SPEED: A deep learning assisted privacy-preserved framework for intelligent transportation systems
- Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 7 (2021), p. 4376-4384
- Full Text: false
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- Description: Roadside cameras in an Intelligent Transportation System (ITS) are used for various purposes, e.g., monitoring the speed of vehicles, violations of laws, and detection of suspicious activities in parking lots, streets, and side roads. These cameras generate big multimedia data, and as a result, the ITS faces challenges like data management, redundancy, and privacy breaching in end-to-end communication. To solve these challenges, we propose a framework, called SPEED, based on a multi-level edge computing architecture and machine learning algorithms. In this framework, data captured by end-devices, e.g., smart cameras, is distributed among multiple Level-One Edge Devices (LOEDs) to deal with data management issue and minimize packet drop due to buffer overflowing on end-devices and LOEDs. The data is forwarded from LOEDs to Level-Two Edge Devices (LTEDs) in a compressed sensed format. The LTEDs use an online Least-Squares Support-Vector Machines (LS-SVMs) model to determine distribution characteristics and index values of compressed sensed data to preserve its privacy during transmission between LTEDs and High-Level Edge Devices (HLEDs). The HLEDs estimate the redundancy in forwarded data using a deep learning architecture, i.e., a Convolutional Neural Network (CNN). The CNN is used to detect the presence of moving objects in the forwarded data. If a movement is detected, the data is forwarded to cloud servers for further analysis otherwise discarded. Experimental results show that the use of a multi-level edge computing architecture helps in managing the generated data. The machine learning algorithms help in addressing issues like data redundancy and privacy-preserving in end-to-end communication. © 2000-2011 IEEE.
Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions
- Authors: Jan, Mian , Cai, Jinjin , Gao, Xiang-Chuan , Khan, Fazlullah , Mastorakis, Spyridon , Usman, Muhammad , Alazab, Mamoun , Watters, Paul
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 175, no. (2021), p.
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- Description: The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Ltd
PAWN: a payload‐based mutual authentication scheme for wireless sensor networks
- Authors: Jan, Mian , Nanda, Priyadarsi , Usman, Muhammad , He, Xiangjian
- Date: 2017
- Type: Text , Journal article
- Relation: Concurrency and computation Vol. 29, no. 17 (2017), p. e3986-n/a
- Full Text: false
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- Description: Summary Wireless sensor networks (WSNs) consist of resource‐starving miniature sensor nodes deployed in a remote and hostile environment. These networks operate on small batteries for days, months, and even years depending on the requirements of monitored applications. The battery‐powered operation and inaccessible human terrains make it practically infeasible to recharge the nodes unless some energy‐scavenging techniques are used. These networks experience threats at various layers and, as such, are vulnerable to a wide range of attacks. The resource‐constrained nature of sensor nodes, inaccessible human terrains, and error‐prone communication links make it obligatory to design lightweight but robust and secured schemes for these networks. In view of these limitations, we aim to design an extremely lightweight payload‐based mutual authentication scheme for a cluster‐based hierarchical WSN. The proposed scheme, also known as payload‐based mutual authentication for WSNs, operates in 2 steps. First, an optimal percentage of cluster heads is elected, authenticated, and allowed to communicate with neighboring nodes. Second, each cluster head, in a role of server, authenticates the nearby nodes for cluster formation. We validate our proposed scheme using various simulation metrics that outperform the existing schemes.
ESMLB : efficient switch migration-based load balancing for multicontroller SDN in IoT
- Authors: Sahoo, Kshira , Puthal, Deepak , Tiwary, Mayank , Usman, Muhammad , Sahoo, Bibhudatta , Wen, Zhenyu , Sahoo, Biswa , Ranjan, Rajiv
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 7 (2020), p. 5852-5860
- Full Text: false
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- Description: In software-defined networks (SDNs), the deployment of multiple controllers improves the reliability and scalability of the distributed control plane. Recently, edge computing (EC) has become a backbone to networks where computational infrastructures and services are getting closer to the end user. The unique characteristics of SDN can serve as a key enabler to lower the complexity barriers involved in EC, and provide better quality-of-services (QoS) to users. As the demand for IoT keeps growing, gradually a huge number of smart devices will be connected to EC and generate tremendous IoT traffic. Due to a huge volume of control messages, the controller may not have sufficient capacity to respond to them. To handle such a scenario and to achieve better load balancing, dynamic switch migrating is one effective approach. However, a deliberate mechanism is required to accomplish such a task on the control plane, and the migration process results in high network delay. Taking it into consideration, this article has introduced an efficient switch migration-based load balancing (ESMLB) framework, which aims to assign switches to an underutilized controller effectively. Among many alternatives for selecting a target controller, a multicriteria decision-making method, i.e., the technique for order preference by similarity to an ideal solution (TOPSIS), has been used in our framework. This framework enables flexible decision-making processes for selecting controllers having different resource attributes. The emulation results indicate the efficacy of the ESMLB. © 2014 IEEE.
