Understanding complementary multi-layer collaborative heuristics for adaptive caching in heterogeneous mobile opportunistic networks

Current research aims to deal with emerging challenges of the opportunistic discovery of content stored in remote mobile publishers and the delivery to the subscribers in heterogeneous mobile opportunistic networks. Innovative network and service architectures leverage in-network caching to improve transmission efficiency, reduce delay and handle disconnections. In this paper, we investigate the influences of multi-dimensional heuristics utilised by our adaptive collaborative caching framework CafRepCache on the performance of content dissemination and query in heterogeneous mobile opportunistic environments. We consider the complementary multi-layer heuristics that combine social driven, resources driven, ego network driven and content popularity driven analytics. We extensively evaluate the performance of each complementary heuristic and discuss the impact of each one on every layer of our caching framework across heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that the multilayer heuristics enable CafRepCache to be responsive to dynamically changing network topology, congestion avoidance and varying patterns of content publishers/subscribers which balances the trade-off that achieves higher cache hit ratio, delivery success ratios while keeping lower delays and packet loss

Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

Understanding complementary multi-layer collaborative heuristics for adaptive caching in heterogeneous mobile opportunistic networks

Current research aims to deal with emerging challenges of the opportunistic discovery of content stored in remote mobile publishers and the delivery to the subscribers in heterogeneous mobile opportunistic networks. Innovative network and service architectures leverage in-network caching to improve transmission efficiency, reduce delay and handle disconnections. In this paper, we investigate the influences of multi-dimensional heuristics utilised by our adaptive collaborative caching framework CafRepCache on the performance of content dissemination and query in heterogeneous mobile opportunistic environments. We consider the complementary multi-layer heuristics that combine social driven, resources driven, ego network driven and content popularity driven analytics. We extensively evaluate the performance of each complementary heuristic and discuss the impact of each one on every layer of our caching framework across heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that the multilayer heuristics enable CafRepCache to be responsive to dynamically changing network topology, congestion avoidance and varying patterns of content publishers/subscribers which balances the trade-off that achieves higher cache hit ratio, delivery success ratios while keeping lower delays and packet loss

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time

Adaptive real-time predictive collaborative content discovery and retrieval in mobile disconnection prone networks

Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to the mobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary real-time distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols