Abstract
Mobile crowdsensing (MCS) is a new perception mode for solving large-scale mobile sensing tasks. Traditional data transmission methods are inapplicable, as the MCS is affected by coverage, user preference, and network access cost. Opportunistic network data transmission schemes in MCS have recently witnessed a surge of research efforts due to their ability of high delivery and low consumption. However, existing works only focus on the impact of the geographical location of nodes on user needs or the interaction between social information and data, which do not take into account the temporal and spatial characteristics of nodes. To address these issues, this paper proposes a multiattribute routing method based on Pareto optimal (MR-Pareto) solution to construct a balance between the energy consumption and resource constraints of nodes in transmission protocols. First, the attribute relationship between nodes is analyzed, which was aimed at selecting the nodes within a contact time threshold. Then, based on a nondominated sorting approach, we achieve a Pareto optimal set of candidate nodes. Finally, the relay nodes for forwarding messages are determined by comparing the cache size and the remaining energy. The experimental results demonstrate that our proposed method has low network overhead, low packet loss, and high message delivery rate, compared to epidemic and prophet routing strategies.
Highlights
With the development of pervasive computing, mobile crowdsensing technology, and intelligent terminal devices, intelligent systems with integrated sensing and computing communication capabilities have been widely deployed
(1) We proposed a multiattribute routing method based on Pareto optimal (MR-Pareto) and evaluated the stability of node connection based on the user motion state
The energy of the energy network, network overhead, packet loss quantity, and message delivery rate of the MR-Pareto routing algorithm was further compared under different simulation times
Summary
With the development of pervasive computing, mobile crowdsensing technology, and intelligent terminal devices, intelligent systems with integrated sensing and computing communication capabilities have been widely deployed. MCS migrates perception tasks from a centralized platform to a distributed computing terminal across the space-time dimension, which can achieve data analysis and understanding and make large-scale and high-precision environment perception possible [1, 2]. In traditional data transmission methods, it is difficult to satisfy the actual needs of data acquisition by only using predeployed dense sensor nodes in an uncertain and large-scale sensing environment, such as limited network resources and heterogeneous sensing terminals. The performance of MCS routing method plays an important role in sensing the task transmission quality. Data forwarding through opportunistic routing transmission mode can leverage the advantages of MCS [4]. The advantages are as follows: (1) opportunistic routing method can reduce the cost of network deployment, make full use of millions of mobile devices to build large-scale sensing networks, and ensure the privacy of user data. Through the opportunity contact between mobile users, the mode of “store-carry-
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