Accelerating the Delivery of Data Services over Uncertain Mobile Crowdsensing Networks

TL;DR

This work tackles efficient data service provisioning in uncertain mobile crowdsensing (MCS) networks. It presents iFAST, a hybrid forward-spot trading framework with overbooking to accelerate data delivery and reduce provisioning failures. The approach combines formal modeling of buyer/seller utilities under uncertainty, a transformation of a multi-objective program into a tractable single-objective problem via the $\epsilon$-constrained method, and a GP-based successive convex algorithm solved with CVX, enabling scalable decision-making. Case studies and simulations using real data show iFAST achieving data quality comparable to full online optimization while substantially reducing decision latency, with several future directions proposed for smart contracts, risk management, and multi-modal resource trading.

Abstract

The challenge of exchanging and processing of big data over mobile crowdsensing (MCS) networks calls for designing seamless data service provisioning mechanisms to enable utilization of resources of mobile devices/users for crowdsensing tasks. Although conventional onsite spot trading of resources based on real-time network conditions can facilitate data sharing, it often suffers from prohibitively long service provisioning delays and unavoidable trading failures due to requiring timely analysis of dynamic network environment. These limitations motivate us to investigate an integrated forward and spot trading mechanism (iFAST), which entails a novel hybrid data trading protocol with time efficiency, over uncertain MCS ecosystems. In iFAST, the sellers (i.e., mobile devices who can contribute data) can provide long-term or temporary sensing services to the buyers (i.e., sensing tasks). Specifically, it enables signing long-term contracts in advance of future transactions through a forward trading mode, via analyzing historical statistics of the network/market, for which the notion of overbooking is introduced and promoted. iFAST further encourages the buyers with unsatisfying service quality to recruit temporary sellers through a spot trading mode, considering the current network/market conditions. We analyze the fundamental blocks of iFAST and provide a case study to demonstrate its performance. Inspirations for future research directions of next-generation sensing and communication are summarized.

Figures (5)

• Figure 1: A schematic of service trading over MCS networks, which consists of an MCS coordinator that supervises the market, multiple buyers and sellers. In each transaction, each seller within the corresponding PoI of a buyer can provide sensing services to that buyer, while receiving a certain payment.
• Figure 2: Timeline and transaction examples of iFAST. Forward contracts are signed among a portion of the buyers and sellers (sellers $s_1-s_7$ are long-term sellers) in advance of future transactions, and will be fulfilled during each practical transaction. During each transaction, $s_8$ can be recruited as a temporary seller, when any buyer’s desired service quality has not been reached, since some long-term sellers may have left the PoI.
• Figure 3: Interactions among sellers and buyers associated with Transaction 2 in Fig. 2. Long-term sellers $s_2, s_4, s_5$ can offer services to the buyers according to the pre-signed contracts, while buyer $b_2$ is selected as a volunteer of $s_4$. Besides, since the desired service quality of $b_2$ may not be satisfied, $s_8$ is determined as a temporary seller that provides one-time data service in the current transaction. Long-term sellers $s_1, s_3, s_6, s_7$ are unavailable during this transaction due to their locations.
• Figure 4: Performance evaluation of service quality upon having different numbers of buyers and sellers.
• Figure 5: Performance evaluation of running time upon having different numbers of buyers and sellers.