A distributed and anonymous data collection framework based on multilevel edge computing architecture
- Authors: Usman, Muhammad , Jan, Mian , Jolfaei, Alireza , Xu, Min , He, Xiangjian , Chen, Jinjun
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 16, no. 9 (2020), p. 6114-6123
- Full Text: false
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- Description: Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. © 2005-2012 IEEE.
Cryptography-based secure data storage and sharing using HEVC and public clouds
- Authors: Usman, Muhammad , Ahmad Jan, Mian , He, Xiangjian
- Date: 2017
- Type: Text , Journal article
- Relation: Information sciences Vol. 387, no. (2017), p. 90-102
- Full Text: false
- Reviewed:
- Description: Mobile devices are widely used for uploading/downloading media files such as audio, video and images to/from the remote servers. These devices have limited resources and are required to offload resource-consuming media processing tasks to the clouds for further processing. Migration of these tasks means that the media services provided by the clouds need to be authentic and trusted by the mobile users. The existing schemes for secure exchange of media files between the mobile devices and the clouds have limitations in terms of memory support, processing load, battery power, and data size. These schemes lack the support for large-sized video files and are not suitable for resource-constrained mobile devices. This paper proposes a secure, lightweight, robust, and efficient scheme for data exchange between the mobile users and the media clouds. The proposed scheme considers High Efficiency Video Coding (HEVC) Intra-encoded video streams in unsliced mode as a source for data hiding. Our proposed scheme aims to support real-time processing with power-saving constraint in mind. Advanced Encryption Standard (AES) is used as a base encryption technique by our proposed scheme. The simulation results clearly show that the proposed scheme outperforms AES-256 by decreasing the processing time up to 4.76% and increasing the data size up to 0.72% approximately. The proposed scheme can readily be applied to real-time cloud media streaming.
PAAL : a framework based on authentication, aggregation, and local differential privacy for internet of multimedia things
- Authors: Usman, Muhammad , Jan, Mian , Puthal, Deepak
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 4 (2020), p. 2501-2508
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- Description: Internet of Multimedia Things (IoMT) applications generate huge volumes of multimedia data that are uploaded to cloud servers for storage and processing. During the uploading process, the IoMT applications face three major challenges, i.e., node management, privacy-preserving, and network protection. In this article, we propose a multilayer framework (PAAL) based on a multilevel edge computing architecture to manage end and edge devices, preserve the privacy of end-devices and data, and protect the underlying network from external attacks. The proposed framework has three layers. In the first layer, the underlying network is partitioned into multiple clusters to manage end-devices and level-one edge devices (LOEDs). In the second layer, the LOEDs apply an efficient aggregation technique to reduce the volumes of generated data and preserve the privacy of end-devices. The privacy of sensitive information in aggregated data is protected through a local differential privacy-based technique. In the last layer, the mobile sinks are registered with a level-two edge device via a handshaking mechanism to protect the underlying network from external threats. Experimental results show that the proposed framework performs better as compared to existing frameworks in terms of managing the nodes, preserving the privacy of end-devices and sensitive information, and protecting the underlying network. © 2014 IEEE.
RaSEC : an intelligent framework for reliable and secure multilevel edge computing in industrial environments
- Authors: Usman, Muhammad , Jolfaei, Alireza , Jan, Mian
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 56, no. 4 (2020), p. 4543-4551
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- Description: Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. © 1972-2012 IEEE.
Security and privacy in IoT using machine learning and blockchain : threats and countermeasures
- Authors: Waheed, Nazar , He, Xiangjian , Ikram, Muhammad , Usman, Muhammad , Hashmi, Saad
- Date: 2021
- Type: Text , Journal article , Review
- Relation: ACM Computing Surveys Vol. 53, no. 6 (2021), p.
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- Description: Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain. © 2020 ACM.
