Shared Communication Channel Research Articles

An efficient federated learning solution for the artificial intelligence of things

Federated Learning (FL) has gained popularity due to its advantages over centralized learning. However, existing FL research has primarily focused on unconstrained wired networks, neglecting the challenges posed by wireless Internet of Things (IoT) environments. The successful integration of FL into IoT networks requires tailored adaptations to address unique constraints, especially in computation and communication. This paper introduces Communication-Aware Federated Averaging (CAFA), a novel algorithm designed to enhance FL operations in wireless IoT networks with shared communication channels. CAFA primarily leverages the latent computational capacities during the communication phase for local training and aggregation. Through extensive and realistic evaluations in dedicated FL-IoT framework, our method demonstrates significant advantages over state-of-the-art approaches. Indeed, CAFA achieves up to a 4x reduction in communication costs and accelerates FL training by as much as 70%, while preserving model accuracy. These achievements position CAFA as a promising solution for the efficient implementation of FL in constrained wireless networks.

COMMUNICATION OF JAMINAN KESEHATAN NASIONAL POLICY (JKN) BY BADAN PENYELENGGARA JAMINAN SOSIAL IN KOTA KEFAMENANU DISTRICT NORTH CENTRAL TIMOR DISTRICT

<p><em>Communication is very important in a policy. When communication in a policy does not work well, it is difficult to implement the policy. This research method is descriptive qualitative. The research results show that clarity of purpose, completeness, conciseness and concise content, feedback, empathy, changing messages according to audience needs, various communication channels, and effective use of informal communication channels. As a result of the research, it can be concluded that the communication of the National Health Insurance Policy (JKN) by the Social Security Administering Body (BPJS) for Health in the City District, North Central Timor Regency (TTU) has been going well but is not yet optimal. This occurs as a result of the first, clarity of objectives where The message conveyed by the organizers was not clear, which made the public unable to differentiate between the JKN and BPJS Health programs. Second, is completeness, where the message conveyed by the organizers is incomplete, which causes the public to not understand the JKN program. Third, brevity where you don't know the main indicators that you need to know about the JKN program. Fourth, feedback where the community will provide feedback when they experience problems or difficulties when wanting to use the BPJS Health card, not during the communication or socialization process for the JKN program. Fifth, sharing communication channels where the organizers have prepared communication channels to disseminate information about the JKN program but the public prefers to go directly to the BPJS Health office to get services or submit complaints.</em></p>

Allocation of Eavesdropping Attacks for Multi-System Remote State Estimation

In recent years, the problem of cyber–physical systems’ remote state estimations under eavesdropping attacks have been a source of concern. Aiming at the existence of eavesdroppers in multi-system CPSs, the optimal attack energy allocation problem based on a SINR (signal-to-noise ratio) remote state estimation is studied. Assume that there are N sensors, and these sensors use a shared wireless communication channel to send their state measurements to the remote estimator. Due to the limited power, eavesdroppers can only attack M channels out of N channels at most. Our goal is to use the Markov decision processes (MDP) method to maximize the eavesdropper’s state estimation error, so as to determine the eavesdropper’s optimal attack allocation. We propose a backward induction algorithm which uses MDP to obtain the optimal attack energy allocation strategy. Compared with the traditional induction algorithm, this algorithm has lower computational cost. Finally, the numerical simulation results verify the correctness of the theoretical analysis.

Multiple Access for Heterogeneous Wireless Networks with Imperfect Channels Based on Deep Reinforcement Learning

In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA’s advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA’s advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04.

