :=[p 1 i , p 2 i , , p M i ] T (1) If the k th subcarrier is not available for transmitter i, p k i = 0. Each node is not only energy limited but also has peak power constraint, i.e., ∑ k∈L i p k i ≤ p max i . The set of all feasible power vector of transmitter i is denoted by P i P i :=  p p p i ⊂ ∏ k∈L i [0, p max i ], ∑ k∈L i p k i ≤ p max i  (2) The signal to interference plus noise ratio (SINR)ofreceiveri over subcarrier k (γ k i )canbe expressed as γ k i (p k i )=α k i (p k j ) · p k i α k i (p k j )= G k i,i ∑ j=i,j∈N G k j,i · p k j + σ 2 i,k (3) where α k i is defined as the channel state information (CSI) which treats all interference as background noise. α k i can be measured at the receiver side and is assumed to be known by the corresponding transmitter through a reciprocal common control channel. When all users divide the spectrum in the same fashion without coordination, it is referred to as a Parallel Gaussian Interference Channel which leads to a tractable inner bound to the capacity region of the interference model. The achievable maximum data rate for each user (Shannon’s capacity formula) is c i ( p p p i ) B k i = ∑ k∈L i c k i  p k i  B k i = ∑ k∈L i , p k i ∈P i log 2  1 + α k i (p k j ) · p k i  (4) where B k i is the equally divided subcarrier bandwidth for transmitter i. Without loss of generality, B k i is assumed to be unity in this work. The noise is assumed to be independent of the symbols and has variance σ 2 for all receivers over entire available subcarrier set. Furthermore, all communicating transmitter and receiver pairs are assumed to have diverse 598 Mobile Ad-Hoc Networks: Protocol Design Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 5 QoS requirements specified by ∑ k∈L i c k i ≥ r tar i ,wherer tar i is the target data rate of transmitter i. In an energy constrained network (such as a wireless sensor network), reception power is not negligible since it is generally comparable to the transmission power. We denote the receiving power as p r i which is treated as a constant value for all receivers 3 . Aiming at achieving high energy efficiency, the energy consumption per information bit for transmitter receiver pair i in each time slot is e i (p p p i ,c i ) := ∑ k∈L i p k i + p r i ∑ k∈L i c k i (5) Let S i (p p p i ,c i ) denote the set of all power and rate allocations satisfying QoS requirements and power limit constraints for transmitter i, and it is given by S i (p p p i ,c i )=  p p p i ,c i : p p p i ∈P i , c i ≥ r tar i , i ∈N  (6) Given the above system assumptions and the objective defined in (5), we end up with the following constrained optimization problem. min p k i ,c k i ∈S i e i (p p p i ,c i ) s.t. c i ( p p p i ) ≥ r tar i ,∀i ∈N ∑ k∈L i p k i ≤ p max i ,∀i ∈N (7) 3. Energy efficient resource allocation algorithm The problem (7) is a combinatorial optimization problem and the objective function is not convex/concave. Constrained optimization techniques can be applied here but with considerable computational complexity. Hence, a two-stage algorithm is proposed in this section to decouple the original problem into an unconstrained problem in order to reduce the search space. After the optimal solution for the unconstrained problem is obtained in stage 1, the power and data rate constraints will be examined in search of the final optimal solution. It should be noted that the solution of the unconstrained problem provides the optimal operating point which can be taken as the benchmark for the system design. 