44 research outputs found
Privacy-Preserving Edge Caching: A Probabilistic Approach
Edge caching (EC) decreases the average access delay of the end-users through
caching popular content at the edge network, however, it increases the leakage
probability of valuable information such as users preferences. Most of the
existing privacy-preserving approaches focus on adding layers of encryption,
which confronts the network with more challenges such as energy and computation
limitations. We employ a chunk-based joint probabilistic caching (JPC) approach
to mislead an adversary eavesdropping on the communication inside an EC and
maximizing the adversary's error in estimating the requested file and
requesting cache. In JPC, we optimize the probability of each cache placement
to minimize the communication cost while guaranteeing the desired privacy and
then, formulate the optimization problem as a linear programming (LP) problem.
Since JPC inherits the curse of dimensionality, we also propose scalable JPC
(SPC), which reduces the number of feasible cache placements by dividing files
into non-overlapping subsets. We also compare the JPC and SPC approaches
against an existing probabilistic method, referred to as disjoint probabilistic
caching (DPC) and random dummy-based approach (RDA). Results obtained through
extensive numerical evaluations confirm the validity of the analytical
approach, the superiority of JPC and SPC over DPC and RDA
Edge Cache-assisted Secure Low-Latency Millimeter Wave Transmission
In this paper, we consider an edge cache-assisted millimeter wave cloud radio
access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local
cache, which can pre-fetch and store the files requested by the actuators.
Multiple RRHs form a cluster to cooperatively serve the actuators, which
acquire their required files either from the local caches or from the central
processor via multicast fronthaul links. For such a scenario, we formulate a
beamforming design problem to minimize the secure transmission delay under
transmit power constraint of each RRH. Due to the difficulty of directly
solving the formulated problem, we divide it into two independent ones:
{\textit{i)}} minimizing the fronthaul transmission delay by jointly optimizing
the transmit and receive beamforming; {\textit{ii)}} minimizing the maximum
access transmission delay by jointly designing cooperative beamforming among
RRHs. An alternatively iterative algorithm is proposed to solve the first
optimization problem. For the latter, we first design the analog beamforming
based on the channel state information of the actuators. Then, with the aid of
successive convex approximation and -procedure techniques, a semidefinite
program (SDP) is formulated, and an iterative algorithm is proposed through SDP
relaxation. Finally, simulation results are provided to verify the performance
of the proposed schemes.Comment: IEEE_IoT, Accep
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
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Blockchain based secure message dissemination in vehicular networks
Vehicular ad-hoc networks (VANETs) are one of the key elements in Intelligent Transportation System (ITS) to enable information exchange among vehicles and Roadside Units (RSUs) via vehicle-to-vehicle (V2V) and vehicle-to- nfrastructure (V2I) communications. With continuously increasing number of vehicles on road, there are numerous security and privacy challenges associated with VANETs. Communication among vehicles is needed to be secure and bandwidth efficient. Also, the messages exchanged between vehicles must be authentic so as to maintain a trusted network in a privacy-preserving manner. Furthermore, a sustainable economic model is required to incentivise honest and cooperative vehicles. Traditional security and privacy solutions in centralised networks are not applicable to VANETs due to its distributed nature, heterogeneity, high mobility and low latency requirements. Meanwhile, the new development of blockchain has been attracting significant interests due to its key features including consensus to evaluate message credibility and immutable storage in distributed ledger, which provides an alternative solution to the security and privacy challenges in VANETs.
This thesis aims to present blockchain solutions for the security and privacy of VANETs meeting the stringent requirements of low latency and bandwidth-efficient message dissemination. VANETs are simulated in OMNeT++ to validate the proposed solutions. Specifically, two novel blockchain consensus algorithms have been developed for message authentication and relay selection in presence of malicious vehicles. The first employs a voting based message validation and relay selection, which reduces the failure rate in message validation by 11% as compared to reputation based consensus. The second utilises federated learning supported by blockchain as a better privacy-preserving solution, which is 65.2% faster than the first voting based solution. Both approaches include blockchain-based incentive mechanisms and game theory analysis to observe strategic behaviour of honest and malicious vehicles. To further study the privacy aspect of vehicular networks, the integration of blockchain with physical layer security is also theoretically analysed in Vehicle-to-Everything (V2X) communications scenarios. The integration results in 8.2 Mbps increased goodput as compared to the blockchain solution alone.
In essence, our research work shows that blockchain can offer better control and security, as compared to centralised solutions, if properly adjusted according to the application and network requirements. Thus, the proposed solutions can provide guidelines for practically feasible application of blockchain in vehicular networks
Optimizing Resource Allocation with Energy Efficiency and Backhaul Challenges
To meet the requirements of future wireless mobile communication which aims to increase the data rates, coverage and reliability while reducing energy consumption and latency, and also deal with the explosive mobile traffic growth which imposes high demands on backhaul for massive content delivery, developing green communication and reducing the backhaul requirements have become two significant trends. One of the promising techniques to provide green communication is wireless power transfer (WPT) which facilitates energy-efficient architectures, e.g. simultaneous wireless information and power transfer (SWIPT). Edge caching, on the other side, brings content closer to the users by storing popular content in caches installed at the network edge to reduce peak-time traffic, backhaul cost and latency. In this thesis, we focus on the resource allocation technology for emerging network architectures, i.e. the SWIPT-enabled multiple-antenna systems and cache-enabled cellular systems, to tackle the challenges of limited resources such as insufficient energy supply and backhaul capacity. We start with the joint design of beamforming and power transfer ratios for SWIPT in MISO broadcast channels and MIMO relay systems, respectively, aiming for maximizing the energy efficiency subject to both the Quality of Service (QoS) constraints and energy harvesting constraints. Then move to the content placement optimization for cache-enabled heterogeneous small cell networks so as to minimize the backhaul requirements. In particular, we enable multicast content delivery and cooperative content sharing utilizing maximum distance separable (MDS) codes to provide further caching gains. Both analysis and simulation results are provided throughout the thesis to demonstrate the benefits of the proposed algorithms over the state-of-the-art methods
Multi-Agent Reinforcement Learning-Based Buffer-Aided Relay Selection in IRS-Assisted Secure Cooperative Networks
This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce the buffer-aided relay to enhance the secrecy performance, but the use of the buffer leads to the cost of delay. Thus, we aim to maximize either the average secrecy rate with a delay constraint or the throughput with both delay and secrecy constraints, by jointly optimizing the buffer-aided relay selection and the IRS reflection coefficients. To obtain the solution of these two optimization problems, we divide each of the problems into two sub-tasks and then develop a distributed multi-agent reinforcement learning scheme for the two cooperative sub-tasks, each relay node represents an agent in the distributed learning. We apply the distributed reinforcement learning scheme to optimize the IRS reflection coefficients, and then utilize an agent on the source to learn the optimal relay selection based on the optimal IRS reflection coefficients in each iteration. Simulation results show that the proposed learning-based scheme uses an iterative approach to learn from the environment for approximating an optimal solution via the exploration of multiple agents, which outperforms the benchmark schemes