English

In this paper, we propose a new data gathering mechanism for large scale wireless sensor networks by introducing mobility into the network. A mobile data collector, or for convenience called M-collector in this paper, could be a mobile robot or a vehicle equipped with a powerful transceiver and battery, works like a mobile base station and gathers data while moving through the field. An M-collector starts the data gathering tour periodically from the static data sink, polls each sensor while traversing the transmission range of the sensor, then collects data directly from the sensor without relay (i.e., in a single hop), finally returns and uploads data to the data sink. Since data packets are gathered directly without relay and collision, the lifetime of sensors is expected to be prolonged, and sensors can be made very simple and inexpensive. We focus on the problem of minimizing the length of each data gathering tour and refer to this problem as the single-hop data gathering problem, or SHDGP for short. We first formalize the SHDGP problem into a mixed integer program and prove its NP-hardness. We then present a heuristic tour-planning algorithm for a single M-collector. For the applications with a strict distance/time constraint for each data gathering tour, we also utilize multiple M-collectors to traverse through several shorter sub-tours concurrently to satisfy the distance/time constraint. Our single-hop data gathering scheme will improve the scalability and solve intrinsic problems of large scale homogeneous networks, and can be used in both connected networks and disconnected networks. The simulation results demonstrate that the new data gathering algorithm can greatly reduce the moving distance of the collectors compared to the covering line approximation algorithm, and is close to the optimal algorithm in small networks. In addition, the proposed data gathering scheme can prolong the network lifetime significantly compared to a network which has only a static data collector, or a network in which the mobile collector can only move along straight lines.

Location-based service (LBS) is a kind of mobile valueadded services in mobile communication systems. The development of mobile positioning techniques promotes application of LBS. This paper presents a type of architecture of LBS. In this architecture, TDOA positioning modules are appended into the mobile basestations in order to implement LBS. LBS gateway and application platform are two vital parts in this architecture. LBS gateway modules are added into MSC of the mobile communication networks. LBS application platform is similar to a mobile location server. TDOA positioning modules deal with rough location testing and simple data computing. LBS gateway is a comprehensive management device which processes location information related to the mobile users. LBS application platform incepts location information processed in LBS gateway, cooperates with the mobile terminal and sends location information to the mobile users by proper data format. LBS application platform is developed based on ArcIMS map server which provides a lot of APIs. The application programming flowchart for LBS is implemented in detail in this paper. The real testing in the mobile communication systems shows that the proposed LBS can provide exact location services for mobile users.

We consider the location service in a mobile ad-hoc network (MANET), where each node needs to maintain its location information in the network by (i) frequently updating its location information within its neighboring region, which is called neighborhood update (NU), and (ii) occasionally updating its location information to a certain (fixed) distributed location server in the network, which is called location server update (LU). A trade-off exists between the costs in location update operations, on one hand, and the additional incurred costs in (position-based) routing due to location errors, on the other hand. In this paper, we develop a stochastic sequential decision framework to analyze this trade-off and provide design guidelines on selecting good location update strategies in practice. Under a Markovian mobility model, the location update decision problem is modeled as a Markovian decision process (MDP). Based on the separable cost structure of the proposed MDP model, we first show that the location update decisions on NU and LU can be independently carried out without loss of optimality. Then we investigate the optimality of simple threshold-based updating rules in NU and LU operations. We finally introduce a model-free learning approach which is practically useful to find a near-optimal solution for the problem.

The developments of Information and Communication Technology (ICT) and Internet of Things (IoT) are being used to enhance quality, performance and interactivity of urban services. The developments of Information and Communication Technology (ICT) and Internet of Things (IoT) are being used to enhance quality, performance and interactivity of urban services. Benefited from the widespread adoption of mobile devices, we can collect amount of mobile data for user mobility analysis. Mining hidden information from users' mobile data is important for builders of smart city to provide better location-based service. This paper focuses on two classical domain-independent prediction models and one improved Markov model that are capable of estimating the next location. By using 27-day-long traffic data of mobile network, we extract trajectories of 4914 individuals for experiments. We find that the original Markov algorithm has a better performance in resource consumption than LZ family algorithms, but its prediction accuracy is lower than prediction accuracy of LeZi Update and Active LeZi algorithm. In order to improve the prediction accuracy of Markov and overcome drawbacks of traditional prediction algorithms, we present a new method based on Markov, which considers both temporal and spatial factors. Extensive experiments demonstrate our improved method has a better performance in location prediction. In addition, we further study the relationship between prediction accuracy and trajectory's regularity, to identify the most suitable prediction algorithm for a trajectory.

