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Service function chaining of network-functions across multiple domains enables next-generation services to be offered efficiently. Multi-domain orchestrator decouples the complexities of setting up such chains across domains on behalf of the users of the service. Privacy preservation is essential in this scenario since domains do not want to expose their topology, available resources, cost, and other operational aspects. Similarly, users try to minimize the exposure of their requirements to the domains and the orchestrator. Prior work relied on abstraction and/or aggregation of information in advertisements to preserve privacy. This paper presents a novel cryptographic mechanism based on bilinear mapping that allows the domains and users to privately advertise their information without revealing anything to the orchestrator and other domains. A private set intersection is used to compute the common elements between domains and users. Security proof for the proposed flows is provided using the universally composable framework. Usage of cryptographic techniques reduces the need for abstraction and provides strong privacy guarantees quantified in the privacy metrics of the paper. The simulation results demonstrate that the performance of the proposed system is at par with other existing work while providing strong cryptographic privacy, which none of the other systems provide. © 2022 IEEE.

The softwarized infrastructure of Software-Defined Industrial Internet of Things (SDIIoT) offers a cost-effective solution to improve flexibility and reliability in network management but faces several critical challenges. First, th Majority of SDIIoT entities operate over wireless channel, which expose them to a variety of attacks (e.g., man-in-the-middle, replay, and impersonation attacks) and also the centralized nature of SDN controller is prone to single point attacks. Second, network traffic in the SDIIoT is associated with large scale, high dimension and redundant data, all of which present significant hurdles in the development of efficient flow analyzer. In this regard, we present a novel blockchain and Deep Learning (DL) integrated framework for protecting confidential information and hunting cyber threats against SDIIoT and their network traffic. First the blockchain module is proposed to securely transmit industrial data from IIoT sensors to controllers of SDN via forwarding nodes (i.e., OpenFLow switches) using Clique Proof-of-Authority (C-PoA) consensus mechanism. A novel flow analyzer based on DL architecture named LSTMSCAE-AGRU is designed by combining Long Short-Term Memory Stacked Contractive AutoEncoder (LSTMSCAE) with Attention-based Gated Recurrent Unit (AGRU) at the control plane. The latter first extracts low-dimensional features in an unsupervised manner, which is then fed to AGRU for hunting anomalous switch requests. The proposed framework can withstand a variety of well-known cyber threats and mitigate the single point of controller failure problem in SDIIoT. © 2022 IEEE.

The advent of Open Radio Access Network (O-RAN) technology enables intelligent edge solutions for base stations in beyond 5G (B5G) networks. O-RAN Working Group 2 (WG2) focuses on the architecture and specifications of AI/ML workflows, allowing AI/ML applications in O-RAN environments to meet different QoS requirements for different use cases over varying time periods. This study shows the technical challenges in mapping AI/ML functionalities at Near-Real Time (RT) RAN Intelligence Controller (RIC) and/or Non-RT RIC for closed loop control-based resource adaptation in O-RAN. We also present a drift-based solution to avoid performance violations if there is decay in prediction accuracy. Results show that drift-based solution outperforms offline models. © 2022 IEEE.

Softwarized Unmanned Aerial Vehicles (UAVs) use network programmability concept of Software-Defined Network (SDN) to separate the hardware control layer from the data layer via OpenFlow protocols. The softwarized UAV enable ubiquitous connection, as well as a flexible, cost-effective, and improved method for upgrading all network services without shutting down the entire system. However, the connectivity of UAVs with OpenFlow switches and their heavy reliance on unsecured communication protocols makes the entire network vulnerable. This is a critical concern, particularly in combat surveillance, where eavesdropping, adding, changing, or deleting messages during communications between deployed UAVs and SDN controller is a possible threat. To mitigate the aforementioned issues, this paper presents a novel secure data sharing framework for softwarized UAV environments that incorporates blockchain and Deep Learning (DL). First we present a blockchain-based technique to reg-ister, verify and thereafter validate the communication entities in softwarized UAV environment using smart contract-based Proof-of-Authentication (PoA) consensus mechanism. Additionally, a new deep neural network architecture-based flow analyzer is designed to detect illegitimate transactions. The latter combines a Stacked Contractive Sparse AutoEncoder with Attention-based Long Short-term Memory Neural Network (SCSAE-ALSTM) to improve intrusion detection process. The effectiveness of our framework over several standard baseline methodologies is demonstrated by security analysis and experimental findings. © 2022 IEEE.

