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Because sensor nodes are deployed in public areas, these sensors are easy to capture by adversaries. Once a sensor is stolen, the sensitive information stored in it is likely to be exposed. Accordingly, designing a secure authentication protocol should consider this issue. Sadri et al. recently proposed a two-factor authentication protocol with anonymity for wireless sensor networks. Unfortunately, we found that their protocol had a design flaw in the user and sensor authentication phase. Besides, their protocol can not resist stolen smart card attacks, sensor capture attacks, and user impersonation attacks. In addition, their protocol does not provide perfect forward security. This paper proposes a provably secure authentication to overcome these weaknesses and flaws. By comparing the security and performance of the proposed protocol with other related protocols, we find that our work has reasonable computation overhead and better security.

In spatial crowdsourcing, mobile workers are recruited to perform special tasks related to location and time. The task assignment has been a focus of the research community due to its importance in spatial crowdsourcing. However, most of the existing efforts focus on optimizing the task requester’s goal and do not consider the requirements and constraints of workers. Unlike the existing methods, we consider task assignment comprising the goals of both the workers and the requesters, that is, the trade-off regarding the reward and travel cost of a worker and the delay of a task. We formulate the delay-sensitive task assignment (DSTA) problem that optimizes the combined goal of minimizing costs and maximizing rewards of a worker; meanwhile, reducing the delay to wait until the task is assigned. The DSTA algorithm based on greed idea (DSTA-G) is proposed to address the problem. To improve the efficiency, we introduce the Geohash algorithm and propose a new solution DSTA-GH, which is scalable to large-scale datasets. Moreover, the task assignment method that only considers the worker’s objective (TAW) is proposed as the baseline. Finally, extensive experiments offer evidence that DSTA and DSTA-GH outperform the baseline in terms of performance and DSTA-GH only takes about 0.03% of the CPU time as compared to DSTA-G.

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