Computer Science - Journal Articles

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5G NR-V2X: Toward connected and cooperative autonomous driving
(Institute of Electrical and Electronics Engineers (IEEE), 2021-03-31) Bagheri, Hamidreza; Noor-A-Rahim, Md; Liu, Zilong; Lee, Haeyoung; Pesch, Dirk; Moessner, Klaus; Xiao, Pei; Horizon 2020; Science Foundation Ireland; European Regional Development Fund
5G New Radio (NR) is touted as a pivotal enabling technology for the genuine realization of connected and cooperative autonomous driving. Despite numerous research efforts in recent years, a systematic overview on the role of 5G NR in future connected autonomous communication networks is missing. To fill this gap and to spark more future research, this article introduces the technology components of 5G NR and discusses the evolution from existing cellular vehicle-to-everything (V2X) technology toward NR-V2X. We primarily focus on the key features and functionalities of the physical layer, sidelink communication and its resource allocation, architecture flexibility, security and privacy mechanisms, and precise positioning techniques. Moreover, we envisage and highlight the potential of machine learning for further performance enhancement in NR-V2X services. Lastly, we show how 5G NR can be configured to support advanced V2X use cases.
6G for vehicle-to-everything (V2X) communications: Enabling technologies, challenges, and opportunities
(IEEE, 2022-05-20) Noor-A-Rahim, Md.; Liu, Zilong; Lee, Haeyoung; Khyam, Mohammad Omar; He, Jianhua; Pesch, Dirk; Moessner, Klaus; Saad, Walid; Poor, H.Vincent; Science Foundation Ireland; Horizon 2020; HORIZON EUROPE Marie Sklodowska-Curie Actions; National Science Foundation
We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.
The ABC of software engineering research
(Association for Computing Machinery (ACM), 2018-07) Stol, Klaas-Jan; Fitzgerald, Brian; Science Foundation Ireland; European Regional Development Fund
A variety of research methods and techniques are available to SE researchers, and while several overviews exist, there is consistency neither in the research methods covered nor in the terminology used. Furthermore, research is sometimes critically reviewed for characteristics inherent to the methods. We adopt a taxonomy from the social sciences, termed here the ABC framework for SE research, which offers a holistic view of eight archetypal research strategies. ABC refers to the research goal that strives for generalizability over Actors (A) and precise measurement of their Behavior (B), in a realistic Context (C). The ABC framework uses two dimensions widely considered to be key in research design: the level of obtrusiveness of the research and the generalizability of research findings. We discuss metaphors for each strategy and their inherent limitations and potential strengths. We illustrate these research strategies in two key SE domains, global software engineering and requirements engineering, and apply the framework on a sample of 75 articles. Finally, we discuss six ways in which the framework can advance SE research.
An adaptive large neighbourhood search algorithm for diameter bounded network design problems
(Springer Nature Switzerland AG, 2021-06-23) Garraffa, Michele; Mehta, Deepak; O'Sullivan, Barry; Ozturk, Cemalettin; Quesada, Luis; Seventh Framework Programme; Science Foundation Ireland
This paper focuses on designing a diameter - constrained network where the maximum distance between any pair of nodes is bounded. The objective considered is to minimise a weighted sum of the total length of the links followed by the total length of the paths between the pairs of nodes. First, the problem is formulated in terms of Mixed Integer Linear Programming and Constraint Programming to provide two alternative exact approaches. Then, an adaptive large neighbourhood search (LNS) to overcome memory and runtime limitations of the exact methods in large size instances is proposed. Such approach is based on computing an initial solution and repeatedly improve it by solving relatively small subproblems. We investigate various alternatives for finding an initial solution and propose two different heuristics for selecting subproblems. We have introduced a tighter lower bound, which demonstrates the quality of the solution obtained by the proposed approach. The performance of the proposed approach is assessed using three real-world network topologies from Ireland, UK and Italy, which are taken from national telecommunication operators and are used to design a transparent optical core network. Our results demonstrate that the LNS approach is scalable to large networks and it can compute very high quality solutions that are close to being optimal.
ALD: adaptive layer distribution for scalable video
(Springer Berlin Heidelberg, 2014-10) Quinlan, Jason J.; Zahran, Ahmed H.; Sreenan, Cormac J.; Science Foundation Ireland; National Telecommunication Regulatory Authority
Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.
