Distributed and Adaptive Medium Access Control for Internet-of-Things-Enabled Mobile Networks
Abstract:
In this paper, we propose a distributed and adaptive hybrid medium access control (DAH-MAC) scheme for a singlehop Internet-of-Things (IoT)-enabled mobile ad hoc network (MANET) supporting voice and data services. A hybrid superframe structure is designed to accommodate packet transmissions from a varying number of mobile nodes generating either delay-sensitive voice traffic or best-effort data traffic. Within each superframe, voice nodes with packets to transmit access the channel in a contention-free period using distributed time division multiple access (TDMA), while data nodes contend for channel access in a contention period using truncated carrier sense multiple access with collision avoidance (T-CSMA/CA). In the contention-free period, by adaptively allocating time slots according to instantaneous voice traffic load, the MAC exploits voice traffic multiplexing to increase the voice capacity. In the contention period, a throughput optimization framework is proposed for the DAH-MAC, which maximizes the aggregate data throughput by adjusting the optimal contention window size according to voice and data traffic load variations. Numerical results show that the proposed MAC scheme outperforms existing QoS-aware MAC schemes for voice and data traffic in the presence of heterogeneous traffic load dynamics.
Existing System:
For a typical IoT-enabled MANET with power-rechargeable mobile nodes [11] (i.e., smartphones, laptops) generating a high volume of heterogeneous traffic, supporting heterogeneous services with differentiated QoS guarantee becomes an important but challenging task. The network is expected to not only provide as high as possible throughput for best-effort data traffic, but also ensure a bounded packet loss rate for delaysensitive voice communications or even multimedia streaming. Therefore, QoS-aware MAC is required to coordinate the channel access for heterogeneous traffic in a differentiated way to satisfy QoS requirements of all individual users [12]. However, the characteristics of MANETs pose technical challenges in the QoS-aware MAC design: 1) Since MANETs do not depend on any central control, distributed MAC is required to coordinate the transmissions of neighboring nodes based on their local information exchanges; 2) Nodes are mobile, making the heterogeneous network traffic load change with time. The traffic load variations can lead to QoS performance degradation. Thus, MAC is expected to be context-aware, which adapts to the changing network traffic load to achieve consistently satisfactory service performance.
Proposed system:
Consider a single-channel fully connected MANET [25] [30] [31], where each node can receive packet transmissions from any other node. The fully connected network scenario can be found in various MANET applications, including office networking in a building or in a university library where users are restricted to move in certain geographical areas [31], users within close proximity are networked with ad hoc mode in a conference site [12], M2M communications in a residential network for a typical IoT-based smart home application where home appliances are normally within the communication range of each other [2]. The channel is assumed error-free, and packet collisions occur when more than one node simultaneously initiate packet transmission attempts. Without any network infrastructure or centralized controller, nodes exchange local information with each other and make their transmission decisions in a distributed manner. The network has two types of nodes, voice nodes and data nodes, generating delay-sensitive voice traffic and best-effort data traffic, respectively. Each node is identified by its MAC address and a unique node identifier (ID) that can be randomly selected and included in each transmitted packet [24]. We use Nv and Nd to denote the total numbers of voice and data nodes in the network coverage area, respectively. Nodes are mobile with a relatively low speed, making Nv and Nd change with time.
Conclusion:
In this paper, we propose a distributed and traffic-adaptive hybrid MAC scheme for a single-hop IoT-enabled MANET. A hybrid MAC superframe structure is devised, in which voice nodes are allocated a time slot in a distributed way by adapting to their instantaneous transmission buffer states and data nodes contend to access the channel in a contention period of each superframe according to the T-CSMA/CA. The proposed hybrid MAC exploits the voice traffic multiplexing to improve the resource utilization while guaranteeing a voice packet loss rate bound, and reduces the congestion level for data nodes by the contention separation between voice and data traffic. A data throughput analytical and optimization framework is developed for the hybrid MAC, in which a closed-form mathematical relationship is established between the MAC layer parameter (i.e., the optimal contention window size) and the number of voice and data nodes in the network. With this framework, the maximum aggregate data throughput can be achieved and be adaptive to variations of the heterogeneous network traffic load. Based on a comparison with two well-known MAC protocols, simulation results demonstrate the effectiveness of our proposed MAC in supporting voice and data services in the presence of heterogeneous traffic load dynamics.
References:
[1] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Gener. Comput. Syst, vol. 29, no. 7, pp. 1645–1660, 2013.
[2] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A survey on enabling technologies, protocols, and applications,” IEEE Commun. Surv. Tutor., vol. 17, no. 4, pp. 2347–2376, 2015.
[3] H. Nishiyama, T. Ngo, S. Oiyama, and N. Kato, “Relay by smart device: Innovative communications for efficient information sharing among vehicles and pedestrians,” IEEE Veh. Technol. Mag., vol. 10, no. 4, pp. 54–62, 2015.
[4] J. Liu and N. Kato, “A markovian analysis for explicit probabilistic stopping-based information propagation in postdisaster ad hoc mobile networks,” IEEE Trans. Wireless Commun., vol. 15, no. 1, pp. 81–90, 2016.
[5] J.-R. Cha, K.-C. Go, J.-H. Kim, and W.-C. Park, “TDMA-based multihop resource reservation protocol for real-time applications in tactical mobile adhoc network,” in Proc. IEEE MILCOM’10, 2010, pp. 1936– 1941.
[6] C. Xu, L. Song, Z. Han, Q. Zhao, X. Wang, X. Cheng, and B. Jiao, “Efficiency resource allocation for device-to-device underlay communication systems: A reverse iterative combinatorial auction based approach,” IEEE J. Select. Areas Commun., vol. 31, no. 9, pp. 348–358, 2013.
[7] J. Qiao, X. Shen, J. W. Mark, Q. Shen, Y. He, and L. Lei, “Enabling device-to-device communications in millimeter-wave 5G cellular networks,” IEEE Commun. Mag., vol. 53, no. 1, pp. 209–215, 2015.
[8] M. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. Grieco, G. Boggia, and M. Dohler, “Standardized protocol stack for the Internet of (important) Things,” IEEE Commun. Surv. Tutor., vol. 15, no. 3, pp. 1389–1406, 2013.
[9] M. Dong, K. Ota, A. Liu, and M. Guo, “Joint optimization of lifetime and transport delay under reliability constraint wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 1, pp. 225– 236, 2016.
[10] M. Park, “IEEE 802.11ah: Sub-1-GHz license-exempt operation for the Internet of Things,” IEEE Commun. Mag., vol. 53, no. 9, pp. 145–151, 2015.