Social and digital media monitoring for nonviolence: a distributed cognition perspective of the precariousness of peace work

PurposeThis paper aims to explore the technologization of peace work through “remote support monitors” that use social and digital media technologies like social media to alert local violence prevention actors to potentially violent situations during demonstrations.Design/methodology/approachUsing a distributed cognition lens, the authors explore the information processing of monitors within peace organizations. The authors adopt a qualitative thematic analysis methodology composed of interviews with monitors and documents from their shared communication and discussion channels. The authors’ analysis seeks to highlight how information is transformed between social and technical actors through the process of monitoring.FindingsThe authors’ analysis identifies that the technologization of monitoring for violence prevention to assist nonviolent activists produces two principal and related forms of transformation: appropriation and hidden attributes. Monitors “appropriate” information from sources to fit new ends and modes of representation throughout the process of detection, verification and dissemination. The verification and dissemination processes likewise render latent supporting informational elements, hiding the aggregative nature of information flow in monitoring. The authors connect the ideas of appropriation and hidden attributes to broader discourses in surveillance and trust that challenge monitoring and its place in peace work going forward.Originality/valueTo the best of the authors’ knowledge, this study is the first to focus on the communicative and information processes of remote support monitors. The authors demonstrate that adoption of social and digital media information of incipient violence and response processes for its mitigation suggests both a social and technical precarity for the role of monitoring.

Decentralized Safe Control for Distributed Cyber-Physical Systems Using Real-Time Reachability Analysis

In this paper, we present a decentralized safe control (DSC) approach for distributed cyber-physical systems based on conducting reachability analysis in real-time. Each agent can periodically compute the local reachable set from its current local time to some time instant in the near future, and then broadcast a message containing the computed reachable set to the other agents via a shared communication channel. By comparing its own reachable set to unsafe regions such as obstacles, and received reachable sets in a peer-to-peer manner, the agent can predict if any collision may exist shortly. In this circumstance, the agent can utilize the unsafe intersection of its computed reachable set, the unsafe regions and the received reachable sets to recalculate a new set of waypoints and update its corresponding control strategies. As a result, our DSC approach can ensure a distributed system achieving a mission goal with a collision-free guarantee. For evaluation, we applied the proposed DSC method to perform a decentralized control, in real-time, a group of quadcopters conducting a distributed patrol mission with safety guarantees.

Quadratic filtering for non-Gaussian stochastic parameter systems with buffer-aided strategy

This paper is concerned with the quadratic filtering problem for a class of linear discrete time-varying systems with non-Gaussian stochastic parameters and intermittent measurements. In order to improve the filtering performance, a buffer-aided strategy is used to store the historical measurement data which is sent to the filter via a shared communication channel at each transmission instant. The main objective of this paper is to design a quadratic filter to estimate the states of the considered non-Gaussian stochastic systems by using the buffer-aided strategy. Specifically, an augmented system is constructed by combining the original system states and their second-order Kronecker powers. As such, the considered quadratic filtering problem amounts to the Kalman filtering problem for the augmented system. The filtering error covariance (FEC) conditioned on the transmission intervals is firstly derived recursively and then is minimized by choosing the suitable filtering gains. Moreover, sufficient conditions are deduced to guarantee the boundedness of both the FEC and the conditional FEC. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed filtering method.

Shared communication for coordinated large-scale reinforcement learning control

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them mathematically. In contrast to classical controllers, DRL alleviates the need for constant parameter tuning, tedious design of control laws, and re-identification procedures in the event of performance degradation. However, the application of DRL algorithms remains fairly modest, and they have not yet established a significant position in process industries. One major obstacle has been their sample inefficiency when facing tasks featuring large state-action spaces. In this work, we show that it is possible to use DRL for plant-wide control by decentralizing and coordinating reinforcement learning. Namely, we express the global policy as a collection of local policies. Every local policy receives local observations and is responsible for controlling a different region of the environment. To enable coordination among local policies, we present a mechanism based on message passing. Messages are encoded by a shared communication channel, which is equipped with a model-based stream to capture the dynamics of the system and enable effective pre-training. The proposed method is evaluated on a set of robotic tasks and a large-scale vinyl acetate monomer (VAM) plant. Experimental results highlight that the proposed model exhibits drastic improvements over baselines in terms of mean scores and sample efficiency.