The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network. IEEE

Mobile Network Operator (MNO)s avail the benefit of providing an isolated service with the network slice in a variety of forms like enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). However, they face challenges in handling situations of overload, congestion, and scaling with the arrival of unexpected control plane User Service Requests (USRs) leading to the dropping of USRs, and hence disturbing the slice’s High Availability (HA). This paper presents a novel High Availability supportive self Reliant NEtwork Slicing System (HARNESS) for 5G Core, powered by intelligent and autonomous Self Organizing Network (SON) paradigm. In HARNESS, we propose algorithms to intelligently schedule and serve the significant portion of control plane USRs for both delay tolerant and delay sensitive slices, to ensure their uninterrupted HA service provision. Along with efficient resource utilization, the HARNESS shows improvements over the traditional HA provisioning methods, in preventing the occluding of crucial control plane USRs by 60% and reducing the average response time for these USRs by 50%. We developed the HARNESS framework in a 5G test-bed system using eXpress Data Path (XDP) and extended Berkeley Packet Filter (eBPF) mechanism for coherent and optimized end-to-end working of it. With this additional gear, HARNESS productively contributes to achieving 3.2% better slice service HA in the minimal two nodes active active cluster configuration. IEEE

Owing to the diverse nature of traffic incidents, accepting and storing relevant data in the form of natural language is more convenient than in constrained value fields. Textual information in such cases can be rich enough for traffic incident analysis and modelling even in the absence of certain fixed set of parameters. However limited studies considered the complexity in processing such information to predict traffic incident duration. In this paper, we propose to represent the textual data from incident reports using BERT word embeddings. These text representations are then inputted into various regressors such as LSTM, XGBoost, RF and SVR to predict traffic incident duration. To demonstrate the significance of this approach, the method is compared with the state-of-the-art approach using LDA representation. Dataset used for the experiment is the Caltrans Performance Measurement System (PeMS). Result analysis indicates that the BERT- LSTM hybrid model is effective in capturing the contextual meaning of textual incident reports to predict the traffic incident duration and outperforms LDA topic modelling with MAE around 11.16 minutes. © 2021 IEEE.

3GPP has introduced C-V2X technology in the Rel. 14 for improving road safety. There are two modes of vehicular communication in C-V2X viz., Mode 3 and Mode 4. Since Mode 4 allows vehicles to communicate with each other even outside the cellular coverage, it is acknowledged as the baseline mode. In Mode 4, vehicles select the best available least interfered radio resource by utilising Semi-Persistent Scheduling (SPS) in a distributed fashion. Medium Access Control (MAC) parameters like pKeep (probability of keeping a radio resource) and Resource Counter (RC) affect the duration of radio resource collision, so these need to be configured carefully for efficient C-V2X communication. This paper explicates the impact of pKeep and RC on the overall performance of C-V2X system. A new algorithm called Traffic-Aware SPS (TA-SPS) is then proposed, which optimally configures these parameters according to the current density of road traffic by estimating it from periodic Cooperative Awareness Messages (CAMs) broadcasted by the vehicles. By reducing the number of resource collisions, TA-SPS increases transmission reliability of the C-V2X system. Through extensive simulation studies, we show how TA-SPS outperforms the traditional SPS in mixed road traffic scenarios. © 2021 IEEE.