A survey on big multimedia data processing and management in smart cities
- Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
- Date: 2019
- Type: Text , Journal article
- Relation: ACM computing surveys Vol. 52, no. 3 (2019), p. 1-29
- Full Text: false
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- Description: Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time.
Frame interpolation for cloud-based mobile video streaming
- Authors: Usman, Muhammad , Xiangjian, He , Kin-Man, Lam , Min, Xu , Bokhari, Syed , Jinjun, Chen
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE transactions on multimedia Vol. 18, no. 5 (2016), p. 831-839
- Full Text: false
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- Description: Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds.
PFARS : Enhancing throughput and lifetime of heterogeneous WSNs through power-aware fusion, aggregation, and routing scheme
- Authors: Khan, Rahim , Zakarya, Muhammad , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Mukhtaj
- Date: 2019
- Type: Text , Journal article
- Relation: International Journal of Communication Systems Vol. 32, no. 18 (Dec 2019), p. 21
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- Description: Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application-specific or too complex that make their implementation unrealistic, specifically, in a resource-constrained environment. In this paper, we propose a novel node-level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.
A survey on representation learning efforts in cybersecurity domain
- Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Chen, Jinjun
- Date: 2020
- Type: Text , Journal article
- Relation: ACM computing surveys Vol. 52, no. 6 (2020), p. 1-28
- Full Text: false
- Reviewed:
- Description: In this technology-based era, network-based systems are facing new cyber-attacks on daily bases. Traditional cybersecurity approaches are based on old threat-knowledge databases and need to be updated on a daily basis to stand against new generation of cyber-threats and protect underlying network-based systems. Along with updating threat-knowledge databases, there is a need for proper management and processing of data generated by sensitive real-time applications. In recent years, various computing platforms based on representation learning algorithms have emerged as a useful resource to manage and exploit the generated data to extract meaningful information. If these platforms are properly utilized, then strong cybersecurity systems can be developed to protect the underlying network-based systems and support sensitive real-time applications. In this survey, we highlight various cyber-threats, real-life examples, and initiatives taken by various international organizations. We discuss various computing platforms based on representation learning algorithms to process and analyze the generated data. We highlight various popular datasets introduced by well-known global organizations that can be used to train the representation learning algorithms to predict and detect threats. We also provide an in-depth analysis of research efforts based on representation learning algorithms made in recent years to protect the underlying network-based systems against current cyber-threats. Finally, we highlight various limitations and challenges in these efforts and available datasets that need to be considered when using them to build cybersecurity systems.
SmartEdge : An end-to-end encryption framework for an edge-enabled smart city application
- Authors: Jan, Mian , Zhang, Wenjing , Usman, Muhammad , Tan, Zhiyuan , Khan, Fazlullah , Luo, Entao
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 137, no. (2019), p. 1-10
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- Description: The Internet of Things (IoT) has the potential to transform communities around the globe into smart cities. The massive deployment of sensor-embedded devices in the smart cities generates voluminous amounts of data that need to be stored and processed in an efficient manner. Long-haul data transmission to the remote cloud data centers leads to higher delay and bandwidth consumption. In smart cities, the delay-sensitive applications have stringent requirements in term of response time. To reduce latency and bandwidth consumption, edge computing plays a pivotal role. The resource-constrained smart devices at the network core need to offload computationally complex tasks to the edge devices located in their vicinity and have relatively higher resources. In this paper, we propose an end-to-end encryption framework, SmartEdge, for a smart city application by executing computationally complex tasks at the network edge and cloud data centers. Using a lightweight symmetric encryption technique, we establish a secure connection among the smart core devices for multimedia streaming towards the registered and verified edge devices. Upon receiving the data, the edge devices encrypts the multimedia streams, encodes them, and broadcast to the cloud data centers. Prior to the broadcasting, each edge device establishes a secured connection with a data center that relies on the combination of symmetric and asymmetric encryption techniques. In SmartEdge, the execution of a lightweight encryption technique at the resource-constrained smart devices, and relatively complex encryption techniques at the network edge and cloud data centers reduce the resource utilization of the entire network. The proposed framework reduces the response time, security overhead, computational and communication costs, and has a lower end-to-end encryption delay for participating entities. Moreover, the proposed scheme is highly resilient against various adversarial attacks.
