Wireless sensor networks are often battery-powered, and hence extending the network lifetime is one of the primary concerns in the ubiquitous deployment of wireless sensor networks. One approach to efficiently utilize the limited energy supplies of the sensors is to have the medium access control (MAC) protocol duty-cycle the sensors, periodically putting the sensors to sleep and waking them up to reduce idle listening, which is energy intensive. Among duty-cycled MAC protocols, some protocols are synchronized so that nodes wake up at the same time in each cycle, and other protocols are asynchronous, where nodes have arbitrary offsets to start their cycles. For protocol designers, it is important to understand which type of duty-cycled MAC protocol should be chosen (synchronized or asynchronous),as well as what values should be assigned to the protocol parameters under a given network scenario in order to achieve a desirable performance for throughput, delay or energy consumption.

The perceptual image hashing is an emerging technique which can be used in image authentication and contentbased image retrieval. Recently, several image hashing schemes based on Radon transform are proposed for image authentication and retrieval. These schemes have no random information to strengthen the security with image hashing. In this paper, we propose a simple key-based secure image hashing scheme by using radon transform and 1-D Discrete Cosine Transform (DCT). Particularly, we introduce a randomization on Radon transform and make the image hash more secure and more robust. Moreover, the discriminative capability is also confirmed in our experimental results.

Abstract-SMAC is a popular duty-cycled MAC protocol, designed for wireless sensor networks to save energy and prolong the network lifetime. However, existing work evaluates the performance of SMAC solely through simulations or field measurements. To the best of our knowledge, there are no analytical models for evaluating the performance of SMAC. In this paper, we propose a Markov model to describe the behavior of SMAC with a finite queue capacity. This model enables us to find the expected throughput of SMAC under variable number of nodes, queue capacities, contention window sizes, and data arrival rates. We validate the model through extensive simulations, which provide throughput values within 5% of the throughput values obtained through our model. Our proposed Markov model can be used to estimate the throughput of SMAC under many different network and node conditions, and more importantly, it provides us with a better understanding of the way that different parameters affect the performance of SMAC. I. INTRODUCTIONWireless sensor networks have attracted much interest in both academia and industry due to their low cost, ease of deployment, and support for various applications ranging from military surveillance and emergency rescue to medical monitoring. However, energy constraints imposed by the battery-powered sensor nodes are a limiting factor, preventing the ubiquitous use of wireless sensor networks. Therefore, much research has focused on how to save energy and prolong the network lifetime In this paper, we focus on throughput analysis of SMAC with a finite queue capacity. We propose two Markov models to describe the behavior of SMAC and to further calculate its throughput with and without retransmissions. We show that the throughput obtained from our analytical model matches simulation results under various scenarios. Our SMAC model can be used to estimate the throughput for a given SMAC configuration. Throughput estimation is important for many applications, like visual surveillance, which generate large

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Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

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Abstract. The confidential access to medical images becomes significant in recent years. In this paper, we propose two types of region-based selective encryption schemes to achieve secure access for medical images. The first scheme randomly flips a subset of the bits belonging to the coefficients in a Region of Interest inside of several wavelet sub-bands, which is performed in compression domain but only incurs little loss on compression efficiency. The second scheme employs AES to encrypt a certain region's data in the code-stream. The size of encrypted bit-stream is not changed and there is no compression overhead generated in the second scheme. Moreover, both of two schemes support backward compatibility so that an encryption-unaware format-compliant player can play the encrypted bit-stream directly without any crash.

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