56 research outputs found
Energy Efficiency in Hybrid Mobile and Wireless Networks
Wireless Internet access is almost pervasive nowadays, and many types of wireless
networks can be used to access the Internet. However, along with this growth,
there is an even greater concern about the energy consumption and efficiency of mobile
devices as well as of the supporting networks, triggering the appearance of the
concept of green communication. While some efforts have been made towards this
direction, challenges still exist and need to be tackled from diverse perspectives.
Cellular networks, WLANs, and ad hoc networks in the form of wireless mesh
networks are the most popular technologies for wireless Internet access. The availability
of such a variety of access networks has also paved the way to explore synergistic
approaches for Internet access, leading to the concept of hybrid networks
and relay communications. In addition, many mobile devices are being equipped
with multiple interfaces, enabling them to operate in hybrid networks. In contrast,
the improvements in the battery technology itself have not matched the pace of the
emerging mobile applications. The situation becomes more sophisticated when a
mobile device functions also as a relay node to forward other station’s data. In the
literature, energy efficiency of mobile devices has been addressed from various perspectives
such as protocol-level efforts, battery management efforts, etc. However,
there is little work on energy efficiency in hybrid mobile and wireless networks
and devices with heterogeneous connections. For example, when there are multiple
networks available to a mobile device, how to achieve optimum long-term energy
consumption of such a device is an open question.
Furthermore, in today’s cellular networks, micro-, pico-, and femto-cells are the
most popular network topologies in order to support high data rate services and
high user density. With the growth of such small-cell solutions, the energy consumption
of these networks is also becoming an important concern for operators.
Towards this direction, various solutions have been proposed, ranging from deployment
strategies for base stations to cooperative techniques etc. However, as base
stations have the largest share in a network’s energy consumption, methods that allow
lightly-loaded base stations sleep or be switched off are possible means as a
feasible step towards green communications.
In this dissertation, we tackle the above mentioned problems from two perspectives,
i.e., mobile station’s and operator’s perspectives. More specifically, by taking
into account the amount of transferred data in uplinks and downlinks individually
for various components in a hybrid network, strategies are proposed to reduce mobile
station’s battery energy consumption. For this purpose, other parameters such as link distance and remaining battery energy can also be considered for handover
decision making, in order to maximize energy efficiency of the mobile station. To
optimize long-term energy consumption of the mobile stations operated in such
scenarios, a Markov decision process-based methodology is proposed as our contribution
to this topic. Moreover, from operator’s perspective, a network energy
conservation scheme which may switch off a base station is proposed for micro- or
pico-cells scenarios. Both deterministic and probabilistic schemes are proposed for
network energy conservation. The problems considered and the solutions proposed
in this dissertation advance the frontiers of the research work within the theme of energy
efficiency for mobile devices as well as hybrid mobile and wireless networks
A system-level power saving approach for cellular networks with microcells/picocells
Network power consumption reduction has recently become an active research topic. In this paper, we propose a novel approach to save power consumption of a three-cell microcellular network. When the traffic load in the middle cell is low, it can be switched-off and its users are covered. This is enabled by increasing the transmission power of one sector antenna in the two neighboring cells. Numerical results show that by increasing antenna transmission power of the two sectors, the overall network power consumption can be reduced
Capacity driven small cell deployment in heterogeneous cellular networks : Outage probability and rate coverage analysis
Author's accepted manuscript.This is the peer reviewed version of the following article: Ullah, A., Haq Abbas, Z., Muhammad, F., Abbas, G. & Lei, J. (2020). Capacity driven small cell deployment in heterogeneous cellular networks: Outage probability and rate coverage analysis. Transactions on Emerging Telecommunications Technologies, 31(6): e3876, which has been published in final form at https://doi.org/10.1002/ett.3876. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.acceptedVersio
Energy Efficiency Maximization in IRS-enabled Phase Cooperative PS-SWIPT based Self-sustainable IoT Network
Power splitting based simultaneous wireless information and power transfer
(PS-SWIPT) appears to be a promising solution to support future
self-sustainable Internet of Things (SS-IoT) networks. However, the performance
of these networks is constrained by radio frequency signal strength and channel
impairments. To address this challenge, intelligent reflecting surfaces (IRSs)
are introduced in PS-SWIPT based SS-IoT networks to improve network efficiency
