Design and Experimental Evaluation of a Cross-Layer Deadline-Based Joint Routing and Spectrum Allocation Algorithm

The design and implementation of a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks is reported in this article. Different traffic classes including text, voice, surveillance video, and threat alert among others need to be handled by these networks. Each of these traffic classes have different quality of service (QoS) based deadline requirements. Additionally, these networks are characterized by dynamic channel and traffic conditions that vary with time and location. Even under these conditions, it is critical to receive packets before the deadline expires to make rapid decisions in the battlefield. Therefore, a tactical ad-hoc network should be able to adapt to these requirements and maximize the number of packets delivered to the destination within the specified deadline. A distributed deadline-based routing and spectrum allocation algorithm is designed to maximize the utilization of the available resources and ensure delivery of packets within the deadline constraints. To this end, a weighted virtual queue (VQ) that is used to construct the network utility function is defined. Accordingly, the optimal session, next hop, transmit power, and frequency is determined by the distributed algorithm to ensure efficient utilization of the available resources. Hence, maximizing the delivery of packets to the intended destination within the specified deadline. The 49 node simulation shows up to 35 percent improvement in effective throughput and 26 percent improvement in reliability as compared to joint ROuting and Spectrum Allocation algorithm (ROSA), which does not adapt according to the deadline requirements of the data flowing through the network. As a secondary objective, this work advances the state of the art of the experimental cross-layer framework to address the challenges involved in having such cross-layer algorithms implemented on a testbed. The required flexibility to change the transmission parameters on-the-fly is provided by the proposed framework. The network is designed to enable the data exchange between neighbors using custom designed control packets (which might be different for different algorithms) since this information is critical for nodes to perform optimization. Cross-layer optimization is achieved by means of data management and control entities that enable information exchange between layers. The practicality of the proposed solution was proven by having the novel algorithm implemented on a five-node software defined radio testbed which leverages the proposed cross-layer framework. In contrast to ROSA, the proposed algorithm demonstrated up to 17 percent improvement in terms of throughput and reliability. The performance improvement achieved is expected to increase on a larger network deployment.

[1]  Matthew Andrews,et al.  Providing quality of service over a shared wireless link , 2001, IEEE Commun. Mag..

[2]  Dimitris A. Pados,et al.  All-Spectrum Cognitive Channelization around Narrowband and Wideband Primary Stations , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[3]  Marco Di Felice,et al.  SEARCH: A routing protocol for mobile cognitive radio ad-Hoc networks , 2009 .

[4]  Jian Ni,et al.  Improved bounds on the throughput efficiency of greedy maximal scheduling in wireless networks , 2011, TNET.

[5]  Sem C. Borst,et al.  Instability of MaxWeight Scheduling Algorithms , 2009, IEEE INFOCOM 2009.

[6]  Xuemin Shen,et al.  QoS Provisioning for Heterogeneous Services in Cooperative Cognitive Radio Networks , 2011, IEEE Journal on Selected Areas in Communications.

[7]  Eylem Ekici,et al.  Delay-Guaranteed Cross-Layer Scheduling in Multihop Wireless Networks , 2010, IEEE/ACM Transactions on Networking.

[8]  P. Bahl,et al.  DSAP: a protocol for coordinated spectrum access , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[9]  J. Walrand,et al.  Sufficient conditions for stability of longest-queue-first scheduling: second-order properties using fluid limits , 2006, Advances in Applied Probability.

[10]  Emrecan Demirors,et al.  RcUBe: Real-time reconfigurable radio framework with self-optimization capabilities , 2015, 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[11]  M. Motani,et al.  Cross-layer design: a survey and the road ahead , 2005, IEEE Communications Magazine.

[12]  Andrew L. Drozd,et al.  Network Throughput Improvement in Cognitive Networks by Joint Optimization of Spectrum Allocation and Cross-layer Routing , 2014 .

[13]  M. Yousof Naderi,et al.  Spectrum Allocation and QoS Provisioning Framework for Cognitive Radio With Heterogeneous Service Classes , 2014, IEEE Transactions on Wireless Communications.

[14]  Jie Wu,et al.  Deadline-sensitive opportunistic utility-based routing in cyclic mobile social networks , 2015, 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[15]  Hussein T. Mouftah,et al.  A Survey on Cross-Layer Quality-of-Service Approaches in WSNs for Delay and Reliability-Aware Applications , 2016, IEEE Communications Surveys & Tutorials.

[16]  Ness B. Shroff,et al.  Understanding the Capacity Region of the Greedy Maximal Scheduling Algorithm in Multihop Wireless Networks , 2008, IEEE/ACM Transactions on Networking.