5G network supports end-to-end logically isolated networks in the form of network slices, catering to the needs of users of various primary network services, namely enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC). Mobile Virtual Network Operators (MVNO)s often face challenges in achieving strong slice isolation and High Availability per slice during overload and scaling situations as the 5G network uses a shared environment for slices with multiple domains, especially considering a variety of services and devices. In this paper, we propose a novel Self Optimizing Network Slicing framework (SONS) leveraging Self Organizing Network by building it as an autonomous slice system in 5G network slicing management for efficient slice sharing and isolation. Precisely, we formulate a system model with Probabilistic Graphical Model (PGM) based Markov Network, building it as an Artificial Intelligence based learning framework. We propose Slice Belief Propagation based algorithms and Deep Learning based Long Short Term Memory (LSTM) methods to aid in serving user requests and reconfiguration of self optimizing slice. Our experiments on the proposed SONS framework shows improvement in serving higher number of users with uninterrupted connectivity by 80% in eMBB, 35% in uRLLC, and 52% in mMTC when compared to standard slice deployments, while handling the worst case of peak traffic in the control plane of 5G Core network. © 2021 IFIP.

Network slicing in 5G forms a pivotal enabler to satisfy unprecedented requirements in terms of data rate, latency, and capacity in the form of heterogeneous network services namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building end to end logical network. In this paper, we study the performance of various network slice deployment modes in a Network Function Virtualization (NFV) based 5G Core (5GC) for High Availability (HA) and Reliable functioning keeping in mind Capital Expenditure (CAPEX) and Operating Expenditure (OPEX). Our experimental results on the in-house 3GPP compliant testbed prototype of 5GC show that the completely shared slice mode saves the resources and its cost (about 66.6% compared to the isolated mode). However, it risks HA and reliability of slice service by consuming additional time (about 40 seconds) during activation, for serving a maximum of 90K subscribers. Whereas, the completely isolated mode caters to reliable slice service using dedicated resources but, with higher CAPEX and OPEX to Telecommunications Service Provider (TSP)s. A reasonable middle-ground partially shared mode of slice functioning balances both reliability and cost concerns. We believe that this work will guide TSPs in building autonomous, smart management, and orchestration of network slice deployments. © 2021 IEEE.

Network slicing is one of the core features of the 5G network to meet the requirements of various network services, namely, enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communications (uRLLC), and massive Machine Type Communication (mMTC) by building an isolated virtual network of resources typically in a Network Function Virtualization (NFV) environment. We have built a 3GPP compliant 5G Core (5GC) testbed and enabled network slicing on it to provide eMBB, uRLLC, and mMTC services to end users. In this demonstration, we showcase the capabilities of our zero-touch framework of 5GC network slicing management and orchestration in an NFV environment with the network functions of 5GC and the applications on eMBB and uRLLC slice services. We believe that this work will guide Mobile Network Operator (MNO)s in building zero-touch autonomous network slices in 5GC along with their smart management and orchestration in closed loop automation. © 2021 IFIP.

Quantum routing protocols seek to distribute entanglement across different nodes of a quantum network. A recently popular approach for quantum routing is to cut down the latency times for sharing entanglement by using virtual edges in addition to physical ones. While a physical edge is associated with the presence of a quantum channel, virtual edges correspond to pre-existing entanglement between some nodes which can be leveraged for entanglement swapping. Distributed routing protocols for quantum networks have been proposed and analyzed using this idea. These analyses have also been backed up by simulations. However, to the best of our knowledge, existing simulation approaches consider a static picture, where the demands for entangled pairs are presented upfront. In this paper, we study routing algorithms through time-driven simulations. Such an approach allows for the demands to emerge in real-time as the simulation proceeds, and therefore mimic realistic scenarios better. This also facilitates studying routing protocols in the presence of dynamic replenishment of entangled pairs, and exposes issues like occurrence of deadlocks in the context of limited quantum resources. As a demonstration of the approach, we show simulation results that analyze the performance of various physical and virtual graph topologies in terms of average latency time. Finally, we show the change in performance and network saturation in the presence of replenishment of entanglement resources. © 2021 IEEE.