Analysis of smartphone user mobility traces for opportunistic data collection in wireless sensor networks
(Elsevier, 2013-07-12) Wu, Xiuchao; Brown, Kenneth N.; Sreenan, Cormac J.; Science Foundation Ireland; Higher Education Authority
The increasing ubiquity of smartphones coupled with the mobility of their users will allow the use of smartphones to enhance the operation of wireless sensor networks. In addition to accessing data from a wireless sensor network for personal use, and the generation of data through participatory sensing, we propose the use of smartphones to collect data from sensor nodes opportunistically. For this to be feasible, the mobility patterns of smartphone users must support opportunistic use. We analyze the dataset from the Mobile Data Challenge by Nokia, and we identify the significant patterns, including strong spatial and temporal localities. These patterns should be exploited when designing protocols and algorithms, and their existence supports the proposal for opportunistic data collection through smartphones.
Analyzing and improving stability of matrix factorization for recommender systems
(Springer, 2022-01-27) D'Amico, Edoardo; Gabbolini, Giovanni; Bernardis, Cesare; Cremonesi, Paolo; Science Foundation Ireland; European Regional Development Fund
Thanks to their flexibility and scalability, collaborative embedding-based models are widely employed for the top-N recommendation task. Their goal is to jointly represent users and items in a common low-dimensional embedding space where users are represented close to items for which they expressed a positive preference. The training procedure of these techniques is influenced by several sources of randomness, that can have a strong impact on the embeddings learned by the models. In this paper we analyze this impact on Matrix Factorization (MF). In particular, we focus on the effects of training the same model on the same data, but with different initial values for the latent representations of users and items. We perform several experiments employing three well known MF implementations over five datasets. We show that different random initializations lead the same MF technique to generate very different latent representations and recommendation lists. We refer to these inconsistencies as instability of representations and instability of recommendations, respectively. We report that stability of item representations is positively correlated to the accuracy of the model. We show that the stability issues affect also the items for which the recommender correctly predicts positive preferences. Moreover, we highlight that the effect is stronger for less popular items. To overcome these drawbacks, we present a generalization of MF called Nearest Neighbors Matrix Factorization (NNMF). The new framework learns the embedding of each user and item as a weighted linear combination of the representations of the respective nearest neighbors. This strategy has the effect to propagate the information about items and users also to their neighbors and allows the embeddings of users and items with few interactions to be supported by a higher amount of information. To empirically demonstrate the advantages of the new framework, we provide a detailed description of the NNMF variants of three common MF techniques. We show that NNMF models, compared to their MF counterparts, largely improve the stability of both representations and recommendations, obtain a higher and more stable accuracy performance, especially on long-tail items, and reach convergence in a fraction of epochs.
An anonymous inter-network routing protocol for the Internet of Things
(River Publishers, 2017-04) Palmieri, Paolo; Calderoni, Luca; Maio, Dario
With the diffusion of the Internet of Things (IoT), computing is becoming increasingly pervasive, and different heterogeneous networks are integrated into larger systems. However, as different networks managed by different parties and with different security requirements are interconnected, security becomes a primary concern. IoT nodes, in particular, are often deployed “in the open”, where an attacker can gain physical access to the device. As nodes can be deployed in unsurveilled or even hostile settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore necessary to secure the communication within IoT networks, and in particular, maintain context information private, including the network topology and the location and identity of the nodes. In this paper, we propose a protocol achieving anonymous routing between different interconnected networks, designed for the Internet of Things and based on the spatial Bloom filter (SBF) data structure. The protocol enables private communication between the nodes through the use of anonymous identifiers, which hide their location and identity within the network. As routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain, the proposed routing strategy preserves context privacy, preventing adversaries from learning the network structure and topology. This, in turn, significantly reduces their ability to gain valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation.
An application of belief merging for the diagnosis of oral cancer
(Elsevier, 2017-04-18) Kareem, Sameem Abdul; Pozos-Parra, Pilar; Wilson, Nic; Universiti Malaya; Consejo Nacional de Ciencia y Tecnología; Universidad Juárez Autónoma de Tabasco
Machine learning employs a variety of statistical, probabilistic, fuzzy and optimization techniques that allow computers to “learn” from examples and to detect hard-to-discern patterns from large, noisy or complex datasets. This capability is well-suited to medical applications, and machine learning techniques have been frequently used in cancer diagnosis and prognosis. In general, machine learning techniques usually work in two phases: training and testing. Some parameters, with regards to the underlying machine learning technique, must be tuned in the training phase in order to best “learn” from the dataset. On the other hand, belief merging operators integrate inconsistent information, which may come from different sources, into a unique consistent belief set (base). Implementations of merging operators do not require tuning any parameters apart from the number of sources and the number of topics to be merged. This research introduces a new manner to “learn” from past examples using a non parametrised technique: belief merging. The proposed method has been used for oral cancer diagnosis using a real-world medical dataset. The results allow us to affirm the possibility of training (merging) a dataset without having to tune the parameters. The best results give an accuracy of greater than 75%.
