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Register a new userTowards a cognitive MAC layer: Predicting the MAC-level performance in Dynamic WSN using Machine learning
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Added by
Merima Kulin
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by the MAC layer. Our approach is data-driven and consists of three steps: extensive experiments for data collection, offline modeling and trace-driven performance evaluation. From our experiments and analysis, we find that a neural networks prediction model shows best performance.
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We propose analytical models that allow us to investigate the performance of long range wide area network (LoRaWAN) uplink in terms of latency, collision rate, and throughput under the constraints of the regulatory duty cycling, when assuming exponential inter-arrival times. Our models take into account sub-band selection and the case of sub-band combining. Our numerical evaluations consider specifically the European ISM band, but the analysis is applicable to any coherent band. Protocol simulations are used to validate the proposed models. We find that sub-band selection and combining have a large effect on the quality of service (QoS) experienced in an LoRaWAN cell for a given load. The proposed models allow for the optimization of resource allocation within a cell given a set of QoS requirements and a traffic model.
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This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.
We present a timed process calculus for modelling wireless networks in which individual stations broadcast and receive messages; moreover the broadcasts are subject to collisions. Based on a reduction semantics for the calculus we define a contextual equivalence to compare the external behaviour of such wireless networks. Further, we construct an extensional LTS (labelled transition system) which models the activities of stations that can be directly observed by the external environment. Standard bisimulations in this LTS provide a sound proof method for proving systems contextually equivalence. We illustrate the usefulness of the proof methodology by a series of examples. Finally we show that this proof method is also complete, for a large class of systems.
In this paper, we study distributed consensus in the radio network setting. We produce new upper and lower bounds for this problem in an abstract MAC layer model that captures the key guarantees provided by most wireless MAC layers. In more detail, we first generalize the well-known impossibility of deterministic consensus with a single crash failure [FLP 1895] from the asynchronous message passing model to our wireless setting. Proceeding under the assumption of no faults, we then investigate the amount of network knowledge required to solve consensus in our model---an important question given that these networks are often deployed in an ad hoc manner. We prove consensus is impossible without unique ids or without knowledge of network size (in multihop topologies). We also prove a lower bound on optimal time complexity. We then match these lower bounds with a pair of new deterministic consensus algorithms---one for single hop topologies and one for multihop topologies---providing a comprehensive characterization of the consensus problem in the wireless setting. From a theoretical perspective, our results shed new insight into the role of network information and the power of MAC layer abstractions in solving distributed consensus. From a practical perspective, given the level of abstraction used by our model, our upper bounds can be easily implemented in real wireless devices on existing MAC layers while preserving their correctness guarantees---facilitating the development of wireless distributed systems.
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