End-to-end network slicing in 5G enables new business models and use cases across all industry verticals. Network slicing is an efficient solution that fulfills the diverse business requirements, characterized by a Service Level Agreement (SLA). 5G service providers offer the network slices through various plans differing in leasing period, allocated resources, and price. With uncertain future demand, it is challenging for businesses to select a cost-effective plan that supports the traffic. In this work, we propose a prediction-based online algorithm that minimizes the cost through plan selection for different applications in industry verticals. First, the future traffic demand is predicted using the Recurrent Neural Network (RNN)-based Long Short-Term Memory (LSTM) model, which continuously learns and adapts to the dynamic requirements with higher prediction accuracy. The traffic prediction model estimates the future demand, and the proposed algorithm leverages it for plan selection. The problem is formulated as an Integer Linear Program (ILP), which provides an offline optimal solution of the problem. Results from extensive simulations, with real-world datasets, illustrate that the proposed algorithm reduces the best worst-case expected Competitive Ratio (CR) by 20% over randomized ski-rental algorithm and 37% over deterministic algorithm. © 2021 IEEE.

Reinforcement learning (RL) is a powerful learning framework which can be used in complex environments such as autonomous driving. Generally, in autonomous driving, vehicles run RL algorithm locally. However doing so will not give a desirable performance as each vehicle will only consider its own environment. So in autonomous driving it is very important that the vehicles make actions with the knowledge learned by other vehicles as well which can be received using V2X technology. However, relying on a single radio or V2X mode of communication is not desirable. In the absence of communication infrastructure on the road side, one can depend on technologies such as 4G/5G for V2N (Vehicle-to-Network) communication and Wi-Fi Direct for V2V (Vehicle-to-Vehicle) communication. Vehicles can depend on cellular technologies for indirect mode of communication (V2N), if direct V2V communication is not possible with other vehicles present in the close vicinity. To reap in the benefits of both federated learning and V2X, we present a federated learning architecture with support from V2X, where all the participant agents make their actions with the knowledge received using V2X, even when they are acting in very different environments. Effectiveness of the proposed V2X federated learning system is demonstrated using collision avoidance application using Flow, Veins, and SUMO simulators. Simulation results suggest that it important to use a federated learning to significantly improve the reliability of of the collision avoidance application. © 2021 IEEE.

The fifth-generation (5G) mobile technology comes with numerous enhancements to support new use cases and services. Two of these enhancements are Numerology and Bandwidth Part (BWP) in the 5G NR (New Radio) standard. While Numerology gives the flexibility for selecting Transmission Time Interval (TTI) for different types of service classes, the BWP allows a gNB (5G NR Base Station) carrier to be divided into multiple parts. Different BWPs can run different Numerologies simultaneously by selecting higher Numerology for low latency use cases. In this paper, we identified the drawbacks of higher Numerology under high traffic scenario and proposed an optimal bandwidth (BW) segmentation algorithm. The proposed solution reduces the downlink latency by selecting Numerologies and BWP based on the traffic conditions of various services. The simulation results show that the proposed algorithm can effectively adapt to the traffic load and reduce the delay and packet loss rate by up to 36% and 18.5%, respectively compared to static Numerology-based scheduling. © 2021 IEEE.

An important idea in quantum networks is that of employing virtual edges or 'short-cuts' between quantum nodes. These edges, which are shared entangled pairs between two nodes that are not directly connected 'physically', are expected to reduce the average latency of entanglement distribution in quantum network routing. In this work, we show how to create such edges on an existing discrete event simulator for quantum networks called SeQUeNCe, and study the impact that these edges have on latency. Additionally, we implement a distributed routing algorithm on the simulator and demonstrate its use in conjunction with the virtual edges. We observe a reduction in latency when short-cuts are used, as is expected. Thus, with these modifications, we believe that SeQUeNCe (and other similar simulators) can be used for experimenting with advanced quantum networking protocols. © 2021 IEEE.