Whittle Index Based Age-of-Information Aware Scheduling for Markovian Channels

The focus of this work is on minimizing the time average of the weighted sum of the Age-of-Information in a multi-sensor system in the setting where all sensors report their measurements to a central monitoring station using a shared communication channel. We consider multiple information settings including complete CSI/delayed CSI/no CSI and two stochastic channel evolution models, i.e., i.i.d. channels and Markovian channels. In all settings considered, we prove the indexability of the scheduling problem. In addition, we compute the Whittle Index in closed form for some of the settings. Indexability for Markovian channels for the objective of minimizing AoI is a key contribution of this work. Further, under i.i.d. channels, we propose a novel efficient implementation for the Whittle Index-based policy. We use simulations to show that Whittle Index-based scheduling policies either outperform or match the performance of the state-of-the-art policies for all the settings considered.

A Wavelet Based Digital Image Watermarking Technique through Encryption with DCT

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. Digital image watermarking is an attractive research area since it protects the multimedia data from unauthorized access. Digital images are widely communicated over the internet. The security of digital images is an essential and challenging task on shared communication channel. In the era of big data and networking, it is necessary to develop a secure and robust digital watermarking scheme with high computational efficiency to protect copyrights of digital works. Digital watermarking is need for the security of digital images to achieve security goals, i.e. confidentiality, integrity and availability. With the use of sophisticated signal/image processing algorithms, manipulations and duplications of audio, images, and videos are much easier. Hence, content authentication through encryption and resistance to general attacks such as noise, compression, and geometric has become an urgent and important issue. Hence in this work a wavelet based digital image watermarking technique through encryption with DCT (Discrete Cosine Transform) is presented that can guarantee robustness of image against salt and pepper attack.

On extended state estimation for nonlinear uncertain systems with round-robin protocol

This work studies the state estimation problem for nonlinear uncertain systems over a shared communication channel. We transform the original system into an extended state system to handle the nonlinear uncertainty. The round-robin protocol is used to overcome the communication capacity constraint by sending the measurements periodically. The measurements’ structure is changed by the scheduling protocol, which may cause the round-robin-induced periodic extended state system (RRIPESS) to be unobservable. A necessary and sufficient condition of a class of RRIPESS’s observability is established. An extended state estimator is designed. The upper bound of the priori estimation error covariance is derived via a set of periodic Riccati equations (PREs). The stability of these PREs is ensured by the detectability and the stabilizability of the original system’s parameters. In addition, we give an iterative algorithm to improve the upper bound by optimizing a set of scalars induced by Young’s inequality. Finally, a simulation example is given to illustrate the effectiveness of the developed scheme.

Competitive Swarm Optimization with Encryption Based Steganography for Digital Image Security

Digital image security is a fundamental and tedious process on shared communication channels. Several methods have been employed for accomplishing security on digital image transmission, such as encryption, steganography, and watermarking. Image stenography and encryption are commonly used models to achieve improved security. Besides, optimal pixel selection process (OPSP) acts as a vital role in the encryption process. With this motivation, this study designs a new competitive swarm optimization with encryption based stenographic technique for digital image security, named CSOES-DIS technique. The proposed CSOES-DIS model aims to encrypt the secret image prior to the embedding process. In addition, the CSOES-DIS model applies a double chaotic digital image encryption (DCDIE) technique to encrypt the secret image, and then embedding method was implemented. Also, the OPSP can be carried out by the design of CSO algorithm and thereby increases the secrecy level. In order to portray the enhanced outcomes of the CSOES-DIS model, a comparative examination with recent methods is performed and the results reported the betterment of the CSOES-DIS model based on different measures.

