## 6 Conclusions and Further Work  
The proposed approach forms a data-centric and hop-count-aware tree infrastructure by interconnecting the sink and LDSs. This tree is reactively updated using data-centric NDN communications according to the consumer's queries/requirements. Under MINDS, MAs use a hop-count-aware Depth-First Search (DFS) algorithm to move through the tree and visit LDSs, which locally capture and aggregate sensory data from (mobile) sensory devices. LDSs are clustered using a data-centric and lightweight clustering technique named Hamming distance, to limit network transmission.  
MINDS offers a number of benefits, including increased data robustness, reduced path length and decreased transmitted network traffic as compared to two benchmarks--ZMA and TBID. It avoids blind/random walk, as it forwards MAs through a minimum hop-count and data-centric tree. MINDS enhance data aggregation robustness, as MAs will be able to capture a greater number of interesting/meaningful sensory recordings when they move through the data-centric tree. In addition, MINDS reduces the network transmissions, as it uses the Hamming distance technique to form data-centric clusters. That way, the network traffic is limited only into intra-cluster communications. MINDS avoids forwarding MAs into overlapped/loop areas to collect redundant data records, because it allows MAs to record itineraries and utilise a hop-count-aware DFS algorithm to monitor data collection paths. However, MINDS underperforms compared to ZMA in ETE. ZMA supports bottom-up MA routing, as it assumes each LDS is equipped with an MA. This reduces ZMA's ETE because there is no need for MAs to move from the sink to LDSs.  
In future, LDS synchronisation needs to be studied further. Synchronisation plays a key role in MINDS, especially for LDS communications. The network will experience frequent message conflicts, network disconnections and/or transmitted traffic congestion with lack of synchronisation. Each LDS should utilise a private timer for synchronisation, especially during clustering and data collection, whereas LDS infrastructure still needs a public or partial synchronisation.  
MINDS still needs to be implemented as a real-world application to investigate the performance of the proposed approach in real test scenarios (e.g., real-world network stress  
Figure 8: Transmitted network traffic.  
test) and study new findings. Due to cost and risk concerns, a simulation was used in this research to test and evaluate MINDS. However, MINDS still needs further evaluations according to a physical implementation using real MAs (e.g., drones) and sensory data collection stations.  
The proposed approach needs to be re-designed if LDSs are mobile. Localising an LDS infrastructure in a wide and crowded urban area is expensive. That way, mobile LDSs--mainly vehicles--would offer benefits to crowd-sensing applications, as they do not need particular infrastructure to set-up. They are able to play LDS roles in an ad-hoc fashion. Indeed, the vehicles move throughout the city and collect crowd-sensing data samples from crowd-sensing devices through an infrastructure-less network.  
Further investigation on MINDS with different LDS network topologies is still required. This research establishes an LDS network in a grid which is widely used to study the average networking behaviour. However, it would be interesting to study the performance of MINDS when LDS networks are established in a mesh or star formation.  
The author would like to extend sincere thanks to University of Nottingham Ningbo China for supporting this research project under UNNC FOSE Grant: I01200900006 and I01200300007.  
The data presented in this study are available on request from the corresponding author.  
The author declares no conflict of interest.