[17]  Bhaskar Krishnamachari,et al.  DAWN: A density adaptive routing for deadline-based data collection in vehicular delay tolerant networks , 2013 .

[18]  Leandros Tassiulas,et al.  Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks , 1990, 29th IEEE Conference on Decision and Control.

[19]  Eylem Ekici,et al.  Delay-Aware Cross-Layer Design for Network Utility Maximization in Multi-Hop Networks , 2011, IEEE Journal on Selected Areas in Communications.

[20]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[21]  Tommaso Melodia,et al.  Platforms and testbeds for experimental evaluation of cognitive ad hoc networks , 2010, IEEE Communications Magazine.

[22]  Sudharman K. Jayaweera,et al.  Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[23]  Lei Ding,et al.  All-Spectrum Cognitive Networking through Joint Distributed Channelization and Routing , 2013, IEEE Transactions on Wireless Communications.

[24]  Tommaso Melodia,et al.  COmBAT: Cross-layer Based testbed with Analysis Tool implemented using software defined radios , 2016, MILCOM 2016 - 2016 IEEE Military Communications Conference.

[25]  Subramanian Ramanathan,et al.  High-Level System Design of IEEE 802.11b Standard-Compliant Link Layer for MATLAB-Based SDR , 2016, IEEE Access.

[26]  G. Veciana,et al.  Throughput optimality of delay-driven MaxWeight scheduler for a wireless system with flow dynamics , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[27]  Ness B. Shroff,et al.  Exploring the inefficiency and instability of Back-Pressure algorithms , 2013, 2013 Proceedings IEEE INFOCOM.

[28]  Tommaso Melodia,et al.  DRS: Distributed Deadline-Based Joint Routing and Spectrum Allocation for Tactical Ad-Hoc Networks , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[29]  Ki-Il Kim,et al.  A Deadline Aware DTN Approach Based on Epidemic Routing , 2014, 2014 IEEE 13th International Symposium on Network Computing and Applications.

[30]  Hari Balakrishnan,et al.  Airblue: A system for cross-layer wireless protocol development , 2010, 2010 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS).

[31]  Sem C. Borst,et al.  Spatial inefficiency of MaxWeight scheduling , 2011, 2011 International Symposium of Modeling and Optimization of Mobile, Ad Hoc, and Wireless Networks.

[32]  Eylem Ekici,et al.  Scheduling in Multihop Wireless Networks Without Back-Pressure , 2010, IEEE/ACM Transactions on Networking.

[33]  Yunnan Wu,et al.  Allocating dynamic time-spectrum blocks in cognitive radio networks , 2007, MobiHoc '07.

[34]  Lei Ding,et al.  Cross-Layer Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks , 2010, IEEE Transactions on Vehicular Technology.

[35]  Rafael P. Laufer,et al.  A Cross-Layer Backpressure Architecture for Wireless Multihop Networks , 2014, IEEE/ACM Transactions on Networking.

[36]  Eylem Ekici,et al.  Capacity Achieving Distributed Scheduling With Finite Buffers , 2015, IEEE/ACM Transactions on Networking.

[37]  Bo Fu,et al.  A Survey of Cross-Layer Designs in Wireless Networks , 2014, IEEE Communications Surveys & Tutorials.

[38]  Ness B. Shroff,et al.  Delay-Based Back-Pressure Scheduling in Multihop Wireless Networks , 2011, IEEE/ACM Transactions on Networking.

[39]  Alexander L. Stolyar,et al.  A Novel Architecture for Reduction of Delay and Queueing Structure Complexity in the Back-Pressure Algorithm , 2011, IEEE/ACM Transactions on Networking.

[40]  Ling Luo,et al.  Analysis of Dynamic Spectrum Access with Heterogeneous Networks: Benefits of Channel Packing Scheme , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[41]  Joel J. P. C. Rodrigues,et al.  A survey on cross-layer solutions for wireless sensor networks , 2011, J. Netw. Comput. Appl..

[42]  Ilenia Tinnirello,et al.  Wireless MAC processors: Programming MAC protocols on commodity Hardware , 2012, 2012 Proceedings IEEE INFOCOM.

[43]  Gustavo de Veciana,et al.  Throughput optimality of delay-driven MaxWeight scheduler for a wireless system with flow dynamics , 2009 .

[44]  Jens-Peter Redlich,et al.  Delay properties of opportunistic back-pressure routing in CSMA-based wireless mesh networks , 2010, 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops.

[45]  Srinivasan Seshan,et al.  Enabling MAC Protocol Implementations on Software-Defined Radios , 2009, NSDI.