Man-in-the-middle attack against cyber-physical systems under random access protocol

This paper investigates the man-in-the-middle (MITM) attack against cyber-physical systems (CPSs) under the random access protocol (RAP) scheduling, where an attacker intercepts and modifies the transmitted data and then forwards them on to degrade the system performance. The RAP schedules the sensing devices to avoid data collisions, where only one node is allowed to access the shared communication channel at each time instant. Hence, it makes the existing stealthy attacks invalid. To overcome the protocol-induced effects, a novel attack model utilizing only part of the measurements at each time instant is proposed, based on which the strictly stealthy and ∊-stealthy attacks are designed. For strictly stealthy attack, the Kullback-Leibler divergence (KLD)-based stealthy constraint is converted into a linear matrix inequality and then a semi-definite program problem is constructed to obtain the optimal attack parameters. In such case, the attack performance is optimal but limited. Furthermore, an ∊-stealthy attack is proposed to achieve higher attack performance, where the analytical attack parameters are obtained by solving an off-line convex optimization problem. Finally, simulations are provided to verify the results.

Event-triggered [formula omitted] control for networked spar-type floating production platforms with active tuned heave plate mechanisms and deception attacks

The service life of floating production platforms can be substantially shortened due to undesirable excessive vibrations caused by dynamic loads. It is thus necessary to develop effective vibration reduction methods for floating production platforms. This article proposes a network-based active control approach for a spar-type floating production platform (SP) against wave exciting loads and deception attacks. First, a novel active tuned heave plate (ATHP) mechanism based on the concept of the active tuned mass damper is developed for the SP. Second, in the context of networked control of the SP-ATHP system, an event-triggering transmission mechanism is introduced to significantly improve communication efficiency. Meanwhile, a Bernoulli distribution and a nonlinear function are employed to character possible deception attacks in shared communication channels. Then, by modelling the network-based closed-loop SP-ATHP system as a time-delay stochastic system, its stability and H∞ performance analysis is derived. Besides, some criteria are obtained to co-design the triggering mechanism and the H∞ controller. Finally, simulation studies demonstrate that compared with some existing heave plate mechanisms, the designed event-triggered H∞ controllers under this ATHP mechanism are more effective to suppress heave motions of the platform and save network resources and control expenditure. Furthermore, even though there are deception attacks, the proposed scheme can still guarantee satisfactory system performance.

A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks

Multiple access channel (MAC) networks use a broadcasting algorithm called the Binary Exponential Backoff (BEB) to mediate access to the shared communication channel by competing nodes and resolve their collisions. While the BEB achieves fair throughput and average packet latency in jamming-free environments and relatively small networks, its performance noticeably degrades when the network is exposed to jamming or its size increases. This paper presents an alternative broadcasting algorithm called the K-tuple Full Withholding (KTFW), which significantly increases MAC networks’ resilience to jamming attacks and network growth. Through simulation, we compare the KTFW with both the BEB and the Queue Backoff (QB), an efficient and high-throughput broadcasting algorithm. We compare the three approaches against two different traffic injection models, each approximating a different environment type. Our results show that the KTFW achieves higher throughput and lower average packet latency against jamming attacks than both the BEB and the QB algorithms. The results also show that the KTFW outperforms the BEB for larger networks with or without jamming.

Empowered AODV Protocol in Wireless Sensor Network using Three Variable RSA Cryptosystem

Background: The Wireless Sensor Network (WSN) is a type of networks which primarily designed for the purpose of monitoring in remote areas. It consists of communicating nodes (called sensor's) which communicate each other to share their data and passing the information to the central node. In many applications like defence requires the secure communication of information. However, due to the numerous characteristics of WSN such as open shared communication channel, limited memory, and processing power of sensors, etc. these networks are vulnerable to various attacks such as black hole, gray hole, etc. Objective: The objective of the paper is to secure the AODV routing protocol in WSN using cryptography techniques. Methods: In this paper, the Ad hoc On-demand Distance Vector (AODV) routing protocol has been chosen for information routing because of their lightweight processing capability. To provide secure communication in WSN, the AODV routing protocol is secured by utilizing the RSA key generation algorithm. Here, RSA with three variables (three prime numbers) is employed instead of two variables. Results: The effectiveness of the proposed approach in handling black hole attack is being verified through the simulation results obtained from the experiments conducted using Network Simulator tool (NS2). The three popular performance metrics namely Average End-to-End Delay, Packet Delivery Ratio, and Average Throughput are used for evaluation purpose. These results are observed under different pose time and varying number of malicious nodes. Conclusion: In this paper, a new three variable RSA cryptosystem-based security model is proposed to protect the communication against the Black Hole (BH) attack in wireless sensor networks. The use of three variables instead of two variables allows our model to provide more security as compared to other methods. Simulation results obtained from the experiments carried out using NS2 tool evident the performance of the proposed model over original AODV and other previous models.

