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Location-based services find a number of applications in vehicular environments such as navigation, parking, infortainment etc. However, the disclosure of vehicles' location information raises multiple privacy issues. To balance the tradeoff between privacy and utility, this paper proposes a framework to preserve users' location privacy while delivering the desired quality of experience (QoE). The proposed framework allows users to quantify the data utility while accessing location-based services under different privacy levels through the QoE metric. The privacy analysis of the proposed framework is provided under two adversary models. Finally, the effectiveness of the proposed framework is demonstrate using the real-world “Dianping” review dataset.

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.

Network coding techniques are usually applied upon network-layer protocols to improve throughput in wireless networks. In scenarios with multiple unicast sessions, fairness is also an important factor. Therefore, a network coding-aware packet-scheduling algorithm is required. A packet-scheduling algorithm determines which packet to send next from a node’s packet backlog. Existing protocols mostly employ a basic round-robin scheduling algorithm to give “equal” opportunities to different packet flows. In fact, this “equal”-opportunity scheduling is neither fair, nor efficient. This paper intends to accentuate the importance of a coding-aware scheduling scheme. With a good scheduling scheme, we can gain more control over the per-flow throughput and fairness. Specifically, we first formulate a static scheduling problem and propose an algorithm to find the optimal scheduling scheme. We then extend the technique to a dynamic setting and, later, to practical routing protocols. Results show that the algorithm is comparatively scalable, and it can improve the throughput gain when the network is not severely saturated. The fairness among flows is drastically improved as a result of this scheduling scheme.

Step counting-based dead-reckoning has been widely accepted as a cheap and effective solution for indoor pedestrian tracking using a hand-held device equipped with motion sensors. To compensate for the accumulating error in a dead-reckoning tracking system, extra techniques are always fused together to form a hybrid system. In this paper, we first propose a map matching (MM) enhanced particle filter (PF) as a robust localization solution, in which MM utilizes the corridor information to calibrate the step direction estimation and PF is applied to filter out impossible locations. To overcome the dependency on manually input corridor information in the MM algorithm, as well as the computational complexity in combining two such algorithms, an improved PF is proposed. By better modelling of the location error, the improved PF calibrates the location estimation, as well as step direction estimation when the map information is available, while keeping the computational complexity the same as the original PF. Experimental results show that in a quite dense map constraint environment with corridors, the proposed methods have similar accuracy, but outperform the original PF in terms of accuracy. When only partial map constraints are applied to simulate a new testbed, the improved PF obtains the most robust and accurate results. Therefore, the improved PF is the recommended DR solution, which is adaptive to various indoor environments.

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.

There have been researchers working on the design of coding-aware routing protocols to exploit the power of network coding in static wireless ad hoc networks. However, most of them have overlooked the fact that routing decisions for multiple flows need to be coordinated in order to achieve maximum throughput. A mechanism to alter prior routing decisions is mandatory for this purpose. In this paper, we propose Self-recommendation coding-aware routing (SCAR), to provide such a mechanism. With intermediate nodes sensing the arrival of new flows, they can send self-recommendations to source nodes, triggering a route-change procedure. In the route-change procedure, adjustments are applied to the route metrics so that the newly recommended route can be fairly weighed without bias. A thorough analysis of coding structures is carried out, and a series of indicators are devised to predict how much throughput benefits we can gain from the mere knowledge of the topology. The rationale behind the protocol design and the effectiveness of the indicators are further justified by a series of simulations. Results show that SCAR can exploit coding opportunities better and provide higher throughput than other coding-aware routing protocols. It is also shown that the opportunity of throughput gain is ubiquitous and it is tested on many typical topologies.