A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
Abstract
:1. Introduction
2. Overview of RPL
2.1. DODAG Construction for Multipoint-to-Point Traffic Flows
2.2. Route Construction for Point-to-Multipoint and Point-to-Point Traffic Flows
2.3. Objective Function and Routing Metrics in RPL
2.4. Supporting Multiple RPL Instances
3. Classification of Schemes for Congestion Control and Load Balancing in RPL
- Congestion detection
- Congestion notification
- Congestion mitigation
- Traffic pattern (upward, downward, and any-to-any)
- Routing metrics
- Adopting cross-layer approaches
- Utilizing path diversity
- Resource control: Resource control is related to provisioning network resources to mitigate congestion. Alternative path selection schemes, multipath schemes, power control, and so on, belong in this category. Most RPL schemes proposed for congestion control adopt resource control approaches including route change, multipath forwarding, power control, and multisink.
- Resource control combined with traffic control: A traffic control approach attempts to mitigate congestion by adjusting the sending rate. Since the primary role of a network layer protocol is to find a good route, RPL schemes are rarely related to adaptation of the sending rate. Nevertheless, some schemes, such as GTCC [16] and OHCA [20], adopt both resource control and traffic control approaches.
4. Congestion Control Schemes for RPL
4.1. Schemes Forwarding Packets via a Single Parent
4.1.1. GTCC
4.1.2. QU-RPL
4.1.3. Residual Energy-Aware Congestion Control
4.1.4. PC-RPL
4.1.5. CA-OF
4.1.6. OHCA
4.1.7. CoAR
4.1.8. MAC-Aware Routing Metrics
4.1.9. Congestion Control using Multiple Sinks
4.2. Schemes Exploiting Path Diversity
4.2.1. BRPL
4.2.2. Multipath Opportunistic RPL Algorithms
4.2.3. LB-RPL
4.2.4. RPL using the Heuristic Load Distribution Algorithm
4.2.5. Energy-Aware Load Balancing
4.2.6. M-RPL
4.2.7. Bird Flocking Congestion Control Algorithm
4.2.8. CA-RPL
4.2.9. H-RPL
4.3. Congestion Control for DAO Packets
5. Discussion
6. Conclusions
Funding
Conflicts of Interest
References
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Schemes | Congestion Detection Metrics |
---|---|
GTCC [16] | Packet generation rate subtracted by packet service rate |
QU-RPL [17] | Queue utilization |
Ullah et al. [18] | Queue utilization |
PC-RPL [19] | Packet losses |
OHCA [20] | Ratio of packet interarrival time to packet service time |
CoAR [21] | Queue utilization, ratio of incoming traffic rate to outgoing traffic rate |
M-RPL [22] | Packet delivery ratio |
HC | ETX | EDC | QU | Link delay | LQI | RSSI | Loss Rate | Traffic Rate | RE | Additional Metrics | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
GTCC | [16] | √ | √ | |||||||||
QU-RPL | [17] | √ | √ | √ | ||||||||
Ullah et al. | [18] | √ | √ | √ | √ | |||||||
PC-RPL | [19] | √ | √ | √ | √ | subtree size | ||||||
CA-OF | [28] | √ | √ | |||||||||
OHCA | [20] | √ | √ | √ | ||||||||
CoAR | [21] | √ | √ | √ | no. of potential children/no. of neighbor nodes | |||||||
Marco et al. | [29] | √ | √ | busy channel prob., reliability constraint | ||||||||
Farooq et al. | [30] | √ | √ | √ | √ | available bandwidth | ||||||
BRPL | [31] | √ | √ | logical link layer channel capacity, change of no. of one-hop neighbors | ||||||||
Pavkovic et al. | [32] | √ | √ | √ | deadline constraints | |||||||
ORPL | [27] | √ | ||||||||||
ORPL-LB | [33] | √ | duty cycle | |||||||||
ORPL-DT | [34] | √ | ||||||||||
LB-RPL | [35] | √ | √ | |||||||||
Moghadam et al. | [36] | √ | √ | √ | ||||||||
Iova et al. | [37] | √ | √ | √ | ||||||||
Nassiri | [38] | √ | √ | √ | √ | superframe distance | ||||||
M-RPL | [22] | PDR | ||||||||||
Hellaoui et al. | [39] | √ | ||||||||||
CA-RPL | [40] | √ | √ | √ | wake-up interval | |||||||
H-RPL | [41] | √ | √ | available/required memory |
Direction of Information Flow | Relevant Metrics and Parameters | How the Layered Architecture is Violated | Objectives | |
---|---|---|---|---|
GTCC [16] | Upward | LQI | Calculating the rank considering LQI | To detect congestion and node failure and unavailability |
Ullah et al. [18], CoAR [21] | Upward | Residual energy | Parent selection considering remaining energy | To avoid selecting energy-constrained nodes |
