Predicting residential building age from map data The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving thisa per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing crossvalidated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points.
This paper is concerned with fully distributed reputation-based mechanisms that improve security in MANETS. We introduce a number of optimisations to the current reputation schemes used in MANETs such as selective deviation tests and adaptive expiration timer that aim to deal with congestion and quick convergence. We use two different centrality measures for evaluation of the individual trust claims and resolving the aggregated ones. We design and build our prototype over AODV and test it in the NS-2 in the presence of variable black hole attacks in highly mobile and sparse networks. Our results show that we achieve increased throughput while delay and jitter decrease and converge to AODV.
This paper is concerned with improving locationprivacy for users accessing location-based services in opportunistic DTNs. We design a protocol that offers location privacy through request/reply location obfuscation technique that uses the nodes' own social network to drive the forwarding heuristic. We propose a fully distributed socialbased location privacy protocol (SLPD) that utilizes social ties between nodes to ensure K-Anonymity, i.e. the requesting node's locations cannot be determined from at least k-1 other nodes in its social network. We evaluate SLPD using extensive simulations and real connectivity data traces. We compare our results to a benchmark protocol that requires centralized trusted server. We show that our distributed protocol is applicable to DTNs with various mobility patterns, and provides the user with the required privacy at less than 30% of the privacy range we define. SLPD achieves success ratios similar to the ones obtained using centralized benchmark solutions up to 15% privacy requirements.
Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.
A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-Social routing protocols are typically used to transfer messages among users and services in mobile opportunistic networks. Adaptive mechanisms are needed for achieving user anonymization and providing sufficient level of user anonymity due to the constant changes in underlying topology, mobility patterns and density of users and their queries. This paper describes a novel flexible and adaptive approach, AdaptAnon that is suitable for dynamic and heterogeneous mobile opportunistic networks. Our approach is multidimensional and combines multiple heuristics based on user profiles, analysis of user connectivity and history of anonymization in order to predict and decide on the best set of nodes that anonymize the sending node. Our results of extensive experiments show that AdaptAnon achieves higher quality of anonymization in terms of both the number of nodes and the diversity of nodes in the anonymization layer for varying query intensity and over different sender and destination degrees of connectivity while neither decreasing success ratios nor increasing latency. We show that AdaptAnon outperforms state of the art single dimensional anonymization approaches when run over three different real-life traces.
Location-tracking is a major privacy problem, especially with high penetration of smart-phones and GPS-enabled devices around the world. In this paper, we propose a Hybrid and Social-aware Location-Privacy in Opportunistic mobile social networks (HSLPO), a collaborative and distributed obfuscation protocol that offers location-privacy k-anonymity. HSLPO discovers the users' own social network and use it to obfuscate requests and hide the original sender's location from the Location based service (LBS) while minimizing the overhead needed to perform obfuscation.We performed extensive simulations over a map-based pseudo realistic environment to compare HSLPO to exiting protocols. Results show that HSLPO can reach higher location-privacy levels and quality of service, in term of success ratio, than other protocols while maintaining low overheads.
Privacy and security in delay-tolerant networks (DTNs) have been an active research topic in the recent years, especially, as people can be involved in these networks and use their mobile devices to forward each other's messages. Such communications require forwarding algorithms that often include replication or context awareness. In this paper, we study the security impact on specific forwarding protocols in both simulated City Scenario and using real connectivity data traces. We propose a hybrid technique combining Erasure Coding and distributed replication to defend against packet dropping malicious attack. We show that replication-alone technique -that is typically expected to improve performance and robustness -is greatly affected by such simple attacks. We show that an attacker can cause up to 50% drop in the success ratio when compromising about 30% of nodes across various scenarios. We use mobile nodes with different speed, transmission range and processing capability, fixed infrastructure access points in our experiments. Results show that using Erasure Coding and message replication at intermediaries achieves up to 250% improvement in the message success ratio compared to using replication only.
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