3.1 Unconstrained energy efficient resource allocation We define the unconstrained energy per bit function as f ( ˆ p p p i ,α α α i ) := ∑ k∈L i ˆ p k i + p r i ∑ k∈L i log 2  1 + α k i · ˆ p k i  (8) 3 In this work, we consider an energy constrained CR ad-hoc wireless network where the throughput requirement is usually not as high as the throughput demanding networks such that the baseband symbol rate is not very high. Thus this baseband power consumption is quite small compared with the power consumption in the RF circuitry. Hence, we neglect the energy consumption of baseband signal processing blocks to simplify the model, and the receiving power equals to the power consumption in the RF circuitry and can be treated as a constant S. Cui (2005) 599 Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 6 Theory and Applications of Ad Hoc Networks where ˆ is used to represent the variables in the unconstrained optimization domain and α α α i =[α 1 i ,α 2 i , ,α k i ]. It is assumed f ( ˆ p p p i ,α α α i ) is a continuous function in R + M .Wedefinethe unconstrained optimal energy per bit for transmitter i of (8) as ˆ ζ ∗ i = min f ( ˆ p p p i ,α α α i ). 3.1.1 Energy efficient waterfilling Theorem 1 Given the channel state information α α α i and noise power, power allocation ˆ p p p ∗ i = [ ˆ p 1∗ i , ˆ p 2∗ i , , ˆ p k∗ i ,k ∈L i ] is defined as the unconstrained optimal power allocation by satisfying f ( ˆ p p p ∗ i ,α α α i ) ≤ f ( ˆ p p p i ,α α α i ) , ∀ ˆ p p p i ⊂ R M + (9) Then the unconstrained optimal power allocation can be obtained by solving the following equations: ˆ p k∗ i = max  log 2 e · ˆ ζ ∗ i − 1 α k i ,0  ˆ ζ ∗ i = ∑ k∈L i ˆ p k∗ i + p r i ∑ k∈L i log 2  1 + α k i · ˆ p k∗ i  (10) Proof: Differentiating f ( ˆ p p p i ,α α α i ) with respect to ˆ p k i (which stands for the power allocated for transmitter i on subcarrier k), we obtain the equations (10). The details of the derivation are given in Appendix A.  The value of ˆ ζ ∗ i can be obtained by using a numerical method which will in turn determine ˆ p p p ∗ i . It is observed that ˆ p p p ∗ i has similar type of rate-adaptive / margin-adaptive waterfilling results, and we name it energy-efficient waterfilling. Whereas, the fundamental difference among them lies in the positions of their respective optimal points. The rate-adaptive waterfilling maximizes the achievable data rate under power upper bound, and margin-adaptive waterfilling minimizes the total transmission power subject to a fixed rate constraint W. Yu (2002), both of which achieve their optimality at the boundary of the constraints. On the contrary, the proposed energy-efficient waterfilling selects the most energy-efficient operating point (in other words, it selects the optimal data rate that minimizes the energy consumption per information bit) while adhering to the QoS requirements and power limits. In this case, optimality is usually obtained in the constraint interval rather than on the boundary. In fact, the rate-adaptive and margin-adaptive waterfilling can be considered as special cases of the energy-efficient waterfilling solved. If we set ∑ k∈L i p k i = p con ≤ p max i or ∑ k∈L i c k i (p k i )=r tar i ,theenergy-efficient allocation problem is reduced to the well explored rate-adaptive or margin-adaptive waterfilling problem. 3.1.2 Feasibility region The existence of the solution for the unconstrained optimization (min f( ˆ p p p i ,α α α i )) depends on the subcarrier condition α k i if we assume other system parameters (e.g. bandwidth, maximal 600 Mobile Ad-Hoc Networks: Protocol Design [...]... Multi-User Resource Allocation in Mobile Ad Hoc Networks Using Multi-Carrier CDMA Modulation”, IEEE JSAC, Vol 26, No 1, pp.70-82, Jan 2008 R Yates, “A Framework for Uplink Power Control in Cellular Radio Systems”, IEEE Journal on Selected areas in Communications, vol 13, no 7, pp 1341 - 1348, Sept 1995 20 614 Theory and Applications of Ad Protocol Design Mobile Ad- Hoc Networks: Hoc Networks S Gao, L Qian, and... should be non-negative, i.e., pτ = log2 · ζ i − α τ ≥ 0 =⇒ ατ ≥ i ln 2 ˆ ζ imax i 8 602 Theory and Applications of Ad Protocol Design Mobile Ad- Hoc Networks: Hoc Networks Fig 3 Partition of the solution space of the constrained optimization problem 2) Sufficiency: We prove this part by contradiction If ατ ≥ i ln 2 ˆ ζ imax and still