by controlling signal reflections. In this article, an IRS-enabled phase
cooperative framework is proposed to improve energy efficiency (EE) of the IoT
network using phase shifts of the user network
, without constraining hardware resources at . We exploit transmit beamforming (BF) at access points (APs) and
phase shifts optimization at the IRS end with phase effective cooperation
between APs to enhance EE performance. The maximization
problem turns out to be NP-hard, so first, an alternating optimization (AO) is
solved for the using low computational complexity
heuristic BF approaches, namely, transmit minimum-mean-square-error and
zero-forcing BF, and phase optimization is performed using semidefinite
relaxation (SDR) approach. To combat the computational complexity of AO, we
also propose an alternative solution by exploiting heuristic BF schemes and an
iterative algorithm, i.e., the element-wise block-coordinate descent method for
phase shifts optimization. Next, EE maximization is solved for the by optimizing the PS ratio and active BF vectors by exploiting
optimal phase shifts of the . Simulation results confirm
that employing IRS phase cooperation in PS-SWIPT based SS-IoT networks can
significantly improve EE performance of without
constraining resources
Physical layer authentication using ensemble learning technique in wireless communications
Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect cyber-attacks. Still, the cyber-physical systems (CPS) have relatively large computational requirements and require more communication resources, thus making it impossible to achieve a low latency target. These methods perform well but only in stationary scenarios. We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios. The features are fed to ensemble learning algorithms, such as AdaBoost, LogitBoost and Gentle Boost, to classify data. The authentication of the received signal is considered a binary classification problem. The transmitted data is labeled as legitimate information, and spoofing data is illegitimate information. Therefore, this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks. It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile. The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time
Analysis of load balancing and interference management in heterogeneous cellular networks
To meet the current cellular capacity demands, proactive offloading is required in heterogeneous cellular networks (HetCNets) comprising of different tiers of base stations (BSs), e.g., small-cell BSs (sBSs) and conventional macro-cell BSs (mBSs). Each tier differs from the others in terms of BS transmit power, spatial density, and association bias. Consequently, the coverage range of each tier BSs is also different from others. Due to low transmit power, a fewer number of users are associated to an sBS as compared with mBS. Thus, inefficient utilization of small-cell resources occurs. To balance the load across the network, it is necessary to push users to the underloaded small cells from the overloaded macro-cells. In co-channel deployed HetCNets, mBSs cause heavy inter-cell interference (ICI) to the offloaded users, which significantly affects the network performance gain. To address this issue, we develop a tractable analytical network model abating ICI using reverse frequency allocation (RFA) scheme along with cell range expansion-based user association. We probabilistically characterize coverage probability and user rate while considering RFA with and without selective sBS deployment. Our results demonstrate that selective sBS deployment outperforms other deployment methods.publishedVersionNivĂĄ
FedDP: A privacy-protecting theft detection scheme in smart grids using federated learning
In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data
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USAF-IoD: Ultralightweight and Secure Authenticated key agreement Framework for Internet of Drones environment
The use of Internet of Drones (IoD) technology has surged across various domains such as logistics, surveying, industrial inspections, emergency response, security, infrastructure monitoring, crop management, and more. However, real-time communication with drones or Unmanned Aerial Vehicles (UAVs) in the IoD environment occurs over an insecure open channel, making it susceptible to various security and privacy vulnerabilities, including unauthorized access, data interception, denial of service attacks, and privacy concerns. Due to their unique characteristics, including long transmission distances, unstable communication environments, resource limitations, and the highly dynamic nature of UAVs, ensuring the security and privacy of IoD systems is of paramount importance for the success of IoD-based applications. Furthermore, drones are resource-constrained devices, and employing expensive security solutions is impractical, as it would significantly reduce the operational capacity of drones. In this paper, we present the design of an ultralightweight, secure, and robust user-authenticated key agreement framework for the IoD environment, named USAF-IoD. The proposed USAF-IoD is developed by incorporating authenticated encryption (ASCON), cryptographic hashing, XOR operations, and the use of physical unclonable functions (PUFs). PUFs are employed to enhance resistance against physical tampering attacks. The security analysis reveals that the proposed USAF-IoD meets the essential security requirements of the IoD environment.