New vehicular applications like Augmented Reality (AR), Virtual Reality (VR), and High Definition Map (HD Map) have computational intensive and latency-sensitive traits and require collaboration among nearby vehicles. Computational offloading is used to improve the accuracy and performance of these applications, as it allows computational jobs to be processed on MEC servers at the cell edge. Here, the challenge is how to effectively take offloading decisions at the MEC server by considering wireless transmission delay and computational delay in the presence of time varying channel conditions due to vehicular mobility. In this work, we aim to maximize the number of jobs offloaded to the MEC server under application's deadline constraints while ensuring fairness among vehicles. First, we formulate the computational offloading as an integer linear programming (ILP) problem where both the transmission delay of 5G NR and MEC computational resources are taken into account. Then, we propose an online heuristic for joint computational offloading and resource allocation, OCTANE, that jointly takes 5G NR radio resources and computational resources into consideration while taking offloading decisions. Further, to provide fairness among vehicles, Transport Block Size (TBS) based Medium Access Layer (MAC) strategy is proposed for allocation of TDMA symbols in the 5G NR uplink. Finally, extensive simulations are performed in the NS-3 5G NR module with mobility traces taken from SUMO using OpenStreetMap to evaluate OCTANE and ILP model. Simulation results show that the proposed OCTANE scheme performs better than a state-of-the-art solution and is close to the ILP model in terms of offloading success rate. © 2021 IEEE.

To access an unlicensed channel, Wi-Fi follows the Listen Before Talk (LBT) mechanism, whereas LTE-U (and future New Radio in Unlicensed (NR-U) Patriciello et al., 2020; Lagen et al., 2019) adopts the ON–OFF duty-cycled mechanism to fairly share the channel with Wi-Fi. These different mechanisms result in quite different performance for Wi-Fi and LTE-U/NR-U based on their relative deployment and density in the environment. In this work, we present an analytical model for the characterization of achievable throughputs of Wi-Fi and LTE-U networks in spatially distributed scenarios with downlink-prevalent traffic. The proposed model is used to study how LTE-U and Wi-Fi coexist with each other in spatially distributed scenarios. Our extensive simulation results show that it is a reliable model for estimating the throughput of both Wi-Fi and LTE-U in a spatially distributed deployment. The impact of various network parameters such as energy detection and carrier sense threshold on Wi-Fi and LTE-U coexistence is explored as a byproduct and corroborated via a National Instrument experimental test bed. Our model gives very high accuracy in throughput estimation and the mean normalized error is less than 3% for a 80-node scenario in which 50% of nodes belong to each of Wi-Fi and LTE-U network. The analytical model is also used to conduct coexistence studies of LTE-U and Wi-Fi. © 2021

Various real-time, latency-sensitive, and high-speed mobile applications are evolving as 5G applications. These applications are realized using Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technologies in the 5G system. MEC platform, MEC service, and MEC application are the main components of an MEC Framework. NFV Orchestrator, Virtualized Infrastructure Manager (VIM), and virtualization technologies such as Virtual Machines (VM) and Containers are the main pillars of the NFV technology. In this paper, we study the impact of the virtualization technologies in the deployment of the MEC framework and its components while. We also study the impact of virtualization technologies on NFV and MEC KPIs such as onboarding time, instantiation time, MEC service and application response times. The experiments and its analysis show that containers perform better than VM to instantiate/terminate MEC components in the NFV framework. The observed MEC service KPIs show that the edge application's performance will be improved to meet the QoE of the applications irrespective of the virtualization technology used. These results can be used as a reference while deploying the MEC components based on their granular functionalities. © 2020 IEEE.

5G Mobile network will reap the benefits from key technologies like Software Defined Networking and Network Function Virtualization. Cloud Radio Access Network architecture (Cloud RAN) is proven to be a promising architecture, but fully centralized Cloud RAN imposes a great bandwidth requirement in the fronthaul link. Different functional split options for 5G RAN have been proposed which lead to a trade-off between centralization and bandwidth requirement. Functional split at different granularity such as per cell, per logical network (slice), per user, or per bearer, have been an area of interest. To explore the effect of slice granularity in adaptive splits for slices, we formulate slice centric functional split in 5G RAN as an ILP to maximize centralization of baseband processing. By varying the slice granularity from macro slicing to micro slicing, we observe how slice centric split can impact centralization benefit of the network. We show that with increasing slice granularity slice centric split can render more centralization benefit in some scenarios but a trade off exists between centralization benefit and migration cost in the network which should be carefully considered in real deployment scenario. © 2020 IEEE.