ARBITER+: Adaptive Rate-Based InTElligent HTTP StReaming Algorithm for Mobile Networks
(IEEE, 2018) Zahran, Ahmed H.; Raca, Darijo; Sreenan, Cormac J.; Science Foundation Ireland
Dynamic adaptive streaming over HTTP (DASH) is widely adopted for video transport by major content providers. However, the inherent high variability in both encoded video and network rates represents a key challenge for designing efficient adaptation algorithms. Accommodating such variability in the adaptation logic design is essential for achieving a high user quality of Experience (QoE). In this paper, we present ARBITER+ as a novel adaptation algorithm for DASH. ARBITER+ integrates different components that are designed to ensure a high video QoE while accommodating inherent system variabilities. These components include a tunable adaptive target rate estimator, hybrid throughput sampling, controlled switching, and short-term actual video rate tracking. We extensively evaluate the streaming performance using real video and cellular network traces. We show that ARBITER+ components work in harmony to balance temporal and visual QoE aspects. Additionally, we show that ARBITER+ enjoys a noticeable QoE margin in comparison to state-of-the-art adaptation approaches in various operating conditions. Furthermore, we show that ARBITER+ also achieves the best application-level fairness when a group of mobile video clients share a cellular base station.
ASAP: Adaptive stall-aware pacing for improved DASH video experience in cellular networks
(Association for Computing Machinery (ACM), 2018-06) Zahran, Ahmed H.; Quinlan, Jason J.; Ramakrishnan, K. K.; Sreenan, Cormac J.; Science Foundation Ireland; National Science Foundation
The dramatic growth of video traffic represents a practical challenge for cellular network operators in providing a consistent streaming Quality of Experience (QoE) to their users. Satisfying this objective has so-far proved elusive, due to the inherent characteristics of wireless networks and varying channel conditions as well as variability in the video bitrate that can degrade streaming performance. In this article, we propose stall-aware pacing as a novel MPEG DASH video traffic management solution that reduces playback stalls and seeks to maintain a consistent QoE for cellular users, even those with diverse channel conditions. These goals are achieved by leveraging both network and client state information to optimize the pacing of individual video flows. We evaluate the performance of two versions of stall-aware pacing techniques extensively, including stall-aware pacing (SAP) and adaptive stall-aware pacing (ASAP), using real video content and clients, operating over a simulated LTE network. We implement state-of-the-art client adaptation and traffic management strategies for direct comparisons with SAP and ASAP. Our results, using a heavily loaded base station, show that SAP reduces the number of stalls and the average stall duration per session by up to 95%. Additionally, SAP ensures that clients with good channel conditions do not dominate available wireless resources, evidenced by a reduction of up to 40% in the standard deviation of the QoE metric across clients. We also show that ASAP achieves additional performance gains by adaptively pacing video streams based on the application buffer state.
Assigning and scheduling service visits in a mixed urban/rural setting
(World Scientific Publishing Company, 2020-06-18) Antunes, Mark; Armant, Vincent; Brown, Kenneth N.; Desmond, Daniel; Escamocher, Guillaume; George, Anne-Marie; Grimes, Diarmuid; O'Keeffe, Mike; Lin, Yiqing; O'Sullivan, Barry; Ozturk, Cemalettin; Quesada, Luis; Siala, Mohamed; Simonis, Helmut; Wilson, Nic; United Technologies Corporation, United States; Science Foundation Ireland; European Regional Development Fund
This paper describes a maintenance scheduling application, which was developed together with an industrial partner. This is a highly combinatorial decision process, to plan and schedule the work of a group of travelling repair technicians, which perform preventive and corrective maintenance tasks at customer locations. Customers are located both in urban areas, where many customers are in close proximity, and in sparsely populated rural areas, where the travel time between customer sites is significant. To balance the workload for the agents, we must consider both the productive working time, as well as the travel between locations. As the monolithic problem formulation is unmanageable, we introduce a problem decomposition into multiple sequential steps, that is compatible with current management practice. We present and compare different models for the solution steps, and discuss results on datasets provided by the industrial partner.
