In TSCH, which is a MAC mechanism set of the IEEE 802.15.4e amendment, calculation, construction, and maintenance of the packet transmission schedules are not defined. Moreover, to ensure optimal throughput, most of the existing scheduling methods are based on the assumption that instantaneous and accurate Channel State Information (CSI) is available. However, due to the inevitable errors in the channel estimation process, this assumption cannot be materialized in many practical scenarios. In this paper, we propose two alternative and realistic approaches. In our first approach, we assume that only the statistical knowledge of CSI is available a priori. Armed with this knowledge, the average packet rate on each link is computed and then, using the results, the throughput-optimal schedule for the assignment of (slotframe) cells to links can be formulated as a max-weight bipartite matching problem, which can be solved efficiently using the well-known Hungarian algorithm. In the second approach, we assume that no CSI knowledge (even statistical) is available at the design stage. For this zeroknowledge setting, we introduce a machine learning-based algorithm by formally modeling the scheduling problem in terms of a combinatorial multi-armed bandit (CMAB) process. Our CMAB-based scheme is widely applicable to many real operational environments, thanks to its reduced reliance on design-time knowledge. Simulation results show that the average throughput obtained by the statistical CSIbased method is within the margin of 15% from the theoretical upper bound associated with perfect instantaneous CSI. The aforesaid margin is around 18% for our learning-theoretic solution.Index Terms-IEEE 802.15.4e, TSCH, Scheduling, CSI, CMAB.

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Energy efficient resource management is critical for prolonging the lifetime of wireless sensor networks (WSNs). Clustering of sensor nodes with the aim of distributing the traffic loads in the network is a proven approach for balanced energy consumption in WSN. The main body of literature in this topic can be classified as hierarchical and distance-based clustering techniques in which multi-hop, multi-level forwarding, and distance-based criteria are utilized for categorization of sensor nodes. In this study, we propose the approximate rank-order wireless sensor networks (ARO-WSNs) clustering algorithm as a combined hierarchical and distance-based clustering approach. Different from absolute distance, ARO-WSN algorithm utilizes a new rank-order distance measure for agglomerative hierarchical clustering. Specifically, for each sensor node, we generate a ranking order list by sorting all other sensor nodes in the neighborhood by absolute distance. Then, the rank-order distance of two sensor nodes is computed using their ranking orders. The designed algorithm iteratively group all sensor nodes into a small number of sub-clusters. The results show that ARO-WSN outperforms the competitive clustering algorithms in terms of efficiency and precision/recall. The lifetime of the network with the first node death criterion improved relative to LEACH, LEACH-C, LEACH with fuzzy descriptors, and BPA-CRP by 60%, 85%, 22%, and 18%, respectively, and with last node death criterion improved relative to K-means, LEACH, LEACH-C, and LEACH with fuzzy descriptors by 42%, 67%, 64%, and 24%, respectively. KEYWORDSapproximate rank-order, clustering, network lifetime, wireless sensor networks INTRODUCTIONWireless sensor networks (WSNs) have been utilized for a wide range of applications such as environmental monitoring, 1 target tracking, 2 health care monitoring, 3 and crisis management. 4 Sensors networks, as a group of collaborative sensors for a specific application, have made significant changes in the way that people interact with the environment. However, the WSNs face strict limitations in terms of communication bandwidth, battery, and computing power. These inherent constraints in sensor nodes have posed great challenges to the WSNs. Therefore, how to find cost-effective solutions to reduce energy consumption in WSN and efficiently exploit the scare resources in the network remains an important research area.Int J Commun Syst. 2020;33:e4313.wileyonlinelibrary.com/journal/dac

IEEE Transactions on Vehicular TechnologyIEEE Transactions on Vehicular Technology 1  Abstract-We consider cooperative communications with energy harvesting (EH) relays, and develop a distributed power control mechanism for the relaying terminals. Unlike prior art which mainly deal with single-relay systems with saturated traffic flow, we address the case of bursty data arrival at the source cooperatively forwarded by multiple half-duplex EH relays. We aim at optimizing the long-run average delay of the source packets under the energy neutrality constraint on power consumption of each relay. While EH relay systems have been predominantly optimized using either offline or online methodologies, we take on a more realistic learning-theoretic approach. Hence, our scheme can be deployed for real-time operation without assuming acausal information on channel realizations, data/energy arrivals as required by offline optimization, nor does it rely on precise statistics of the system processes as is the case with online optimization. We formulate the problem as a partially observable identical payoff stochastic game (PO-IPSG) with factored controllers, in which the power control policy of each relay is adaptive to its channel and energy states as well as to the state of the source buffer. We equip each relay with a reinforcement learning procedure, and prove that the parallel execution of this procedure is convergent to (at least) a locally optimal solution of the formulated PO-IPSG. The proposed algorithm operates without explicit message exchange between the relays, while inducing only little source-relay signaling overhead. By simulation, we contrast the delay performance of the proposed method against existing heuristics for throughput maximization. It is shown that compared with these heuristics, the systematic approach adopted in this paper has a smaller sub-optimality gap once evaluated against a centralized optimal policy armed with perfect statistics. (IUST), Tehran, Iran. His current research mainly focuses on cognitive control of computer networks using stochastic control theory, and game-theoretic learning.M. Dehghan (M'10) received his B.

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