Network slicing is an integral part of 5G, which supports next-generation wireless applications over a shared network infrastructure. It paves the way to leverage the full potential of 5G by increasing the efficiencies through differentiation and faster time-to-market. In this work, we propose a Mobile Virtual Network Operator (MVNO) Slice Resource Allocation Architecture (MSRAA) for supporting different network slices in the 5G data plane. MSRAA supports QoS parameters, including Guaranteed Bit Rate (GBR) and Maximum Delay Budget. Using long short-term memory (LSTM) neural networks, we predict network slices bandwidth requirements for efficiently allocating the resources. To reduce revenue loss to the network operators due to forecasting errors, the proposed Bandwidth Admission Control (BAC) algorithm, reallocates resources from lower priority slices (e.g., best-effort users) to higher priority slices (e.g., guaranteed service users). Using Mondrain Random Forests in our Delay Admission Control (DAC) algorithm, we predict the end-to-end delay and admit flows into slices that can satisfy delay requirements. We implement MSRAA on our advanced 5G Core testbed and evaluate User Service Request (USR) acceptances and do a complete cost-benefit analysis of our architecture. We show that for eMBB-GBR and eMBB-Non-GBR slices, our algorithm is showing a significant reduction in costs.

The recent adoption of virtualized technologies in Next Generation Radio Access Network (NG-RAN) has driven a significant impact on energy consumption by subsequently decreasing the number of active base stations. The base station (gNodeB) of 5G is segregated into cost-efficient Central Units (CU) hosted on virtual platforms and cheaper & smaller Distributed Units (DU) present at the cell sites. Multiple CUs are pooled together in a single powerful central cloud, known as CU pool. The logical connection between DU and CU can be dynamically adjusted and can potentially affect the energy consumption of the CU pool. The deployment of NG-RAN imposes strict latency requirements on the fronthaul link that connects DUs to CU. To relax these strict latency requirements, various alternate architectures such as Flexible RAN Functional Splits have been proposed by 3GPP. In this paper, we first evaluate the energy consumption of DU and CU for various functional split options using OpenAirInterface (OAI), a real-time open source software radio solution. We find that lower layer splits have high energy consumption at CU as compared to higher layer split options. We also observe the variation in energy consumption due to traffic heterogeneity. Motivated by the above study, we formulate an optimization model, Apt-RAN, that optimizes the energy consumption of the CU pool and the number of handovers, considering different functional splits. To address the computational complexity of solving the optimization model, a lightweight polynomial time heuristic algorithm is proposed. Simulation results demonstrate that our proposed model outperforms existing state-of-art schemes.

LTE-Wi-Fi radio level interworking is realized by employing traffic steering from IP layer or PDCP layer of LTE eNodeB protocol stack to Wi-Fi protocol stack. The challenges associated with radio level interworking architectures such as reliability, fine granularity in traffic steering, and minimizing out-of-order packet delivery for maximizing link aggregation benefits are not well addressed by IP and PDCP layer interworking architectures. LTE-Wi-Fi radio level interworking at RLC layer (LWIR) is a promising integration architecture which not only addresses the above mentioned problems but also improves the interworking benefits significantly. We have designed and implemented an LWIR prototype system using OpenAirInterface (OAI) as LTE network and Cisco Access Point/Atheros device with Hostapd as Wi-Fi network. In this work, we demonstrate the aggregation benefits of using LWIR architecture by employing byte level traffic steering across LTE and Wi-Fi links.

