Due to the increased capabilities of mobile devices and through wireless opportunistic contacts, users can experience new ways to share and retrieve content anywhere and anytime, even in the presence of link intermittency. Due to the significant number of available routing solutions, it is difficult to understand which one has the best performance, since all of them follow a different evaluation method. This paper proposes an assessment model, based on a new taxonomy, which comprises an evaluation guideline with performance metrics and experimental setup to aid designers in evaluating solutions through fair comparisons. Simulation results based on the proposed model revisit the performance results published by Epidemic, PROPHET, and BubbleRap, showing how they perform under the same set of metrics and scenario.
(2004) @cite_12 classify proposals based on knowledge from network oracles or route computation and determination. A routing evaluation framework is provided considering the different knowledge oracles to evaluate the amount of knowledge each proposal requires in specific application scenarios.
@cite_12
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Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, and although the introduction of regions serves to consolidate the sparse data, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality. We present a route planning technique solely based on the concept of node contraction. The nodes are first ordered by 'importance'. A hierarchy is then generated by iteratively contracting the least important node. Contracting a node υ means replacing shortest paths going through v by shortcuts. We obtain a hierarchical query algorithm using bidirectional shortest-path search. The forward search uses only edges leading to more important nodes and the backward search uses only edges coming from more important nodes. For fastest routes in road networks, the graph remains very sparse throughout the contraction process using rather simple heuristics for ordering the nodes. We have five times lower query times than the best previous hierarchical Dijkstra-based speedup techniques and a negative space overhead, i.e., the data structure for distance computation needs less space than the input graph. CHs can be combined with many other route planning techniques, leading to improved performance for many-to-many routing, transit-node routing, goal-directed routing or mobile and dynamic scenarios. We propose a routing strategy to improve the transportation efficiency on complex networks. Instead of using the routing strategy for shortest path, we give a generalized routing algorithm to find the so-called efficient path, which considers the possible congestion in the nodes along actual paths. Since the nodes with the largest degree are very susceptible to traffic congestion, an effective way to improve traffic and control congestion, as our strategy, can be redistributing traffic load in central nodes to other noncentral nodes. Simulation results indicate that the network capability in processing traffic is improved more than 10 times by optimizing the efficient path, which is in good agreement with the analysis. DOI: 10.1103 PhysRevE.73.046108 PACS numbers: 89.75.Hc Since the seminal work on scale-free networks by Barabasi and Albert BA model1 and on the small-world phenomenon by Watts and Strogatz 2, the structure and dynamics of complex networks have recently attracted a tremendous amount of interest and attention from the physics community see the review papers 3‐5 and references therein. The increasing importance of large communication networks such as the Internet 6, upon which our society survives, calls for the need for high efficiency in handling and delivering information. In this light, to find optimal strategies for traffic routing is one of the important issues we have to address. There have been many previous studies to understand and control traffic congestion on networks, with a basic assumption that the network has a homogeneous structure 7‐11. However, many real networks display both scale-free and small-world features, and thus it is of great interest to study the effect of network topology on traffic flow and the effect of traffic on network evolution. present a formalism that can cope simultaneously with the searching and traffic dynamics in parallel transportation systems 12. This formalism can be used to optimize network structure under a local search algorithm, while to obtain the formalism one should know the global information of the whole networks. Holme and Kim provide an in-depth analysis on the vertex edge overload cascading breakdowns based on evolving networks, and suggest a method to avoid Since ad hoc and sensor networks can be composed of a very large number of devices, the scalability of network protocols is a major design concern. Furthermore, network protocols must be designed to prolong the battery lifetime of the devices. However, most existing routing techniques for ad hoc networks are known not to scale well. On the other hand, the so-called geographical routing algorithms are known to be scalable but their energy efficiency has never been extensively and comparatively studied. In a geographical routing algorithm, data packets are forwarded by a node to its neighbor based on their respective positions. The neighborhood of each node is constituted by the nodes that lie within a certain radio range. Thus, from the perspective of a node forwarding a packet, the next hop depends on the width of the neighborhood it perceives. The analytical framework proposed in this paper allows to analyze the relationship between the energy efficiency of the routing tasks and the extension of the range of the topology knowledge for each node. A wider topology knowledge may improve the energy efficiency of the routing tasks but increases the cost of topology information due to signaling packets needed to acquire this information. The problem of determining the optimal topology knowledge range for each node to make energy efficient geographical routing decisions is tackled by integer linear programming. It is shown that the problem is intrinsically localized, i.e., a limited topology knowledge is sufficient to make energy efficient forwarding decisions. The leading forwarding rules for geographical routing are compared in this framework, and the energy efficiency of each of them is studied. Moreover, a new forwarding scheme, partial topology knowledge forwarding (PTKF), is introduced, and shown to outperform other existing schemes in typical application scenarios. A probe-based distributed protocol for knowledge range adjustment (PRADA) is finally introduced that allows each node to efficiently select online its topology knowledge range. PRADA is shown to rapidly converge to a near-optimal solution.