Robustness optimization of scale-free IoT Networks
- Authors: Usman, Muhammad , Javaid, Nadeem , Khalid, Adia , Nasser, Nidal , Imran, Muhammad
- Date: 2020
- Type: Text , Conference paper
- Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyrprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2240-2244
- Full Text: false
- Reviewed:
- Description: In today's modern world, people are cultivating towards the Internet of Things (IoT) networks due to their various demands in health monitoring, smart homes, traffic management, and industrial optimization, etc., IoT networks comprise of sensor nodes that have multiple functionalities to fulfill the demands of individuals. With the advancement in technology, the need for IoT networks is increasing as the devices are getting smarter day by day. The scale-free topology is considered to be the best topology for IoT networks because it is more robust against the attacks. For a scale-free network, robustness optimization is essential. Therefore, in this paper, to enhance the robustness, we have optimized a scale-free network through proposed the Improved Scale-Free Network (ISFN) technique. In ISFN, the edges are swapped based on their degree and nodes distance operation. This technique does not change the degree of the nodes of original topology which makes the optimized topology remains scale-free. Through experiments, we have compared the ISFN with two existing techniques, i.e., ROSE and SA. The results prove that by increasing the number of nodes, ISFN outperforms these existing techniques. © 2020 IEEE.
Security hardening of implantable cardioverter defibrillators
- Authors: Jaffar, Iram , Usman, Muhammad , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1173-1178
- Full Text:
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- Description: Contemporary healthcare has witnessed a wide deployment of Implantable Cardioverter Defibrillators (ICDs), which have the capability to be controlled remotely, making them equally accessible from both home and hospitals. The therapeutic benefits of ICDs seem to outweigh potential security concerns, yet overlooking the presence of malicious attacks cannot be justified. This study investigates the scenario where an adversary falsifies a controller command and sends instructions to issue high electric shocks in succession. We propose a novel security hardening mechanism to protect data communications between ICD and controller from malicious data manipulations. Our proposed method verifies the correctness of an external command with respect to the history of heart rhythms. The proposed method is evaluated using real data. Multi-aspect analyses show the effectiveness of the proposed scheme.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
An improved congestion-controlled routing protocol for IoT applications in extreme environments
- Authors: Adil, Muhammad , Usman, Muhammad , Jan, Mian , Abulkasim, Hussein , Farouk, Ahmed , Jin, Zhanpeng
- Date: 2024
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 11, no. 3 (2024), p. 3757-3767
- Full Text: false
- Reviewed:
- Description: The Internet of Things (IoT) has shown its presence in applications that require monitoring extreme environments, such as wildfires, military operations, and coastal areas, among others. In these applications, the IoT nodes are deployed in hazardous terrains where humanistic access is hard or not possible. Hence, to ensure reliable data transmission in these applications, novel routing protocols need to be designed due to the multihop nature of communication possessed by the deployed nodes. Currently, most of the routing protocols utilized by IoT nodes follow traditional approaches, which creates congestion and contention in the network. As a result, the network performance is degraded in terms of various communication metrics. To address this problem and improve the communication statistics in extreme environments, we propose a deep- Q -learning-enable-destination-sequenced distance-vector (DQL-DSDV) framework. DQL-DSDV focuses on selecting the next hop during communication. Initially, the DSDV protocol updates routing information for connected nodes. This information is subsequently utilized by the deep- Q -learning (DQL) algorithm to compute the next hop count. This computation is based on reward functions, known as Q-values, which are conceptualized as the distance between connected nodes by taking into account the traffic flow. These distinguishing operational features of DQL and DSDV ensure that DQL-DSDV minimizes the packet lost ratio, congestion, end-to-end delay, and communication cost with improved Quality of Service (QoS). During simulations, we observed significant improvement in these performance metrics, in the presence of the existing schemes. Despite that, we checked the computation complexity of the proposed approach with existing protocols, which demonstrated noteworthy outcomes just like the other metrics. © 2014 IEEE.