An axiomatic framework for influence diagram computation with partially ordered preferences
(Elsevier B.V., 2020-07-08) Wilson, Nic; Marinescu, Radu
This paper presents an axiomatic framework for influence diagram computation, which allows reasoning with partially ordered values of utility. We show how an algorithm based on sequential variable elimination can be used to compute the set of maximal values of expected utility (up to an equivalence relation). Formalisms subsumed by the framework include decision making under uncertainty based on multi-objective utility, or on interval-valued utilities, as well as a more qualitative decision theory based on order of magnitude probabilities and utilities. Consequently, we also introduce the order of magnitude influence diagram to model and solve partially specified sequential decision problems when only qualitative (or imprecise) information is available.
A bio-inspired managed video delivery service using HTTP-based adaptive streaming
(Springer Nature Switzerland AG, 2022-02-14) Sani, Yusuf; Quinlan, Jason J.; Sreenan, Cormac J.; Science Foundation Ireland
As consumers switch to video-on-demand services, over the best effort Internet, the importance of service level agreement enforcement schemes cannot be over emphasised. For these agreements to be effective, content providers must be able to enforce business policies in a simple and scalable manner, typically without access to the functionality within the core of the content delivery infrastructure. The option of relying on Media Presentation Description (MPD) attributes for video rate restriction is neither flexible or effective. Hence, in this paper, we present a bio-inspired solution that exploits the inherent features of an HTTP-based adaptive streaming service to enable content providers guarantee service level agreements. We utilise concepts from mathematical ecology that model species competing for a limited resource. In the proposed solution, distributed clients are assisted with global information using SDN. To enhance the scalability of the system, a business policy is enforced through parameter optimisation. To demonstrate the applicability of the proposed service, we built a test-bed and implemented a number of business policies. Evaluation results show that business policies are enforced in a fair and stable manner.
Blockchain-based digital twins collaboration for smart pandemic alerting: Decentralized COVID-19 pandemic alerting use case
(Hindawi, 2022-01-13) Sahal, Radhya; Alsamhi, Saeed H.; Brown, Kenneth N.; O'Shea, Donna; Science Foundation Ireland; European Regional Development Fund; H2020 Marie Skłodowska-Curie Actions; Taif University
Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.
Bloom filter variants for multiple sets: a comparative assessment
(Graz University of Technology, Austria, 2022-02-28) Calderoni, Luca; Maio, Dario; Palmieri, Paolo
In this paper we compare two probabilistic data structures for association queries derived from the well-known Bloom filter: the shifting Bloom filter (ShBF), and the spatial Bloom filter (SBF). With respect to the original data structure, both variants add the ability to store multiple subsets in the same filter, using different strategies. We analyse the performance of the two data structures with respect to false positive probability, and the inter-set error probability (the probability for an element in the set of being recognised as belonging to the wrong subset). As part of our analysis, we extended the functionality of the shifting Bloom filter, optimising the filter for any non-trivial number of subsets. We propose a new generalised ShBF definition with applications outside of our specific domain, and present new probability formulas. Results of the comparison show that the ShBF provides better space efficiency, but at a significantly higher computational cost than the SBF.
Broadcast performance analysis and improvements of the LTE-V2V autonomous mode at road intersection
(Institute of Electrical and Electronics Engineers (IEEE), 2019-08-21) Noor-A-Rahim, Md.; Ali, G. G. Md. Nawaz; Guan, Yong Liang; Ayalew, Beshah; Chong, Peter Han Joo; Pesch, Dirk; Horizon 2020; Science Foundation Ireland; NTU-NXP; U.S. Department of Energy
An autonomous V2V communication mode (also known as side-link mode 4), which facilitates V2V communication in out of eNB coverage areas, has recently been introduced into the Long term evolution (LTE) standard. Recent research has studied the performance of this LTE-V2V autonomous mode for a highway use case. However, performance analysis for a highway use case cannot be easily applied to an intersection use case as it may contain non-line-of-sight (NLOS) communication links. In this paper, we analyze and evaluate the safety message broadcasting performance of LTE-V2V autonomous mode in an urban intersection scenario. Considering practical path loss models, we present the impact of NLOS communication link on the overall message dissemination performance. Through the analytical and simulation results, we show that the overall message dissemination performance degrades drastically with increasing vehicle density and increasing distance of the transmitting vehicle from the intersection. To improve the performance, we propose a vehicle-assisted relaying scheme in which the relaying vehicle is selected in an autonomous manner. We also present two resource allocation strategies for the relaying vehicle. For low to medium vehicle density along the street, we observe significant improvement in message dissemination through relaying compared to the scheme without relaying.