Wireless Sensor Networks (WSNs) have become an indistinguishable part of any Internet of Things (IoT) based system. In an IoT-based system, WSN monitors different physical objects and forwards the collected data to the base station or gateway node for further processing. Existing routing protocols designed for WSNs are not suitable for IoT-based systems. An intelligent and high-performance routing protocol is essential for real-time data analysis and decision-making. Therefore, this paper proposes a mobile sink-based intelligent data routing scheme for WSNs. Initially, Particle Swarm Optimization is used for optimal cluster formation. After cluster formation, the optimal number of rendezvous points are selected, and a data-gathering tour is planned for the mobile sink. Furthermore, a mobile sink-based intelligent data gathering scheme is proposed to collect the data from the cluster heads. Extensive simulations and mathematical analysis are performed to verify the effectiveness of the proposed scheme. The simulation results are compared with state-of-the-art algorithms in terms of stability period, network lifetime, throughput, average energy consumption, fairness index, data transmission latency, and message overhead.

Wireless Sensor Networks (WSNs) mostly uses static sink to collect data from the sensor nodes randomly deployed in the sensor region. In the static sink based approach, the data packets are flooded across the network to reach the mobile base station in multi-hop communication. Due to this, the static sink is inefficient in energy utilization. Recently, mobile sink are used for data gathering, has less energy utilization which in turn increases the network lifetime. Thus, the sink mobility has difficulties in finding the routing path for the data packets. This paper proposes an effective clustering approach with data aggregation using multiple mobile sinks for heterogeneous WSN. The proposed algorithm achieves network lifetime increases with limited energy utilization.

Mobile devices with Global Positioning System enable various kinds of location information searches using location based services (LBS). Location information and query contents of searchers are privacy information. Privacy protection requires to hide location information and query contents of searchers from not only third parties such as eavesdroppers but also location database servers which answer queries. This paper focuses on privacy protection in nearest neighbor search. There are several researches of secure computation in nearest neighbor search for other than LBS. This paper proposes a new method of privacy protection in LBS. The method is based on Asymmetric Scalar-product Preserving Encryption. This paper also shows that the proposed method is faster and more secure than conventional privacy protection methods in LBS.

Several multicast protocols for mobile ad hoc networks have been proposed, which build multicast trees by using location information that is available from the Global Positioning System (GPS) or localization algorithms and use geographic forwarding to forward packets down the multicast trees. These stateless multicast protocols carry encoded membership, location, and tree information in each packet and are more efficient and robust than stateful protocols (for example, ADMR and ODMRP), as they avoid the difficulty of maintaining distributed state in the presence of frequent topology changes. However, current stateless multicast protocols are not scalable to large groups because of the per-packet encoding overhead, and the centralized group membership and location management. In this paper I propose a novel Robust and Scalable Geographic Multicast Protocol (RSGM). Several virtual architectures are used in the protocol without need of maintaining state information for more robust and scalable membership management and packet forwarding in the presence of high network dynamics due to unstable wireless channels and node movements. Specifically, scalable and efficient group membership management is performed through a virtual-zone-based structure, and the location service for group members is integrated with the membership management. Both the control messages and data packets are forwarded along efficient tree-like paths, but there is no need to explicitly create and actively maintain a tree structure. The stateless virtual-tree-based structures significantly reduce the tree management overhead, support more efficient transmissions, and make the transmissions much more robust to dynamics. Geographic forwarding is used to achieve further scalability and robustness. To avoid periodic flooding of the source information throughout the network, an efficient source tracking mechanism is designed to focus on minimizing join latency, repair latency, and control overhead. I have design a routing protocol based on these principles and demonstrate its effectiveness through simulations.

In mobile ad hoc networks, routing schemes using location information have been proposed. Most of these schemes assume that the source node already knows the location information of the destination node. However, since all nodes are always moving, it is difficult to apply this assumption to the real mobile ad hoc environment. In order to cope this difficulty, this paper presents a new routing scheme HRLI, a Hybrid Routing with Location Information, which using the threshold for the communication range of all nodes. And it considers the location and velocity information of the destination node and the neighboring nodes. In HRLI, when a source node creates a route to a destination node, the future location of the destination node predicted by the source node is calculated using these location and velocity information. And the source node sends data packets based on the predicted location information of destination node and neighboring nodes. This paper shows that HRLI achieves high data delivery ratio and fewer overheads for using the threshold of communication range and the predicted location through simulation experiments

With the prevalence of mobile devices and location-based services, forecasting human mobility has become a critical topic in ubiquitous computing. Existing forecasting approaches usually adopt frameworks with a centralized mobility data holder. However, mobility data typically pertains to independent organizations, introducing two learning challenges. First, since each organization only holds a location domain subset, none can tackle a forecasting model that covers the whole location domain. Second, distributed mobility data compromises the spatio-temporal correlation between locations hindering learning. Hence, reducing the forecasting accuracy. This work proposes a mobility vertical federated forecasting (MVFF) framework that allows the learning process to be jointly conducted over vertically partitioned data belonging to multiple organizations. MVFF enables the forecasting of mobility predictions covering a joint location domain. We evaluate MVFF's performance over two real-world datasets using different spatial and temporal neural network algorithms. Experimental results demonstrate that the two datasets' mean percentage error performance gains are up to 12% and 4% compared to the state-of-the-art, respectively.

