In the last few years, the Internet of Things (IoT) has proved to be an interesting and promising paradigm that aims to contribute to countless applications by connecting more physical “things” to the Internet. Although it emerged as a major enabler for many next generation applications, it also introduced new challenges to already saturated networks. The IoT is already coming to life especially in healthcare and smart environment applications adding a large number of low powered sensors and actuators to improve life style and introduce new services to the community. The Internet Engineering Task Force (IETF) developed RPL as the routing protocol for low power and lossy networks (LLNs) and standardized it in RFC6550 in 2012. RPL quickly gained interest and many research papers were introduced to evaluate and improve its performance in different applications. In this paper, we present a discussion of the main aspects of RPL and the advantages and disadvantages of using it in different IoT applications. We also review the available research related to RPL in a systematic manner, based on the enhancement area and the service type. In addition to that, we compare related RPL-based protocols in terms of energy efficiency, reliability, flexibility, robustness and security. Finally, we present our conclusions and discuss the possible future directions of RPL and its applicability in the Internet of the future.

By 2020, 50 billion devices (things) are expected to be connected to the Internet to form the Internet of Things (IoT) world. In general, two main categories of networks are used in the IoT: short-range and long-range low-power networks. IPv6 over low-power wireless personal area networks (6LoWPAN) are considered to be a crucial network in short-range low-power networks where 6LoWPAN motes will account for the majority of short-range low-power things. Also, LoRaWAN and SigFox are two major networking landscapes and players in long-range, low-power networks or oftentimes called low-power wide area networks (LPWAN). This paper reviews and compares 6LoWPAN and LPWAN in terms of network architecture, protocol stack, applications and security. In general, LPWAN networks are more demanding in terms of node and link constraints than 6LoWPAN networks (e.g. very low payload size, very low data rate and very limited resources). Also, as yet, LPWAN networks do not have IPv6 addressing capabilities. Each network category has its unique characteristics and strengths and each category has its important role in the IoT world.

The Internet of Things (IoT) has been considered as an emerging research area where the IPv6 over low-power wireless personal area network (6LoWPAN) protocol stack is considered as one of the most important protocol suite for the IoT. Recently, the Internet Engineering Task Force has developed a set of IPv6-based protocols to alleviate the challenges of connecting resource limited sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and effects the quality of service aspects, e.g., throughput, end-to-end delay and energy consumption. In this paper, we formulate the congestion problem as a noncooperative game framework where the nodes (players) behave uncooperatively and demand high data rate in a selfish way. Then, the existence and uniqueness of Nash equilibrium is proved and the optimal game solution is computed by using Lagrange multipliers and Karush-Kuhn-Tucker conditions. Based on this framework, we propose a novel and simple congestion control mechanism called game theory-based congestion control framework (GTCCF) specially tailored for IEEE 802.15.4, 6LoWPAN networks. GTCCF is aware of node priorities and application priorities to support the IoT application requirements. The proposed framework has been tested and evaluated through two different scenarios by using Contiki OS and compared with comparative algorithms. Simulation results show that GTCCF improves performance in the presence of congestion by an overall average of 30.45%, 39.77%, 26.37%, 91.37%, and 13.42% in terms of throughput, end-to-end delay, energy consumption, number of lost packets, and weighted fairness index (WFI), respectively, as compared to duty cycle-aware congestion control for 6LoWPAN network algorithm.

The IPv6 over low-power wireless personal area network (6LoWPAN) protocol stack is a key part of the Internet of Things (IoT) where the 6LoWPAN motes will account for the majority of the IoT “things.” In 6LoWPAN networks, heavy network traffic causes congestion which significantly effects the network performance and the quality of service metrics. Generally, two main strategies are used to control and alleviate congestion in 6LoWPAN networks: 1) resource control and 2) traffic control. All the existing work of congestion control in 6LoWPAN networks use one of these. In this paper, we propose a novel congestion control algorithm called optimization-based hybrid congestion alleviation (OHCA) which combines both strategies into a hybrid solution. OHCA utilizes the positive aspects of each strategy and efficiently uses the network resources. The proposed algorithm uses a multiattribute optimization methodology called gray relational analysis for resource control by combining three routing metrics (buffer occupancy, expected transmission count, and queuing delay) and forwarding packets through noncongested parents. Also, OHCA uses optimization theory and network utility maximization framework to achieve traffic control when the noncongested parent is not available where the optimal nodes’ sending rate are computed by using Lagrange multipliers and Karush–Kuhn–Tucker conditions. The proposed algorithm is aware of node priorities and application priorities to support the IoT application requirements where the applications’ sending rate allocation is modeled as a constrained optimization problem. OHCA has been tested and evaluated through simulation by using Contiki OS and compared with comparative algorithms. Simulation results show that OHCA improves performance in the presence of congestion by an overall average of 28.36%, 28.02%, 48.07%, 31.97%, and 90.35% in terms of throughput, weighted fairness index, end-to-end delay, energy consumption, and buffer dropped packets as compared to duty cycle-aware congestion control for 6LoWPAN networks and queue utilization-based-IPv6 routing protocol for low-power and lossy network.

The Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard routing protocol for the Internet of Things (IoT). This paper investigates the use of RPL in dynamic networks and presents an enhanced RPL for different applications with dynamic mobility and diverse network requirements. This implementation of RPL is designed with a new dynamic Objective-Function (D-OF) to improve the Packet Delivery Ratio (PDR), end-to-end delay and energy consumption while maintaining low packet overhead and loop-avoidance. We propose a controlled reverse-trickle timer based on received signal strength identification (RSSI) readings to maintain high responsiveness with minimum overhead and consult the objective function when a movement or an inconsistency is detected to help nodes make an informed decision. Simulations are done using Cooja with random waypoint mobility scenario for healthcare applications considering multi-hop routing. The results show that the proposed dynamic RPL (D-RPL) adapts to the nodes mobility and has a higher PDR, slightly lower end-to-end delay and reasonable energy consumption compared to related existing protocols.