An Energy-Efficient Distributed Algorithm for k-Coverage Problem in Wireless Sensor Networks

Wireless sensor networks (WSNs) have recently achieved a great deal of attention due to its numerous attractive applications in many different fields. Sensors and WSNs possesses a number of special characteristics that make them very promising in many applications, but also put on them lots of constraints that make issues in sensor network particularly difficult. These issues may include topology control, routing, coverage, security, and data management. In this thesis, we focus our attention on the coverage problem. Firstly, we define the Sensor Energy-efficient Scheduling for k-coverage (SESK) problem. We then solve it by proposing a novel, completely localized and distributed scheduling approach, naming Distributed Energy-efficient Scheduling for k-coverage (DESK) such that the energy consumption among all the sensors is balanced, and the network lifetime is maximized while still satisfying the k-coverage requirement. Finally, in related work section we conduct an extensive survey of the existing work in literature that focuses on with the coverage problem

Prediction-based Routing with Packet Scheduling under Temporal Constraint in Delay Tolerant Networks

is a challenging problem due to the intermittent connectivity between the nodes. Researchers have proposed many routing protocols that adapt to the temporary connections of DTNs. One classification of routing protocols makes use of historical information to predict future contact patterns for any pair of nodes. However, most existing protocols focus on the probability of a path from the source to the destination without considering the information in a packet which includes the source, destination, size, TTL (Time-To-Live) and limited resources such as available buffer size and bandwidth. In this paper, we propose a new prediction-based routing algorithm that takes into account packet information under the conditions of limited transmission opportunities. The goal of this protocol is to increase the overall delivery ratio through scheduling packets at each node. Meanwhile, this protocol may sacrifice some messages ’ delivery delay time to some extent. Extensive simulation results with real traces show that our protocol with packet scheduling has better performance than the pure probabilistic routing algorithms in term of delivery ratio. Our protocol’s performance advantage is more obvious for nodes with higher packet intensity and shorter TTL in packets. I

Data Collection and Capacity Analysis in Large-scale Wireless Sensor Networks

In this dissertation, we study data collection and its achievable network capacity in Wireless Sensor Networks (WSNs). Firstly, we investigate the data collection issue in dual-radio multi-channel WSNs under the protocol interference model. We propose a multi-path scheduling algorithm for snapshot data collection, which has a tighter capacity bound than the existing best result, and a novel continuous data collection algorithm with comprehensive capacity analysis. Secondly, considering most existing works for the capacity issue are based on the ideal deterministic network model, we study the data collection problem for practical probabilistic WSNs. We design a cell-based path scheduling algorithm and a zone-based pipeline scheduling algorithm for snapshot and continuous data collection in probabilistic WSNs, respectively. By analysis, we show that the proposed algorithms have competitive capacity performance compared with existing works. Thirdly, most of the existing works studying the data collection capacity issue are for centralized synchronous WSNs. However, wireless networks are more likely to be distributed asynchronous systems. Therefore, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs and propose a data collection algorithm with fairness consideration. Theoretical analysis of the proposed algorithm shows that its achievable network capacity is order-optimal as centralized and synchronized algorithms do and independent of network size. Finally, for completeness, we study the data aggregation issue for realistic probabilistic WSNs. We propose order-optimal scheduling algorithms for snapshot and continuous data aggregation under the physical interference model

An Efficient Context-Aware Privacy Preserving Approach for Smartphones

With the proliferation of smartphones and the usage of the smartphone apps, privacy preservation has become an important issue. The existing privacy preservation approaches for smartphones usually have less efficiency due to the absent consideration of the active defense policies and temporal correlations between contexts related to users. In this paper, through modeling the temporal correlations among contexts, we formalize the privacy preservation problem to an optimization problem and prove its correctness and the optimality through theoretical analysis. To further speed up the running time, we transform the original optimization problem to an approximate optimal problem, a linear programming problem. By resolving the linear programming problem, an efficient context-aware privacy preserving algorithm (CAPP) is designed, which adopts active defense policy and decides how to release the current context of a user to maximize the level of quality of service (QoS) of context-aware apps with privacy preservation. The conducted extensive simulations on real dataset demonstrate the improved performance of CAPP over other traditional approaches

Application and challenges of a metaverse in medicine

Metaverse has been confirmed as a relatively amorphous concept of innovation, which refers to technological advancement. Metaverse, i.e., a coalition between reality world and virtual world, has created significant significance and convenience in education, communication, economy, etc. The COVID-19 outbreak has stimulated the growth of metaverse applications in medicine. The above-mentioned technology has broad applications while comprising online remote medical treatment, online conferences, medical education, preparation of surgical plans, etc. Moreover, technical, security, and financial challenges should be tackled down by the future widespread use of metaverse. Metaverse is limitlessly promising, and it will exert a certain effect on future scientific and technological advancements in the medical industry. The review article primarily aims to summarize the application of the metaverse in medicine and their challenge in the future of medicine

Construction Algorithms for k-Connected m-Dominating Sets in Wireless Sensor Networks

A Connected Dominating Set (CDS) working as a virtual backbone is an effective way to decrease the overhead of routing in a wireless sensor network. Furthermore, a k-Connected m-Dominating Set (kmCDS) is necessary for fault tolerance and routing flexibility. Some approximation algorithms have been proposed to construct a kmCDS. However, most of them only consider some special cases where k = 1, 2 or k ≤ m, or are not easy to implement, or have high message complexity. In this paper, we propose a novel distributed algorithm LDA with low message complexity to construct a kmCDS for general k and m whose size is guaranteed to be within a small constant factor of the optimal solution when the maximum node degree is a constant. We also propose one centralized algorithm ICGA with a constant performance ratio to construct a kmCDS. Theoretical analysis as well as simulation results are shown to evaluate the proposed algorithms