In the past few years, infectious diseases like COVID-19 have caused serious distress to the global society and the economy. To prevent its spread, the early detection and assessment of infectious diseases based on molecular tests or antigen testing of bodily have led to countless labor and material costs. Fortunately, with the rapid development of mobile localization and web techniques, the collected massive mobile trajectory data provides a promising solution for detecting positive cases. However, existing mobility data-driven infection case detection methods are limited in terms of modeling the complicated epidemic spreading processes and preserving user privacy of the mobility data. In this paper, we propose a novel graph convolutional networks (GCN) model for detecting high risk infection cases, where we incorporate a spatio-temporal hypergraph to model the complex interaction of individuals. Then, we elaborately design a privacy-preserving framework tightly coupled with the structure of the spatio-temporal hypergraph, which includes a mobility data obfuscation module to protect privacy and an accompanying confidence-aware mechanism to mitigate the consequent performance decline. Moreover, we introduce a causal propagation mechanism to further guarantee the temporal dependency and causal effect of the feature propagation in our spatio-temporal hypergraph, which introduces both the causal transform of node features and the causal gathering of edge features. Finally, extensive experiments on a large mobility dataset collected from location-based services (LBS) show that the proposed model improves the performance of infection case detection by at least \(12.47\%\) when compared with several widely adopted baselines. Besides, our code and datasets are available at the link 1 .

The ubiquity of mobile devices with global positioning functionality (e.g., GPS and Assisted GPS) and Internet connectivity (e.g., 3G and Wi-Fi) has resulted in widespread development of location-based services (LBS). Typical examples of LBS include local business search, e-marketing, social networking, and automotive traffic monitoring. Although LBS provide valuable services for mobile users, revealing their private locations to potentially untrusted LBS service providers pose privacy concerns. In general, there are two types of LBS, namely, snapshot and continuous LBS. For snapshot LBS, a mobile user only needs to report its current location to a service provider once to get its desired information. On the other hand, a mobile user has to report its location to a service provider in a periodic or on-demand manner to obtain its desired continuous LBS. Protecting user location privacy for continuous LBS is more challenging than snapshot LBS because adversaries may use the spatial and temporal correlations in the user's a sequence of location samples to infer the user's location information with a higher degree of certainty. Such user spatial trajectories are also very important for many applications, e.g., business analysis, city planning, and intelligent transportation. However, publishing original spatial trajectories to the public or a third party for data analysis could pose serious privacy concerns. Privacy protection in continuous LBS and trajectory data publication has increasingly drawn attention from the research community and industry. In this chapter, we describe the state-of-the-art privacy-preserving techniques for continuous LBS and trajectory publication.

Joint routing and location-based service in VANETs

Power-aware routing, have been studied to avoid energy-scarce sensors and achieve network lifetime improvement but recently, it was noticed that sink mobility can also be used to improve network lifetime by avoiding excessive transmission overhead at nodes that are close to the location that would be occupied by a static sink. Also in many situations, a static sink may be unfeasible because of deployment or security constraints. In this paper, an energy-efficient method is introduced which can deliver data packets to mobile sink nodes. As can observed from simulation results, the mobile sink directed diffusion (MSDD) offers a magnificent way to upgrade directed diffusion algorithm to support sink mobility with minimum energy overhead and acceptable performance. Furthermore, in the proposed algorithm, no location service or predictable sink trajectory is assumed which is a common requirement in most of mobile sink routing algorithms. Simulation results show that MSDD algorithm can reach the packet delivery rate of over 55% in the worst case with the sink speed of 30 m/s with acceptable energy overhead.

Smart cities are driven by huge amount of data collected from sensors deployed across the city. Sensors typically form a multi-hop network with a base station (BS ) in order to send their data to the command and control center. However, sparse deployment of sensors can leave subsets of the network partitioned from the rest of the network. In such a case, isolated partitions cannot forward their data to the BS . Consequently, network coverage and data fidelity decline. A possible solution to link partitions and provide connectivity is employing mobile data collectors (MDCs). A smart vehicle supporting wireless communication can act as an MDC and carry data between sensors and the BS . Using a single MDC extends the average tour length. To minimize the maximum tour length, multiple MDCs can be employed. To identify sensors to be visited by each MDC, this paper clusters partitions as many as the number of MDCs and assigns an MDC for each cluster. Then two different cooperative data collection schemes are considered based on the availability of inter-MDC data exchange. If MDCs collaborate in data delivery, they meet at certain meeting points for data exchange. Such a cooperation avoids the requirement of visiting the BS for some MDCs and reduces tour lengths. On the other hand, MDCs closer to the BS can experience data loss due to buffer overflow given the higher volume of the accumulated data. Presented approaches are evaluated in terms of maximum tour length, data latency, and data loss. The smart city application is simulated with deployment of sensors on certain amenity types. Geographic data is obtained from a volunteered geographic information system and MDC mobility is restricted with the road network. Obtained results indicate that MDC cooperation decreases maximum tour length at the expense of increased rate of data loss and data latency.