full utilization of VMIMO and increase the time saving out of concurrent data uploading, MWR associates the sensors evenly to the compatible sensors. The performance of MWR is evaluated by comparing with optimal solution which is obtained by CPLEX based on the formulation modelling and MWR is shown to achieve a close performance to the optimal solution. Compared with other algorithms, MWR effectively reduces the total data collection delay in different network scenarios. Furthermore, it requires less overall network energy consump- tion(especially in sparse networks). The simulation results demonstrate that the proposed algorithm is desirable to be applied in time-sensitive data collection sce- narios (e.g. military defence applications and real-time environment monitoring applications) with well-balanced trade-off between data collection latency and network energy consumption.
6.2
Future work
In this section, the future research directions are discussed in following two aspects to further improve the proposed algorithms in the area of efficient data collection with VMIMO and mobile sink techniques.
Firstly, the proposed algorithms can be improved and extended for practical scenarios. The cost of sharing control information for VMIMO transmission, interference of data transmission among sensors and channel state information (CSI) are all not considered in our proposed algorithms, which is not practical in realistic networks. The improved algorithms could be developed in network simulator considering the physical interference model and imperfect knowledge of CSI. It is worth investigating the effects of these practical conditions for data collection in WSNs. Furthermore, the organization and formulation of compatible sensors are desired to be improved in a distributed manner, to avoid the large centralization control message overhead in large-scale networks.
Secondly, the advantages of applying multiple mobile sinks in the proposed data collection algorithms could be investigated. Multiple mobile sinks shorten the moving tour length for each sink to decrease the total data collection latency. Moreover, mobile sinks share their information so that they learn what others already learn to obtain the full view of overall network information, which enables faster task completion and potentially bandwidth saving due to the reduction of the control message overhead. In this research work, the joint optimization of routing algorithms and the design of moving trajectory for each mobile sink is a significant and challenging issue. Furthermore, the number of mobile sinks, the cooperativeness between sinks, the velocities and positions are all important influence factors and should also be evaluated.
Thirdly, software-defined networking (SDN) [135] paradigm can be used in WSNs. The decoupling of the control logic and the data forwarding is the foundation of SDN, which could bring benefits in WSNs. First, the centralized controller maintains a global view of the network which reduces the power consumption by sensors in order to explore and maintain that view locally. Second, it reduces the control overhead for topology discovery and also improves routing algorithm performance due to the accurate location information. However, there are still problems to be solved. In the current SDN based WSNs studies, a master node is normally selected as the controller and the core of the network. It is important to address the problem such as: How to limit the energy of the master node? Moreover, the centralized controller may raise some security questions such as: What would be the effect of attacks on the controller? These issues and questions need to be investigated and answered.
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