The comparative analysis further highlights the effectiveness of the proposed USAF-IoD, notably excelling in terms of security and functionality characteristics when compared to existing benchmark schemes, and showcasing competitive performance in computation, communication, and energy overheads
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Blockchain-assisted lightweight authenticated key agreement security framework for smart vehicles-enabled Intelligent Transportation System
Intelligent Transportation Systems (ITS) supported by smart vehicles have revolutionized modern transportation, offering a wide range of applications and services, such as electronic toll collection, collision avoidance alarms, real-time parking management, and traffic planning. However, the open communication channels among various entities, including smart vehicles, roadside infrastructure, and fleet management sys- tems, introduce security and privacy vulnerabilities. To address these concerns, we propose a novel security framework, named blockchain-assisted lightweight authenticated key agreement se- curity framework for smart vehicles-enabled ITS (BASF-ITS), which ensures data protection both during transit and while stored on cloud servers. BASF-ITS employs a combination of efficient cryptographic primitives, including hash functions, XOR operator, ASCON, elliptic curve cryptography, and physical unclonable functions (PUF), to design authenticated key agree- ment schemes. The inclusion of PUF significantly enhances the system’s resistance to physical attacks, preventing tampering attempts. To ensure data integrity when stored on the cloud, our framework incorporates blockchain technology. By leveraging the immutability and decentralization of the blockchain, BASF-ITS effectively safeguards data at rest, providing an additional layer of security. We rigorously analyze the security of BASF-ITS and demonstrate its strong resistance against potential security ass aults, making it a robust and reliable solution for smart vehicle- enabled ITS. In a comparative analysis with contemporary competing schemes, BASF-ITS emerges as a promising approach, offering superior functionality traits, enhanced security features, and reduced computation, communication, and storage costs. Furthermore, we present a practical implementation of BASF- ITS using blockchain technology, showcasing the computational time versus the “transactions per block” and the “number of mined blocks”, confirming its efficiency and viability in real- world scenarios
Two Teletraffic-based Schemes for Energy Saving in Cellular Networks with Micro-cells
Abstract — The energy consumption of Base Stations (BSs) is known to constitute a major part of the power consumption in a cellular network. In this paper, we propose a novel approach which may switch a BS off under light traffic conditions in order to conserve the power consumption of such networks. More specifically, when the traffic load in the middle cell of a network with three micro-cells is sufficiently low, the corresponding BS can be switched off and its users will be covered by increasing the transmission power of one sector antenna in each of the two neighboring cells. Two teletraffic-based power saving schemes are proposed in our study. The first scheme analyzes the expected sojourn times of different channel occupancies and switches off the BS deterministically when the switching thresholds are met. The second scheme instead switches off the BS probabilistically based on a policy designed using a Finite Markov Decision Process (FMDP). Numerical results for the first scheme demonstrate that a reasonable amount of network power can be saved at the cost of slightly higher transmission power. The results for the second scheme indicate that a lower limit on the long-term network transmission power can be obtained using the FMDP-based analysis. Index Terms — Micro-cell, teletraffic, power saving, energy, Markov chain, BS, FMDP, optimization
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