###### Abstract  
Mobile agents have the potential to offer benefits, as they are able to either independently or cooperatively move throughout networks and collect/aggregate sensory data samples. They are programmed to autonomously move and visit sensory data stations through optimal paths, which are established according to the application requirements. However, mobile agent routing protocols still suffer heavy computation/communication overheads, lack of route planning accuracy and long-delay mobile agent migrations. For this, mobile agent route planning protocols aim to find the best-fitted paths for completing missions (e.g., data collection) with minimised delay, maximised performance and minimised transmitted traffic. This article proposes a mobile agent route planning protocol for sensory data collection called MINDS. The key goal of this MINDS is to reduce network traffic, maximise data robustness and minimise delay at the same time. This protocol utilises the Hamming distance technique to partition a sensor network into a number of data-centric clusters. In turn, a named data networking approach is used to form the cluster-heads as a data-centric, tree-based communication infrastructure. The mobile agents utilise a modified version of the Depth-First Search algorithm to move through the tree infrastructure according to a hop-count-aware fashion. As the simulation results show, MINDS reduces path length, reduces network traffic and increases data robustness as compared with two conventional benchmarks (ZMA and TBID) in dense and large wireless sensor networks. M 2021 13 April 2021 22 April 2021 22 April 2021 22 April 2021 22 April 2021 22 April 2021 22 April 2021 22 2  
MA sensory data collection utilises mobile devices to move across the network and capture sensory recordings. This offers benefits such as enhanced data robustness, load balancing, scalability, resource conservation and reduce delay as compared to client/server [4]. An MA has the ability to autonomously move and collect sensory data samples. This can be in two forms: software and hardware. Software MAs are supported by mobile code and/or remote object access techniques to move throughout a network [5], whereas hardware MAs (e.g., unmanned aerial vehicles) move over a network to visit the sensory data station and collect data samples [6]. However, path planning and network resource consumption (e.g., bandwidth) are still the key drawbacks of MA data collection. To resolve this, MAs should intelligently move throughout the network to capture data samples and avoid blind/random walking to conserve network resources. Named data networking (NDN) is able to offer MAs a number of path planning benefits in sensory data collection applications [7; 8]. Using NDN routing, MAs are forwarded to visit source nodes residing on data-centric routes and capture interesting/meaningful data samples. That way, MAs do not blindly walk throughout the network to find event regions; they only move through NDN routes which forward them into source nodes. Although NDN suffers from limited connectivity and node mobility in mobile sensor networks [9], this can be tuned by distributed adaptive caching strategy technique to reduce transmitted traffic and communication overheads [10]. NDN is a routing technique that is used to establish data-centric network paths [11]. This utilises data names rather than address configuration to establish the links [12]. In fact, an NDN route is established by a number of (source) nodes having correlated data names. According to [13], basic NDN forwards data interests (DIs) throughout the network to ask for data contents. As Figure 1 shows, a receiving node replies back and provides the requested data record if the DI matches. Otherwise, this checks its own pending interest table (PIT) and forwards DI if this is new and fits message forwarding strategies which are provided by the forwarding information base (FIB). This article proposes MINDS, NDN-enabled \"MA itinerary planning in wireless sensor networks\".