Urban congestion and environmental pollution are pressing issues in urban sustainability. This study introduces the Streamlined Traffic Optimizer (STO), an AIoT-based system designed to improve urban traffic flow and reduce energy consumption. The STO algorithm dynamically adapts traffic signals using real-time data from the Uber Movement dataset. Initial results from simulations show a significant reduction in average travel time from 35 minutes to 28 minutes and an increase in average speed from 30 km/h to 36 km/h. Additionally, congestion levels dropped from 40% to 25%, while fuel consumption decreased by 18%, from 10,000 liters to 8,200 liters. These improvements are accompanied by a reduction in CO2 emissions from 1,200 to 950 tons per year. The STO system offers a scalable and flexible solution for cities aiming to reduce their environmental impact while optimizing traffic efficiency.

The commercialization of 5G has been initiated for a while. Furthermore, millimeter wave (mmWave) has been introduced to small cells with small coverage due to its strong linearity and non-winding characteristics. On the other hand, in connected autonomous vehicles (CAV s), where various traffic systems can cooperatively perform recognition, decision-making, and execution, communication is assumed to be always connected. Therefore, to use low latency mm Wave for high-speed moving CAV, existing beamforming cannot follow them at high speed. This paper proposes an improved beam tracking algorithm for high-speed CAVs, which can be evaluated in a more general environment using a traffic simulator. We proposed an adaptive algorithm for a general road environment by increasing the number of beam searches and search dimensions.

The rapid increase in the number of connected vehicles on roads has made vehicular ad-hoc networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This article proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a secure and efficient blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a homomorphic encryption system (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experimental results show that the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.

Federated learning (FL) enables clients to train models locally, enhancing privacy by avoiding data centralization. Traditional FL assumes all clients have adequate resources, an often unrealistic expectation in heterogeneous networks with resource constraints like limited battery, memory, and bandwidth. These limitations can hinder performance, prolong convergence times, and lead to inaccurate models. To address these challenges, we introduce the Client Eligibility-based Lightweight Protocol (CELP), optimized for resource-constrained environments. CELP employs a sample-based pruning mechanism and a re-parameterized FedAvg algorithm, enhancing its management of resource variability. It also integrates an intrusion detection system to safeguard against malicious activities. Our results show that CELP significantly reduces communication overhead by up to 81.01% compared to FedAvg and up to 72.54% compared to FedProx and enhances system stability, achieving 93% accuracy on the MNIST dataset and 83% accuracy on CIFAR-10. These improvements demonstrate CELP’s ability to deliver robust performance and efficiency in diverse FL scenarios.

Federated Learning (FL) is a new paradigm of Machine Learning (ML) that enables on-device computation via decentralized data training. However, traditional FL algorithms impose strict requirements on the clients' selection and its ratio. Moreover, the data training becomes inefficient when the client's computational resources are limited. Towards this goal, we aim to extend FL, a decentralized learning framework that efficiently works with heterogeneous clients in practical industrial scenarios. To this end, we propose a Clients' Eligibility Protocol (CEP), a resource-aware FL solution, for a heterogeneous environment. To this end, we use a Trusted Authority (TA) between the clients and the cloud server, which calculates the client's eligibility score based on local computing resources such as bandwidth, memory, and battery life and selects the most resourceful clients for training. If a client gives a slow response or infuses an incorrect model, the TA declares that the client is ineligible for future training. Besides, the proposed CEP leverages the asynchronous FL model, which avoids a long delay in a client's response. The empirical results proves that the proposed CEP gains the benefits of resource-aware clients selection and achieves 88 % and 93 % of accuracy on AlexNet and LeNet, respectively.

Federated learning (FL) is a promising technique for collaboratively training machine-learning models on massively distributed clients data under privacy constraints. However, the existing FL literature focuses on speeding up the learning process and ignores minimizing the communication cost which is critical for resource-constrained clients. To this end, in this article, we propose a novel 3-way hierarchical framework (THF) to promote communication efficiency in FL. Using the proposed framework, only a cluster head (CH) communicates with the cloud server through edge aggregation in order to minimize the communication cost of clients. In particular, the clients upload their local models to their respective CHs, which are responsible to forward them to the corresponding edge server. The edge server averages the local models and iterates until it achieves the edge accuracy. Afterward, each edge server uploads the edge models to the cloud server for global aggregation. In this way, model downloading and uploading requires less bandwidth due to the short distance from source to destination that makes an efficient 3-way hierarchical network structure. In addition, we formulate a joint communication and computation resource management scheme through efficient client selection in order to achieve global cost minimization in FL. We conduct extensive empirical evaluations on diverse data learning tasks on multiple data sets to signify that THF achieves global cost savings and converges within fewer communication rounds compared to other FL approaches.

