Enhanced stability of cluster-based location service mechanism for urban vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) are gaining tremendous research interest in developing an Intelligent Transportation System (ITS) for smart cities. The position of vehicles plays a significant role in ITS applications and services such as public emergency, vehicles tracking, resource discovery, traffic monitoring and position-based routing. The location service is used to keep up-to-date records of current positions of vehicles. A review of previous literatures, found various locationbased service mechanisms have been proposed to manage the position of vehicles. The cluster-based location service mechanisms have achieved growing attention due to their advantages such as scalability, reliability and reduced communication overhead. However, the performance of the cluster-based location service mechanism depends on the stability of the cluster, and the stability of the cluster depends on the stability of the Cluster Head (CH), Cluster Member (CM) and cluster maintenance. In the existing cluster-based location service schemes, the issue of CH instability arises due to the non-optimal cluster formation range and unreliable communication link with Road Side Unit (RSU). The non-optimal cluster formation range causes CH instability due to lack of uniqueness of Centroid Vehicle (CV), uncertainty of participating vehicles in the CH election process and unreliability of the Cluster Head Election Value (CHEV). Also, the unreliable link with RSU does not guarantee that CH is stable with respect to its CMs and RSU simultaneously. The issue of CM instability in the existing cluster-based location service schemes occurs due to using instantaneous speed of the CH and fixed CM affiliation threshold values. The instantaneous speed causes the CM to switch the clusters frequently and fixed CM affiliation threshold values increase isolated vehicles. The frequent switching of isolated vehicles augment the CM instability. Moreover, the inefficient cluster maintenance due to non-optimal cluster merging and cluster splitting also contributes to cluster instability. The merging conditions such as fixed merging threshold time and uncertain movement of overlapping CHs within merging threshold time cause the cluster instability. Furthermore, the unnecessary clustering during cluster splitting around the intersection due to CH election parameters also increases cluster instability. Therefore, to address the aforementioned cluster instability issues, Enhanced Stability of Cluster-based Location Service (ESCLS) mechanism was proposed for urban VANETs. The proposed ESCLS mechanism consists of three complementary schemes which are Reliable Cluster Head Election (RCHE), Dynamic Cumulative Cluster Member Affiliation (DCCMA) and Optimized Cluster Maintenance (OCM). Firstly, the aim of the RCHE scheme was to enhance the stability of the CH through optimizing the cluster formation range and by considering communication link reliability with the RSU. Secondly, the DCCMA scheme focussed on improving the stability of the CMs by considering the Cumulative Moving Average Speed (CMAS) of the CH and dynamic CM affiliation threshold values, and finally, the OCM scheme enhanced the cluster stability by improving cluster merging conditions and reducing unnecessary clustering in cluster splitting. The results of the simulation verified the improved performance of the ESCLS in terms of increasing the location query success rate by 34%, and decreasing the query response delay and localization error by 24% and 35% respectively as compared to the existing cluster-based location service schemes such as HCBLS, CBLS and MoGLS. In conclusion, it is proven that ESCLS is a suitable location service mechanism for a wide range of position-based applications of VANETs that require timely and accurate vehicle locations

Спосіб кластеризації в мобільних SDN мережах VANET на основі fuzzy logic алгоритмів

Магістерська дисертація присвячена вирішенню проблем пов’язаних із високою динамічністю топології та масштабованістю мережі. Застосування ефективного алгоритму кластеризації в мобільних мережах VANET дозволяю вирішити ці проблеми, а також сприяють покращенню процесів маршрутизації, безпеки та керування системи. У цій роботі запропоновано універсальний гібридний алгоритм кластеризації для мобільний мереж, що базується на поєднанні технологій машинного навчання та нечіткої логіки. Згідно аналізу отриманих результатів експериментального дослідження, запропонований спосіб продемонстрував найбільш релевантні показники по формуванню кластерів, що може достатньо зменшити загальну вартість транспортної мережі. Більш того, це також сприяє зниженню кількості затримок та кількості стрибків повідомлення у межах одного кластеру.The master's dissertation is devoted to solving problems related to the high dynamics of topology and scalability of the network. An efficient clustering algorithm usage in VANET mobile networks allows us to solve these problems and contribute to the improvement of routing, security and system management processes. This paper proposes a universal hybrid-clustering algorithm for mobile networks based on a combination of machine learning technologies and fuzzy logic. According to the analysis of the results of the experimental study, the proposed method demonstrated the most relevant indicators for the formation of clusters, which can significantly reduce the total cost of the transport network. Moreover, it also helps to reduce the number of delays and the number of message jumps within a single cluster

Stable dynamic feedback-based predictive clustering protocol for vehicular ad hoc networks

Scalability presents a significant challenge in vehicular communication, particularly when there is no hierarchical structure in place to manage the increasing number of vehicles. As the number of vehicles increases, they may encounter the broadcast storm problem, which can cause network congestion and reduce communication efficiency. Clustering can solve these issues, but due to high vehicle mobility, clustering in vehicular ad hoc networks (VANET) suffers from stability issues. Existing clustering algorithms are optimized for either cluster head or member, and for highways or intersections. The lack of intelligent use of mobility parameters like velocity, acceleration, direction, position, distance, degree of vehicles, and movement at intersections, also contributes to cluster stability problems. A dynamic clustering algorithm that efficiently utilizes all mobility parameters can resolve these issues in VANETs. To provide higher stability in VANET clustering, a novel robust and dynamic mobility-based clustering algorithm called junction-based clustering protocol for VANET (JCV) is proposed in this dissertation. Unlike previous studies, JCV takes into account position, distance, movement at the junction, degree of a vehicle, and time spent on the road to select the cluster head (CH). JCV considers transmission range, the moving direction of the vehicle at the next junction, and vehicle density in the creation of a cluster. JCV's performance is compared with two existing VANET clustering protocols in terms of the average cluster head duration, the average cluster member (CM) duration, the average number of cluster head changes, and the percentage of vehicles participating in the clustering process, etc. To evaluate the performance of JCV, we developed a new cloud-based VANET simulator (CVANETSIM). The simulation results show that JCV outperforms the existing algorithms and achieves better stability in terms of the average CH duration (4%), the average CM duration (8%), the number of CM (6%), the ratio of CM (22%), the average CH change rate (14%), the number of CH (10%), the number of non-cluster vehicles (7%), and clustering overhead (35%). The dissertation also introduced a stable dynamic feedback-based predictive clustering (SDPC) protocol for VANET, which ensures cluster stability in both highway and intersection scenarios, irrespective of the road topology. SDPC considers vehicle relative velocity, acceleration, position, distance, transmission range, moving direction at the intersection, and vehicle density to create a cluster. The cluster head is selected based on the future construction of the road, considering relative distance, movement at the intersection, degree of vehicles, majority-vehicle, and probable cluster head duration. The performance of SDPC is compared with four existing VANET clustering algorithms in various road topologies, in terms of the average cluster head change rate, duration of the cluster head, duration of the cluster member, and the clustering overhead. The simulation results show that SDPC outperforms existing algorithms, achieving better clustering stability in terms of the average CH change rate (50%), the average CH duration (15%), the average CM duration (6%), and the clustering overhead (35%)