The Impact of Message Replication on the Performance of Opportunistic Networks for Sensed Data Collection

Abstract

Opportunistic networks (OppNets) provide a scalable solution for collecting delay-tolerant data from sensors to their respective gateways. Portable handheld user devices contribute significantly to the scalability of OppNets since their number increases according to user population and they closely follow human movement patterns. Hence, OppNets for sensed data collection are characterised by high node population and degrees of spatial locality inherent to user movement. We study the impact of these characteristics on the performance of existing OppNet message replication techniques. Our findings reveal that the existing replication techniques are not specifically designed to cope with these characteristics. This raises concerns regarding excessive message transmission overhead and throughput degradations due to resource constraints and technological limitations associated with portable handheld user devices. Based on concepts derived from the study, we suggest design guidelines to augment existing message replication techniques. We also follow our design guidelines to propose a message replication technique, namely Locality Aware Replication (LARep). Simulation results show that LARep achieves better network performance under high node population and degrees of spatial locality as compared with existing techniques.

An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks

“…This will likely cause a low quality of service from networks to clients. On the other hand, when the cache of nodes is insufficient, they cannot store and process the data information obtained from message carriers [46]. Consequently, this paper comprehensively considers the significant two-factor including cache space and energy consumption through the fuzzy inference CMFI system.…”