Related papers: Secure and Privacy-Preserving Data Aggregation Pro…
In this survey paper, our goal is to discuss recent advances of compressive sensing (CS) based solutions in wireless sensor networks (WSNs) including the main ongoing/recent research efforts, challenges and research trends in this area. In…
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by expensive communication and privacy concerns. Both of these…
Energy preservation is one of the most important challenges in wireless sensor networks. In most applications, sensor networks consist of hundreds or thousands nodes that are dispersed in a wide field. Hierarchical architectures and data…
Secure access of communication networks has become an increasingly important area of consideration for the communication service providers of present day. Broadband Wireless Access (BWA) networks are proving to be an efficient and cost…
To develop Smart City, the growing popularity of Machine Learning (ML) that appreciates high-quality training datasets generated from diverse IoT devices raises natural questions about the privacy guarantees that can be provided in such…
Wireless sensor networks increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future. However, deploying new technology without security in mind has often proved to be…
Wireless sensor networks are harshly restricted by storage capacity, energy and computing power. So it is essential to design effective and energy aware protocol in order to enhance the network lifetime. In this paper, a review on routing…
Over the past few years, federated learning has become widely used in various classical machine learning fields because of its collaborative ability to train data from multiple sources without compromising privacy. However, in the area of…
With the rapid advancement of wireless network technology, usage of WSN in real time applications like military, forest monitoring etc. found increasing. Generally WSN operate in an unattended environment and handles critical data.…
In wireless systems, neighbor discovery (ND) is a fundamental building block: determining which devices are within direct radio communication is an enabler for networking protocols and a wide range of applications. To thwart abuse of ND and…
Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's…
We consider the problem of providing a rigorous model for programming wireless sensor networks. Assuming that collisions, packet losses, and errors are dealt with at the lower layers of the protocol stack, we propose a Calculus for Sensor…
In this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for aggregating large corpora of internally incoherent probability…
Privacy-preserving machine learning (PPML) enables clients to collaboratively train deep learning models without sharing private datasets, but faces privacy leakage risks due to gradient leakage attacks. Prevailing methods leverage secure…
Wireless Sensor Network (WSN) consists of large number of low-cost, resource-constrained sensor nodes. The constraints of the wireless sensor node is their characteristics which include low memory, low computation power, they are deployed…
Wireless sensor networks (WSNs) are made up of a large number of tiny sensors, which can sense, analyze, and communicate information about the outside world. These networks play a significant role in a broad range of fields, from crucial…
Security and Privacy are two important parameters that need to be considered when dealing with wireless sensor networks as WSN operate in an unattended environment and carry sensitive information critical to the application. However,…
In this paper, we consider a particular type of deployment scenario of a distributed sensor network (DSN), where sensors of different types and categories are densely deployed in the same target area. In this network, the sensors are…
Data aggregation is a fundamental technique in wireless sensor networks (WSNs) in which sensory data collected by intermediate nodes is merged by in-network computation using maximum, average, or sum functions. Because sensors run on…
Secure Multiparty Computation (MPC) can improve the security and privacy of data owners while allowing analysts to perform high quality analytics. Secure aggregation is a secure distributed mechanism to support federated deep learning…