PC-RPL [19] | Back and forth | RSSI | Jointly controlling transmission power and routing topology | To address the hidden terminal problem |
Marco et al. [29] | Upward, Downward | LQI, Busy channel probability, Max. no. of backoffs and retransmissions at the MAC layer, Reliability constraint | Calculating link reliability and traffic load distribution considering bad channel probability (LQI), busy channel probability, MAC parameters, and power consumption, reliability constraint given by the application | To extend reliability metric by considering packet losses due to MAC contention To distribute load while keeping the minimum reliability required by the application |
Pavkovic et al. [32] | Downward | Deadline constraints | Selecting a node that satisfies the deadline constraint which is assigned on packet generation | To meet the deadline that is assigned to each packet |
ORPL-LB [33] | Back and forth | Wake-up interval | Adapting the wake-up interval by comparing the current duty cycle and the target duty cycle | To balance load by adjusting nodes’ wake-up intervals |
Iova et al. [37] | Upward | Residual energy | Identifying the bottleneck nodes in terms of Expected Lifetime metric (=time until a node will run out of energy) | To detect energy bottleneck nodes and to spread the traffic among them |
Nassiri [38] | Upward | RSSI, Residual energy, Superframe distance, | Using RSSI for the initial DODAG construction and considering the upstream energy estimate and superframe distance for periodic parent selection | To prevent selecting parents with low quality or low energy and to reduce end-to-end delay in the beacon-enabled mode |
H-RPL [41] | Upward | Residual energy, Available/required memory | Determining the routing type adaptively considering the critical resource status such as residual energy and available/required memory | To balance the routing workload between nodes considering the resource status and requirement |
Main Feature | Routing Metrics & Constraints | Evaluation Tool | Evaluation Metrics | |
---|---|---|---|---|
GTCC [16] | Designing the parent change procedure using a potential game. Detecting congestion based on the difference of packet transmission rate and the packet service rate. Notifying congestion explicitly by sending a DIO packet with the CN bit set. | Link quality indicator (LQI), transmission rate | Cooja simulation | Packet losses, throughput, average hop count |
QU-RPL [17] | Selecting a parent based on queue utilization, hop count, and ETX | ETX, queue utilization, hop count | Real testbed experiment | PDR, routing overhead, scalability |
Ullah et al. [18] | Selecting a parent based on queue utilization, residual energy, and ETX | ETX, queue utilization, residual energy | Cooja simulation | PDR, power consumption |
PC-RPL [19] | Addressing the hidden terminal problem and load imbalance by jointly controlling transmission power and routing topology | Hop count, ETX, queue losses, link losses, RSSI | Real testbed experiment | PRR, hop count, end-to-end ETX, parent change frequency, routing overhead, total transmission count |
CA-OF [28] | Using a weighted sum of ETX and buffer occupancy | ETX, buffer occupancy | Cooja simulation | PDR, number of lost packets, throughput, energy consumption |
OHCA [20] | Adopting both resource control (parent change if available) and traffic control (sending rate adaptation). Detecting congestion by monitoring if the arrival rate exceeds the service rate | Buffer occupancy, ETX, queuing delay | Cooja simulation | Throughput, weighed fairness index, end-to-end delay, energy consumption, number of lost packets |
CoAR [21] | Using TOPSIS to select the best alternative parent. Adjusting the threshold for congestion detection according to the buffer occupancy and the incoming/outgoing traffic | ETX, queue utilization, residual energy, neighborhood index | Cooja simulation | PRR, end-to-end delay, packet loss rate, throughput, power consumption |
Marco et al. [29,46] | Proposing two metrics exploiting the information from the MAC layer: R-metric representing end-to-end reliability and Q-metric representing the optimal traffic for balanced energy consumption | LQI, busy channel probability, traffic rate | Testbed experiment (using TelosB platforms) | End-to-end node reliability, average node power consumption |