Optimal sensor attacks in cyber-physical systems with Round-Robin protocol

This paper investigates the stealthy attack problem in cyber-physical systems (CPSs) under the scheduling effects of the Round-Robin protocol (RRP), where the transmission order of the sensor nodes is modeled as a periodic function and only one node is allowed to access the shared communication channel at each time instant. The adoption of the RRP can efficiently avoid data collisions, while it makes the existing innovation-based stealthy attack strategies invalid. Thus, the aim is to design an attack strategy under the protocol-induced behaviors to maximize the attack effect and remain strictly stealthy simultaneously. Different from the existing results, where only single-sensor application scenarios were considered, multi-sensor ones with limited bandwidth are investigated in this work. A new attack model is proposed to overcome the protocol-introduced effects, which utilizes only part of the historical and the current innovations at each time instant. Based on the attack model, the stealthy condition is obtained and converted into the form of linear matrix inequality (LMI). Then, the error covariance under the attack and the RRP is derived to quantify the attack effect. Subsequently, the optimal attack strategy is obtained by solving a semi-definite programming (SDP) problem. Finally, simulations are performed to demonstrate the results.

Age of Information of One-Hop Broadcast Communications in a CSMA Network

We consider a network of agents that send periodic updates to their neighbors. A trade-off between load on the shared communication channel and data timeliness is obtained by looking at the Age of Information (AoI) metric. We develop a model of a Carrier-Sense Multiple Access (CSMA) network with partial sensing, to calculate the AoI of one-hop broadcast messages exchanged among the agents. The model is applied to beacon messages in a vehicular network to gain insight into the impact of system parameters.

Ultimately Bounded Filtering for Time-Delayed Nonlinear Stochastic Systems with Uniform Quantizations under Random Access Protocol.

This paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information transmissions over the shared communication channel. The scheduling behavior of the RAP is characterized by a Markov chain with known transition probabilities. On the other hand, the measurement outputs are quantized by the uniform quantizer before being transmitted via the communication channel. The objective of this paper is to devise a nonlinear filter such that, in the simultaneous presence of RAP and UQEs, the filtering error dynamics is exponentially ultimately bounded in mean square (EUBMS). By resorting to the stochastic analysis technique and the Lyapunov stability theory, sufficient conditions are obtained under which the desired nonlinear filter exists, and then the filter design algorithm is presented. At last, two simulation examples are given to validate the proposed filtering strategy.

Optimal Estimation for Discrete-Time Linear System with Communication Constraints and Measurement Quantization

This paper focuses on the linear minimum mean square estimator for a networked discrete time-varying linear system subject to data quantification and communication constraints. The communication limitation is that only one transmission node can get access to the shared communication channel at each time step, and that different transmission nodes in the networked systems are scheduled to transmit information according to a Markov protocol. Then the remote estimator completes the estimation with only partially available observations, which are quantified. Suppose that the Markov chain is unknown to the remote estimator. By using orthogonal projection principle and innovation analysis method, a Kalman type filter is designed in a recurrence form. It is shown that estimation performance depends on the transition probability matrix of the Markov chain, quantization error, and the shared channel weighting parameter. Finally, an illustrative example is given to show the effectiveness of the proposed method.