Vehicle-to-vehicle networks have gained significant interest in recent times from researchers all around the world. V2V/V2I are emerging as an efficient solution for achieving road safety and securely transmitting data from one vehicle to other. However, in such opportunistic environments such as a sparse vehicular network where disruption, dynamic network topology, fast vehicle movement, and environmental conditions are the major concerns, data forwarding is extremely challenging. Traditional ad-hoc routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) fail to deliver in such laborious conditions. Routing protocols of Delay/Disruption Tolerant Networks(DTN) exploit the Store-Carry-Forward Mechanism (SCF) strategy in these cases. Taking advantage of temporary connections to disseminate information is the focus of DTN. DTN helps to increase information propagation coverage in sparse areas where there are very few devices. In this paper, we first compare the performance of AllJoyn framework and Direct Delivery protocol in a single-hop scenario and then draw the attention towards a multi-hop scenario by comparing other three routing protocols that use DTN as the underlying paradigm. These protocols are Flooding, Epidemic, and PRoPHET. The performances are evaluated by transmitting the files of size 1, 4, and 10 MB from a static sender to a mobile receiver in the multi-hop scenario. We also develop an Android application that contains the implementation of these routing protocols along with file sharing functionality. From the outcomes of this experiment, it can be said that Epidemic routing protocol performs the best in our considered multi-hop scenario in terms of transfer delay, coverage, and throughput whereas, Direct Delivery routing protocol performs better than AllJoyn in single-hop communication.

The mobile network operators are moving towards Virtual Network Functions (VNFs), in place of traditional dedicated hardwares for the 5G deployment. The VNFs require low maintenance and give high flexibility compared to dedicated hardwares which are prone to failures. Typically, VNFs are run on top of Virtual Machines (VMs) which run on physical nodes in data centers. For each class of service, based on its policy, the traffic has to pass through a set of VNFs in sequence termed as Service Function Chains (SFCs). Selection of available VNFs to steer the SFC requests with guaranteed end-to-end latency is still an open problem. In this work, we design an algorithm named DAVIS (Delay-Aware VNF selectIon algorithm for SFC) to increase the SFC acceptance rate by efficiently selecting VNFs. Simulation results show that DAVIS improves the throughput of accepted requests by 42% while guaranteeing SLA requirements.

Service Function Chaining (SFC) is used to steer the traffic to a specific set of Network Functions (NFs) (such as load balancer, proxy, firewall, etc.) based on the type of traffic and operator policy. Handling the massive amount of user traffic envisioned in the next generation networks using traditional techniques is costly and tedious. By leveraging advanced technologies such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN), NFs can be deployed as software instances on Virtual Machines (VMs) (also called as Virtual Network Function (VNF)). Network operators widely place different types of VNFs at different locations to meet the user traffic demands. Multiple VNF instances on the same physical server compete for common resources such as network I/O bandwidth, CPU cycles, cache memory, and main memory which can lead to severe performance interference, which is ignored in existing NF selection mechanisms. However, increasing the SFC acceptance rate of SFC requests with an effective selection of required VNFs under the constraint of end-to-end latency is still an open problem. Since this problem is NP-Hard, we propose a heuristic algorithm based on dynamic programming which efficiently selects the required VNFs and steers the traffic by considering the interference effect. Results show that the proposed algorithm improves the average SFC acceptance rate by 29% as compared with existing methods.

3GPP has chosen Service Based Architecture (SBA) for 5G Core (5GC). In this work, we build a prototype of SBA of 5GC from scratch using open source tools in Network Functions Virtualization (NFV) environment. In SBA, Network Functions (NFs) are designed to expose capabilities to consumers with interfaces, called Service Based Interface (SBI). To reduce the latency and the load on NFs, we use gRPC, a modern open-source Remote Procedure Call (RPC) framework, instead of REST for implementing SBI. We present a distributed service registration and discovery framework for 5GC using a software solution named Consul. We then propose usage of a Look Aside Load Balancer (LALB) for load balancing multiple NFs of 5GC. The control plane latency is reduced by routing traffic across multiple instances of Access and Mobility Management Function (AMF) via an LALB. Experimental results suggest that carefully chosen load balancing algorithms can significantly lessen the control plane latency when compared to simple random or round-robin schemes.

