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.

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%.

Accurate and timely mobility support in Internet of Things (IoT) applications is a challenging issue, considering the inherent scarce resources of IoT devices. However, the computational, memory and communication burden may be pushed into more ”muscled” Software Defined Network (SDN) controllers. A centralised controller can exploit its global view of the network and predict the handovers and update the routing tables just in time to keep the mobile nodes connected. However, it requires the mobility solution to be enhanced with extra link quality information to estimate the distance between mobile and static nodes while avoiding collision between the new control overhead for localization and other communications in the network. In this work we present SDMob, an SDN-based mobility management architecture that lifts the burden of computation intensive filtering algorithms from resource constrained nodes and achieves accurate and fast handovers upon nodes’ mobility under Routing Protocol for Lossy Low-power Networks (RPL) and IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). We show that SDMob improves the baseline RPL and the state-of-the-art mRPL in terms of packet delivery ratio leveraging more reliable routing and applying Particle filter and variations of Kalman filter on radio signal strength data enables more accurate localization for complex real world trajectories.

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-west or ”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%.

Routing Protocol for Low-power and lossy networks (RPL), as the de-facto routing protocol for IoT networks, neglects to exploit IoT devices’ full capacity to tune their transmission power. One of the reasons is that optimizing the transmission power in parallel with the routing strategy is challenging, given the dynamic nature of wireless links and the constrained resources in IoT devices. Optimizing the transmission power requires evaluating the probability of packet collisions, energy consumption, the number of hops, and interference. We propose Adaptive Control of Transmission pOwer for RPL (ACTOR) for dynamic optimization of transmission power. ACTOR aims at improving throughput in dense networks by passively exploring different transmission power levels. The extent of resources used for this exploration significantly affects the network throughput. Thus, the exploration needs to adapt to dynamism in the environment. We formulate this exploration strategy using the Multi-Armed Bandit framework. The classic solutions of bandit theory including Upper Confidence Bound and Discounted Upper Confidence Bound accelerate the convergence of the exploration and guarantee its optimality. We also enhance ACTOR by mechanisms from RPL to blacklist undesirable transmission power levels and stabilize the topology. Results of the experiments on our 40-node testbed and simulations show that ACTOR achieves higher throughput (increasing the packet delivery ratio by 20%), energy consumption, end-to-end delay, and the number of retransmissions are significantly improved against the standard RPL and the selected benchmark.

There is a growing interest in increasing the flexibility and the mobility of industrial infrastructures to support novel industrial applications. To support this change, industrial networks must provide real-time guarantees while integrating a great variety of traffic over a cohesive network infrastructure. Specifically, there is special interest in the integration of wired and wireless communications in the industrial domain. We present a solution that integrates a Time-Sensitive Networking Ethernet network with a low-power 6TiSCH IoT network and discuss the implementation alternatives and their implications on delay and resource consumption. The main goal is to experimentally identify the research challenges and necessary enhancements for interoperability between constrained battery-driven wireless communication with the time sensitive wired networks.