No Arabic abstract
The IETF 6TiSCH working group fosters the adaptation of IPv6-based protocols into Internet of Things by introducing the 6TiSCH Operation Sublayer (6top). The 6TiSCH architecture integrates the high reliability and low-energy consumption of IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) with IPv6. IEEE 802.15.4e TSCH defines only the communication between nodes through a schedule but it does not specify how the resources are allocated for communication between the nodes in 6TiSCH networks. We propose a distributed algorithm for bandwidth allocation, called Local Voting, that adapts the schedule to the network conditions. The algorithm tries to equalize the link load (defined as the ratio of the queue length plus the new packet arrivals, over the number of allocated cells) through cell reallocation by calculating the number of cells to be added or released by 6top. Simulation results show that equalizing the load throughout 6TiSCH network provides better fairness in terms of load, reduces the queue sizes and packets reach the root faster compared to representative algorithms from the literature. Local Voting combines good delay performance and energy efficiency that are crucial features for Industrial Internet-of-Things applications.
An efficient and fair node scheduling is a big challenge in multihop wireless networks. In this work, we propose a distributed node scheduling algorithm, called Local Voting. The idea comes from the finding that the shortest delivery time or delay is obtained when the load is equalized throughout the network. Simulation results demonstrate that Local Voting achieves better performance in terms of average delay, maximum delay, and fairness compared to several representative scheduling algorithms from the literature. Despite being distributed, Local Voting has a very close performance to a centralized algorithm that is considered to have the optimal performance.
Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu and Twitch are gaining more momentum recently. Allocating the limited resources in a cost-effective manner while maximizing the Quality of Service (QoS) through real-time delivery and the provision of the appropriate representations for all viewers is a challenging problem. In our paper, we introduce a machine-learning based predictive resource allocation framework for geo-distributed cloud sites, considering the delay and quality constraints to guarantee the maximum QoS for viewers and the minimum cost for content providers. First, we present an offline optimization that decides the required transcoding resources in distributed regions near the viewers with a trade-off between the QoS and the overall cost. Second, we use machine learning to build forecasting models that proactively predict the approximate transcoding resources to be reserved at each cloud site ahead of time. Finally, we develop a Greedy Nearest and Cheapest algorithm (GNCA) to perform the resource allocation of real-time broadcasted videos on the rented resources. Extensive simulations have shown that GNCA outperforms the state-of-the art resource allocation approaches for crowdsourced live streaming by achieving more than 20% gain in terms of system cost while serving the viewers with relatively lower latency.
Control of multihop Wireless networks in a distributed manner while providing end-to-end delay requirements for different flows, is a challenging problem. Using the notions of Draining Time and Discrete Review from the theory of fluid limits of queues, an algorithm that meets delay requirements to various flows in a network is constructed. The algorithm involves an optimization which is implemented in a cyclic distributed manner across nodes by using the technique of iterative gradient ascent, with minimal information exchange between nodes. The algorithm uses time varying weights to give priority to flows. The performance of the algorithm is studied in a network with interference modelled by independent sets.
We present an algorithm which computes a planar 2-spanner from an Unit Disk Graph when the node density is sufficient. The communication complexity in terms of number of nodes identifier sent by the algorithm is $6n$, while the computational complexity is $O(nDelta)$, with $Delta$ the maximum degree of the communication graph. Furthermore, we present a simple and efficient routing algorithm dedicated to the computed graph. Last but not least, using traditional Euclidean coordinates, our algorithm needs the broadcast of as few as $3n$ nodes identifiers. Under the hypothesis of sufficient node density, no broadcast at all is needed, reducing the previous best known complexity of an algorithm to compute a planar spanner of an Unit Disk Graph which was of $5n$ broadcasts.
Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator (RO), which can be deployed between base stations (BSs) and fog nodes to coordinate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that the proposed algorithm can minimize the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that the proposed algorithm converges much faster than some other existing algorithms. The joint network slicing with both bandwidth and computational resources can offer around 15% overall latency reduction compared to network slicing with only a single resource.