Characterization of multi-channel interference

Multi-channel communication protocols in wireless networks usually assume perfect orthogonality between wireless channels or consider only the use of interference-free channels. The first approach may overestimate the performance whereas the second approach may fail to utilize the spectrum efficiently. Therefore, a more realistic approach would be the careful use of interfering channels by controlling the interference at an acceptable level. We present a methodology to estimate the packet error rate (PER) due to inter-channel interference in a wireless network. The methodology experimentally characterizes the multi-channel interference and analytically estimates it based on the observations from the experiments. Furthermore, the analytical estimation is used in simulations to derive estimates of the capacity in larger networks. Simulation results show that the achievable network capacity, which is defined as the number of simultaneous transmissions, significantly increases with realistic interfering channels compared with the use of only orthogonal channels. When we consider the same number of channels, the achievable capacity with realistic interfering channels can be close to the capacity of idealistic orthogonal channels. This shows that overlapping channels which constitute a much smaller band, provides more efficient use of the spectrum. Finally, we explore the correctness of channel orthogonality and show why this assumption may fail in a practical setting

Multi-Channel Scheduling for Fast Convergecast in Wireless Sensor Networks

We explore the following fundamental question - how fast can information be collected from a wireless sensor network? We consider a number of design parameters such as, power control, time and frequency scheduling, and routing. There are essentially two factors that hinder efficient data collection - interference and the half-duplex single-transceiver radios. We show that while power control helps in reducing the number of transmission slots to complete a convergecast under a single frequency channel, scheduling transmissions on different frequency channels is more efficient in mitigating the effects of interference (empirically, 6 channels suffice for most 100-node networks). With these observations, we define a receiver-based channel assignment problem, and prove it to be NP-complete on general graphs. We then introduce a greedy channel assignment algorithm that efficiently eliminates interference, and compare its performance with other existing schemes via simulations. Once the interference is completely eliminated, we show that with half-duplex single-transceiver radios the achievable schedule length is lower-bounded by max(2nk − 1,N), where nk is the maximum number of nodes on any subtree and N is the number of nodes in the network. We modify an existing distributed time slot assignment algorithm to achieve this bound when a suitable balanced routing scheme is employed. Through extensive simulations, we demonstrate that convergecast can be completed within up to 50% less time slots, in 100-node networks, using multiple channels as compared to that with single-channel communication. Finally, we also demonstrate further improvements that are possible when the sink is equipped with multiple transceivers or when there are multiple sinks to collect data

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Taha Toros Arşivi, Dosya No: 447/A-Vedat Kosa

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Taha Toros Arşivi, Dosya No: 447/A-Vedat Kosa

Algorithms for Fast Aggregated Convergecast in Sensor Networks

Fast and periodic collection of aggregated data is of considerable interest for mission-critical and continuous monitoring applications in sensor networks. In the many-to-one communication paradigm, referred to as convergecast, we focus on applications wherein data packets are aggregated at each hop en-route to the sink along a tree-based routing topology, and address the problem of minimizing the convergecast schedule length by utilizing multiple frequency channels. The primary hindrance in minimizing the schedule length is the presence of interfering links. We prove that it is NP-complete to determine whether all the interfering links in an arbitrary network can be removed using at most a constant number of frequencies. We give a sufficient condition on the number of frequencies for which all the interfering links can be removed, and propose a polynomial time algorithm that minimizes the schedule length in this case. We also prove that minimizing the schedule length for a given number of frequencies on an arbitrary network is NP-complete, and describe a greedy scheme that gives a constant factor approximation on unit disk graphs. When the routing tree is not given as an input to the problem, we prove that a constant factor approximation is still achievable for degree-bounded trees. Finally, we evaluate our algorithms through simulations and compare their performance under different network parameters

Multi-channel wireless sensor networks : protocols, design and evaluation

Pervasive systems, which are described as networked embedded systems integrated with everyday environments, are considered to have the potential to change our daily lives by creating smart surroundings and by their ubiquity, just as the Internet. In the last decade, “Wireless Sensor Networks” have appeared as one of the real-world examples of pervasive systems by combining automated sensing, embedded computing and wireless networking into tiny embedded devices.\ud A wireless sensor network typically comprises a large number of spatially distributed, tiny, battery-operated, embedded sensor devices that are networked to cooperatively collect, process, and deliver data about a phenomenon that is of interest to the users. Traditionally, wireless sensor networks have been used for monitoring applications based on low-rate data collection with low periods of operation. Current wireless sensor networks are considered to support more complex operations ranging from target tracking to health care which require efficient and timely collection of large amounts of data. Considering the low-bandwidth, low-power operation of the radios on the sensor devices, interference and contention over the wireless medium and the energy-efficiency requirements due to the battery-operated devices, fulfilling the mentioned data-collection requirements in complex applications becomes a challenging task. This thesis focuses on the efficient delivery of large amounts of data in bandwidth-limited wireless sensor networks by making use of the multi-channel capability of the sensor radios and by using optimal routing topologies. We start with experimenting the operation of the sensor radios to characterize the behavior of multi-channel communication. We propose a set of algorithms to increase the throughput and timely delivery of the data and analyze the bounds on the data collection capacity of the wireless sensor networks

Federated Learning on Edge Sensing Devices: A Review

The ability to monitor ambient characteristics, interact with them, and derive information about the surroundings has been made possible by the rapid proliferation of edge sensing devices like IoT, mobile, and wearable devices and their measuring capabilities with integrated sensors. Even though these devices are small and have less capacity for data storage and processing, they produce vast amounts of data. Some example application areas where sensor data is collected and processed include healthcare, environmental (including air quality and pollution levels), automotive, industrial, aerospace, and agricultural applications. These enormous volumes of sensing data collected from the edge devices are analyzed using a variety of Machine Learning (ML) and Deep Learning (DL) approaches. However, analyzing them on the cloud or a server presents challenges related to privacy, hardware, and connectivity limitations. Federated Learning (FL) is emerging as a solution to these problems while preserving privacy by jointly training a model without sharing raw data. In this paper, we review the FL strategies from the perspective of edge sensing devices to get over the limitations of conventional machine learning techniques. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized. We conclude with a discussion on open issues and future research directions on FL for further studie

FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition

Motion sensors integrated into wearable and mobile devices provide valuable information about the device users. Machine learning and, recently, deep learning techniques have been used to characterize sensor data. Mostly, a single task, such as recognition of activities, is targeted, and the data is processed centrally at a server or in a cloud environment. However, the same sensor data can be utilized for multiple tasks and distributed machine-learning techniques can be used without the requirement of the transmission of data to a centre. This paper explores Federated Transfer Learning in a Multi-Task manner for both sensor-based human activity recognition and device position identification tasks. The OpenHAR framework is used to train the models, which contains ten smaller datasets. The aim is to obtain model(s) applicable for both tasks in different datasets, which may include only some label types. Multiple experiments are carried in the Flower federated learning environment using the DeepConvLSTM architecture. Results are presented for federated and centralized versions under different parameters and restrictions. By utilizing transfer learning and training a task-specific and personalized federated model, we obtained a similar accuracy with training each client individually and higher accuracy than a fully centralized approach.Comment: Subimtted to Asian Conference in Machine Learning (ACML) 2023, Pattern Recognition in Health Analysis Workshop, 7 pages, 3 figure