Information dissemination rather than traditional routing is useful for many application domains in mobile ad hoc networks. Context-adaptive information dissemination is a special variant, which was originally designed for vehicular ad hoc networks (VANETs), but could also be used in other domains, for example, disaster management. In this work, we address the security of this dissemination scheme. The attack analysis shows weaknesses but also how the adaption to context already limits the impact of many attacks. We present and evaluate a sensor-based protection system helping to reduce the margin for attacks to a level of local disturbance, while the overall dissemination is not affected significantly any more.(5) Information dissemination with in-network data processing.One-hop communication and multi-hop broadcasting are essential communication patterns that form the basis of many more complex mechanisms in all kinds of dynamic networks. In traditional MANET research, mainly bidirectional, end-to-end connections between two nodes are considered, followed by various multi-/anycast schemes. In contrast, WSNs have different requirements: typically multiple sensors need to propagate sensed information to the closest base station, and often sensor data are aggregated within the network to reduce transmissions and thus save energy. So WSNs often implement the last two of the listed patterns. However, the often assumed static topologies lessen some communication problems.
Big Data and IoT applications require highly-scalable database management system (DBMS), preferably operated in the cloud to ensure scalability also on the resource level. As the number of existing distributed DBMS is extensive, the selection and operation of a distributed DBMS in the cloud is a challenging task. While DBMS benchmarking is a supportive approach, existing frameworks do not cope with the runtime constraints of distributed DBMS and the volatility of cloud environments. Hence, DBMS evaluation frameworks need to consider DBMS runtime and cloud resource constraints to enable portable and reproducible results. In this paper we present Mowgli, a novel evaluation framework that enables the evaluation of non-functional DBMS features in correlation with DBMS runtime and cloud resource constraints. Mowgli fully automates the execution of cloud and DBMS agnostic evaluation scenarios, including DBMS cluster adaptations. The evaluation of Mowgli is based on two IoT-driven scenarios, comprising the DBMSs Apache Cassandra and Couchbase, nine DBMS runtime configurations, two cloud providers with two different storage backends. Mowgli automates the execution of the resulting 102 evaluation scenarios, verifying its support for portable and reproducible DBMS evaluations. The results provide extensive insights into the DBMS scalability and the impact of different cloud resources. The significance of the results is validated by the correlation with existing DBMS evaluation results.Since the era of RDBMS, their selection is guided by domain-specific benchmarks that have evolved together with distributed DBMSs.
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