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Routing Protocol for Low-Power and Lossy Networks(RPL) as the most widely used routing protocol for constrained Internet of Things (IoT) devices optimizes the number of routing states that nodes maintain to minimize resource consumption.Given that the routes are optimized for data collection, this leads to selecting sub-optimal routes, particularly in case of east-westor ”transversal” traffic. Additionally, RPL neglects interactions with a central entity in the network for monitoring or managing routes and enabling more flexibility and responsiveness to the system.In this paper, we present RPL with Route Projection (RPL-RP)that enables collecting siblings’ relations at the root node in order to inject routing states to the routers. This backward-compatible RPL extension still favors collection-based traffic patterns but it enriches the way routing protocol handles other flow directions.We address different advantages of RPL-RP in contrast to standard RPL and evaluate its overhead and improvements in terms of end-to-end delay, control overhead and packet delivery ratio. Overall, RPL-RP halves the end-to-end delay and increases network reliability by 5% while increasing network overhead by only 3%

Internet-of-Things (IoT) applications are envisaged to evolve to support mobility of devices while providing quality of service in the system. To keep the connectivity of the constrained nodes upon topological changes, it is of vital importance to enhance the standard protocol stack, including the Routing Protocol for Lossy Low-power Networks (RPL), with accurate and real-time control decisions. We argue that devising a centralized mobility management solution based on a lightweight Software Defined Networking (SDN) controller provides seamless handoff with reasonable communication overhead. A centralized controller can exploit its global view of the network, computation capacity, and flexibility, to predict and significantly improve the responsiveness of the network. This approach requires the controller to be fed with the required input and to get involved in the distributed operation of the standard RPL. We present SDMob, which is a lightweight SDN-based mobility management architecture that integrates an external controller within a constrained IoT network. SDMob lifts the burden of computation-intensive filtering algorithms away from the resource-constrained nodes to achieve seamless handoffs upon nodes’ mobility. The current work extends our previous work, by supporting multiple mobile nodes, networks with a high density of anchors, and varying hop-distance from the controller, as well as harsh and realistic mobility patterns. Through analytical modeling and simulations, we show that SDMob outperforms the baseline RPL and the state-of-the-art ARMOR in terms of packet delivery ratio and end-to-end delay, with an adjustable and tolerable overhead. With SDMob, the network provides close to 100% packet delivery ratio (PDR) for a limited number of mobile nodes, and maintains sub-meter accuracy in localization under random mobility patterns and varying network topologies.

Low-power wireless Internet of Things (IoT) devices employ Time Slotted Channel Hopping (TSCH) Medium Access Control to achieve predictable timing behaviour. TSCH aims at collision-free scheduling by exploiting diversity over time (slots) and frequency (channels). However, existing works on performance and worst-case analysis are based on deterministic models, which lead to rather pessimistic non-realistic results, i.e. tools for probabilistic performance analysis of TSCH schedulers are still lacking. In this context, we devised a Stochastic Network Calculus model that enables to calculate end-to-end delays for specific traffic flows and (deadline) violation probability, building on Moment Generating Functions. We instantiate this SNC model and provide bounds for three widely used TSCH schedulers, namely Minimal Scheduling Function, Orchestra, and a custom collision-free scheduler, with different parameters such as radio duty-cycle, radio link quality, and traffic arrival rate. We demonstrate that our proposed model closely follows the simulation results, under different network scenarios.

RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

Applications of the Internet of Things (IoT), particularly within Industrial IoT, impose stricter reliability and efficiency requirements on low-power wireless technologies. This has driven the creation of new medium access protocols, such as Time Slotted Channel Hopping (TSCH). Recently, autonomous schedulers, which manage wireless links without node negotiation, are gaining popularity due to their lightweight and reliable operation. However, challenges arise with node mobility and dynamic traffic, as current schedulers use a static allocation method. To overcome this gap, we propose Forte, a hybrid scheduler that combines autonomous scheduling for basic connectivity with a centralized on-demand scheduler that allocates extra timeslots and frequency channels so that nodes adapt to the dynamic requirements. The centralized module formulates a Lyapunov optimization to guarantee queue stability while minimizing negotiation overhead and nodes’ duty-cycles. Forte outperforms the state-of-the-art by reducing packet end-to-end delay and increasing packet delivery ratio, all with minimal duty-cycle increase.