no optimal solution exists, which implies that the power allocated on... Defined Radio Networks , IEEE International Conference on Computer Communications (INFOCOM), pp.1-9, May 2007 Y Shi, Y T Hou, “Optimal Power Control for Multi-Hop Software Defined Radio Networks , IEEE International Conference on Computer Communications (INFOCOM), pp .169 4-1702, May 2007 29 Theory and Applications of Ad Hoc Networks Takuo Nakashima Tokai University Japan 1 Introduction In ad hoc mobile networks. .. proactive and reactive routing protocols has been explored in the last decade To realize the real environment, the selection of the mobile pattern and the size of nodes are key element of simulation Marinoni et al (3) discussed routing protocol performance in a realistic environment New mobility model has introduced and installed in 616 Mobile Ad- Hoc Networks: Protocol Design ns-2 simulator The discussion,... source, which carries the route traversed by the Route Request 618 Mobile Ad- Hoc Networks: Protocol Design packet received In addition, the TTL field constrains the reachable distance, and the sequence number filed prevents double transmission and loops 2.1.2 Ad hoc On-demand Distance Vector Ad hoc on-demand distance vector (AODV) routing protocol uses an on-demand approach for finding routes, that is,... message without loss 2.5 Simulation model We adopted the LBNL network simulator (ns) (6) to evaluate the effects of performance of ad hoc routing protocols The ns is a very popular software for simulating advanced TCP/IP algorithms and wireless ad hoc networks Our experiments configure two stationary end nodes and intermediate mobile nodes in the ad hoc networks The TCP connection will be limited between... and Mobile Systems, pp 174-181, 2004 I F Akyildiz, Won-Yeol Lee, and Kaushik R Chowdhury “CRAHNs: Cognitive Radio Ad Hoc Networks , Ad Hoc Networks, vol 7, pp 810 - 836, 2009 I F Akyildiz, W Lee, M C Vuran, and S Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless network: A survey.”, Computer Networks, vol 50, no 13, pp 2127 - 2159, Sep 2006 J Mitola et al, “Cognitive radio:... and Applications of Ad Protocol Design Mobile Ad- Hoc Networks: Hoc Networks 0.18 Total Power Tx2 Total Power Tx1 Power (W) 0.17 0 .16 0.15 Power Convergence 0.14 0.13 1 1.5 2 2.5 3 3.5 4 Steps for Power Convergence 4.5 5 5.5 6 0.028 0.026 Power (W0 0.024 Subcarrier6 Converge 0.022 0.02 Subcarrier1 Tx1 Subcarrier6 Tx1 Subcarrier1 Tx2 Subcarrier6 Tx2 Subcarrier1 Converge 0.018 0. 016 0.014 1 1.5 2 2.5... generate one TCP traffic through an appropriate route controlled by ad hoc routing protocols While mobile nodes are located randomly on the 500 (m) * 400 (m) square area, and move through this area with the same speed In our simulations, the speed of mobile node will be changed at 5, 10, 15 and 20 620 Mobile Ad- Hoc Networks: Protocol Design (m/sec) to emulate a bicycle and a normal car speed The direction... increases in terms of the number of node In addition, we captured the following three features of TCP throughput performances for different protocols Firstly, the TCP throughput on AODV 624 Mobile Ad- Hoc Networks: Protocol Design with mobile intermediate nodes degrades about 20 % compared to stationary intermediate nodes in dense node condition, and exponentially degrades until 10 (m/sec) node speeds in a . receiver pairs are assumed to have diverse 598 Mobile Ad- Hoc Networks: Protocol Design Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 5 QoS requirements specified by ∑ k∈L i c k i ≥. parameters (e.g. bandwidth, maximal 600 Mobile Ad- Hoc Networks: Protocol Design Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 7 0 1 2 3 4 5 6 x 10 −3 −0.2 −0.15 −0.1 −0.05 0 0.05 0.1 0.15 0.2 ζ i k 0. requirement under the maximal power bound. 602 Mobile Ad- Hoc Networks: Protocol Design Energy Efficient Resource Allocation in Cognitive Radio Wireless Ad Hoc Networks 9 Based on (7) and (10), we can