In the era of advanced technologies, mobile devices are equipped with computing and sensing capabilities that gather excessive amounts of data. These amounts of data are suitable for training different learning models. Cooperated with Deep Learning (DL) advancements, these learning models empower numerous useful applications, e.g., image processing, speech recognition, healthcare, vehicular network, and many more. Traditionally, Machine Learning (ML) approaches require data to be centralised in cloud-based data-centres. However, this data is often large in quantity and privacy-sensitive, preventing logging into these data-centres for training the learning models. In turn, this results in critical issues of high latency and communication inefficiency. Recently, in light of new privacy legislation in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models without sharing the privacy-sensitive data. Usually, these mobile users have slow network connections to the data-centre where the global model is maintained. Moreover, in a complicated and extensive scale network, heterogeneous devices with various energy constraints are involved. This raises the challenge of communication cost when implementing FL at a large scale. To this end, in this research, we begin with the fundamentals of FL, and then we highlight the recent FL algorithms and evaluate their communication efficiency with detailed comparisons. Furthermore, we propose a set of solutions to alleviate the existing FL problems from a communication perspective and a privacy perspective.

Rehabilitation on a regular basis is essential to recover functional ability for patients with physical disabilities. However, performing correct movements without the therapist's guidance is considered to be a challenging problem. To this end, we propose an interactive system to support patients through rehabilitation, where a movement guidance agent is included. Our system recognizes and assists patients while engaging in rehabilitation activities. The proposed approach consists of three significant steps: (1) the estimation of patient movement through the camera; (2) the detection of patient movement; and (3) the detected movements, are then observed by an agent in order to instruct correct movement, if the movement was incorrectly performed. To this end, we propose an agent-based system that allows users to perform rehabilitation exercises anywhere with the proper guidance system. The proposed system is applied to track patient activities in real-time, with lightweight devices such as mobile phones.

Applications of Machine Learning (ML) algorithms in cybersecurity provide significant performance enhancement over traditional rule-based algorithms. These intelligent cyber-security solutions demand careful integration of the learning algorithms to develop a significant cyber incident detection system to formulate security analysts' industrial level. The development of advanced malware programs poses a critical threat to cybersecurity systems. Hence, an efficient, robust, and scalable malware recognition module is essential for every cybersecurity product. Conventional Signature-based methods struggle in terms of robustness and effectiveness during malware detection, specifically in the case of zero-day and polymorphic viruses attacks. In this paper, we design an adaptive Machine Learning based active malware detection framework which provides a cybersecurity solution against phishing attacks. The proposed framework utilize ML algorithms in a multilayered feed-forwarding approach to successfully detect the malware by examining the static features of the web pages. The proposed framework successfully extracts the features from the web pages and performs a successful detection process for the phishing attack. In the multilayered feed-forwarding framework, the first layer utilizes Random Forest (RF), Support Vector Machine (SVN), and K-Nearest Neighbor (K-NN) classifiers to build a model for detecting malware from the real-time input. The output of the first layer passes to the Ensemble Voting (EV) algorithm, which accumulates earlier classifiers' performance. At the third layer, adaptive frameworks investigate second layer input data and formulate the phishing detection model. We analyze the proposed framework's performance on three different phishing datasets and validate the higher accuracy rate.

Designing and development of energy effective path planning algorithm is very key research domain in order to tackle the issues of limited life-time for Wireless Sensor Networks (WSNs). So in WSNs, energy efficiency is major concern of researchers. Overall advancement in routing protocols prove that clustering is much better approach as compared to flat and location-based energy efficient routing protocols. Due to better performance, multiple energy efficient clustering routing protocols have been proposed. But existing clustering algorithms are centralized or distributed, which are not intelligent enough and do not produce hybrid cluster-head selection. In this paper, we propose a cluster structured path planning algorithm named, Hybrid Advance Distributed Centralized Clustering (HADCC) path planning energy efficient algorithm. HADCC proposed model is fascinated with hybrid cluster head selection algorithm. This hybrid algorithm makes decision of cluster head selection of nodes. In order to execute proposed model we have also proposed an advance network topology, in which the whole network region is divided into two physical levels. First physical level consists of a circular region, containing homogeneous normal nodes and all important Base Station. While, second physical level is outer region of the circle that contains advanced heterogeneous nodes. Simulation results indicate that HADCC prolongs the network lifetime as compared to existing advanced clustering routing protocols for both homogeneous and heterogeneous WSNs. HADCC outperforms in case of stability and network life time as compared to the existing clustering algorithms.