Cancer and breast cancer awareness interventions in an intellectual disability context: A review of the literature
(SAGE Publications, 2019-05-19) Walsh, Susan; O'Mahony, Mairin; Lehane, Elaine; Farrell, D.; Taggart, L.; Kelly, L.; Sahm, Laura; Byrne, A.; Corrigan, M.; Caples, Maria; Martin, Anne-Marie; Tabirca, Sabin; Corrigan, M. A.; Hegarty, Josephine
Background: Women with an intellectual disability (ID) have a similar risk of developing breast cancer as women in the general population yet present with later stage breast cancers, which have poorer outcomes. Aim: To identify whether there is a need to develop a breast cancer awareness intervention for women with an ID. Methods: Interventions aimed at increasing cancer awareness and breast cancer awareness for people with an ID were identified and critically appraised. Results: Five interventions to increase cancer awareness or breast cancer awareness in people with an ID were identified. Conclusion: The review highlighted the paucity of theoretically underpinned breast cancer awareness interventions specifically aimed at women with an ID. Facilitating breast cancer awareness for women with an ID could potentially lead to earlier presentation of potential symptoms of breast cancer, earlier treatment, better prognosis and ultimately, improved survival. This article establishes that there is a need for an intervention underpinned by theory to increase breast cancer awareness in women with an ID.
Candidate selection and instance ordering for realtime algorithm configuration
(IOS Press, 2019-03-14) Fitzgerald, Tadhg; O'Sullivan, Barry; Science Foundation Ireland
Many modern combinatorial solvers have a variety of parameters through which a user can customise their behaviour. Algorithm configuration is the process of selecting good values for these parameters in order to improve performance. Time and again algorithm configuration has been shown to significantly improve the performance of many algorithms for solving challenging computational problems. Automated systems for tuning parameters regularly out-perform human experts, sometimes but orders of magnitude. Online algorithm configurators, such as ReACTR, are able to tune a solver online without incurring costly offline training. As such ReACTR’s main focus is on runtime minimisation while solving combinatorial problems. To do this ReACTR adopts a one-pass methodology where each instance in a stream of instances to be solved is considered only as it arrives. As such ReACTR’s performance is sensitive to the order in which instances arrive. It is still not understood which instance orderings positively or negatively effect the performance of ReACTR. This paper investigates the effect of instance ordering and grouping by empirically evaluating different instance orderings based on difficulty and feature values. Though the end use is generally unable to control the order in which instances arrive it is important to understand which orderings impact Re- ACTR’s performance and to what extent. This study also has practical benefit as such orderings can occur organically. For example as business grows the problems it may encounter, such as routing or scheduling, often grow in size and difficulty. ReACTR’s performance also depends strongly configuration selection procedure used. This component controls which configurations are selected to run in parallel from the internal configuration pool. This paper evaluates various ranking mechanisms and different ways of combining them to better understand how the candidate selection procedure affects realtime algorithm configuration. We show that certain selection procedures are superior to others and that the order which instances arrive in determines which selection procedure performs best. We find that both instance order and grouping can significantly affect the overall solving time of the online automatic algorithm configurator ReACTR. One of the more surprising discoveries is that having groupings of similar instances can actually negatively impact on the overall performance of the configurator. In particular we show that orderings based on nearly any instance feature values can lead to significant reductions in total runtime over random instance orderings. In addition, certain candidate selection procedures are more suited to certain orderings than others and selecting the correct one can show a marked improvement in solving times.
Capacity and contention-based joint routing and gateway selection for machine-type communications
(Elsevier, 2017-04-20) Farooq, Muhammad O.; Sreenan, Cormac J.; Brown, Kenneth N.
Typically, in machine-type communications (MTC) devices communicate with servers over the Internet. In a large-scale machine-to-machine area (M2M) network, the devices may not connect directly to the Internet due to radio transmission and energy limitations. Therefore, the devices collaborate wirelessly to relay their data to a gateway. A large-scale M2M area network may have multiple gateways, selecting a proper gateway for the devices can have immense impact on the networks performance. We present the channel capacity and contention-based joint routing and gateway selection methods for MTC. Based on channel capacity and contention, our methods select the best gateway on per-packet, per-flow, and per-node basis. We compare the methods performance with existing methods using simulation and test-bed experiments. We analyse the impact of the number of gateways, physical distribution of transmitters, control overhead, and duty-cycling on the performance of the gateway selection methods. Our results demonstrate that, in duty-cycled operations, the methods performance depends on control overhead and making a good trade-off between load imbalance to different gateways and a forwarding paths length. Otherwise only the latter impacts the methods performance. In general, our node-based best gateway selection method makes a better trade-off and exhibits lower control overhead, hence it demonstrates better performance. Moreover, our methods demonstrate better performance as compared to an existing state-of-the-art joint routing and gateway selection method.

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