Browsing by Author "Farhan M"

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    BatchHL+: batch dynamic labelling for distance queries on large-scale networks
    (Springer-Verlag GmbH Germany, part of Springer Nature, 2024-01) Farhan M; Koehler H; Wang Q
    Many real-world applications operate on dynamic graphs to perform important tasks. In this article, we study batch-dynamic algorithms that are capable of updating distance labelling efficiently in order to reflect the effects of rapid changes on such graphs. To explore the full pruning potentials, we first characterize the minimal set of vertices being affected by batch updates. Then, we reveal patterns of interactions among different updates (edge insertions and edge deletions) and leverage them to design pruning rules for reducing update search space. These interesting findings lead us to developing a new batch-dynamic method, called BatchHL+ , which can dynamize labelling for distance queries much more efficiently than existing work. We provide formal proofs for the correctness and minimality of BatchHL+ which are non-trivial and require a delicate analysis of patterns of interactions. Empirically, we have evaluated the performance of BatchHL+ on 15 real-world networks. The results show that BatchHL+ significantly outperforms the state-of-the-art methods with up to 3 orders of magnitude faster in reflecting updates of rapidly changing graphs for distance queries.
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    Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios.
    (MDPI (Basel, Switzerland), 2024-08-19) Ahmed S; Hossain MA; Chong PHJ; Ray SK; Farhan M; Mahmood K; Jabbar S
    The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.
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    Stable Tree Labelling for Accelerating Distance Queries on Dynamic Road Networks
    (OpenProceedings.org, 2024-11-11) Koehler H; Farhan M; Wang Q
    Finding the shortest-path distance between two arbitrary vertices is an important problem in road networks. Due to real-time traffic conditions, road networks undergo dynamic changes all the time. Current state-of-the-art methods incrementally maintain a distance labelling based on a hierarchy among vertices to support efficient distance computation. However, their labelling sizes are often large and cannot be efficiently maintained. To combat these issues, we present a simple yet efficient labelling method, namely Stable Tree Labelling (STL), for answering distance queries on dynamic road networks. We observe that the properties of an underlying hierarchy play an important role in improving and balancing query and update performance. Thus, we introduce the notion of stable tree hierarchy which lays the ground for developing efficient maintenance algorithms on dynamic road networks. Based on stable tree hierarchy, STL can be efficiently constructed as a 2-hop labelling. A crucial ingredient of STL is to only store distances within subgraphs in labels, rather than distances in the entire graph, which restricts the labels affected by dynamic changes. We further develop two efficient maintenance algorithms upon STL: Label Search algorithm and Pareto Search algorithm. Label Search algorithm identifies affected ancestors in a stable tree hierarchy and performs efficient searches to update labels from those ancestors. Pareto Search algorithm explores the interaction between search spaces of different ancestors, and combines searches from multiple ancestors into only two searches for each update, eliminating duplicate graph traversals. The experiments show that our algorithms significantly outperform state-of-the-art dynamic methods in maintaining the labelling and query processing, while requiring an order of magnitude less space.