With the exponential growth in mobile data traffic, mobile operators are facing the unfortunate limit on the availability of licensed spectrum which has however, led to the popularity of Long Term Evolution (LTE) in unlicensed spectrum (LTE-U). Undeniably, it is expected from LTE-U that it fairly shares the spectrum with Wi-Fi. Along with fair sharing, efficient utilization of the unlicensed spectrum is also equally important, which in some sense requires coordination between the two Radio Access Technologies (RATs) viz., LTE-U and Wi-Fi. In particular, the hidden terminal scenario between LTE-U and Wi-Fi, resulting mainly due to lack of coordination, threatens the spectrum utilization of unlicensed spectrum. Focusing on this hidden terminal problem between LTE-U and Wi-Fi, we highlight the deficiency of existing technologies from the Wi-Fi perspective, both at the user level and at the network level. We then propose a novel coexistence technique (similar to RTS-CTS mechanism in Wi-Fi) that solves the hidden terminal problem between LTE-U and Wi-Fi, and subsequently addresses the spectrum underutilization problem caused by hidden terminal collisions. The proposed mechanism achieves this by using a modified CTS frame of Wi-Fi. We have validated our proposed mechanism using a mathematical framework demonstrating its credibility.

The recent adoption of virtualized technologies in Next Generation Radio Access Network (NGRAN) has driven a significant impact on energy consumption. In C-RAN, the protocol stack of Next Generation NodeB (gNB) has been split into two components: Central Unit (CU) and Distributed Unit (DU). CUs are moved to central-offices/data-centers (commonly referred as BBU pool in CRAN literature) where cloud computing and virtualization mechanisms can be used as the key enabling technologies whereas the DUs remain at cell sites. The upper layers of the gNB protocol stack (e.g., PDCP and RRC) commonly reside in the CU whereas the lower layers (e.g., PHY, MAC, and RLC) are kept at the DU. Both CU and DU communicate through an interface also known as a fronthaul interface adhered to strict latency and bandwidth requirements. Several functional split options have been suggested by 3GPP for gNB. Each functional split option has got specific requirements such as fronthaul bandwidth, strict latency demand, and capability to support advanced services. Nevertheless, the functional split in C-RAN between CU and DU is flexible and influences the energy consumption in CU pool. In this work, a realtime testbed has been setup using an open-source implementation of 3GPP compliant cellular stack, OpenAirInterface (OAI) along with USRP-B210 SDR and commercial general purpose processor (GPP) servers. From the above prototype, we measure energy consumption for different split using running average power limit (RAPL) tool and model specific register MSR register. Further, we evaluates the variation of energy consumption in CU pool sure to various load at DU side. Key observations are studied from the above prototype to design and develop a mathematical model to study and evaluate the cloud-native implementation of CUs in data center environments. Further, novel and efficient optimization MILP model Apt-RAN that adopts flexible functional splits based on available fronthaul bandwidth and link dela

In the Next Generation Radio Access Network (NGRAN) defined by 3GPP for the fifth generation of mobile communications (5G), the next generation NodeB (gNB) is split into a Radio Unit (RU), a Distributed Unit (DU), and a Central Unit (CU). RU, DU, and CU are connected through the fronthaul (RU-DU) and midhaul (DU-CU) segments. If the RAN is also virtualised RAN (VRAN), DU and CU are deployed in virtual machines or containers. Different latency and jitter requirements are demanded on the midhaul according to the distribution of the protocol functions between DU and CU. This study shows that, in VRAN, the virtualisation technologies, the functional split option, and the number of elements deployed in the same computational resource affect the latency budget available for the midhaul. Moreover, it provides an expression for the midhaul allowable latency as a function of the aforementioned parameters. Finally, it shows that, the virtualised DUs featuring a lower layer split option shall be deployed not in the same computational resources where other vDUs are deployed.