## 2 Related Works  
A great number of WSN data communication protocols have been proposed in a wide range of applications, from healthcare to urban resource management. They are typically categorised as conforming to one of two models: client/server and MA [3]. For client/server, source sensors (clients) forward data sample through direct or indirect links to the data collection points (server). However, client/server is not very beneficial for WSN applications, especially if the sensory devices are highly mobile and/or equipped with restricted resources--mainly power and bandwidth [18]. A frequent link update to keep/re-establish the communication ties results in increased power and bandwidth consumption. According to [19], a client/server WSN has been proposed to continuously monitor the health status of hypertensive patients using smartphones. Client-side pre-processing is performed to capture and extract electrocardiogram (ECG) features, whereas a cloud server-side computation is performed to analyse and predict healthcare patterns using machine learning techniques, such as neural network and random forest. In reference [20], they utilised a device-to-device paradigm to forward crowd sensing data packets in mobile networks over 4G links. The mobile sensory devices utilise a greedy algorithm to form location-based clusters. Sensory data records are transmitted within each cluster until the target device/station is reached. MA sensory data collection offers a number of benefits as compared to client/server [3]: (1) The transmission rate is reduced. Client/server forwards raw data samples, whereas MAs collect data samples if they are meaningful and match the query. (2) reduced network resource consumption--mainly bandwidth, as the transmission rate is decreased. (3) Increased scalability due to the invariance of MA data collection performance to the network size. (4) Improved system extensibility, as MA is able to carry out task-adaptiveprocesses, aiming to extend the system capacities. (5) Enhanced network stability due to MA's ability to support offline (asynchronous) message delivery. (6) Increased load balancing by symmetric workload scattering as compared to client/server in which data communications focus on particular nodes--mainly crowd-sensing data stations. MAs plan data collection itineraries in two ways: proactive and reactive [4]. The proactive scheme reduces MA migration delay and resource consumption, as routes are established in advance. However, this needs to have all the routing information required to establish paths. This would be challenging, especially in large networks where nodes are highly mobile. Reactive MA path planning is utilised to establish routes on-the-fly using acquired information in a data collection journey. However, MA's blind movements (or random walk) would result in increased migration delay and power consumption. Besides, this can reduce data collection robustness, as a number of source nodes, which have meaningful and interesting data records matching the consumer's query, might be missed by MAs. For this reason, MAs should be programmed to intelligently plan itineraries to collect and aggregate sensory data samples with minimum cost (e.g., power consumption) and delay, and maximum data robustness. The near-optimal itinerary design algorithm (NOID) [21] utilises synchronous MAs to collaboratively collect sensory data samples from target regions. Each MA starts its journey from the sink and moves via routes which are established using a greedy algorithm to minimise a cost function of the path's hop count and the node's remaining power level. This approach monitors data collection volume at each node. This helps to control MA size and forwards an MA back to the sink if the data load is heavy. However, NOID still suffers from redundant data collection, as collaborative MAs blindly capture and report events in overlapping areas. Tree-based itinerary design (TBID) [17] forms a zone-based network to forward MAs through multiple spanning trees (SPTs) for data aggregation.