Routing protocol for Low-Power and Lossy Networks for heterogeneous traffic network

Abstract

In a real network deployment, the diverse sensor applications generate a heterogeneous traffic pattern which may include basic sensing measurements such as temperature readings or high-volume multimedia traffic. In a heterogeneous traffic network, the two standardized objective functions (OFs), i.e., objective function zero (OF0) and the Minimum Rank with Hysteresis Objective Function (MRHOF) for routing protocol for Low-Power and Lossy Networks (RPL) perform poor routing decisions by selecting an already congested parent node and cause more retransmissions across the network. Therefore, careful consideration is required in designing a new OF for heterogeneous traffic scenarios. In this study, we examine the RPL protocol under a heterogeneous traffic pattern and proposed a new protocol based on queue and workload-based condition (QWL-RPL). The aim of the proposed protocol is to achieve a reliable path with better overall performance. The proposed OF model considers the link workload in addition to mapping the congestion status of the node using the packet queue. We implement the proposed routing model in the Contiki operating system (OS) Cooja environment to compare with the existing technique. The simulation results show that QWL-RPL can improve the performance of a heterogeneous traffic network as compared with both OF0 and MRHOF, specifically in terms of the amount of overhead, packets reception ratio (PRR), average delay, and jitter. Final results indicate that on average, there is a 5%-30% improvement in PRR, 25%-45% reduction in overheads, 12%-30% reduction in average delay, and 20%-40% reduction in jitter.The Internet of Things (IoT) is a large system of interconnected heterogeneous devices to sense our physical environment and communicate the sensed information. Each sensor detects its vicinity and transmits the sensed information to the sink node via its neighboring nodes using multihop communication [1]. The practical applications of such a system include smart cities [2] [3], industrial and building automation, disaster management [4], smart grid, and smart healthcare [5]. A sensor discovers its neighbors, builds the topology, and routes the sensed data. An IoT system requires an efficient networking mechanism to facilitate such an interconnected heterogeneous flow of traffic. An efficient routing mechanism must consider these device characteristics and constrained resources. Furthermore, the IoT sensors usually face unfavorable environmental factors. Thus, designing of such routing protocols is a complicated task due to the resource limitations of these devices, such as limited energy, limited memory, and limited processing power [6]. For any pair of communicating nodes, a reasonable goal for such a protocol is to improve communication performance while maintaining energy consumption at a minimum level. However, achieving this goal requires careful consideration of the lossy nature of the network, fluctuating traffic patterns, varying link qualities, routing loops, and convergence time [7].Several rou...

RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

“…The proposed method # of times // R= Root (sink) node 1 // N= Other node 1 //N0=Neighbor Routing Table 1 [44]. The time complexity of operations performed in Cooja simulation has been computed as O (n).…”

“…The RPL incurs control overheads (DIO, DAO, and DIS) during the DODAG construction. Each node transmits DIO messages using the trickle timer based on the network status and the frequency of DIO messages also depends on the network stability [44].…”

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

“…CPU = (CPU × 0.426 × 3)/32, 768 (13) Tx = (Tx × 17.4 × 3)/32, 768 (14) Rx = (Rx × 18.8 × 3)/32, 768 (15) We utilize the Energest function (energest_flush()) in Contiki OS to obtain the LPM, CPU, Tx, and Rx tick values. The LPM energy consumptions of all protocols are depicted in Figure 12.…”

“…The overall network quality can be improved by enhancing the capability of the device at the lower layers of the open systems interconnection (OSI) model. Similarly, the network performance can be improved by enhancing the device capabilities to learn traffic heterogeneity and diversity [ 13 ]. Thus, cross-layer optimization along with intelligent communication protocols are key to attaining optimal performance in heterogeneous data traffic scenarios.…”

A Comprehensive Study on RPL Challenges

“…Another work [6] introduced a new protocol for heterogeneous traffic, QW-RPL (Queue and Workload based RPL) to provide a reliable path by improving the network performance. IoT is an interconnection of varieties of heterogeneous devices.…”