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We define authenticated video as decoded video that results from those received packets whose authenticities have been verified. Generic data stream authentication methods usually impose overhead and dependency among packets for verification. Therefore, the conventional rate-distortion (R-D) optimized video streaming techniques produce highly sub-optimal R-D performance for authenticated video, since they do not account for the overhead and additional dependencies for authentication. In this paper, we study this practical problem and propose an Rate-Distortion-Authentication (R-D-A) optimized streaming technique for authenticated video. Based on packets' importance in terms of both video quality and authentication dependencies, the proposed technique computes a packet transmission schedule that minimizes the expected end-to-end distortion of the authenticated video at the receiver subject to a constraint on the average transmission rate. Simulation results based on H.264 JM 10.2 and NS-2 demonstrate that our proposed R-D-A optimized streaming technique substantially outperforms both prior (authentication-unaware) R-D optimized streaming techniques and data stream authentication techniques. In particular, when the channel capacity is below the source rate, the PSNR of authenticated video quickly drops to unacceptable levels using conventional R-D optimized streaming techniques, while the proposed R-D-A Optimization technique still maintains optimized video quality. Furthermore, we examine a low-complexity version of the proposed algorithm, and also an enhanced version which accounts for the multiple deadlines associated with each packet, which is introduced by stream authentication. [ABSTRACT FROM AUTHOR]

Energy awareness is a crucial component in the design of wireless sensor networks at all layers. This paper looks into efficient energy utilization of a target-tracking sensor network by predicting a target's trajectory through experience. While this is not new, the chief novelty comes in conserving energy through both dynamic spatial and temporal management of sensors while assuming minimal locality information. We adapted our target trajectory model from the Gauss-Markov mobility model, formulated the tracking problem as a hierarchical Markov decision process (HMDP), and solved it through neurodynamic programming. Our HMDP for target-tracking (HMTT) algorithm conserves energy by reducing the rate of sensing (temporal management) but maintains an acceptable tracking accuracy through trajectory prediction (spatial management) of multiple targets. We derived some theoretical bounds on accuracy and energy utilization of HMTT. Simulation results demonstrated the effectiveness of HMTT in energy conservation and tracking accuracy against two other predictive tracking algorithms, with accuracy of up to 47% higher and energy savings of up to 200%. [ABSTRACT FROM AUTHOR]

A wearable sensor system enables continuous and remote health monitoring and is widely considered as the next generation of healthcare technology. The performance, the packet error rate (PER) in particular, of a wearable sensor system may deteriorate due to a number of factors, particularly the interference from the other wearable sensor systems in the vicinity. We systematically evaluate the performance of the wearable sensor system in terms of PER in the presence of such interference in this paper. The factors that affect the performance of the wearable sensor system, such as density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital are all considered. Simulation results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER is less than 0.05) even in the off-peak period in hospital. We then suggest some interference mitigation schemes based on the performance evaluation results in the case study. [ABSTRACT FROM AUTHOR]

When a body sensor network (BSN) that is linked to the backbone via a wireless network interface moves from one coverage zone to another, a handover is required to maintain network connectivity. This paper presents an optimized handover scheme with movement trend awareness for BSNs. The proposed scheme predicts the future position of a BSN user using the movement trend extracted from the historical position, and adjusts the handover decision accordingly. Handover initiation time is optimized when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized. The proposed handover scheme is simulated in a BSN deployment area in a hospital environment in UK. Simulation results show that the proposed scheme reduces the outage probability by 22% as compared with the existing hysteresis-based handover scheme under the constraint of acceptable handover rate. [ABSTRACT FROM AUTHOR]