MAC for Internet of Things (IoT)

Internet of Things (IoT) networks are expected to consist of a large number of resource constrained devices that gather data by sensing their environment and communicate dynamically with access points or neighboring devices to communicate these small amount of location specific delay-sensitive data. A IoT MAC protocol must be able to support the high-intensity and short-lived demands of these IoT networks. The basic design questions to be addressed are, one, why endure a high-overhead and large-delay MAC protocol in IoT networks when only a few intermittent packets need to be sent and received? Two, how to ensure energy efficiency even when energy harvesting is available? Three, what kind of access technique should be employed; grant based or grant free? In this talk, we take a look at how existing wireless MAC protocols are being adapted to cater to the specific needs of IoT networks which is imperative to address the basic design questions. Recent research proposals for IoT MAC protocols that endeavor to address the needs shall also be examined for their efficacy and promise

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Privacy in wireless sensor networks using ring signature

AbstractThe veracity of a message from a sensor node must be verified in order to avoid a false reaction by the sink. This verification requires the authentication of the source node. The authentication process must also preserve the privacy such that the node and the sensed object are not endangered. In this work, a ring signature was proposed to authenticate the source node while preserving its spatial privacy. However, other nodes as signers and their numbers must be chosen to preclude the possibility of a traffic analysis attack by an adversary. The spatial uncertainty increases with the number of signers but requires larger memory size and communication overhead. This requirement can breach the privacy of the sensed object. To determine the effectiveness of the proposed scheme, the location estimate of a sensor node by an adversary and enhancement in the location uncertainty with a ring signature was evaluated. Using simulation studies, the ring signature was estimated to require approximately four members from the same neighbor region of the source node to sustain the privacy of the node. Furthermore, the ring signature was also determined to have a small overhead and not to adversely affect the performance of the sensor network

Adaptive Prototypical Networks

Prototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space. However, in this process, the support set samples for a task are embedded independently of one other, and hence, the inter-class closeness is not taken into account. Thus, in the presence of similar-looking classes in a task, the embeddings will tend to be close to each other in the embedding space and even possibly overlap in some regions, which is not desirable for classification. In this paper, we propose an approach that intuitively pushes the embeddings of each of the classes away from the others in the meta-testing phase, thereby grouping them closely based on the distinct class labels rather than only the similarity of spatial features. This is achieved by training the encoder network for classification using the support set samples and labels of the new task. Extensive experiments conducted on benchmark data sets show improvements in meta-testing accuracy when compared with Prototypical Networks and also other standard few-shot learning models

FAM: fast adaptive federated meta-learning

In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model, which can then be personalized locally on individual clients. Federated learning enables multiple clients to collaborate to train a model without sharing data. Clients with insufficient data or data diversity participate in federated learning to learn a model with superior performance. Nonetheless, learning suffers when data distributions diverge. There is a need to learn a global model that can be adapted using client's specific information to create personalized models on clients is required. MRI data suffers from this problem, wherein, one, due to data acquisition challenges, local data at a site is sufficient for training an accurate model and two, there is a restriction of data sharing due to privacy concerns and three, there is a need for personalization of a learnt shared global model on account of domain shift across client sites. The global model is sparse and captures the common features in the MRI. This skeleton network is grown on each client to train a personalized model by learning additional client-specific parameters from local data. Experimental results show that the personalization process at each client quickly converges using a limited number of epochs. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism. Additionally, the sparse parameter set to be communicated during federated learning drastically reduced communication overhead, which makes the scheme viable for networks with limited resources.Comment: 13 Pages, 1 figur

Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment

In Federated Learning, model training is performed across multiple computing devices, where only parameters are shared with a common central server without exchanging their data instances. This strategy assumes abundance of resources on individual clients and utilizes these resources to build a richer model as user's models. However, when the assumption of the abundance of resources is violated, learning may not be possible as some nodes may not be able to participate in the process. In this paper, we propose a sparse form of federated learning that performs well in a Resource Constrained Environment. Our goal is to make learning possible, regardless of a node's space, computing, or bandwidth scarcity. The method is based on the observation that model size viz a viz available resources defines resource scarcity, which entails that reduction of the number of parameters without affecting accuracy is key to model training in a resource-constrained environment. In this work, the Lottery Ticket Hypothesis approach is utilized to progressively sparsify models to encourage nodes with resource scarcity to participate in collaborative training. We validate Equitable-FL on the $MNIST$, $F-MNIST$, and $CIFAR-10$ benchmark datasets, as well as the $Brain-MRI$ data and the $PlantVillage$ datasets. Further, we examine the effect of sparsity on performance, model size compaction, and speed-up for training. Results obtained from experiments performed for training convolutional neural networks validate the efficacy of Equitable-FL in heterogeneous resource-constrained learning environment.Comment: 12 pages, 7 figure