A comprehensive survey of security threats and their mitigation techniques for next‐generation SDN controllers
- Authors: Han, Tao , Jan, Syed , Tan, Zhiyuan , Usman, Muhammad , Jan, Mian , Khan, Rahim , Xu, Yongzhao
- Date: 2020
- Type: Text , Journal article
- Relation: Concurrency and computation Vol. 32, no. 16 (2020), p. n/a
- Full Text: false
- Reviewed:
- Description: Summary Software Defined Network (SDN) and Network Virtualization (NV) are emerged paradigms that simplified the control and management of the next generation networks, most importantly, Internet of Things (IoT), Cloud Computing, and Cyber‐Physical Systems. The Internet of Things (IoT) includes a diverse range of a vast collection of heterogeneous devices that require interoperable communication, scalable platforms, and security provisioning. Security provisioning to an SDN‐based IoT network poses a real security challenge leading to various serious security threats due to the connection of various heterogeneous devices having a wide range of access protocols. Furthermore, the logical centralized controlled intelligence of the SDN architecture represents a plethora of security challenges due to its single point of failure. It may throw the entire network into chaos and thus expose it to various known and unknown security threats and attacks. Security of SDN controlled IoT environment is still in infancy and thus remains the prime research agenda for both the industry and academia. This paper comprehensively reviews the current state‐of‐the‐art security threats, vulnerabilities, and issues at the control plane. Moreover, this paper contributes by presenting a detailed classification of various security attacks on the control layer. A comprehensive state‐of‐the‐art review of the latest mitigation techniques for various security breaches is also presented. Finally, this paper presents future research directions and challenges for further investigation down the line.
Performance evaluation of high definition video streaming over mobile ad hoc networks
- Authors: Usman, Muhammad , Jan, Mian , He, Xiangjian , Alam, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: Signal processing Vol. 148, no. (2018), p. 303-313
- Full Text: false
- Reviewed:
- Description: A unique combinational approach of QoS and QoE for multimedia traffic in MANETs is proposed in this paper.•A feedback-based multi-path transmission strategy is adopted to minimize the data loss during HD video streaming.•An EC technique based on multi-threading-based parallel processing is used to recover the lost video frames.•We use multiple objective evaluation metrics to test the performance of the EC technique on recovered video frames.•Subjective analysis is combined with objective evaluation using subjective scores and statistical analysis to maintain QoE. Video Service Providers (VSPs) can collect and analyze an enormous amount of multimedia data from various cloud storage centers using real-time big data systems for supporting various online customers. The infrastructure-less nature of Mobile Ad Hoc Networks (MANETs) makes the video streaming a challenging task for VSPs. High packet-loss probability in MANETs can create a notable distortion in the received video quality. In this paper, High Definition (HD) videos are streamed over MANETs. First, a transmission model is designed followed by a distortion model to estimate network distortions, such as packet-loss rate and end-to-end delay. Based on the proposed models, a video streaming framework is designed to efficiently utilize the available bandwidth in MANETs, minimize the network distortions, and improve Quality of Service (QoS). Later, an Error Concealment (EC) technique is used to conceal the lost/dropped video frames to improve the Quality of Experience (QoE). Experimental results show that our proposed video streaming framework outperforms the state-of-the-art routing protocols designed for MANETs, such as Destination-Sequenced Distance Vector (DSDV) and Optimized Link Sate Routing (OLSR) protocols. In the end, both subjective and objective evaluations are performed to evaluate the perceptual quality of the concealed video data.
A Joint framework for QoS and QoE for video transmission over wireless multimedia sensor networks
- Authors: Usman, Muhammad , Ning, Yang , Jan, Mian , Xiangjian, He , Min, Xu , Kin-Man, Lam
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE transactions on mobile computing Vol. 17, no. 4 (2018), p. 746-759
- Full Text: false
- Reviewed:
- Description: With the emergence of Wireless Multimedia Sensor Networks (WMSNs), the distribution of multimedia contents have now become a reality. Without proper management, the transmission of multimedia data over WMSNs affects the performance of networks due to excessive packet-drop. The existing studies on Quality of Service (QoS) mostly deal with simple Wireless Sensor Networks (WSNs) and as such do not account for an increasing number of sensor nodes and an increasing volume of data. In this paper, we propose a novel framework to support QoS in WMSNs along with a light-weight Error Concealment (EC) scheme. The EC schemes play a vital role to enhance Quality of Experience (QoE) by maintaining an acceptable quality at the receiving ends. The main objectives of the proposed framework are to maximize the network throughput and to cover-up the effects produced by dropped video packets. To control the data-rate, Scalable High efficiency Video Coding (SHVC) is applied at multimedia sensor nodes with variable Quantization Parameters (QPs). Multi-path routing is exploited to support real-time video transmission. Experimental results show that the proposed framework can efficiently adjust large volumes of video data under certain network distortions and can effectively conceal lost video frames by producing better objective measurements.