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Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a…
The issue of optimizing the limited and often non-renewable energy of sensor nodes due to its direct impact on network lifetime dominates every aspect of wireless sensor networks. Existing techniques for optimizing energy consumption are…
Many energy-aware routing protocols have been proposed for wireless sensor networks. Most of them are only energy savers and do not take care about energy balancing. The energy saver protocols try to decrease the energy consumption of the…
Wi-Fi fingerprinting remains one of the most practical solutions for indoor positioning, however, its performance is often limited by the size and heterogeneity of fingerprint datasets, strong Received Signal Strength Indicator variability,…
Wireless Sensor networks are dense networks of small, low-cost sensors, which collect and disseminate environmental data and thus facilitate monitoring and controlling of physical environment from remote locations with better accuracy. The…
5G heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs…
The wireless network places vital role in the present day communication scenario. The ad hoc nature of wireless communication adds flavour to suit various real world applications. This improves the performance of the network tremendously…
This invited paper presents some novel ideas on how to enhance the performance of consensus algorithms in distributed wireless sensor networks, when communication costs are considered. Of particular interest are consensus algorithms that…
The nodes in wireless sensor networks (WSNs) contain limited energy resources, which are needed to transmit data to base station (BS). Routing protocols are designed to reduce the energy consumption. Clustering algorithms are best in this…
In this paper, we study a federated learning system at the wireless edge that uses over-the-air computation (AirComp). In such a system, users transmit their messages over a multi-access channel concurrently to achieve fast model…
Chromatic dispersion compensation (CDC), implemented in either the time-domain or frequency-domain, is crucial for enhancing power efficiency in the digital signal processing of modern optical fiber communication systems. Developing…
The purpose of this paper is to study the algorithm FCM and some of its famous innovations, analyse and discover the method of applying hedge algebra theory that uses algebra to represent linguistic-valued variables, to FCM. Then, this…
In this paper, we propose Regional Energy Efficient Cluster Heads based on Maximum Energy (REECH-ME) Routing Protocol for Wireless Sensor Networks (WSNs) . The main purpose of this protocol is to improve the network lifetime and…
Federated Learning (FL) is a machine learning paradigm that safeguards privacy by retaining client data on edge devices. However, optimizing FL in practice can be challenging due to the diverse and heterogeneous nature of the learning…
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of FL considering the…
Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resource-constrained environments, these IoT devices face challenges related…
We present a novel approach to partitioning network nodes into non-overlapping communities - a key step in revealing network modularity and hierarchical organization. Our methodology, applicable to networks with both weighted and unweighted…
Objective-The main purpose of this paper is to construct a data accuracy model for the maximal set of sensor nodes that sense a point event and forms a cluster with fully connected network between them. We determine the minimal set of…
We present here the Temporal Clustering Algorithm (TCA), an incremental learning algorithm applicable to problems of anticipatory computing in the context of the Internet of Things. This algorithm was tested in a specific prediction…
Fuzzy clustering is a famous unsupervised learning method used to collecting similar data elements within cluster according to some similarity measurement. But, clustering algorithms suffer from some drawbacks. Among the main weakness…