MINDS: Mobile Agent Itinerary Planning Using Named Data Networking in Wireless Sensor Networks

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.

Assessing the efficacy of a novel adaptive fuzzy c-means (AFCM) based clustering algorithm for mobile agent itinerary planning in wireless sensor networks using validity indices

“…[1], Aloui et al [2], Spawn Multi Mobile Agent Itinerary Planning (SMIP) [3], Daramola et al [4] • Tree structured: Minimum Spanning Tree MIP (MST-MIP) [5], Tree Based Itinerary Design (TBID) [6], Mobile Agent Itinerary Planning Using Named Data Networking (MINDS) [7], Near Optimal Itinerary Design (NOID) [8], Iterative Local Search (ILS) [9],…”

“…The source code of the mobile agent is compact, allowing it to compute results from the network [2], [3]. The mobile agent is designed to operate even in situations where there is a disconnection [4], [2]. It requires a network only to move to the next node in order to complete its assigned task.…”

Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security

“…This is because in MMTM-RPL, the clustering algorithm responds swiftly to the arrival and departure of nodes in the mobile networks. Secondly, the use of MAs also makes the proposed MMTM-RPL efficient [52], since they use dynamic itineraries. Thirdly, as the control message overhead in MMTM-RPL is low compared to the state of the art, the network performance plays an important role in decreasing the detection time.…”