Farooq et al. [30] | Extending the original RPL for a wireless sensor network with multiple sinks | Hop count and tie-breaking metrics (available bandwidth, delay, MAC layer queue occupancy, ETX) | Cooja simulation | PDR, delay, total retransmissions |
Main Feature | Routing Metrics & Constraints | Evaluation Tool | Evaluation Metrics | |
---|---|---|---|---|
BRPL [31] | Switching smoothly and adaptively between the RPL OF with backpressure routing. Considering mobility. | Queue backlogs, ETX | Cooja simulation, FIT IoT-LAB testbed | Packet loss, end-to-end delay, communication overhead |
Pavkovic et al. [32] | Opportunistically forwarding packets over multiple parents satisfying the delay constraint on a per-packet basis | Delay to the sink, deadline constraints, ETX | WSNet/Worldsens | PDR, incurred delay, overhead |
ORPL [27] | Opportunistic routing protocol supporting upward, downward, and any-to-any on-demand traffic | EDC (Estimated Duty Cycled wake-ups) | Indriya testbed experiment | PDR, latency, duty cycle |
ORPL-LB [33] | Opportunistic forwarding. Adjusting the wake-up interval depending on the traffic load | EDC, average duty cycle | Indriya testbed experiment | PDR, latency, duty cycle, |
ORPL-DT [34] | Opportunistic forwarding. Improving the reliability of routes by updating the routing metric using both upward and downward traffic | EDC | Real testbed experiment | Uplink/downlink packet reception ratio (PRR), PRR fairness, no. of routing messages |
LB-RPL [35] | Forwarding data using multiple parent nodes. Setting the DIO message transmission timer in proportion to workload | Packet drop probability, buffer utilization counter | Ns-2 simulation | Packet delivery ratio, packet loss, packet delivery delay |
Moghadam et al. [36] | Equalizing traffic load share between parents of an equal depth from the sink while minimizing transmission cost | Hop count, ETX | Simulation using MATLAB, OMNET++ | Network life time, throughput, collisions, link drops |
Iova et al. [37] | Splitting traffic between multiple parents to spread traffic load uniformly among energy bottleneck nodes | ELT (Expected Lifetime) | WSNet | PDR, energy consumption, network lifetime, no. of parent changes |
Nassiri [38] | Adopting a cross-layer approach using RSSI and superframe distance. | RSSI, superframe distance, remaining energy, load | WSNet | PDR, Delay, Lifetime |
M-RPL [22] | Initially establishing a single path but providing temporary multipath routing during period of congestion. Detecting congestion relying on average PDR | Cooja simulation | Throughput, end-to-end delay, energy consumption | |
Hellaoui et al. [39] | Using a bird flocking model | Buffer filling ratio | Cooja simulation | Latency, Packet loss ratio |
CA-RPL [40] | Using a delay metric calculated considering the duty cycle at the MAC layer. Splitting traffic over two best parents | ETX, minimized delay to the DODAG root, no. of received packets | Cooja simulation | Throughput, packet loss ratio, latency |
H-RPL [41] | Allowing heterogeneous, mixed, adaptive routing types based on available/required resource. Shifting routing workload from nodes with less resource to nodes with more resources. Load balancing based on queue utilization. | Available/memory, required memory, expected routing lifetime | Ns-2 simulation | PDR, energy consumption, network lifetime |
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Lim, C. A Survey on Congestion Control for RPL-Based Wireless Sensor Networks. Sensors 2019, 19, 2567. https://doi.org/10.3390/s19112567
Lim C. A Survey on Congestion Control for RPL-Based Wireless Sensor Networks. Sensors. 2019; 19(11):2567. https://doi.org/10.3390/s19112567
Chicago/Turabian StyleLim, Chansook. 2019. "A Survey on Congestion Control for RPL-Based Wireless Sensor Networks" Sensors 19, no. 11: 2567. https://doi.org/10.3390/s19112567
APA StyleLim, C. (2019). A Survey on Congestion Control for RPL-Based Wireless Sensor Networks. Sensors, 19(11), 2567. https://doi.org/10.3390/s19112567