A Game Theory Based Congestion Control Protocol for Wireless Personal Area Networks

Abstract

In wireless sensor networks (WSNs), the presence of congestion increases the ratio of packet loss and energy consumption and reduces the network throughput. Particularly, this situation will be more complex in Internet of Things (IoT) environment, which is composed of thousands of heterogeneous nodes. RPL is an IPv6 routing protocol in low power and lossy networks standardized by IETF. However, the RPL can induce problems under network congestion, such as frequently parent changing and throughput degradation. In this paper, we address the congestion problem between parent nodes and child nodes in RPL-enabled networks, which typically consist of low power and resource constraint devices. To mitigate the effect of network congestion, we design a parent-change procedure by game theory strategy, by which the child nodes can change next hop neighbors toward the sink. Comparing to the ContikiRPL implementation, the simulation results show that our protocol can achieve more than two times improvement in throughput and reduce packet loss rate with less increasing of average hop count.

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

“…Others such as control overhead packets [41], [42], [48], network efficiency [38], [50], hop count [41], [51], [52] and quality of data (QoD) [50].…”

“…The least congested node in each ZoR and ZoA is selected as next two hops to route a packet through them. Also, the proposed algorithm uses two parameters, Q ZoR [51], [52] • Selects alternative less congested paths by using Game Theory • Improves throughput and packet loss ratio • Increases control overhead packets • Does not have a policy to reduce source rate when non-congestion nodes (paths) are not available • Does not support the hybrid application type CA-RPL [93] • Mitigates congestion by distributing heavy traffic to different paths • Improves packet loss and delay • Does not aware when high packet overflow occurs at nodes' queue • Does not have a policy to reduce source rate when non-congestion nodes (paths) are not available • Does not support the hybrid application type CA-OF RPL [44] • Selects less congested nodes (paths) by using buffer occupancy as a routing metric • Improves packet loss due to buffer drops, throughput, packet delivery ratio, and energy consumption • Does not have a policy to reduce source rate when non-congestion nodes (paths) are not available • Does not support the hybrid application type Lodhi's M-RPL [95] • Splits the forwarding rate among multiple paths • Improves throughput, latency, and energy consumption • Does not have a policy to reduce source rate when non-congestion nodes (paths) are not available • Does not the support hybrid application type MLEq [96] • Achieves load balancing and distribution based on water flow behavior working principle • Supports and is aware of multiple gateways in the network • Improves throughput, fairness, and control overhead The proposed solution has been implemented by using the Contiki OS simulator, Cooja, and compared with Confirmable (CON) and Non-confirmable (NON) transactions of CoAP. The proposed mechanism is tested within 50 nodes which are distributed in an area of 201 m x 201 m during 300 seconds simulation time.…”

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

“…In [19] and [20], the authors proposed a congestion control mechanism called Game Theory Congestion Control (GTCC) for 6LoWPAN networks. The proposed protocol detects congestion by using the network packet flow rate which is packet generation rate subtracted by packet service rate.…”

Congestion Control for 6LoWPAN Networks: A Game Theoretic Framework

“…A short review of these mechanisms is given below. However, according to our best knowledge, none of the proposed algorithms in congestion control literature for WSNs and 6LoWPAN networks: (i) uses game theory for traffic control (rate adaptation) [12], [13] to solve the congestion problem (the work in [14] and [21] (both papers are the same work) use game theory for parent selection (routing)) and (ii) supports and is aware of both node priorities and application priorities. However, the non-cooperative game theory provides an analytical framework suited for characterizing the interactions and decision making process among several players with conflicting interests [15].…”

“…In [14] and [21], the authors proposed a congestion control mechanism called Game Theory Congestion Control (GTCC) for 6LoWPAN networks. The proposed protocol detects congestion by using the network packet flow rate which is packet generation rate subtracted by packet service rate.…”